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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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Home Market Research

Survey Research: Definition, Examples and Methods

Survey Research is a quantitative research method used for collecting data from a set of respondents. It has been perhaps one of the most used methodologies in the industry for several years due to the multiple benefits and advantages that it has when collecting and analyzing data.

LEARN ABOUT: Behavioral Research

In this article, you will learn everything about survey research, such as types, methods, and examples.

Survey Research Definition

Survey Research is defined as the process of conducting research using surveys that researchers send to survey respondents. The data collected from surveys is then statistically analyzed to draw meaningful research conclusions. In the 21st century, every organization’s eager to understand what their customers think about their products or services and make better business decisions. Researchers can conduct research in multiple ways, but surveys are proven to be one of the most effective and trustworthy research methods. An online survey is a method for extracting information about a significant business matter from an individual or a group of individuals. It consists of structured survey questions that motivate the participants to respond. Creditable survey research can give these businesses access to a vast information bank. Organizations in media, other companies, and even governments rely on survey research to obtain accurate data.

The traditional definition of survey research is a quantitative method for collecting information from a pool of respondents by asking multiple survey questions. This research type includes the recruitment of individuals collection, and analysis of data. It’s useful for researchers who aim to communicate new features or trends to their respondents.

LEARN ABOUT: Level of Analysis Generally, it’s the primary step towards obtaining quick information about mainstream topics and conducting more rigorous and detailed quantitative research methods like surveys/polls or qualitative research methods like focus groups/on-call interviews can follow. There are many situations where researchers can conduct research using a blend of both qualitative and quantitative strategies.

LEARN ABOUT: Survey Sampling

Survey Research Methods

Survey research methods can be derived based on two critical factors: Survey research tool and time involved in conducting research. There are three main survey research methods, divided based on the medium of conducting survey research:

• Online/ Email:   Online survey research is one of the most popular survey research methods today. The survey cost involved in online survey research is extremely minimal, and the responses gathered are highly accurate.
• Phone:  Survey research conducted over the telephone ( CATI survey ) can be useful in collecting data from a more extensive section of the target population. There are chances that the money invested in phone surveys will be higher than other mediums, and the time required will be higher.
• Face-to-face:  Researchers conduct face-to-face in-depth interviews in situations where there is a complicated problem to solve. The response rate for this method is the highest, but it can be costly.

Further, based on the time taken, survey research can be classified into two methods:

• Longitudinal survey research:  Longitudinal survey research involves conducting survey research over a continuum of time and spread across years and decades. The data collected using this survey research method from one time period to another is qualitative or quantitative. Respondent behavior, preferences, and attitudes are continuously observed over time to analyze reasons for a change in behavior or preferences. For example, suppose a researcher intends to learn about the eating habits of teenagers. In that case, he/she will follow a sample of teenagers over a considerable period to ensure that the collected information is reliable. Often, cross-sectional survey research follows a longitudinal study .
• Cross-sectional survey research:  Researchers conduct a cross-sectional survey to collect insights from a target audience at a particular time interval. This survey research method is implemented in various sectors such as retail, education, healthcare, SME businesses, etc. Cross-sectional studies can either be descriptive or analytical. It is quick and helps researchers collect information in a brief period. Researchers rely on the cross-sectional survey research method in situations where descriptive analysis of a subject is required.

Survey research also is bifurcated according to the sampling methods used to form samples for research: Probability and Non-probability sampling. Every individual in a population should be considered equally to be a part of the survey research sample. Probability sampling is a sampling method in which the researcher chooses the elements based on probability theory. The are various probability research methods, such as simple random sampling , systematic sampling, cluster sampling, stratified random sampling, etc. Non-probability sampling is a sampling method where the researcher uses his/her knowledge and experience to form samples.

LEARN ABOUT: Survey Sample Sizes

The various non-probability sampling techniques are :

• Convenience sampling
• Snowball sampling
• Consecutive sampling
• Judgemental sampling
• Quota sampling

Process of implementing survey research methods:

• Decide survey questions:  Brainstorm and put together valid survey questions that are grammatically and logically appropriate. Understanding the objective and expected outcomes of the survey helps a lot. There are many surveys where details of responses are not as important as gaining insights about what customers prefer from the provided options. In such situations, a researcher can include multiple-choice questions or closed-ended questions . Whereas, if researchers need to obtain details about specific issues, they can consist of open-ended questions in the questionnaire. Ideally, the surveys should include a smart balance of open-ended and closed-ended questions. Use survey questions like Likert Scale , Semantic Scale, Net Promoter Score question, etc., to avoid fence-sitting.

LEARN ABOUT: System Usability Scale

• Finalize a target audience:  Send out relevant surveys as per the target audience and filter out irrelevant questions as per the requirement. The survey research will be instrumental in case the target population decides on a sample. This way, results can be according to the desired market and be generalized to the entire population.

LEARN ABOUT:  Testimonial Questions

• Send out surveys via decided mediums:  Distribute the surveys to the target audience and patiently wait for the feedback and comments- this is the most crucial step of the survey research. The survey needs to be scheduled, keeping in mind the nature of the target audience and its regions. Surveys can be conducted via email, embedded in a website, shared via social media, etc., to gain maximum responses.
• Analyze survey results:  Analyze the feedback in real-time and identify patterns in the responses which might lead to a much-needed breakthrough for your organization. GAP, TURF Analysis , Conjoint analysis, Cross tabulation, and many such survey feedback analysis methods can be used to spot and shed light on respondent behavior. Researchers can use the results to implement corrective measures to improve customer/employee satisfaction.

Reasons to conduct survey research

The most crucial and integral reason for conducting market research using surveys is that you can collect answers regarding specific, essential questions. You can ask these questions in multiple survey formats as per the target audience and the intent of the survey. Before designing a study, every organization must figure out the objective of carrying this out so that the study can be structured, planned, and executed to perfection.

LEARN ABOUT: Research Process Steps

Questions that need to be on your mind while designing a survey are:

• What is the primary aim of conducting the survey?
• How do you plan to utilize the collected survey data?
• What type of decisions do you plan to take based on the points mentioned above?

There are three critical reasons why an organization must conduct survey research.

• Understand respondent behavior to get solutions to your queries:  If you’ve carefully curated a survey, the respondents will provide insights about what they like about your organization as well as suggestions for improvement. To motivate them to respond, you must be very vocal about how secure their responses will be and how you will utilize the answers. This will push them to be 100% honest about their feedback, opinions, and comments. Online surveys or mobile surveys have proved their privacy, and due to this, more and more respondents feel free to put forth their feedback through these mediums.
• Present a medium for discussion:  A survey can be the perfect platform for respondents to provide criticism or applause for an organization. Important topics like product quality or quality of customer service etc., can be put on the table for discussion. A way you can do it is by including open-ended questions where the respondents can write their thoughts. This will make it easy for you to correlate your survey to what you intend to do with your product or service.
• Strategy for never-ending improvements:  An organization can establish the target audience’s attributes from the pilot phase of survey research . Researchers can use the criticism and feedback received from this survey to improve the product/services. Once the company successfully makes the improvements, it can send out another survey to measure the change in feedback keeping the pilot phase the benchmark. By doing this activity, the organization can track what was effectively improved and what still needs improvement.

Survey Research Scales

There are four main scales for the measurement of variables:

• Nominal Scale:  A nominal scale associates numbers with variables for mere naming or labeling, and the numbers usually have no other relevance. It is the most basic of the four levels of measurement.
• Ordinal Scale:  The ordinal scale has an innate order within the variables along with labels. It establishes the rank between the variables of a scale but not the difference value between the variables.
• Interval Scale:  The interval scale is a step ahead in comparison to the other two scales. Along with establishing a rank and name of variables, the scale also makes known the difference between the two variables. The only drawback is that there is no fixed start point of the scale, i.e., the actual zero value is absent.
• Ratio Scale:  The ratio scale is the most advanced measurement scale, which has variables that are labeled in order and have a calculated difference between variables. In addition to what interval scale orders, this scale has a fixed starting point, i.e., the actual zero value is present.

Benefits of survey research

In case survey research is used for all the right purposes and is implemented properly, marketers can benefit by gaining useful, trustworthy data that they can use to better the ROI of the organization.

Other benefits of survey research are:

• Minimum investment:  Mobile surveys and online surveys have minimal finance invested per respondent. Even with the gifts and other incentives provided to the people who participate in the study, online surveys are extremely economical compared to paper-based surveys.
• Versatile sources for response collection:  You can conduct surveys via various mediums like online and mobile surveys. You can further classify them into qualitative mediums like focus groups , and interviews and quantitative mediums like customer-centric surveys. Due to the offline survey response collection option, researchers can conduct surveys in remote areas with limited internet connectivity. This can make data collection and analysis more convenient and extensive.
• Reliable for respondents:  Surveys are extremely secure as the respondent details and responses are kept safeguarded. This anonymity makes respondents answer the survey questions candidly and with absolute honesty. An organization seeking to receive explicit responses for its survey research must mention that it will be confidential.

Survey research design

Researchers implement a survey research design in cases where there is a limited cost involved and there is a need to access details easily. This method is often used by small and large organizations to understand and analyze new trends, market demands, and opinions. Collecting information through tactfully designed survey research can be much more effective and productive than a casually conducted survey.

There are five stages of survey research design:

• Decide an aim of the research:  There can be multiple reasons for a researcher to conduct a survey, but they need to decide a purpose for the research. This is the primary stage of survey research as it can mold the entire path of a survey, impacting its results.
• Filter the sample from target population:  Who to target? is an essential question that a researcher should answer and keep in mind while conducting research. The precision of the results is driven by who the members of a sample are and how useful their opinions are. The quality of respondents in a sample is essential for the results received for research and not the quantity. If a researcher seeks to understand whether a product feature will work well with their target market, he/she can conduct survey research with a group of market experts for that product or technology.
• Zero-in on a survey method:  Many qualitative and quantitative research methods can be discussed and decided. Focus groups, online interviews, surveys, polls, questionnaires, etc. can be carried out with a pre-decided sample of individuals.
• Design the questionnaire:  What will the content of the survey be? A researcher is required to answer this question to be able to design it effectively. What will the content of the cover letter be? Or what are the survey questions of this questionnaire? Understand the target market thoroughly to create a questionnaire that targets a sample to gain insights about a survey research topic.
• Send out surveys and analyze results:  Once the researcher decides on which questions to include in a study, they can send it across to the selected sample . Answers obtained from this survey can be analyzed to make product-related or marketing-related decisions.

Survey examples: 10 tips to design the perfect research survey

Picking the right survey design can be the key to gaining the information you need to make crucial decisions for all your research. It is essential to choose the right topic, choose the right question types, and pick a corresponding design. If this is your first time creating a survey, it can seem like an intimidating task. But with QuestionPro, each step of the process is made simple and easy.

Below are 10 Tips To Design The Perfect Research Survey:

• Set your SMART goals:  Before conducting any market research or creating a particular plan, set your SMART Goals . What is that you want to achieve with the survey? How will you measure it promptly, and what are the results you are expecting?
• Choose the right questions:  Designing a survey can be a tricky task. Asking the right questions may help you get the answers you are looking for and ease the task of analyzing. So, always choose those specific questions – relevant to your research.
• Begin your survey with a generalized question:  Preferably, start your survey with a general question to understand whether the respondent uses the product or not. That also provides an excellent base and intro for your survey.
• Enhance your survey:  Choose the best, most relevant, 15-20 questions. Frame each question as a different question type based on the kind of answer you would like to gather from each. Create a survey using different types of questions such as multiple-choice, rating scale, open-ended, etc. Look at more survey examples and four measurement scales every researcher should remember.
• Prepare yes/no questions:  You may also want to use yes/no questions to separate people or branch them into groups of those who “have purchased” and those who “have not yet purchased” your products or services. Once you separate them, you can ask them different questions.
• Test all electronic devices:  It becomes effortless to distribute your surveys if respondents can answer them on different electronic devices like mobiles, tablets, etc. Once you have created your survey, it’s time to TEST. You can also make any corrections if needed at this stage.
• Distribute your survey:  Once your survey is ready, it is time to share and distribute it to the right audience. You can share handouts and share them via email, social media, and other industry-related offline/online communities.
• Collect and analyze responses:  After distributing your survey, it is time to gather all responses. Make sure you store your results in a particular document or an Excel sheet with all the necessary categories mentioned so that you don’t lose your data. Remember, this is the most crucial stage. Segregate your responses based on demographics, psychographics, and behavior. This is because, as a researcher, you must know where your responses are coming from. It will help you to analyze, predict decisions, and help write the summary report.
• Prepare your summary report:  Now is the time to share your analysis. At this stage, you should mention all the responses gathered from a survey in a fixed format. Also, the reader/customer must get clarity about your goal, which you were trying to gain from the study. Questions such as – whether the product or service has been used/preferred or not. Do respondents prefer some other product to another? Any recommendations?

Having a tool that helps you carry out all the necessary steps to carry out this type of study is a vital part of any project. At QuestionPro, we have helped more than 10,000 clients around the world to carry out data collection in a simple and effective way, in addition to offering a wide range of solutions to take advantage of this data in the best possible way.

From dashboards, advanced analysis tools, automation, and dedicated functions, in QuestionPro, you will find everything you need to execute your research projects effectively. Uncover insights that matter the most!

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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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Survey Research — Types, Methods and Example Questions

Survey research The world of research is vast and complex, but with the right tools and understanding, it's an open field of discovery. Welcome to a journey into the heart of survey research. What is survey research? Survey research is the lens through which we view the opinions, behaviors, and experiences of a population. Think of it as the research world's detective, cleverly sleuthing out the truths hidden beneath layers of human complexity. Why is survey research important? Survey research is a Swiss Army Knife in a researcher's toolbox. It’s adaptable, reliable, and incredibly versatile, but its real power? It gives voice to the silent majority. Whether it's understanding customer preferences or assessing the impact of a social policy, survey research is the bridge between unanswered questions and insightful data. Let's embark on this exploration, armed with the spirit of openness, a sprinkle of curiosity, and the thirst for making knowledge accessible. As we journey further into the realm of survey research, we'll delve deeper into the diverse types of surveys, innovative data collection methods, and the rewards and challenges that come with them. Types of survey research Survey research is like an artist's palette, offering a variety of types to suit your unique research needs. Each type paints a different picture, giving us fascinating insights into the world around us. Cross-Sectional Surveys: Capture a snapshot of a population at a specific moment in time. They're your trusty Polaroid camera, freezing a moment for analysis and understanding. Longitudinal Surveys: Track changes over time, much like a time-lapse video. They help to identify trends and patterns, offering a dynamic perspective of your subject. Descriptive Surveys: Draw a detailed picture of the current state of affairs. They're your magnifying glass, examining the prevalence of a phenomenon or attitudes within a group. Analytical Surveys: Deep dive into the reasons behind certain outcomes. They're the research world's version of Sherlock Holmes, unraveling the complex web of cause and effect. But, what method should you choose for data collection? The plot thickens, doesn't it? Let's unravel this mystery in our next section. Survey research and data collection methods Data collection in survey research is an art form, and there's no one-size-fits-all method. Think of it as your paintbrush, each stroke represents a different way of capturing data. Online Surveys: In the digital age, online surveys have surged in popularity. They're fast, cost-effective, and can reach a global audience. But like a mysterious online acquaintance, respondents may not always be who they say they are. Mail Surveys: Like a postcard from a distant friend, mail surveys have a certain charm. They're great for reaching respondents without internet access. However, they’re slower and have lower response rates. They’re a test of patience and persistence. Telephone Surveys: With the sound of a ringing phone, the human element enters the picture. Great for reaching a diverse audience, they bring a touch of personal connection. But, remember, not all are fans of unsolicited calls. Face-to-Face Surveys: These are the heart-to-heart conversations of the survey world. While they require more resources, they're the gold standard for in-depth, high-quality data. As we journey further, let’s weigh the pros and cons of survey research. Advantages and disadvantages of survey research Every hero has its strengths and weaknesses, and survey research is no exception. Let's unwrap the gift box of survey research to see what lies inside. Advantages: Versatility: Like a superhero with multiple powers, surveys can be adapted to different topics, audiences, and research needs. Accessibility: With online surveys, geographical boundaries dissolve. We can reach out to the world from our living room. Anonymity: Like a confessional booth, surveys allow respondents to share their views without fear of judgment. Disadvantages: Response Bias: Ever met someone who says what you want to hear? Survey respondents can be like that too. Limited Depth: Like a puddle after a rainstorm, some surveys only skim the surface of complex issues. Nonresponse: Sometimes, potential respondents play hard to get, skewing the data. Survey research may have its challenges, but it also presents opportunities to learn and grow. As we forge ahead on our journey, we dive into the design process of survey research. Limitations of survey research Every research method has its limitations, like bumps on the road to discovery. But don't worry, with the right approach, these challenges become opportunities for growth. Misinterpretation: Sometimes, respondents might misunderstand your questions, like a badly translated novel. To overcome this, keep your questions simple and clear. Social Desirability Bias: People often want to present themselves in the best light. They might answer questions in a way that portrays them positively, even if it's not entirely accurate. Overcome this by ensuring anonymity and emphasizing honesty. Sample Representation: If your survey sample isn't representative of the population you're studying, it can skew your results. Aiming for a diverse sample can mitigate this. Now that we're aware of the limitations let's delve into the world of survey design. {loadmoduleid 430} Survey research design Designing a survey is like crafting a roadmap to discovery. It's an intricate process that involves careful planning, innovative strategies, and a deep understanding of your research goals. Let's get started. Approach and Strategy Your approach and strategy are the compasses guiding your survey research. Clear objectives, defined research questions, and an understanding of your target audience lay the foundation for a successful survey. Panel The panel is the heartbeat of your survey, the respondents who breathe life into your research. Selecting a representative panel ensures your research is accurate and inclusive. 9 Tips on Building the Perfect Survey Research Questionnaire Keep It Simple: Clear and straightforward questions lead to accurate responses. Make It Relevant: Ensure every question ties back to your research objectives. Order Matters: Start with easy questions to build rapport and save sensitive ones for later. Avoid Double-Barreled Questions: Stick to one idea per question. Offer a Balanced Scale: For rating scales, provide an equal number of positive and negative options. Provide a ‘Don't Know’ Option: This prevents guessing and keeps your data accurate. Pretest Your Survey: A pilot run helps you spot any issues before the final launch. Keep It Short: Respect your respondents' time. Make It Engaging: Keep your respondents interested with a mix of question types. Survey research examples and questions Examples serve as a bridge connecting theoretical concepts to real-world scenarios. Let's consider a few practical examples of survey research across various domains. User Experience (UX) Imagine being a UX designer at a budding tech start-up. Your app is gaining traction, but to keep your user base growing and engaged, you must ensure that your app's UX is top-notch. In this case, a well-designed survey could be a beacon, guiding you toward understanding user behavior, preferences, and pain points. Here's an example of how such a survey could look: "On a scale of 1 to 10, how would you rate the ease of navigating our app?" "How often do you encounter difficulties while using our app?" "What features do you use most frequently in our app?" "What improvements would you suggest for our app?" "What features would you like to see in future updates?" This line of questioning, while straightforward, provides invaluable insights. It enables the UX designer to identify strengths to capitalize on and weaknesses to improve, ultimately leading to a product that resonates with users. Psychology and Ethics in survey research The realm of survey research is not just about data and numbers, but it's also about understanding human behavior and treating respondents ethically. Psychology: In-depth understanding of cognitive biases and social dynamics can profoundly influence survey design. Let's take the 'Recency Effect,' a psychological principle stating that people tend to remember recent events more vividly than those in the past. While framing questions about user experiences, this insight could be invaluable. For example, a question like "Can you recall an instance in the past week when our customer service exceeded your expectations?" is likely to fetch more accurate responses than asking about an event several months ago. Ethics: On the other hand, maintaining privacy, confidentiality, and informed consent is more than ethical - it's fundamental to the integrity of the research process. Imagine conducting a sensitive survey about workplace culture. Ensuring respondents that their responses will remain confidential and anonymous can encourage more honest responses. An introductory note stating these assurances, along with a clear outline of the survey's purpose, can help build trust with your respondents. Survey research software In the age of digital information, survey research software has become a trusted ally for researchers. It simplifies complex processes like data collection, analysis, and visualization, democratizing research and making it more accessible to a broad audience. LimeSurvey, our innovative, user-friendly tool, brings this vision to life. It stands at the crossroads of simplicity and power, embodying the essence of accessible survey research. Whether you're a freelancer exploring new market trends, a psychology student curious about human behavior, or an HR officer aiming to improve company culture, LimeSurvey empowers you to conduct efficient, effective research. Its suite of features and intuitive design matches your research pace, allowing your curiosity to take the front seat. For instance, consider you're a researcher studying consumer behavior across different demographics. With LimeSurvey, you can easily design demographic-specific questions, distribute your survey across various channels, collect responses in real-time, and visualize your data through intuitive dashboards. This synergy of tools and functionalities makes LimeSurvey a perfect ally in your quest for knowledge. Conclusion If you've come this far, we can sense your spark of curiosity. Are you eager to take the reins and conduct your own survey research? Are you ready to embrace the simple yet powerful tool that LimeSurvey offers? If so, we can't wait to see where your journey takes you next! In the world of survey research, there's always more to explore, more to learn and more to discover. So, keep your curiosity alive, stay open to new ideas, and remember, your exploration is just beginning! We hope that our exploration has been as enlightening for you as it was exciting for us. Remember, the journey doesn't end here. With the power of knowledge and the right tools in your hands, there's no limit to what you can achieve. So, let your curiosity be your guide and dive into the fascinating world of survey research with LimeSurvey! Try it out for free now! Happy surveying! {loadmoduleid 429}

Table Content

Survey research.

The world of research is vast and complex, but with the right tools and understanding, it's an open field of discovery. Welcome to a journey into the heart of survey research.

What is survey research?

Survey research is the lens through which we view the opinions, behaviors, and experiences of a population. Think of it as the research world's detective, cleverly sleuthing out the truths hidden beneath layers of human complexity.

Why is survey research important?

Survey research is a Swiss Army Knife in a researcher's toolbox. It’s adaptable, reliable, and incredibly versatile, but its real power? It gives voice to the silent majority. Whether it's understanding customer preferences or assessing the impact of a social policy, survey research is the bridge between unanswered questions and insightful data.

Let's embark on this exploration, armed with the spirit of openness, a sprinkle of curiosity, and the thirst for making knowledge accessible. As we journey further into the realm of survey research, we'll delve deeper into the diverse types of surveys, innovative data collection methods, and the rewards and challenges that come with them.

Types of survey research

Survey research is like an artist's palette, offering a variety of types to suit your unique research needs. Each type paints a different picture, giving us fascinating insights into the world around us.

• Cross-Sectional Surveys: Capture a snapshot of a population at a specific moment in time. They're your trusty Polaroid camera, freezing a moment for analysis and understanding.
• Longitudinal Surveys: Track changes over time, much like a time-lapse video. They help to identify trends and patterns, offering a dynamic perspective of your subject.
• Descriptive Surveys: Draw a detailed picture of the current state of affairs. They're your magnifying glass, examining the prevalence of a phenomenon or attitudes within a group.
• Analytical Surveys: Deep dive into the reasons behind certain outcomes. They're the research world's version of Sherlock Holmes, unraveling the complex web of cause and effect.

But, what method should you choose for data collection? The plot thickens, doesn't it? Let's unravel this mystery in our next section.

Survey research and data collection methods

Data collection in survey research is an art form, and there's no one-size-fits-all method. Think of it as your paintbrush, each stroke represents a different way of capturing data.

• Online Surveys: In the digital age, online surveys have surged in popularity. They're fast, cost-effective, and can reach a global audience. But like a mysterious online acquaintance, respondents may not always be who they say they are.
• Mail Surveys: Like a postcard from a distant friend, mail surveys have a certain charm. They're great for reaching respondents without internet access. However, they’re slower and have lower response rates. They’re a test of patience and persistence.
• Telephone Surveys: With the sound of a ringing phone, the human element enters the picture. Great for reaching a diverse audience, they bring a touch of personal connection. But, remember, not all are fans of unsolicited calls.
• Face-to-Face Surveys: These are the heart-to-heart conversations of the survey world. While they require more resources, they're the gold standard for in-depth, high-quality data.

As we journey further, let’s weigh the pros and cons of survey research.

Advantages and disadvantages of survey research

Every hero has its strengths and weaknesses, and survey research is no exception. Let's unwrap the gift box of survey research to see what lies inside.

Advantages:

• Versatility: Like a superhero with multiple powers, surveys can be adapted to different topics, audiences, and research needs.
• Accessibility: With online surveys, geographical boundaries dissolve. We can reach out to the world from our living room.
• Anonymity: Like a confessional booth, surveys allow respondents to share their views without fear of judgment.

Disadvantages:

• Response Bias: Ever met someone who says what you want to hear? Survey respondents can be like that too.
• Limited Depth: Like a puddle after a rainstorm, some surveys only skim the surface of complex issues.
• Nonresponse: Sometimes, potential respondents play hard to get, skewing the data.

Survey research may have its challenges, but it also presents opportunities to learn and grow. As we forge ahead on our journey, we dive into the design process of survey research.

Limitations of survey research

Every research method has its limitations, like bumps on the road to discovery. But don't worry, with the right approach, these challenges become opportunities for growth.

Misinterpretation: Sometimes, respondents might misunderstand your questions, like a badly translated novel. To overcome this, keep your questions simple and clear.

Social Desirability Bias: People often want to present themselves in the best light. They might answer questions in a way that portrays them positively, even if it's not entirely accurate. Overcome this by ensuring anonymity and emphasizing honesty.

Sample Representation: If your survey sample isn't representative of the population you're studying, it can skew your results. Aiming for a diverse sample can mitigate this.

Now that we're aware of the limitations let's delve into the world of survey design.

•   Create surveys in 40+ languages
•   Unlimited number of users
•   Ready-to-go survey templates
•   So much more...

Survey research design

Designing a survey is like crafting a roadmap to discovery. It's an intricate process that involves careful planning, innovative strategies, and a deep understanding of your research goals. Let's get started.

Approach and Strategy

Your approach and strategy are the compasses guiding your survey research. Clear objectives, defined research questions, and an understanding of your target audience lay the foundation for a successful survey.

The panel is the heartbeat of your survey, the respondents who breathe life into your research. Selecting a representative panel ensures your research is accurate and inclusive.

9 Tips on Building the Perfect Survey Research Questionnaire

• Keep It Simple: Clear and straightforward questions lead to accurate responses.
• Make It Relevant: Ensure every question ties back to your research objectives.
• Order Matters: Start with easy questions to build rapport and save sensitive ones for later.
• Avoid Double-Barreled Questions: Stick to one idea per question.
• Offer a Balanced Scale: For rating scales, provide an equal number of positive and negative options.
• Provide a ‘Don't Know’ Option: This prevents guessing and keeps your data accurate.
• Pretest Your Survey: A pilot run helps you spot any issues before the final launch.
• Keep It Short: Respect your respondents' time.
• Make It Engaging: Keep your respondents interested with a mix of question types.

Survey research examples and questions

Examples serve as a bridge connecting theoretical concepts to real-world scenarios. Let's consider a few practical examples of survey research across various domains.

User Experience (UX)

Imagine being a UX designer at a budding tech start-up. Your app is gaining traction, but to keep your user base growing and engaged, you must ensure that your app's UX is top-notch. In this case, a well-designed survey could be a beacon, guiding you toward understanding user behavior, preferences, and pain points.

Here's an example of how such a survey could look:

• "On a scale of 1 to 10, how would you rate the ease of navigating our app?"
• "How often do you encounter difficulties while using our app?"
• "What features do you use most frequently in our app?"
• "What improvements would you suggest for our app?"
• "What features would you like to see in future updates?"

This line of questioning, while straightforward, provides invaluable insights. It enables the UX designer to identify strengths to capitalize on and weaknesses to improve, ultimately leading to a product that resonates with users.

Psychology and Ethics in survey research

The realm of survey research is not just about data and numbers, but it's also about understanding human behavior and treating respondents ethically.

Psychology: In-depth understanding of cognitive biases and social dynamics can profoundly influence survey design. Let's take the 'Recency Effect,' a psychological principle stating that people tend to remember recent events more vividly than those in the past. While framing questions about user experiences, this insight could be invaluable.

For example, a question like "Can you recall an instance in the past week when our customer service exceeded your expectations?" is likely to fetch more accurate responses than asking about an event several months ago.

Ethics: On the other hand, maintaining privacy, confidentiality, and informed consent is more than ethical - it's fundamental to the integrity of the research process.

Imagine conducting a sensitive survey about workplace culture. Ensuring respondents that their responses will remain confidential and anonymous can encourage more honest responses. An introductory note stating these assurances, along with a clear outline of the survey's purpose, can help build trust with your respondents.

Survey research software

In the age of digital information, survey research software has become a trusted ally for researchers. It simplifies complex processes like data collection, analysis, and visualization, democratizing research and making it more accessible to a broad audience.

LimeSurvey, our innovative, user-friendly tool, brings this vision to life. It stands at the crossroads of simplicity and power, embodying the essence of accessible survey research.

Whether you're a freelancer exploring new market trends, a psychology student curious about human behavior, or an HR officer aiming to improve company culture, LimeSurvey empowers you to conduct efficient, effective research. Its suite of features and intuitive design matches your research pace, allowing your curiosity to take the front seat.

For instance, consider you're a researcher studying consumer behavior across different demographics. With LimeSurvey, you can easily design demographic-specific questions, distribute your survey across various channels, collect responses in real-time, and visualize your data through intuitive dashboards. This synergy of tools and functionalities makes LimeSurvey a perfect ally in your quest for knowledge.

If you've come this far, we can sense your spark of curiosity. Are you eager to take the reins and conduct your own survey research? Are you ready to embrace the simple yet powerful tool that LimeSurvey offers? If so, we can't wait to see where your journey takes you next!

In the world of survey research, there's always more to explore, more to learn and more to discover. So, keep your curiosity alive, stay open to new ideas, and remember, your exploration is just beginning!

We hope that our exploration has been as enlightening for you as it was exciting for us. Remember, the journey doesn't end here. With the power of knowledge and the right tools in your hands, there's no limit to what you can achieve. So, let your curiosity be your guide and dive into the fascinating world of survey research with LimeSurvey! Try it out for free now!

Happy surveying!

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What is survey research.

15 min read Find out everything you need to know about survey research, from what it is and how it works to the different methods and tools you can use to ensure you’re successful.

Survey research is the process of collecting data from a predefined group (e.g. customers or potential customers) with the ultimate goal of uncovering insights about your products, services, or brand overall .

As a quantitative data collection method, survey research can provide you with a goldmine of information that can inform crucial business and product decisions. But survey research needs careful planning and execution to get the results you want.

So if you’re thinking about using surveys to carry out research, read on.

Get started with our free survey maker tool

Types of survey research

Calling these methods ‘survey research’ slightly underplays the complexity of this type of information gathering. From the expertise required to carry out each activity to the analysis of the data and its eventual application, a considerable amount of effort is required.

As for how you can carry out your research, there are several options to choose from — face-to-face interviews, telephone surveys, focus groups (though more interviews than surveys), online surveys , and panel surveys.

Typically, the survey method you choose will largely be guided by who you want to survey, the size of your sample , your budget, and the type of information you’re hoping to gather.

Here are a few of the most-used survey types:

Face-to-face interviews

Before technology made it possible to conduct research using online surveys, telephone, and mail were the most popular methods for survey research. However face-to-face interviews were considered the gold standard — the only reason they weren’t as popular was due to their highly prohibitive costs.

When it came to face-to-face interviews, organizations would use highly trained researchers who knew when to probe or follow up on vague or problematic answers. They also knew when to offer assistance to respondents when they seemed to be struggling. The result was that these interviewers could get sample members to participate and engage in surveys in the most effective way possible, leading to higher response rates and better quality data.

Telephone surveys

While phone surveys have been popular in the past, particularly for measuring general consumer behavior or beliefs, response rates have been declining since the 1990s .

Phone surveys are usually conducted using a random dialing system and software that a researcher can use to record responses.

This method is beneficial when you want to survey a large population but don’t have the resources to conduct face-to-face research surveys or run focus groups, or want to ask multiple-choice and open-ended questions .

The downsides are they can: take a long time to complete depending on the response rate, and you may have to do a lot of cold-calling to get the information you need.

You also run the risk of respondents not being completely honest . Instead, they’ll answer your survey questions quickly just to get off the phone.

Focus groups (interviews — not surveys)

Focus groups are a separate qualitative methodology rather than surveys — even though they’re often bunched together. They’re normally used for survey pretesting and designing , but they’re also a great way to generate opinions and data from a diverse range of people.

Focus groups involve putting a cohort of demographically or socially diverse people in a room with a moderator and engaging them in a discussion on a particular topic, such as your product, brand, or service.

They remain a highly popular method for market research , but they’re expensive and require a lot of administration to conduct and analyze the data properly.

You also run the risk of more dominant members of the group taking over the discussion and swaying the opinions of other people — potentially providing you with unreliable data.

Online surveys

Online surveys have become one of the most popular survey methods due to being cost-effective, enabling researchers to accurately survey a large population quickly.

Online surveys can essentially be used by anyone for any research purpose – we’ve all seen the increasing popularity of polls on social media (although these are not scientific).

Using an online survey allows you to ask a series of different question types and collect data instantly that’s easy to analyze with the right software.

There are also several methods for running and distributing online surveys that allow you to get your questionnaire in front of a large population at a fraction of the cost of face-to-face interviews or focus groups.

This is particularly true when it comes to mobile surveys as most people with a smartphone can access them online.

However, you have to be aware of the potential dangers of using online surveys, particularly when it comes to the survey respondents. The biggest risk is because online surveys require access to a computer or mobile device to complete, they could exclude elderly members of the population who don’t have access to the technology — or don’t know how to use it.

It could also exclude those from poorer socio-economic backgrounds who can’t afford a computer or consistent internet access. This could mean the data collected is more biased towards a certain group and can lead to less accurate data when you’re looking for a representative population sample.

When it comes to surveys, every voice matters.

Find out how to create more inclusive and representative surveys for your research.

Panel surveys

A panel survey involves recruiting respondents who have specifically signed up to answer questionnaires and who are put on a list by a research company. This could be a workforce of a small company or a major subset of a national population. Usually, these groups are carefully selected so that they represent a sample of your target population — giving you balance across criteria such as age, gender, background, and so on.

Panel surveys give you access to the respondents you need and are usually provided by the research company in question. As a result, it’s much easier to get access to the right audiences as you just need to tell the research company your criteria. They’ll then determine the right panels to use to answer your questionnaire.

However, there are downsides. The main one being that if the research company offers its panels incentives, e.g. discounts, coupons, money — respondents may answer a lot of questionnaires just for the benefits.

This might mean they rush through your survey without providing considered and truthful answers. As a consequence, this can damage the credibility of your data and potentially ruin your analyses.

What are the benefits of using survey research?

Depending on the research method you use, there are lots of benefits to conducting survey research for data collection. Here, we cover a few:

1.   They’re relatively easy to do

Most research surveys are easy to set up, administer and analyze. As long as the planning and survey design is thorough and you target the right audience , the data collection is usually straightforward regardless of which survey type you use.

2.   They can be cost effective

Survey research can be relatively cheap depending on the type of survey you use.

Generally, qualitative research methods that require access to people in person or over the phone are more expensive and require more administration.

Online surveys or mobile surveys are often more cost-effective for market research and can give you access to the global population for a fraction of the cost.

3.   You can collect data from a large sample

Again, depending on the type of survey, you can obtain survey results from an entire population at a relatively low price. You can also administer a large variety of survey types to fit the project you’re running.

4.   You can use survey software to analyze results immediately

Using survey software, you can use advanced statistical analysis techniques to gain insights into your responses immediately.

Analysis can be conducted using a variety of parameters to determine the validity and reliability of your survey data at scale.

5.   Surveys can collect any type of data

While most people view surveys as a quantitative research method, they can just as easily be adapted to gain qualitative information by simply including open-ended questions or conducting interviews face to face.

How to measure concepts with survey questions

While surveys are a great way to obtain data, that data on its own is useless unless it can be analyzed and developed into actionable insights.

The easiest, and most effective way to measure survey results, is to use a dedicated research tool that puts all of your survey results into one place.

When it comes to survey measurement, there are four measurement types to be aware of that will determine how you treat your different survey results:

Nominal scale

With a nominal scale , you can only keep track of how many respondents chose each option from a question, and which response generated the most selections.

An example of this would be simply asking a responder to choose a product or brand from a list.

You could find out which brand was chosen the most but have no insight as to why.

Ordinal scale

Ordinal scales are used to judge an order of preference. They do provide some level of quantitative value because you’re asking responders to choose a preference of one option over another.

Ratio scale

Ratio scales can be used to judge the order and difference between responses. For example, asking respondents how much they spend on their weekly shopping on average.

Interval scale

In an interval scale, values are lined up in order with a meaningful difference between the two values — for example, measuring temperature or measuring a credit score between one value and another.

Step by step: How to conduct surveys and collect data

Conducting a survey and collecting data is relatively straightforward, but it does require some careful planning and design to ensure it results in reliable data.

Step 1 – Define your objectives

What do you want to learn from the survey? How is the data going to help you? Having a hypothesis or series of assumptions about survey responses will allow you to create the right questions to test them.

Step 2 – Create your survey questions

Once you’ve got your hypotheses or assumptions, write out the questions you need answering to test your theories or beliefs. Be wary about framing questions that could lead respondents or inadvertently create biased responses .

Step 3 – Choose your question types

Your survey should include a variety of question types and should aim to obtain quantitative data with some qualitative responses from open-ended questions. Using a mix of questions (simple Yes/ No, multiple-choice, rank in order, etc) not only increases the reliability of your data but also reduces survey fatigue and respondents simply answering questions quickly without thinking.

Find out how to create a survey that’s easy to engage with

Step 4 – Test your questions

Before sending your questionnaire out, you should test it (e.g. have a random internal group do the survey) and carry out A/B tests to ensure you’ll gain accurate responses.

Step 5 – Choose your target and send out the survey

Depending on your objectives, you might want to target the general population with your survey or a specific segment of the population. Once you’ve narrowed down who you want to target, it’s time to send out the survey.

After you’ve deployed the survey, keep an eye on the response rate to ensure you’re getting the number you expected. If your response rate is low, you might need to send the survey out to a second group to obtain a large enough sample — or do some troubleshooting to work out why your response rates are so low. This could be down to your questions, delivery method, selected sample, or otherwise.

Step 6 – Analyze results and draw conclusions

Once you’ve got your results back, it’s time for the fun part.

Break down your survey responses using the parameters you’ve set in your objectives and analyze the data to compare to your original assumptions. At this stage, a research tool or software can make the analysis a lot easier — and that’s somewhere Qualtrics can help.

Get reliable insights with survey software from Qualtrics

Gaining feedback from customers and leads is critical for any business, data gathered from surveys can prove invaluable for understanding your products and your market position, and with survey software from Qualtrics, it couldn’t be easier.

Used by more than 13,000 brands and supporting more than 1 billion surveys a year, Qualtrics empowers everyone in your organization to gather insights and take action. No coding required — and your data is housed in one system.

Get feedback from more than 125 sources on a single platform and view and measure your data in one place to create actionable insights and gain a deeper understanding of your target customers .

Automatically run complex text and statistical analysis to uncover exactly what your survey data is telling you, so you can react in real-time and make smarter decisions.

We can help you with survey management, too. From designing your survey and finding your target respondents to getting your survey in the field and reporting back on the results, we can help you every step of the way.

And for expert market researchers and survey designers, Qualtrics features custom programming to give you total flexibility over question types, survey design, embedded data, and other variables.

No matter what type of survey you want to run, what target audience you want to reach, or what assumptions you want to test or answers you want to uncover, we’ll help you design, deploy and analyze your survey with our team of experts.

Ready to find out more about Qualtrics CoreXM?

Get started with our free survey maker tool today

Related resources

Survey bias types 24 min read, post event survey questions 10 min read, best survey software 16 min read, close-ended questions 7 min read, survey vs questionnaire 12 min read, response bias 13 min read, double barreled question 11 min read, request demo.

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A Short Introduction to Survey Research

• First Online: 20 November 2018

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This chapter offers a brief introduction into survey research. In the first part of the chapter, students learn about the importance of survey research in the social and behavioral sciences, substantive research areas where survey research is frequently used, and important cross-national survey such as the World Values Survey and the European Social Survey. In the second, I introduce different types of surveys.

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In the literature, such reversed causation is often referred to as an endogeneity problem.

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Further Reading

Why do we need survey research.

Converse, J. M. (2017). Survey research in the United States: Roots and emergence 1890–1960. New York: Routledge. This book has more of an historical ankle. It tackles the history of survey research in the United States.

Davidov, E., Schmidt, P., & Schwartz, S. H. (2008). Bringing values back in: The adequacy of the European Social Survey to measure values in 20 countries. Public Opinion Quarterly, 72 (3), 420–445. This rather short article highlights the importance of conducting a large pan-European survey to measure European’s social and political beliefs.

Schmitt, H., Hobolt, S. B., Popa, S. A., & Teperoglou, E. (2015). European parliament election study 2014, voter study. GESIS Data Archive, Cologne. ZA5160 Data file Version , 2 (0). The European Voter Study is another important election study that researchers and students can access freely. It provides a comprehensive battery of variables about voting, political preferences, vote choice, demographics, and political and social opinions of the electorate.

Applied Survey Research

Almond, G. A., & Verba, S. (1963). The civic culture: Political attitudes and democracy in five nations. Princeton: Princeton University Press. Almond’s and Verba’s masterpiece is a seminal work in survey research measuring citizens’ political and civic attitudes in key Western democracies. The book is also one of the first books that systematically uses survey research to measure political traits.

Inglehart, R., & Welzel, C. (2005). Modernization, cultural change, and democracy: The human development sequence . Cambridge: Cambridge University Press. This is an influential book, which uses data from the World Values Survey to explain modernization as a process that changes individual’s values away from traditional and patriarchal values and toward post-materialist values including environmental protection, minority rights, and gender equality.

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

Survey Research is a quantitative research method that involves collecting standardized data from a sample of individuals or groups through the use of structured questionnaires or interviews. The data collected is then analyzed statistically to identify patterns and relationships between variables, and to draw conclusions about the population being studied.

Survey research can be used to answer a variety of questions, including:

• What are people’s opinions about a certain topic?
• What are people’s experiences with a certain product or service?
• What are people’s beliefs about a certain issue?

Survey Research Methods

Survey Research Methods are as follows:

• Telephone surveys: A survey research method where questions are administered to respondents over the phone, often used in market research or political polling.
• Face-to-face surveys: A survey research method where questions are administered to respondents in person, often used in social or health research.
• Mail surveys: A survey research method where questionnaires are sent to respondents through mail, often used in customer satisfaction or opinion surveys.
• Online surveys: A survey research method where questions are administered to respondents through online platforms, often used in market research or customer feedback.
• Email surveys: A survey research method where questionnaires are sent to respondents through email, often used in customer satisfaction or opinion surveys.
• Mixed-mode surveys: A survey research method that combines two or more survey modes, often used to increase response rates or reach diverse populations.
• Computer-assisted surveys: A survey research method that uses computer technology to administer or collect survey data, often used in large-scale surveys or data collection.
• Interactive voice response surveys: A survey research method where respondents answer questions through a touch-tone telephone system, often used in automated customer satisfaction or opinion surveys.
• Mobile surveys: A survey research method where questions are administered to respondents through mobile devices, often used in market research or customer feedback.
• Group-administered surveys: A survey research method where questions are administered to a group of respondents simultaneously, often used in education or training evaluation.
• Web-intercept surveys: A survey research method where questions are administered to website visitors, often used in website or user experience research.
• In-app surveys: A survey research method where questions are administered to users of a mobile application, often used in mobile app or user experience research.
• Social media surveys: A survey research method where questions are administered to respondents through social media platforms, often used in social media or brand awareness research.
• SMS surveys: A survey research method where questions are administered to respondents through text messaging, often used in customer feedback or opinion surveys.
• IVR surveys: A survey research method where questions are administered to respondents through an interactive voice response system, often used in automated customer feedback or opinion surveys.
• Mixed-method surveys: A survey research method that combines both qualitative and quantitative data collection methods, often used in exploratory or mixed-method research.
• Drop-off surveys: A survey research method where respondents are provided with a survey questionnaire and asked to return it at a later time or through a designated drop-off location.
• Intercept surveys: A survey research method where respondents are approached in public places and asked to participate in a survey, often used in market research or customer feedback.
• Hybrid surveys: A survey research method that combines two or more survey modes, data sources, or research methods, often used in complex or multi-dimensional research questions.

Types of Survey Research

There are several types of survey research that can be used to collect data from a sample of individuals or groups. following are Types of Survey Research:

• Cross-sectional survey: A type of survey research that gathers data from a sample of individuals at a specific point in time, providing a snapshot of the population being studied.
• Longitudinal survey: A type of survey research that gathers data from the same sample of individuals over an extended period of time, allowing researchers to track changes or trends in the population being studied.
• Panel survey: A type of longitudinal survey research that tracks the same sample of individuals over time, typically collecting data at multiple points in time.
• Epidemiological survey: A type of survey research that studies the distribution and determinants of health and disease in a population, often used to identify risk factors and inform public health interventions.
• Observational survey: A type of survey research that collects data through direct observation of individuals or groups, often used in behavioral or social research.
• Correlational survey: A type of survey research that measures the degree of association or relationship between two or more variables, often used to identify patterns or trends in data.
• Experimental survey: A type of survey research that involves manipulating one or more variables to observe the effect on an outcome, often used to test causal hypotheses.
• Descriptive survey: A type of survey research that describes the characteristics or attributes of a population or phenomenon, often used in exploratory research or to summarize existing data.
• Diagnostic survey: A type of survey research that assesses the current state or condition of an individual or system, often used in health or organizational research.
• Explanatory survey: A type of survey research that seeks to explain or understand the causes or mechanisms behind a phenomenon, often used in social or psychological research.
• Process evaluation survey: A type of survey research that measures the implementation and outcomes of a program or intervention, often used in program evaluation or quality improvement.
• Impact evaluation survey: A type of survey research that assesses the effectiveness or impact of a program or intervention, often used to inform policy or decision-making.
• Customer satisfaction survey: A type of survey research that measures the satisfaction or dissatisfaction of customers with a product, service, or experience, often used in marketing or customer service research.
• Market research survey: A type of survey research that collects data on consumer preferences, behaviors, or attitudes, often used in market research or product development.
• Public opinion survey: A type of survey research that measures the attitudes, beliefs, or opinions of a population on a specific issue or topic, often used in political or social research.
• Behavioral survey: A type of survey research that measures actual behavior or actions of individuals, often used in health or social research.
• Attitude survey: A type of survey research that measures the attitudes, beliefs, or opinions of individuals, often used in social or psychological research.
• Opinion poll: A type of survey research that measures the opinions or preferences of a population on a specific issue or topic, often used in political or media research.
• Ad hoc survey: A type of survey research that is conducted for a specific purpose or research question, often used in exploratory research or to answer a specific research question.

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

Quantitative survey research is a method of collecting numerical data from a sample of participants through the use of standardized surveys or questionnaires. The purpose of quantitative survey research is to gather empirical evidence that can be analyzed statistically to draw conclusions about a particular population or phenomenon.

In quantitative survey research, the questions are structured and pre-determined, often utilizing closed-ended questions, where participants are given a limited set of response options to choose from. This approach allows for efficient data collection and analysis, as well as the ability to generalize the findings to a larger population.

Quantitative survey research is often used in market research, social sciences, public health, and other fields where numerical data is needed to make informed decisions and recommendations.

Qualitative survey research is a method of collecting non-numerical data from a sample of participants through the use of open-ended questions or semi-structured interviews. The purpose of qualitative survey research is to gain a deeper understanding of the experiences, perceptions, and attitudes of participants towards a particular phenomenon or topic.

In qualitative survey research, the questions are open-ended, allowing participants to share their thoughts and experiences in their own words. This approach allows for a rich and nuanced understanding of the topic being studied, and can provide insights that are difficult to capture through quantitative methods alone.

Qualitative survey research is often used in social sciences, education, psychology, and other fields where a deeper understanding of human experiences and perceptions is needed to inform policy, practice, or theory.

Data Analysis Methods

There are several Survey Research Data Analysis Methods that researchers may use, including:

• Descriptive statistics: This method is used to summarize and describe the basic features of the survey data, such as the mean, median, mode, and standard deviation. These statistics can help researchers understand the distribution of responses and identify any trends or patterns.
• Inferential statistics: This method is used to make inferences about the larger population based on the data collected in the survey. Common inferential statistical methods include hypothesis testing, regression analysis, and correlation analysis.
• Factor analysis: This method is used to identify underlying factors or dimensions in the survey data. This can help researchers simplify the data and identify patterns and relationships that may not be immediately apparent.
• Cluster analysis: This method is used to group similar respondents together based on their survey responses. This can help researchers identify subgroups within the larger population and understand how different groups may differ in their attitudes, behaviors, or preferences.
• Structural equation modeling: This method is used to test complex relationships between variables in the survey data. It can help researchers understand how different variables may be related to one another and how they may influence one another.
• Content analysis: This method is used to analyze open-ended responses in the survey data. Researchers may use software to identify themes or categories in the responses, or they may manually review and code the responses.
• Text mining: This method is used to analyze text-based survey data, such as responses to open-ended questions. Researchers may use software to identify patterns and themes in the text, or they may manually review and code the text.

Applications of Survey Research

Here are some common applications of survey research:

• Market Research: Companies use survey research to gather insights about customer needs, preferences, and behavior. These insights are used to create marketing strategies and develop new products.
• Public Opinion Research: Governments and political parties use survey research to understand public opinion on various issues. This information is used to develop policies and make decisions.
• Social Research: Survey research is used in social research to study social trends, attitudes, and behavior. Researchers use survey data to explore topics such as education, health, and social inequality.
• Academic Research: Survey research is used in academic research to study various phenomena. Researchers use survey data to test theories, explore relationships between variables, and draw conclusions.
• Customer Satisfaction Research: Companies use survey research to gather information about customer satisfaction with their products and services. This information is used to improve customer experience and retention.
• Employee Surveys: Employers use survey research to gather feedback from employees about their job satisfaction, working conditions, and organizational culture. This information is used to improve employee retention and productivity.
• Health Research: Survey research is used in health research to study topics such as disease prevalence, health behaviors, and healthcare access. Researchers use survey data to develop interventions and improve healthcare outcomes.

Examples of Survey Research

Here are some real-time examples of survey research:

• COVID-19 Pandemic Surveys: Since the outbreak of the COVID-19 pandemic, surveys have been conducted to gather information about public attitudes, behaviors, and perceptions related to the pandemic. Governments and healthcare organizations have used this data to develop public health strategies and messaging.
• Political Polls During Elections: During election seasons, surveys are used to measure public opinion on political candidates, policies, and issues in real-time. This information is used by political parties to develop campaign strategies and make decisions.
• Customer Feedback Surveys: Companies often use real-time customer feedback surveys to gather insights about customer experience and satisfaction. This information is used to improve products and services quickly.
• Event Surveys: Organizers of events such as conferences and trade shows often use surveys to gather feedback from attendees in real-time. This information can be used to improve future events and make adjustments during the current event.
• Website and App Surveys: Website and app owners use surveys to gather real-time feedback from users about the functionality, user experience, and overall satisfaction with their platforms. This feedback can be used to improve the user experience and retain customers.
• Employee Pulse Surveys: Employers use real-time pulse surveys to gather feedback from employees about their work experience and overall job satisfaction. This feedback is used to make changes in real-time to improve employee retention and productivity.

Survey Sample

Purpose of survey research.

The purpose of survey research is to gather data and insights from a representative sample of individuals. Survey research allows researchers to collect data quickly and efficiently from a large number of people, making it a valuable tool for understanding attitudes, behaviors, and preferences.

Here are some common purposes of survey research:

• Descriptive Research: Survey research is often used to describe characteristics of a population or a phenomenon. For example, a survey could be used to describe the characteristics of a particular demographic group, such as age, gender, or income.
• Exploratory Research: Survey research can be used to explore new topics or areas of research. Exploratory surveys are often used to generate hypotheses or identify potential relationships between variables.
• Explanatory Research: Survey research can be used to explain relationships between variables. For example, a survey could be used to determine whether there is a relationship between educational attainment and income.
• Evaluation Research: Survey research can be used to evaluate the effectiveness of a program or intervention. For example, a survey could be used to evaluate the impact of a health education program on behavior change.
• Monitoring Research: Survey research can be used to monitor trends or changes over time. For example, a survey could be used to monitor changes in attitudes towards climate change or political candidates over time.

When to use Survey Research

there are certain circumstances where survey research is particularly appropriate. Here are some situations where survey research may be useful:

• When the research question involves attitudes, beliefs, or opinions: Survey research is particularly useful for understanding attitudes, beliefs, and opinions on a particular topic. For example, a survey could be used to understand public opinion on a political issue.
• When the research question involves behaviors or experiences: Survey research can also be useful for understanding behaviors and experiences. For example, a survey could be used to understand the prevalence of a particular health behavior.
• When a large sample size is needed: Survey research allows researchers to collect data from a large number of people quickly and efficiently. This makes it a useful method when a large sample size is needed to ensure statistical validity.
• When the research question is time-sensitive: Survey research can be conducted quickly, which makes it a useful method when the research question is time-sensitive. For example, a survey could be used to understand public opinion on a breaking news story.
• When the research question involves a geographically dispersed population: Survey research can be conducted online, which makes it a useful method when the population of interest is geographically dispersed.

How to Conduct Survey Research

Conducting survey research involves several steps that need to be carefully planned and executed. Here is a general overview of the process:

• Define the research question: The first step in conducting survey research is to clearly define the research question. The research question should be specific, measurable, and relevant to the population of interest.
• Develop a survey instrument : The next step is to develop a survey instrument. This can be done using various methods, such as online survey tools or paper surveys. The survey instrument should be designed to elicit the information needed to answer the research question, and should be pre-tested with a small sample of individuals.
• Select a sample : The sample is the group of individuals who will be invited to participate in the survey. The sample should be representative of the population of interest, and the size of the sample should be sufficient to ensure statistical validity.
• Administer the survey: The survey can be administered in various ways, such as online, by mail, or in person. The method of administration should be chosen based on the population of interest and the research question.
• Analyze the data: Once the survey data is collected, it needs to be analyzed. This involves summarizing the data using statistical methods, such as frequency distributions or regression analysis.
• Draw conclusions: The final step is to draw conclusions based on the data analysis. This involves interpreting the results and answering the research question.

Advantages of Survey Research

There are several advantages to using survey research, including:

• Efficient data collection: Survey research allows researchers to collect data quickly and efficiently from a large number of people. This makes it a useful method for gathering information on a wide range of topics.
• Standardized data collection: Surveys are typically standardized, which means that all participants receive the same questions in the same order. This ensures that the data collected is consistent and reliable.
• Cost-effective: Surveys can be conducted online, by mail, or in person, which makes them a cost-effective method of data collection.
• Anonymity: Participants can remain anonymous when responding to a survey. This can encourage participants to be more honest and open in their responses.
• Easy comparison: Surveys allow for easy comparison of data between different groups or over time. This makes it possible to identify trends and patterns in the data.
• Versatility: Surveys can be used to collect data on a wide range of topics, including attitudes, beliefs, behaviors, and preferences.

Limitations of Survey Research

Here are some of the main limitations of survey research:

• Limited depth: Surveys are typically designed to collect quantitative data, which means that they do not provide much depth or detail about people’s experiences or opinions. This can limit the insights that can be gained from the data.
• Potential for bias: Surveys can be affected by various biases, including selection bias, response bias, and social desirability bias. These biases can distort the results and make them less accurate.
• L imited validity: Surveys are only as valid as the questions they ask. If the questions are poorly designed or ambiguous, the results may not accurately reflect the respondents’ attitudes or behaviors.
• Limited generalizability : Survey results are only generalizable to the population from which the sample was drawn. If the sample is not representative of the population, the results may not be generalizable to the larger population.
• Limited ability to capture context: Surveys typically do not capture the context in which attitudes or behaviors occur. This can make it difficult to understand the reasons behind the responses.
• Limited ability to capture complex phenomena: Surveys are not well-suited to capture complex phenomena, such as emotions or the dynamics of interpersonal relationships.

Following is an example of a Survey Sample:

Welcome to our Survey Research Page! We value your opinions and appreciate your participation in this survey. Please answer the questions below as honestly and thoroughly as possible.

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

• A) Less than high school
• B) High school or equivalent
• C) Some college or technical school
• D) Bachelor’s degree
• E) Graduate or professional degree

3. What is your current employment status?

• A) Employed full-time
• B) Employed part-time
• C) Self-employed
• D) Unemployed

4. How often do you use the internet per day?

•  A) Less than 1 hour
• B) 1-3 hours
• C) 3-5 hours
• D) 5-7 hours
• E) More than 7 hours

5. How often do you engage in social media per day?

6. Have you ever participated in a survey research study before?

7. If you have participated in a survey research study before, how was your experience?

• A) Excellent
• E) Very poor

8. What are some of the topics that you would be interested in participating in a survey research study about?

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

9. How often would you be willing to participate in survey research studies?

• A) Once a week
• B) Once a month
• C) Once every 6 months
• D) Once a year

10. Any additional comments or suggestions?

Thank you for taking the time to complete this survey. Your feedback is important to us and will help us improve our survey research efforts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Open Access

Peer-reviewed

Research Article

Reporting Guidelines for Survey Research: An Analysis of Published Guidance and Reporting Practices

* E-mail: [email protected]

Affiliation Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, Canada

Affiliations Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, Canada, Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Canada

Affiliation Canadian Institutes of Health Research, Ottawa, Canada

Affiliation Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Canada

Affiliations Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, Canada, Department of Medicine, University of Ottawa, Ottawa, Canada

• Carol Bennett, 
• Sara Khangura, 
• Jamie C. Brehaut, 
• Ian D. Graham, 
• David Moher, 
• Beth K. Potter, 
• Jeremy M. Grimshaw

• Published: August 2, 2011
• https://doi.org/10.1371/journal.pmed.1001069
• Reader Comments

Research needs to be reported transparently so readers can critically assess the strengths and weaknesses of the design, conduct, and analysis of studies. Reporting guidelines have been developed to inform reporting for a variety of study designs. The objective of this study was to identify whether there is a need to develop a reporting guideline for survey research.

Methods and Findings

We conducted a three-part project: (1) a systematic review of the literature (including “Instructions to Authors” from the top five journals of 33 medical specialties and top 15 general and internal medicine journals) to identify guidance for reporting survey research; (2) a systematic review of evidence on the quality of reporting of surveys; and (3) a review of reporting of key quality criteria for survey research in 117 recently published reports of self-administered surveys. Fewer than 7% of medical journals (n = 165) provided guidance to authors on survey research despite a majority having published survey-based studies in recent years. We identified four published checklists for conducting or reporting survey research, none of which were validated. We identified eight previous reviews of survey reporting quality, which focused on issues of non-response and accessibility of questionnaires. Our own review of 117 published survey studies revealed that many items were poorly reported: few studies provided the survey or core questions (35%), reported the validity or reliability of the instrument (19%), defined the response rate (25%), discussed the representativeness of the sample (11%), or identified how missing data were handled (11%).

Conclusions

There is limited guidance and no consensus regarding the optimal reporting of survey research. The majority of key reporting criteria are poorly reported in peer-reviewed survey research articles. Our findings highlight the need for clear and consistent reporting guidelines specific to survey research.

Please see later in the article for the Editors' Summary

Citation: Bennett C, Khangura S, Brehaut JC, Graham ID, Moher D, Potter BK, et al. (2011) Reporting Guidelines for Survey Research: An Analysis of Published Guidance and Reporting Practices. PLoS Med 8(8): e1001069. https://doi.org/10.1371/journal.pmed.1001069

Academic Editor: Rachel Jewkes, Medical Research Council, South Africa

Received: December 23, 2010; Accepted: June 17, 2011; Published: August 2, 2011

Copyright: © 2011 Bennett et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: Funding, in the form of salary support, was provided by the Canadian Institutes of Health Research [MGC – 42668]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Editors' Summary

Surveys, or questionnaires, are an essential component of many types of research, including health, and usually gather information by asking a sample of people questions on a specific topic and then generalizing the results to a larger population. Surveys are especially important when addressing topics that are difficult to assess using other approaches and usually rely on self reporting, for example self-reported behaviors, such as eating habits, satisfaction, beliefs, knowledge, attitudes, opinions. However, the methods used in conducting survey research can significantly affect the reliability, validity, and generalizability of study results, and without clear reporting of the methods used in surveys, it is difficult or impossible to assess these characteristics and therefore to have confidence in the findings.

Why Was This Study Done?

This uncertainty in other forms of research has given rise to Reporting Guidelines—evidence-based, validated tools that aim to improve the reporting quality of health research. The STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) Statement includes cross-sectional studies, which often involve surveys. But not all surveys are epidemiological, and STROBE does not include methods' and results' reporting characteristics that are unique to surveys. Therefore, the researchers conducted this study to help determine whether there is a need for a reporting guideline for health survey research.

What Did the Researchers Do and Find?

The researchers identified any previous relevant guidance for survey research, and any evidence on the quality of reporting of survey research, by: reviewing current guidance for reporting survey research in the “Instructions to Authors” of leading medical journals and in published literature; conducting a systematic review of evidence on the quality of reporting of surveys; identifying key quality criteria for the conduct of survey research; and finally, reviewing how these criteria are currently reported by conducting a review of recently published reports of self-administered surveys.

The researchers found that 154 of the 165 journals searched (93.3%) did not provide any guidance on survey reporting, even though the majority (81.8%) have published survey research. Only three of the 11 journals that provided some guidance gave more than one directive or statement. Five papers and one Internet site provided guidance on the reporting of survey research, but none used validated measures or explicit methods for development. The researchers identified eight papers that addressed the quality of reporting of some aspect of survey research: the reporting of response rates; the reporting of non-response analyses in survey research; and the degree to which authors make their survey instrument available to readers. In their review of 117 published survey studies, the researchers found that many items were poorly reported: few studies provided the survey or core questions (35%), reported the validity or reliability of the instrument (19%), discussed the representativeness of the sample (11%), or identified how missing data were handled (11%). Furthermore, (88 [75%]) did not include any information on consent procedures for research participants, and one-third (40 [34%]) of papers did not report whether the study had received research ethics board review.

What Do These Findings Mean?

Overall, these results show that guidance is limited and consensus lacking about the optimal reporting of survey research, and they highlight the need for a well-developed reporting guideline specifically for survey research—possibly an extension of the guideline for observational studies in epidemiology (STROBE)—that will provide the structure to ensure more complete reporting and allow clearer review and interpretation of the results from surveys.

Additional Information

Please access these web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001069 .

• More than 100 reporting guidelines covering a broad spectrum of research types are indexed on the EQUATOR Networks web site
• More information about STROBE is available on the STROBE Statement web site

Introduction

Surveys are a research method by which information is typically gathered by asking a subset of people questions on a specific topic and generalising the results to a larger population [1] , [2] . They are an essential component of many types of research including public opinion, politics, health, and others. Surveys are especially important when addressing topics that are difficult to assess using other approaches (e.g., in studies assessing constructs that require individual self-report about beliefs, knowledge, attitudes, opinions, or satisfaction). However, there is substantial literature to show that the methods used in conducting survey research can significantly affect the reliability, validity, and generalisability of study results [3] , [4] . Without clear reporting of the methods used in surveys, it is difficult or impossible to assess these characteristics.

Reporting guidelines are evidence-based, validated tools that employ expert consensus to specify minimum criteria for authors to report their research such that readers can critically appraise and interpret study findings [5] – [7] . More than 100 reporting guidelines covering a broad spectrum of research types are indexed on the EQUATOR Network's website ( www.equator-network.org ). There is increasing evidence that reporting guidelines are achieving their aim of improving the quality of reporting of health research [8] – [11] .

Given the growth in the number and range of reporting guidelines, the need for guidance on how to develop a guideline has been addressed [7] . A well-structured development process for reporting guidelines includes a review of the literature to determine whether a reporting guideline already exists (i.e., a needs assessment) [7] . The needs assessment should also include a search for evidence on the quality of reporting of published research in the domain of interest [7] .

The series of studies reported here was conducted to help determine whether there is a need for survey research reporting guidelines. We sought to identify any previous relevant guidance for survey research, and any evidence on the quality of reporting of survey research. The objectives of our study were:

• to identify current guidance for reporting survey research in the “Instructions to Authors” of leading medical journals and in published literature;
• to conduct a systematic review of evidence on the quality of reporting of surveys; and
• to identify key quality criteria for the conduct of survey research and to review how they are being reported through a review of recently published reports of self-administered surveys.

Part 1: Identification of Current Guidance for Survey Research

Identifying guidance in “instructions to authors” sections in peer reviewed journals..

Using a strategy originally developed by Altman [12] to assess endorsement of CONSORT by top medical journals, we identified the top five journals from each of 33 medical specialties, and the top 15 journals from the general and internal medicine category, using Web of Science citation impact factors (list of journals available on request). The final sample consisted of 165 unique journals (15 appeared in more than one specialty).

We reviewed each journal's “Instructions to Authors” web pages as well as related PDF documents between January 12 and February 9, 2009. We used the “find” features of the Firefox web browser and Adobe Reader software to identify the following search terms: survey, questionnaire, response, response rate, respond, and non-responder. Web pages were hand searched for statements relevant to survey research. We also conducted an electronic search (MEDLINE 1950 – February Week 1, 2009; terms: survey, questionnaire) to identify whether the journals have published survey research.

Any relevant text was summarized by journal into categories: “No guidance” (survey related term found; however, no reporting guidance provided); “One directive” (survey related term(s) found that included one brief statement, directive or reference(s) relevant to reporting survey research); and “Guidance” (survey related term(s) including more than one statement, instruction and/or directive relevant to reporting survey research). Coding was carried out by one coder (SK) and verified by a second coder (CB).

Identifying published survey reporting guidelines.

MEDLINE (1950 – April Week 1, 2011) and PsycINFO (1806 – April Week 1, 2011) electronic databases were searched via Ovid to identify relevant citations. The MEDLINE electronic search strategy ( Text S1 ), developed by an information specialist, was modified as required for the PsycINFO database. For all papers meeting eligibility criteria, we hand-searched the reference lists and used the “Related Articles” feature in PubMed. Additionally, we reviewed relevant textbooks and web sites. Two reviewers (SK, CB) independently screened titles and abstracts of all unique citations to identify English language papers and resources that provided explicit guidance on the reporting of survey research. Full-text reports of all records passing the title/abstract screen were retrieved and independently reviewed by two members of the research team; there were no disagreements regarding study inclusion and all eligible records passing this stage of screening were included in this review. One researcher (CB) undertook a thematic analysis of identified guidance (e.g., sample selection, response rate, background, etc.), which was subsequently reviewed by all members of the research team. Data were summarized as frequencies.

Part 2: Systematic Review of Published Studies on the Quality of Survey Reporting

The results of the above search strategy ( Text S1 ) were also screened by the two reviewers to identify publications providing evidence on the quality of reporting of survey research in the health science literature. We identified the aspects of reporting survey research that were addressed in these evaluative studies and summarized their results descriptively.

Part 3: Assessment of Quality of Survey Reporting

The results from Part 1 and Part 2 identified items critical to reporting survey research and were used to inform the development of a data abstraction tool. Thirty-two items were deemed most critical to the reporting of survey research on that basis. These were compiled and categorized into a draft data abstraction tool that was reviewed and modified by all the authors, who have expertise in research methodology and survey research. The resulting draft data abstraction instrument was piloted by two researchers (CB, SK) on a convenience sample of survey articles identified by the authors. Items were added and removed and the wording was refined and edited through discussion and consensus among the coauthors. The revised final data abstraction tool ( Table S1 ) comprised 33 items.

Aiming for a minimum sample size of 100 studies, we searched the top 15 journals (by impact factor) from each of four broad areas of health research: health science, public health, general/internal medicine, and medical informatics. These categories, identified through Web of Science, were known to publish survey research and covered a broad range of the biomedical literature. An Ovid MEDLINE search of these 57 journals (three were included in more than one topic area) included Medical Subject Heading (MeSH) terms (“Questionnaires,” “Data Collection,” and “Health Surveys”) and keyword terms (“survey” and “questionnaire”). The search was limited to studies published between January 2008 and February 2009.

We defined a survey as a research method by which information is gathered by asking people questions on a specific topic and the data collection procedure is standardized and well defined. The information is gathered from a subset of the population of interest with the intent of generating summary statistics that are generalisable to the larger population [1] , [2] .

Two reviewers (CB, SK) independently screened all citations (title and abstract) to determine whether the study used a survey instrument consistent with our definition. The same reviewers screened all full-text articles of citations meeting our inclusion criteria, and those whose eligibility remained unclear. We included all primary reports of self-administered surveys, excluding secondary analyses, longitudinal studies, or surveys that were administered openly through the web (i.e., studies that lacked a clearly defined sampling frame). Duplicate data extraction was completed by the two reviewers. Inconsistencies were resolved by discussion and consensus.

Part 1: Identification of Current Guidance for Survey Research – “Instructions to Authors”

Of the 165 journals searched, 154 (93.3%) did not provide any guidance on survey reporting. Of these 154, 126 (81.8%) have published survey research, while 28 have not. Of the 11 journals providing some guidance, eight provided a brief phrase, statement of guidance, or reference; and three provided more substantive guidance, including more than one directive or statement. Examples are provided in Table 1 . Although no reporting guidelines for survey research were identified, several journals referenced the EQUATOR Network's web site. The EQUATOR Network includes two papers relevant to reporting survey research [13] , [14] .

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https://doi.org/10.1371/journal.pmed.1001069.t001

The EQUATOR Network also links to the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) Statement ( www.strobe-statement.org ). Although the STROBE Statement includes cross-sectional studies, a class of studies that subsumes surveys, not all surveys are epidemiological. Additionally, STROBE does not include Methods ' and Results ' reporting characteristics that are unique to surveys ( Table S1 ).

Part 1: Identification of Current Guidance for Survey Research - Published Survey Reporting Guidelines

Our search identified 2,353 unique records ( Figure 1 ), which were title-screened. One-hundred sixty-four records were included in the abstract screen, from which 130 were excluded. The remaining 34 records were retrieved for full-text screening to determine eligibility. There was substantial agreement between reviewers across all the screening phases (kappa  =  0.73; 95% CI 0.69–0.77).

https://doi.org/10.1371/journal.pmed.1001069.g001

We identified five papers [13] – [17] and one internet site [18] that provided guidance on the reporting of survey research. None of these sources reported using valid measures or explicit methods for development. In all cases, in addition to more descriptive details, the guidance was presented in the form of a numbered or bulleted checklist. One checklist was excluded from our descriptive analysis as it was very specific to the reporting of internet surveys [16] . Two checklists were combined for analysis because one [14] was a slightly modified version of the other [17] .

Amongst the four checklists, 38 distinct reporting items were identified and grouped in eight broad themes: background, methods, sample selection, research tool, results, response rates, interpretation and discussion, and ethics and disclosure ( Table 2 ). Only two items appeared in all four checklists: providing a description of the questionnaire instrument and describing the representativeness of the sample to the population of interest. Nine items appear in three checklists, 17 items appear in two checklists, and 10 items appear in only one checklist.

https://doi.org/10.1371/journal.pmed.1001069.t002

Screening results are presented in Figure 1 . Eight papers were identified that addressed the quality of reporting of some aspect of survey research. Five studies [19] – [23] addressed the reporting of response rates; three evaluated the reporting of non-response analyses in survey research [20] , [21] , [24] ; and two assessed the degree to which authors make their survey instrument available to readers ( Table 3 ) [25] , [26] .

https://doi.org/10.1371/journal.pmed.1001069.t003

Part 3: Assessment of Quality of Survey Reporting from the Biomedical Literature

Our search identified 1,719 citations: 1,343 citations were excluded during title/abstract screening because these studies did not use a survey instrument as their primary research tool. Three hundred seventy-six citations were retrieved for full-text review. Of those, 259 did not meet our eligibility criteria; reasons for their exclusion are reported in Figure 2 . The remaining 117 articles, reporting results from self-administered surveys, were retained for data abstraction.

https://doi.org/10.1371/journal.pmed.1001069.g002

The 117 articles were published in 34 different journals: 12 journals from health science, seven from medical informatics, 10 from general/internal medicine, and eight from public health ( Table S2 ). The median number of pages per study was 8 (range 3–26). Of the 33 items that were assessed using our data abstraction form, the median number of items reported was 18 (range 11–25).

Reporting Characteristics: Title, Abstract, and Introduction

The majority (113 [97%]) of articles used the term “survey” or “questionnaire” in the title or abstract; four articles did not use a term to indicate that the study was a survey. While all of the articles presented a background to their research, 17 (15%) did not identify a specific purpose, aim, goal, or objective of the study. ( Table 4 )

https://doi.org/10.1371/journal.pmed.1001069.t004

Reporting Characteristics: Methods

Approximately one-third (40 [34%]) of survey research reports did not provide access to the questionnaire items used in the study in either the article, appendices, or an online supplement. Of those studies that reported the use of existing survey questionnaires, the majority (40/52 [77%]) did not report the psychometric properties of the tool (although all but two did reference their sources). The majority of studies that developed a novel questionnaire (91/111 [82%]) failed to clearly describe the development process and/or did not describe the methods used to pre-test the tool; the majority (89/111 [80%]) also failed to report the reliability or validity of a newly developed survey instrument. For those papers which used survey instruments that required scoring (n = 95), 63 (66%) did not provide a description of the scoring procedures.

With respect to a description of sample selection methods, 104 (89%) studies did not describe the sample's representativeness of the population of interest. The majority (110 [94%]) of studies also did not present a sample size calculation or other justification of the sample size.

There were 23 (20%) papers for which we could not determine the mode of survey administration (i.e., in-person, mail, internet, or a combination of these). Forty-one (35%) articles did not provide information on either the type (i.e. phone, e-mail, postal mail) or the number of contact attempts. For 102 (87%) papers, there was no description of who was identified as the organization/group soliciting potential research subjects for their participation in the survey.

Twelve (10%) papers failed to provide a description of the methods used to analyse the data (i.e., a description of the variables that were analysed, how they were manipulated, and the statistical methods used). However, for a further 55 (47%) studies, the data analysis would be a challenge to replicate based on the description provided in the research report. Very few studies provided methods for analysis of non-response error, calculating response rates, or handling missing item data (15 [13%], 5 [4%], and 13 [11%] respectively). The majority (112 [96%]) of the articles did not provide a definition or cut-off limit for partial completion of questionnaires.

Reporting Characteristics: Results

While the majority (89 [76%]) of papers provided a defined response rate, 28 studies (24%) failed to define the reported response rate (i.e., no information was provided on the definition of the rate or how it was calculated), provided only partial information (e.g., response rates were reported for only part of the data, or some information was reported but not a response rate), or provided no quantitative information regarding a response rate. The majority (104 [87%]) of studies did not report the sample disposition (i.e., describing the number of complete and partial returned questionnaires according to the number of potential participants known to be eligible, of unknown eligibility, or known to be ineligible). More than two-thirds (80 [68%]) of the reports provided no information on how non-respondents differed from respondents.

Reporting Characteristics: Discussion and Ethical Quality Indicators

While all of the articles summarized their results with regard to the objectives, and the majority (110 [94%]) described the limitations of their study, most (90 [77%]) did not outline the strengths of their study and 70 (60%) did not include any discussion of the generalisability of their results.

When considering the ethical quality indicators, reporting was varied. While three-quarters (86 [74%]) of the papers reported their source of funding, approximately the same proportion (88 [75%]) did not include any information on consent procedures for research participants. One-third (40 [34%]) of papers did not report whether the study had received research ethics board review.

Our comprehensive review, to identify relevant guidance for survey research and evidence on the quality of reporting of surveys, substantiates the need for a reporting guideline for survey research. Overall, our results show that few medical journals provide guidance to authors regarding survey research. Furthermore, no validated guidelines for reporting surveys currently exist. Previous reviews of survey reporting quality and our own review of 117 published studies revealed that many criteria are poorly reported.

Surveys are common in health care research; we identified more than 117 primary reports of self-administered surveys in 34 high-impact factor journals over a one-year period. Despite this, the majority of these journals provided no guidance to authors for reporting survey research. This may stem, at least partly, from the fact that validated guidelines for survey research do not exist and that recommended quality criteria vary considerably. The recommended reporting criteria that we identified in the published literature are not mutually exclusive, and there is perhaps more overlap if one takes into account implicit and explicit considerations. Regardless of these limitations, the lack of clear guidance has contributed to inconsistency in the literature; both this work and that of others [19] – [26] shows that key survey quality characteristics are often under-reported.

Self-administered sample surveys are a type of observational study and for that reason they can fall within the scope of STROBE. However, there are methodological features relevant to sample surveys that need to be highlighted in greater detail. For example, surveys that use a probability sampling design do so in order to be able to generalise to a specific target population (many other types of observational research may have a more “infinite” target population); this emphasizes the importance of coverage error and non-response error – topics that have received attention in the survey literature. Thus, in our data abstraction tool, we placed emphasis on specific methodological details excluded from STROBE – such as non-response analysis, details of strategies used to increase response rates (e.g., multiple contacts, mode of contact of potential participants), and details of measurement methods (e.g., making the instrument available so that readers can consider questionnaire formatting, question framing, choice of response categories, etc.).

Consistent with previous work [25] , [26] , fully one-third of our sample failed to provide access to any survey questions used in the study. This poses challenges both for critical analysis of the studies and for future use of the tools, including replication in new settings. These challenges will be particularly apparent as the articles age and study authors become more difficult to contact [25] .

Assessing descriptions of the study population and sampling frame posed particular challenges in this study. It was often unclear whom the authors considered to be the population of interest. To standardise our assessment of this item, we used a clearly delineated definition of “survey population” and “sampling frame” [3] , [27] . A survey reporting guideline could help this issue by clearly defining the difference between the terms and descriptions of “population” and “sampling frame.”

Our results regarding reporting of response rates and non-response analysis were similar to previously published studies [19] – [24] . In our sample, 24% of papers assessed did not provide a defined response rate and 68% did not provide results from non-response analysis. The wide variation in how response rates are reported in the literature is perhaps a historical reflection of the limited consensus or explicit journal policy for response rate reporting [22] , [28] , [29] . However, despite lack of explicit policies regarding acceptable standards for response rates or the reporting of response rates, journal editors are known to have implicit policies for acceptable response rates when considering the publication of surveys [17] , [22] , [29] , [30] . Given the concern regarding declining response rates to surveys [31] , there is a need to ensure that aspects of the survey's design and conduct are well reported so that reviewers can adequately assess the degree of bias that may be present and allay concerns over the representativeness of the survey population.

With regard to the ethical quality indicators, sources of study funding were often reported (74%) in this sample of articles. However, the reporting of research ethics board approval and subject consent procedures were reported far less often. In particular, the reporting of informed consent procedures was often absent in studies where physicians, residents, other clinicians or health administrators were the subjects. This finding may suggest that researchers do not perceive doctors and other health-care professionals and administrators to be research subjects in the same way they perceive patients and members of the public to be. It could also reflect a lack of current guidelines that specifically address the ethical use of health services professionals and staff as research subjects.

Our research is not without limitations. With respect to the review of journals' “Instructions to Authors,” the study was cross-sectional in contrast with the dynamic nature of web pages. Since our searches in early 2009, several journals have updated their web pages. It has been noted that at least one has added a brief reference to the reporting of survey research.

A second limitation is that our sample included only the contents of “Instructions to Authors” web pages for higher-impact factor journals. It is possible that journals with lower impact factors contain guidance for reporting survey research. We chose this approach, which replicates previous similar work [12] , to provide a defensible sample of journals.

</?twb=.3w?>Third, the problem of identifying non-randomised studies in electronic searches is well known and often related to the inconsistent use of terminology in the original papers. It is possible that our search strategy failed to identify relevant articles. However, it is unlikely that there is an existing guideline for survey research that is in widespread use, given our review of actual surveys, instructions to authors, and reviews of reporting quality.

Fourth, although we restricted our systematic review search strategy to two health science databases, our hand search did identify one checklist that was not specific to the health science literature [18] . The variation in recommended reporting criteria amongst the checklists may, in part, be due to variation in the different domains (i.e., health science research versus public opinion research).

Additionally, we did not critically appraise the quality of evidence for items included in the checklists nor the quality of the studies that addressed the quality of reporting of some aspect of survey research. For our review of current reporting practices for surveys, we were unable to identify validated tools for evaluation of these studies. While we did use a comprehensive and iterative approach to develop our data abstraction tool, we may not have captured information on characteristics deemed important by other researchers. Lastly, our sample was limited to self-administered surveys, and the results may not be generalisable to interviewer-administered surveys.

Recently, Moher and colleagues outlined the importance of a structured approach to the development of reporting guidelines [7] . Given the positive impact that reporting guidelines have had on the quality of reporting of health research [8] – [11] , and the potential for a positive upstream effect on the design and conduct of research [32] , there is a fundamental need for well-developed reporting guidelines. This paper provides results from the initial steps in a structured approach to the development of a survey reporting guideline and forms the foundation for our further work in this area.

In conclusion, there is limited guidance and no consensus regarding the optimal reporting of survey research. While some key criteria are consistently reported by authors publishing their survey research in peer-reviewed journals, the majority are under-reported. As in other areas of research, poor reporting compromises both transparency and reproducibility, which are fundamental tenets of research. Our findings highlight the need for a well developed reporting guideline for survey research – possibly an extension of the guideline for observational studies in epidemiology (STROBE) – that will provide the structure to ensure more complete reporting and allow clearer review and interpretation of the results from surveys.

Supporting Information

Data abstraction tool items and overlap with STROBE.

https://doi.org/10.1371/journal.pmed.1001069.s001

Journals represented by 117 included articles.

https://doi.org/10.1371/journal.pmed.1001069.s002

Ovid MEDLINE search strategy.

https://doi.org/10.1371/journal.pmed.1001069.s003

Acknowledgments

We thank Risa Shorr (Librarian, The Ottawa Hospital) for her assistance with designing the electronic search strategy used for this study.

Author Contributions

Conceived and designed the experiments: JG DM CB SK JB IG BP. Analyzed the data: CB SK JB DM JG. Contributed to the writing of the manuscript. CB SK JB IG DM BP JG. ICMJE criteria for authorship read and met. CB SK JB IG DM BP JG. Acqusition of data: CB SK.

• 1. Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, et al. (2004) Survey Methodology. Hoboken (New Jersey): John Wiley & Sons, Inc.
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• 27. Dillman DA (2007) Mail and Internet Surveys: The Tailored Design Method. Hoboken (New Jersey): John Wiley & Sons, Inc.

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Survey Research: An Effective Design for Conducting Nursing Research

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• • Describe the steps of the survey research project.
• • Differentiate survey research methods.
• a. social desirability
• b. social status.
• c. validated practice.
• d. validated response.
• a. Web-based
• b. Face-to-face interviews
• c. U.S. mail
• a. They have the potential for researcher bias.
• b. They are time consuming.
• c. They reach too many participants.
• d. They have the potential for subject bias.
• a. A signed consent form from each participant is required.
• b. Approval from an institutional review board is not needed.
• c. Informed consent is implied when the survey is completed and returned.
• d. Respondents cannot be asked for information that would identify them.
• a. Purposive sample
• b. Population study
• c. Target survey
• d. Subset sample
• a. A questionnaire sent by registered mail
• b. A questionnaire that is at least 10 pages long
• c. Four contacts by mail followed by a "special" contact
• d. The addition of a form letter to the questionnaire
• a. outcome validity.
• b. inter-rater validity.
• c. face validity.
• d. construct validity.
• a. Outcome validity
• b. Inter-rater validity
• c. Face validity
• d. Construct validity
• a. inter-rater reliability.
• b. intra-rater reliability.
• c. concept validity.
• d. database validity.
• a. send the surveys out in waves.
• b. send all surveys out at one time.
• c. hold data entry until the end of data collection.
• d. hold data cleaning until the end of data collection.
• a. Statistical techniques should be independent of the design.
• b. Statistical techniques should match the design.
• c. Regression models should be used in the analysis.
• d. Pattern testing should be used in the analysis.
• c. Data analysis
• d. Discussion
• • Describe the steps of the survey research project. 1 2 3 4 5 ______________
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• 2 Were the authors knowledgeable about the subject? 1 2 3 4 5 ______________
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• 5 Was the article useful to you in your work? 1 2 3 4 5 ______________
• 6 Was there enough time allotted for this activity? 1 2 3 4 5 ______________ Comments: ______________ ______________ ______________ ______________ ______________ ______________
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How To Write a Good Research Question: Guide with Definition, Tips & Examples

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Research questions form the backbone of any study, guiding researchers in their search for knowledge and understanding. Framing relevant research questions is the first essential step for ensuring the research is effective and produces valuable insights.

In this blog, we’ll explore what research questions are, tips for crafting them, and a variety of research question examples across different fields to help you formulate a well-balanced research questionnaire.

Let’s begin.

What Is a Research Question?

A research question is a specific inquiry or problem statement guiding a research study, outlining the researcher’s intention to investigate. Think of it as a roadmap for your paper or thesis – it tells you exactly what you want to explore, giving your work a clear purpose.

A good research question not only helps you focus your writing but also guides your readers. It gives them a clear idea of what your research is about and what you aim to achieve. Before you start drafting your paper and even before you conduct your study, it’s important to write a concise statement of what you want to accomplish or discover.

This sets the stage for your research and ensures your work is focused and purposeful.

Why Are Research Questions Important?

Research questions are the cornerstone of any academic or scientific inquiry. They serve as a guide for the research process, helping to focus the study, define its goals, and structure its methodology. 

Below are some of its most significant impacts, along with hypothetical examples to help you understand them better:

1. Guidance and Focus

Research questions provide a clear direction for the study, enabling researchers to narrow down the scope of their investigation to a manageable size. Research efforts can become scattered and unfocused without a well-defined question without a well-defined question, leading to wasted time and resources.

For example, consider a researcher interested in studying the effects of technology on education. A broad interest in technology and education could lead to an overwhelming range of topics to cover. However, by formulating a specific research question such as, “ How does the use of interactive digital textbooks in high school science classes affect students’ learning outcomes?” the researcher can focus their study on a specific aspect of technology in education, making the research more manageable and directed.

2. Defining the Research Objectives

A well-crafted research question helps to clearly define what the researcher aims to discover, examine, or analyze. This clarity is crucial for determining the study’s objectives and ensures that every step of the research process contributes toward achieving these goals.

For example, in a study aimed at understanding the impact of remote work on employee productivity, a research question such as “ Does remote work increase productivity among information technology professionals? ” directly sets the objective of the study to measure productivity levels among a specific group when working remotely.

3. Determining the Research Methodology

The research question influences the choice of methodology, including the design, data collection methods, and analysis techniques. It dictates whether the study should be qualitative, quantitative, or mixed methods and guides the selection of tools and procedures for conducting the research.

For example, in a research question like “ What are the lived experiences of first-generation college students? ” a qualitative approach using interviews or focus groups might be chosen to gather deep, nuanced insights into students’ experiences. In contrast, a question such as “ What percentage of first-generation college students graduate within four years?” would require a quantitative approach, possibly utilizing existing educational data sets for analysis.

4. Enhancing Relevance and Contribution

A well-thought-out research question ensures that the study addresses a gap in the existing literature or solves a real-world problem. This relevance is crucial for the contribution of the research to the field, as it helps to advance knowledge, inform policy, or offer practical solutions.

For example, in a scenario where existing research has largely overlooked the environmental impacts of single-use plastics in urban waterways, a question like “ What are the effects of single-use plastic pollution on the biodiversity of urban waterways?” can fill this gap, contributing valuable new insights to environmental science and potentially influencing urban environmental policies.

5. Facilitating Data Interpretation and Analysis

Clear research questions help in structuring the analysis, guiding the interpretation of data, and framing the discussion of results. They ensure that the data collected is directly relevant to the questions posed, making it easier to draw meaningful conclusions.

For example, in a study asking, “ How do social media algorithms influence political polarization among users? ” the data analysis would specifically focus on the mechanisms of algorithmic content delivery and its effects on user behavior and political views. This focus makes it straightforward to interpret how algorithm-induced echo chambers might contribute to polarization.

Types of Research Questions

Understanding the different types of research questions is essential for researchers to effectively design and conduct studies that align with their research objectives and methodologies

These questions can be broadly categorized into three main types: quantitative, qualitative, and mixed-method research questions.

Let’s explore each type in-depth, along with some examples.

Type A: Quantitative Research Questions

Quantitative research involves the collection and analysis of numerical data to answer specific research questions or hypotheses. It focuses on quantifying relationships, patterns, and phenomena, often using statistical methods for analysis. Quantitative research questions are typically structured and aim to explore relationships between variables or assess the impact of interventions.

Quantitative research questions can again be subcategorized into three distinct types:

1. Descriptive Questions :

Descriptive questions aim to describe characteristics, behaviors, or phenomena within a population. These questions often start with words like “ how much ,” “ how many ,” or “ what is the frequency of .” They provide a snapshot of a particular situation or phenomenon.

Example: “ What is the average age of first-time homebuyers in the United States?”

2. Comparative Questions :

Comparative questions seek to compare two or more groups, conditions, or variables to identify differences or similarities. They often involve the use of statistical tests to determine the significance of observed differences or associations.

Example: “Is there a significant difference in academic performance between students who receive tutoring and those who do not?”

3. Relationship Questions:

Relationship questions explore the associations or correlations between variables. They aim to determine the strength and direction of relationships, allowing researchers to assess the predictive power of one variable on another.

Example: “What is the relationship between exercise frequency and levels of anxiety among adults?”

Type B: Qualitative Research Questions

Qualitative research involves the exploring and understanding of complex phenomena through an in-depth examination of individuals’ experiences, behaviors, and perspectives. It aims to uncover meaning, patterns, and underlying processes within a specific context, often through techniques such as interviews, observations, and content analysis.

Types of qualitative research questions:

1. Exploratory Questions:

Exploratory questions seek to understand a particular phenomenon or issue in depth. They aim to uncover new insights, perspectives, or dimensions that may not have been previously considered.

Example: “What are the experiences of LGBTQ+ individuals in accessing healthcare services in rural communities?”

2. Descriptive Questions:

Descriptive questions aim to provide a detailed description or portrayal of a phenomenon or social context. They focus on capturing the intricacies and nuances of a particular situation or setting.

Example: “What are the communication patterns within multicultural teams in a corporate setting?”

3. Explanatory Questions:

Explanatory questions delve into the underlying reasons, mechanisms, or processes that influence a phenomenon or behavior. They aim to uncover the ‘why’ behind observed patterns or relationships.

Example: “What factors contribute to employee turnover in the hospitality industry?”

Type C: Mixed-Methods Research Questions

Mixed-methods research integrates both quantitative and qualitative approaches within a single study, allowing researchers to gain a comprehensive understanding of a research problem. Mixed-method research questions are designed to address complex phenomena from multiple perspectives, combining the strengths of both quantitative and qualitative methodologies.

Types of Mixed-Methods Research Questions:

1. Sequential Questions:

Sequential questions involve the collection and analysis of quantitative and qualitative data in separate phases or stages. The findings from one phase inform the design and implementation of the subsequent phase.

Example: “Quantitatively, what are the prevalence rates of mental health disorders among adolescents? Qualitatively, what are the factors influencing help-seeking behaviors among adolescents with mental health concerns?”

2. Concurrent Questions:

Concurrent questions involve the simultaneous collection and analysis of quantitative and qualitative data. Researchers triangulate findings from both methods to provide a more comprehensive understanding of the research problem.

Example: “How do students’ academic performance (quantitative) correlate with their perceptions of school climate (qualitative)?”

3. Transformative Questions:

Transformative questions aim to use mixed-methods research to bring about social change or inform policy decisions. They seek to address complex societal issues by combining quantitative data on prevalence rates or trends with qualitative insights into lived experiences and perspectives.

Example: “What are the barriers to accessing healthcare services for underserved communities, and how can healthcare policies be redesigned to address these barriers effectively?”

Steps to Developing a Good Research Question

Developing a good research question is a crucial first step in any research endeavor. A well-crafted research question serves as the foundation for the entire study, guiding the researcher in formulating hypotheses, selecting appropriate methodologies, and conducting meaningful analyses.

Here are the steps to developing a good research question:

Identify a Broad Topic

Begin by identifying a broad area of interest or a topic that you would like to explore. This could stem from your academic discipline, professional interests, or personal curiosity. However, make sure to choose a topic that is both relevant and feasible for research within the constraints of your resources and expertise.

Conduct Preliminary Research

Before refining your research question, conduct preliminary research to familiarize yourself with existing literature and identify gaps, controversies, or unanswered questions within your chosen topic. This step will help you narrow down your focus and ensure that your research question contributes to the existing body of knowledge.

Narrow Down Your Focus

Based on your preliminary research, narrow down your focus to a specific aspect, problem, or issue within your chosen topic. Consider the scope of your study, the availability of resources, and the feasibility of addressing your research question within a reasonable timeframe. Narrowing down your focus will help you formulate a more precise and manageable research question.

Define Key Concepts and Variables

Clearly define the key concepts, variables, or constructs that are central to your research question. This includes identifying the main variables you will be investigating, as well as any relevant theoretical or conceptual frameworks that will guide your study. Clarifying these aspects will ensure that your research question is clear, specific, and focused.

Formulate Your Research Question

Based on your narrowed focus and defined key concepts, formulate your research question. A good research question is concise, specific, and clearly articulated. It should be phrased in a way that is open-ended and leads to further inquiry. Avoid vague or overly broad questions that are difficult to answer or lack clarity.

Consider the Type of Research

Consider whether your research question is best suited for quantitative, qualitative, or mixed-methods research. The type of research question will influence your choice of methodologies, data collection techniques, and analytical approaches. Tailor your research question to align with the goals and requirements of your chosen research paradigm.

Evaluate the Significance and Relevance

Evaluate the significance and relevance of your research question within the context of your academic discipline, field of study, or practical implications. Consider how your research question fills gaps in knowledge, addresses practical problems, or advances theoretical understanding. A good research question should be meaningful and contribute to the broader scholarly conversation.

Refine and Revise

Finally, refine and revise your research question based on feedback from colleagues, advisors, or peers. Consider whether the question is clear, feasible, and likely to yield meaningful results. Be open to making revisions as needed to ensure that your research question is well-constructed and aligned with the goals of your study.

Examples of Research Questions

Below are some example research questions from various fields to provide a glimpse into the diverse array of inquiries within each field.

1. Psychology Research Questions:

• How does childhood trauma influence the development of personality disorders in adulthood?
• What are the effects of mindfulness meditation on reducing symptoms of anxiety and depression?
• How does social media usage impact self-esteem among adolescents?
• What factors contribute to the formation and maintenance of romantic relationships in young adults?
• What are the cognitive mechanisms underlying decision-making processes in individuals with addiction?
• How does parenting style affect the development of resilience in children?
• What are the long-term effects of early childhood attachment patterns on adult romantic relationships?
• What role does genetics play in the predisposition to mental health disorders such as schizophrenia?
• How does exposure to violent media influence aggressive behavior in children?
• What are the psychological effects of social isolation on mental health during the COVID-19 pandemic?

2. Business Research Questions:

• What are the key factors influencing consumer purchasing behavior in the e-commerce industry?
• How does organizational culture impact employee job satisfaction and retention?
• What are the strategies for successful international market entry for small businesses?
• What are the effects of corporate social responsibility initiatives on brand reputation and consumer loyalty?
• How do leadership styles influence organizational innovation and performance?
• What are the challenges and opportunities for implementing sustainable business practices in emerging markets?
• What factors contribute to the success of startups in the technology sector?
• How do economic fluctuations affect consumer confidence and spending behavior?
• What are the impacts of globalization on supply chain management practices?
• What are the determinants of successful mergers and acquisitions in the corporate sector?

3. Education Research Questions:

• What teaching strategies are most effective for promoting student engagement in online learning environments?
• How does socioeconomic status impact academic achievement and educational attainment?
• What are the barriers to inclusive education for students with disabilities?
• What factors influence teacher job satisfaction and retention in urban schools?
• How does parental involvement affect student academic performance and school outcomes?
• What are the effects of early childhood education programs on later academic success?
• How do culturally responsive teaching practices impact student learning outcomes in diverse classrooms?
• What are the best practices for implementing technology integration in K-12 education?
• How do school leadership practices influence school climate and student outcomes?
• What interventions are most effective for addressing the achievement gap in STEM education?

4. Healthcare Research Questions:

• What are the factors influencing healthcare-seeking behavior among underserved populations?
• How does patient-provider communication affect patient satisfaction and treatment adherence?
• What are the barriers to implementing telemedicine services in rural communities?
• What interventions are effective for reducing hospital readmissions among elderly patients?
• How does access to healthcare services impact health disparities among marginalized communities?
• What are the effects of nurse staffing levels on patient outcomes in acute care settings?
• How do socioeconomic factors influence access to mental healthcare services?
• What are the best practices for managing chronic disease patients in primary care settings?
• What are the impacts of healthcare reform policies on healthcare delivery and patient outcomes?
• How does cultural competence training for healthcare providers affect patient trust and satisfaction?

5. Computer Science Research Questions:

• What are the security vulnerabilities of blockchain technology, and how can they be mitigated?
• How can machine learning algorithms be used to detect and prevent cyber-attacks?
• What are the privacy implications of data mining techniques in social media platforms?
• How can artificial intelligence be used to improve medical diagnosis and treatment?
• What are the challenges and opportunities for implementing edge computing in IoT systems?
• How can natural language processing techniques be applied to improve human-computer interaction?
• What are the impacts of algorithmic bias on fairness and equity in decision-making systems?
• How can quantum computing algorithms be optimized for solving complex computational problems?
• What are the ethical considerations surrounding the use of autonomous vehicles in transportation systems?
• How does the design of user interfaces influence user experience and usability in mobile applications?

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By following these guidelines, we are confident that your research questions will be well-designed, focused, and capable of yielding valuable insights.

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Good research questions are clear, specific, relevant, and feasible. For example, “How does childhood trauma influence the development of personality disorders in adulthood?”

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Good research questions are focused and relevant, such as “What factors influence employee job satisfaction in the hospitality industry?” Bad research questions are vague or trivial, like “What is the favorite color of employees in the hospitality industry?”

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Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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• Emla Fitzsimons

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Psychological safety and the critical role of leadership development

When employees feel comfortable asking for help, sharing suggestions informally, or challenging the status quo without fear of negative social consequences, organizations are more likely to innovate quickly , unlock the benefits of diversity , and adapt well to change —all capabilities that have only grown in importance during the COVID-19 crisis. 1 Jonathan Emmett, Gunnar Schrah, Matt Schrimper, and Alexandra Wood, “ COVID-19 and the employee experience: How leaders can seize the moment ,” June 2020, McKinsey.com; Tera Allas, David Chinn, Pal Erik Sjatil, and Whitney Zimmerman, “ Well-being in Europe: Addressing the high cost of COVID-19 on life satisfaction ,” June 2020, McKinsey.com. Yet a McKinsey Global Survey conducted during the pandemic confirms that only a handful of business leaders often demonstrate the positive behaviors that can instill this climate, termed psychological safety , in their workforce. 2 The online survey was in the field from May 14–29, 2020, and garnered responses from 1,574 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, we analyzed the results of 1,223 participants who said they were a member of a team that they did not lead, where a team is defined as two or more people who work together to achieve a common goal. CEOs were included in the findings if they said that a) their organization had a board of directors and b) they were not the board’s chair, so that they could think of their board when asked questions about their team.

As considerable prior research shows, psychological safety is a precursor to adaptive, innovative performance—which is needed in today’s rapidly changing environment—at the individual, team, and organization levels. 3 Amy C. Edmondson, The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth, first edition, Hoboken, NJ: John Wiley & Sons, November 2018; Shirley A. Ashauer and Therese Macan, “How can leaders foster team learning? Effects of leader-assigned mastery and performance goals and psychological safety,” Journal of Psychology, November–December 2013, Volume 147, Number 6, pp. 541–61, tandfonline.com; Anne Boon et al., “Team learning beliefs and behaviours in response teams,” European Journal of Training and Development, May 2013, Volume 37, Number 4, pp. 357–79, emerald.com; Daphna Brueller and Abraham Carmeli, “Linking capacities of high-quality relationships to team learning and performance in service organizations,” Human Resource Management, July–August 2011, Volume 50, Number 4, pp. 455–77, wileyonlinelibrary.com; M. Lance Frazier et al., “Psychological safety: A meta-analytic review and extension,” Personnel Psychology, February 2017, Volume 70, Number 1, pp. 113–65, onlinelibrary.wiley.com; Nikos Bozionelos and Konstantinos C. Kostopoulos, “Team exploratory and exploitative learning: Psychological safety, task conflict, and team performance,” Group & Organization Management, June 2011, Volume 36, Number 3, pp. 385–415, journals.sagepub.com; Rosario Ortega et al., “The emotional impact of bullying and cyberbullying on victims: A European cross-national study,” Aggressive Behavior, September–October 2012, Volume 38, Issue 5, pp. 342–56, onlinelibrary.wiley.com; Corinne Post, “Deep-level team composition and innovation: The mediating roles of psychological safety and cooperative learning,” Group & Organizational Management, October 2012, Volume 37, Number 5, pp. 555–88, journals.sagepub.com; Charles Duhigg, “What Google learned from its quest to build the perfect team,” New York Times, February 25, 2016, nytimes.com. Amy Edmondson’s 1999 research previously found—and our survey findings confirm—that higher psychological safety predicts a higher degree of boundary-spanning behavior, which is accessing and coordinating with those outside of an individual’s team to accomplish goals. For example, successfully creating a “ network of teams ”—an agile organizational structure that empowers teams to tackle problems quickly by operating outside of bureaucratic or siloed structures—requires a strong degree of psychological safety.

Fortunately, our newest research suggests how organizations can foster psychological safety. Doing so depends on leaders at all levels learning and demonstrating specific leadership behaviors that help their employees thrive. Investing in and scaling up leadership-development programs  can equip leaders to embody these behaviors and consequently cultivate psychological safety across the organization.

A recipe for leadership that promotes psychological safety

Leaders can build psychological safety by creating the right climate, mindsets, and behaviors within their teams. In our experience, those who do this best act as catalysts, empowering and enabling other leaders on the team—even those with no formal authority—to help cultivate psychological safety by role modeling and reinforcing the behaviors they expect from the rest of the team.

Our research finds that a positive team climate—in which team members value one another’s contributions, care about one another’s well-being, and have input into how the team carries out its work—is the most important driver of a team’s psychological safety. 4 Past research by Frazier et al. (2017) found three categories to be the main drivers of psychological safety: positive leader relations, work-design characteristics, and a positive team climate. We conducted multiple regression with relative-importance analysis to understand which category matters most, and our results show that a positive team climate has a significantly stronger direct effect on psychological safety than the other two. Based on these results, we tested a structural-equation model (SEM) in which the frequency with which team leaders displayed four leadership behaviors predicted psychological safety both directly and indirectly via positive team climate. Exploratory analyses were conducted to determine whether the effect of the leadership behaviors affected psychological safety at different levels of team climate. By setting the tone for the team climate through their own actions, team leaders have the strongest influence on a team’s psychological safety. Moreover, creating a positive team climate can pay additional dividends during a time of disruption. Our research finds that a positive team climate has a stronger effect on psychological safety in teams that experienced a greater degree of change in working remotely than in those that experienced less change during the COVID-19 pandemic. Yet just 43 percent of all respondents report a positive climate within their team.

Positive team climate is the most important driver of psychological safety and most likely to occur when leaders demonstrate supportive, consultative behaviors, then begin to challenge their teams.

During the pandemic, we have seen an accelerated shift away from the traditional command-and-control leadership style known as authoritative leadership, one of the four well-established styles of leadership behavior we examined to understand which ones encourage a positive team climate and psychological safety . The survey finds that team leaders’ authoritative-leadership behaviors are detrimental to psychological safety, while consultative- and supportive-leadership behaviors promote psychological safety.

The results also suggest that leaders can further enhance psychological safety by ensuring a positive team climate (Exhibit 1). Both consultative and supportive leadership help create a positive team climate, though to varying degrees and through different types of behaviors.

With consultative leadership, which has a direct and indirect effect on psychological safety, leaders consult their team members, solicit input, and consider the team’s views on issues that affect them. 5 The standardized regression coefficient between consultative leadership and psychological safety was 0.54. The survey measured consultative-leadership behaviors by asking respondents how frequently their team leaders demonstrate the following behaviors: ask the opinions of others before making important decisions, give team members the autonomy to make their own decisions, and try to achieve team consensus on decisions. Supportive leadership has an indirect but still significant effect on psychological safety by helping to create a positive team climate; it involves leaders demonstrating concern and support for team members not only as employees but also as individuals. 6 The survey measured supportive leadership behaviors by asking respondents how frequently their team leaders demonstrate the following behaviors: create a sense of teamwork and mutual support within the team, and demonstrate concern for the welfare of team members. These behaviors also can encourage team members to support one another.

Another set of leadership behaviors can sometimes strengthen psychological safety—but only when a positive team climate is in place. This set of behaviors, known as challenging leadership, encourages employees to do more than they initially think they can. A challenging leader asks team members to reexamine assumptions about their work and how it can be performed in order to exceed expectations and fulfill their potential. Challenging leadership has previously been linked with employees expressing creativity, feeling empowered to make work-related changes, and seeking to learn and improve. 7 Giles Hirst, Helen Shipton, and Qin Zhou, “Context matters: Combined influence of participation and intellectual stimulation on the promotion focus–employee creative relationship,” Journal of Organizational Behavior, October 2012, Volume 33, Number 7, pp. 894–909, onlinelibrary.wiley.com; Le Cong Thuan, “Motivating follower creativity by offering intellectual stimulation,” International Journal of Organizational Analysis, December 2019, Volume 28, Number 4, pp. 817–29, emerald.com; Jie Li et al., “Not all transformational leadership behaviors are equal: The impact of followers’ identification with leader and modernity on taking charge,” Journal of Leadership and Organizational Studies, August 2017, Volume 24, Number 3, pp. 318–34, journals.sagepub.com; Susana Llorens-Gumbau, Marisa Salanova Soria, and Israel Sánchez-Cardona, “Leadership intellectual stimulation and team learning: The mediating role of team positive affect,” Universitas Psychologica, March 2018, Volume 17, Number 1, pp. 1–16, revistas.javeriana.edu.co. However, the survey findings show that the highest likelihood of psychological safety occurs when a team leader first creates a positive team climate, through frequent supportive and consultative actions, and then challenges their team; without a foundation of positive climate, challenging behaviors have no significant effect. And employees’ experiences look very different depending on how their leaders behave, according to Amy Edmondson, the Novartis Professor of Leadership and Management at Harvard Business School (interactive).

What’s more, the survey results show that a climate conducive to psychological safety starts at the very top of an organization. We sought to understand the effects of senior-leader behavior on employees’ sense of safety and found that senior leaders can help create a culture of inclusiveness that promotes positive leadership behaviors throughout an organization by role-modeling these behaviors themselves. Team leaders are more likely to exhibit supportive, consultative, and challenging leadership if senior leaders demonstrate inclusiveness—for example, by seeking out opinions that might differ from their own and by treating others with respect.

The importance of developing leaders at all levels

Our findings show that investing in leadership development across an organization—for all leadership positions—is an effective method for cultivating the combination of leadership behaviors that enhance psychological safety. Employees who report that their organizations invest substantially in leadership development are more likely to also report that their team leaders frequently demonstrate consultative, supportive, and challenging leadership behaviors. They also are 64 percent more likely to rate senior leaders as more inclusive (Exhibit 2). 8 We measured investing in leadership development by asking about agreement with the following statements: “my organization places a great deal of importance on developing its leaders,” and “my organization devotes significant resources to developing its leaders.” However, the results suggest that the effectiveness of these programs varies depending upon the skills they address.

Reorient the skills developed in leadership programs

Organizations often attempt to cover many topics in their leadership-development programs . But our findings suggest that focusing on a handful of specific skills and behaviors in these learning programs can improve the likelihood of positive leadership behaviors that foster psychological safety and, ultimately, of strong team performance. Some of the most commonly taught skills at respondents’ organizations—such as open-dialogue skills, which allow leaders to explore disagreements and talk through tension in a team—are among the ones most associated with positive leadership behaviors. However, several relatively untapped skill areas also yield beneficial results (Exhibit 3).

Two of the less-commonly addressed skills in formal programs are predictive of positive leadership. Training in sponsorship—that is, enabling others’ success ahead of one’s own—supports both consultative- and challenging-leadership behaviors, yet just 26 percent of respondents say their organizations include the skill in development programs. And development of situational humility, which 36 percent of respondents say their organizations address, teaches leaders how to develop a personal-growth mindset and curiosity. Addressing this skill is predictive of leaders displaying consultative behaviors.

Development at the top is equally important

According to the data, fostering psychological safety at scale begins with companies’ most senior leaders developing and embodying the leadership behaviors they want to see across the organization. Many of the same skills that promote positive team-leader behaviors can also be developed among senior leaders to promote inclusiveness. For example, open-dialogue skills and development of social relationships within teams are also important skill sets for senior leaders.

In addition, several skills are more important at the very top of the organization. Situational and cultural awareness, or understanding how beliefs can be developed based on selective observations and the norms in different cultures, are both linked with senior leaders’ inclusiveness.

Looking ahead

Given the quickening pace of change and disruption and the need for creative, adaptive responses from teams at every level, psychological safety is more important than ever. The organizations that develop the leadership skills and positive work environment that help create psychological safety can reap many benefits, from improved innovation, experimentation, and agility to better overall organizational health and performance. 9 We define organizational health as an organization’s ability to align on a clear vision, strategy, and culture; to execute with excellence; and to renew the organization’s focus over time by responding to market trends.

As clear as this call to action may be, “How do we develop psychological safety?” and, more specifically, “Where do we start?” remain the most common questions we are asked. These survey findings show that there is no time to waste in creating and investing in leadership development at scale to help enhance psychological safety. Organizations can start doing so in the following ways:

• Go beyond one-off training programs and deploy an at-scale system of leadership development. Human behaviors aren’t easily shifted overnight. Yet too often we see companies try to do so by using targeted training programs alone. Shifting leadership behaviors within a complex system at the individual, team, and enterprise levels begins with defining a clear strategy aligned to the organization’s overall aspiration and a comprehensive set of capabilities that are required to achieve it. It’s critical to develop a taxonomy of skills (having an open dialogue, for example) that not only supports the realization of the organization’s overall identity but also fosters learning and growth and applies directly to people’s day-to-day work. Practically speaking, while the delivery of learning may be sequenced as a series of trainings—and rapidly codified and scaled for all leaders across a cohort or function of the organization—those trainings will be even more effective when combined with other building blocks of a broader learning system, such as behavioral reinforcements. While learning experiences look much different now than before the COVID-19 pandemic , digital learning provides large companies with more opportunities to break down silos and create new connections across an organization through learning.
• Invest in leadership-development experiences that are emotional, sensory, and create aha moments. Learning experiences that are immersive and engaging are remembered more clearly and for a longer time. Yet a common pitfall of learning programs is an outsize focus on the content—even though it is usually not a lack of knowledge that holds leaders back from realizing their full potential. Therefore, it’s critical that learning programs prompt leaders to engage with and shift their underlying beliefs, assumptions, and emotions to bring about lasting mindset changes. This requires a learning environment that is both conducive to the often vulnerable process of learning and also expertly designed. Companies can begin with facilitated experiences that push learners toward personal introspection through targeted reflection questions and small, intimate breakout conversations. These environments can help leaders achieve increased self-awareness, spark the desire for further growth, and, with the help of reflection and feedback, drive collective growth and performance.
• Build mechanisms to make development a part of leaders’ day-to-day work. Formal learning and skill development serve as springboards in the context of real work; the most successful learning journeys account for the rich learning that happens in day-to-day work and interactions. The use of learning nudges (that is, daily, targeted reminders for individuals) can help learners overcome obstacles and move from retention to application of their knowledge. In parallel, the organization’s most senior leaders need to be the first adopters of putting real work at the core of their development, which requires senior leaders to role model—publicly—their own processes of learning. In this context, the concept of role models has evolved; rather than role models serving as examples of the finished product, they become examples of the work in progress, high on self-belief but low on perfect answers. These examples become strong signals for leaders across the organization that it is safe to be practicing, failing, and developing on the job.

The contributors to the development and analysis of this survey include Aaron De Smet , a senior partner in McKinsey’s New Jersey office; Kim Rubenstein, a research-science specialist in the New York office; Gunnar Schrah, a director of research science in the Denver office; Mike Vierow, an associate partner in the Brisbane office; and Amy Edmondson , the Novartis Professor of Leadership and Management at Harvard Business School.

This article was edited by Heather Hanselman, an associate editor in the Atlanta office.

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ORIGINAL RESEARCH article

Decomposing the inequalities in the catastrophic health expenditures on the hospitalization in india: empirical evidence from national sample survey data.

• 1 Department of Social and Public Health, College of Health Sciences and Professions, Ohio University, Athens, OH, United States
• 2 Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
• 3 Chettinad Hospital and Research Institute, Chennai, Tamil Nadu, India
• 4 Taibah University, Medina, Saudi Arabia

Introduction: Sustainable Development Goal (SDG) Target 3.8.2 entails financial protection against catastrophic health expenditure (CHE) by reducing out-of-pocket expenditure (OOPE) on healthcare. India is characterized by one of the highest OOPE on healthcare, in conjunction with the pervasive socio-economic disparities entrenched in the population. As a corollary, India has embarked on the trajectory of ensuring financial risk protection, particularly for the poor, with the launch of various flagship initiatives. Overall, the evidence on wealth-related inequities in the incidence of CHE in low- and middle-Income countries has been heterogenous. Thus, this study was conducted to estimate the income-related inequalities in the incidence of CHE on hospitalization and glean the individual contributions of wider socio-economic determinants in influencing these inequalities in India.

Methods: The study employed cross-sectional data from the nationally represented survey on morbidity and healthcare (75th round of National Sample Survey Organization) conducted during 2017–2018, which circumscribed a sample size of 1,13,823 households and 5,57,887 individuals. The inequalities and need-adjusted inequities in the incidence of CHE on hospitalization care were assessed via the Erreygers corrected concentration index. Need-standardized concentration indices were further used to unravel the inter- and intra-regional income-related inequities in the outcome of interest. The factors associated with the incidence of CHE were explored using multivariate logistic regression within the framework of Andersen’s model of behavioral health. Additionally, regression-based decomposition was performed to delineate the individual contributions of legitimate and illegitimate factors in the measured inequalities of CHE.

Results: Our findings revealed pervasive wealth-related inequalities in the CHE for hospitalization care in India, with a profound gap between the poorest and richest income quintiles. The negative value of the concentration index (EI: −0.19) indicated that the inequalities were significantly concentrated among the poor. Furthermore, the need-adjusted inequalities also demonstrated the pro-poor concentration (EI: −0.26), denoting the unfair systemic inequalities in the CHE, which are disadvantageous to the poor. Multivariate logistic results indicated that households with older adult, smaller size, vulnerable caste affiliation, poorest income quintile, no insurance cover, hospitalization in a private facility, longer stay duration in the hospital, and residence in the region at a lower level of epidemiological transition level were associated with increased likelihood of incurring CHE on hospitalization. The decomposition analysis unraveled that the contribution of non-need/illegitimate factors (127.1%) in driving the inequality was positive and relatively high vis-à-vis negative low contribution of need/legitimate factors (35.3%). However, most of the unfair inequalities were accounted for by socio-structural factors such as the size of the household and enabling factors such as income group and utilization pattern.

Conclusion: The study underscored the skewed distribution of CHE as the poor were found to incur more CHE on hospitalization care despite the targeted programs by the government. Concomitantly, most of the inequality was driven by illegitimate factors amenable to policy change. Thus, policy interventions such as increasing the awareness, enrollment, and utilization of Publicly Financed Health Insurance schemes, strengthening the public hospitals to provide improved quality of specialized care and referral mechanisms, and increasing the overall budgetary share of healthcare to improve the institutional capacities are suggested.

1 Introduction

The Universal Health Coverage (UHC) has been proclaimed as the third major transition in health, after the demographic and epidemiological transitions ( 1 ) and has become the focal point of health policy discourse as the world made transition from millennium development goals (MDSs) to sustainable development goals (SDGs). Goal 3.8 of the SDG Agenda enunciates to achieve the UHC and encompasses two components: (i) Indicator 3.8.1–Coverage of essential health services (defined as average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, and service capacity and access, among the general and most disadvantaged population). (ii) Indicator 3.8.2–Incidence of catastrophic health spending (defined as the proportion of the population with large household expenditures on health as a share of total household expenditure or income). Despite the institutional commitment, there is an inordinate reliance on out-of-pocket-expenditure (OOPE) to finance healthcare due to the severely underfunded health system. For India, specifically, the public health expenditure as a share of GDP (1.25%) is the lowest in the world. Furthermore, the estimates from the National Health Accounts of India divulged that abysmally low coverage of government-sponsored pre-payment schemes coupled with the dearth of private health insurance has impelled households to have excessive reliance on out-of-pocket payments (58.7% of total health expenditure) for healthcare ( 2 ).

Healthcare expenditures or costs are incurred whenever a person accesses the healthcare system and utilizes the healthcare services. Health expenditures could be broadly defined as any expense that is spent on healthcare and related activities, including paying premiums for private or public health insurance coverage ( 3 ). A multitude of cost components encompasses healthcare payments on hospitalization, such as direct medical costs related to user fees, made at the time of health service use, incorporating charges ranging from registration, consultation, drugs, diagnostics, bed charges, etc. A legion of studies examining the impact of user fees on healthcare-seeking behavior in LMICs have conceded that the higher user fee/increase in prices can lead to decreased healthcare utilization and vice-versa ( 4 – 6 ). Literature in the Indian context underscores the impact of user charges and direct medical costs, specifically on drugs and diagnostics ( 7 , 8 ). In addition to the direct cost, indirect costs, such as expenses on food, lodging, and transportation, also account for a large proportion of OOPE, as evinced in the literature from LMICs and India ( 9 – 13 ). Furthermore, other invisible costs that were not incurred because of medical management of disease but rather of other incurred losses, such as lost wages, lost productivity, and costs resulting from the need for home care and child care otherwise not incurred, also pose a formidable barrier to access.

The unprecedented level of financial burden posed by healthcare expenditures has two-pronged implications. First, at the macroeconomic level, the burden posed by forgone care due to affordability barriers has a deleterious impact on the economic growth of the region due to loss in productivity. Second, out-of-pocket health payments precipitate an adverse shock on the financial stability of households incurring such expenditure, subsequently rendering the households vulnerable to catastrophic health expenditure and impoverishment due to income shocks perpetuated via health shocks, which can further potentially culminate into a trans-generational cycle of poverty, bearing long-term consequences. Health shock is the most common idiosyncratic income shock and one of the most pertinent reasons for the descent of households into poverty in LMICs ( 14 ).

The out-of-pocket payments for healthcare are usually the most inequitable type of finance due to its tendency to hit the poor the hardest by being a barrier to healthcare/by denying individuals’ financial protection from catastrophic illness ( 15 ). Studies from India have established the Inverse Care Law, i.e., individuals with the greatest need for healthcare have the greatest difficulty in accessing healthcare services ( 16 – 18 ). There is strong evidence that financial access to healthcare is very low among those residing in rural areas, uneducated, lowest wealth quintile, and otherwise marginalized sections of society ( 19 ). In a resource-poor setting, there are substantial heterogeneities in healthcare measures and capacity to pay thereof; as a corollary, pervasive income-based inequalities in the economic burden of care on the households are pronounced in these settings as well. A systematic review of LMICs has evinced that across all the LMICs, the risk of incurring CHE is six times more concentrated among the poor ( 20 ). Furthermore, evidence on hospitalization from countries such as Argentina, China, India, and Tanzania also revealed the disproportionate impact of CHE on the poor ( 21 ). Although there is some literature on the impact of socio-economic inequalities on the incidence of catastrophic payments in the Indian context ( 22 – 24 ), the evidence is rather exiguous and does not commensurate with the policy implications.

In India, the National Health Policy 2017 ( 25 ) directed that budgetary allocations would ensure horizontal equity by targeting specific population subgroups, geographical areas, healthcare services, and gender-related issues. Horizontal equity entails equal treatment for equal needs, irrespective of other socio-economic characteristics such as income, education, place of residence, and social group. Meanwhile, vertical equity connotes unequal treatment for unequal needs. However, the measurement of horizontal inequities is quite complex vis-a-vis vertical inequality, as need is a rather elusive concept both in terms of the choice of measurable indicators and also normative ethical considerations ( 26 ). However, the degree to which health inequality is considered inequitable is estimated via the need-adjustment of inequality. Literature commonly suggests that people with similar health statuses have the same needs and persons with dissimilar health statuses have different needs ( 27 ). The need-based variables are not amenable to the policy intervention and, thus, considered as fair or legitimate variables, whereas non-need variables are due to systemic inequalities and are amenable to policy intervention, thus, considered as unfair or illegitimate. Therefore, standardizing the inequality in health outcomes by need results in systematic disparities and captures the degree to which the inequality is inequitable.

The systemic inequalities along the socio-economic gradient with respect to the burden of healthcare payments continue to pose an unprecedented challenge in India despite the launch of various initiatives to provide financial risk protection to the poor and vulnerable. Previous studies have revealed that the incidence of CHE on hospitalization care has increased in the last few decades in India ( 24 ). However, the evidence of the impact of these initiatives in reducing the catastrophic burden among poor households remains elusive. Thus, it becomes imperative to explore the dimension of equity w.r.t. incidence of the catastrophic burden of out-of-pocket payments to correct existing interventions and promulgate inclusive policies.

However, there is a dearth of literature to study the need-adjusted inequities in the incidence of CHE for hospitalization care, and, further, to the best of our knowledge, no study has been conducted to decompose the effect of the legitimate and illegitimate factors causing the inequalities in the CHE. At the same time, it is pertinent to decompose and identify the need and non-need factors that affect the health and financial protection in the household to enable the targeted policy response. Thus, this study was conducted to estimate the degree of inequalities and need-adjusted inequities in the incidence of CHE for hospitalization care using a modified Erreygers concentration index. Furthermore, wider socio-economic-contextual determinates influencing the CHE on hospitalization care were unraveled succinctly within a conceptual framework. Additionally, the study also attempted to measure the relative contributions of need and non-need factors driving the inequality in the CHE by conducting a robust regression-based decomposition of the inequalities to identify the key variables for the policy response.

The study employed national representative unit-level cross-sectional data from the 75th round of the National Sample Survey Organization (Household Social Consumption in India: Health) . The survey was conducted under the stewardship of the Ministry of Statistics and Program Implementation , Government of India, during the time period of July 2017–June 2018. The survey schedule collects information pertaining to the demographic - socio-economic characteristics , morbidity status , utilization of healthcare services, and healthcare expenditure across ambulatory, inpatient, delivery, and immunization care for households and individuals. A two-stage stratified random sampling design was adopted in the survey with census villages and urban blocks as the First Stage Units for rural and urban areas, respectively, and households as the Second Stage Units. The overall sample size consisted of 1,13,823 households and 5,57,887 individuals (including the death cases). The analysis, however, circumscribed 66,237 individuals who were hospitalized in the last 365 days of the survey (without childbirth episodes). For this study, the information encompassing both medical expenses such as doctor’s/surgeon’s fee, medicines, diagnostic tests, bed charges, and consumables, viz. blood, oxygen, etc., and non-medical expenses such as expenses incurred on transportation, food, and lodging on account of treatment was employed in the study. Detailed information on the survey design can be found in the official report released by the National Sample Survey Organization ( 28 ).

2.2 Measures

The following measures were assessed in the study: (a) Extent of CHE on hospitalization cases in India; (b) Wealth-related inequities in the incidence of CHE on hospitalization; (c) Socio-economic-demographic factors impacting the CHE on hospitalization cases; and (d) Relative contribution of the factors in driving the wealth-based inequality in the CHE for hospitalization cases.

2.2.1 Outcome measure

The survey encompasses information on the expenses incurred in hospital treatment (medical and non-medical). The medical component subsumed data on the expenses toward the doctor’s/surgeon’s fee, medicines, diagnostics, bed charges, physiotherapy, personal medical appliances, and other consumables such as oxygen and blood. However, the non-medical component incorporated the expenses incurred on other ancillary payments, such as transportation, lodging, and food for the patient and caretaker, on account of the treatment. Given the information, the out-of-pocket expenditure (OOPE) is then defined as the direct payments made by the patients at the time of treatment, net of any reimbursements by the insurance provider. The CHE can be defined via two approaches, i.e., (a) capacity-to-pay approach and (b) budget-share approach. Under the capacity-to-pay approach, the OOPE on healthcare is considered catastrophic if a household’s financial contributions to the healthcare treatment exceed the 40% of income remaining after the subsistence needs have been met ( 29 , 30 ). Meanwhile, under the Budget-share approach, the OOPE is catastrophic if a household’s financial contribution to the treatment equals or exceeds 10% of the household’s total expenditure ( 31 , 32 ). In this study, the CHE was computed using the budget-share approach, where a 10% threshold of total household expenditure was considered. The outcome variable of interest in the study was binary in nature, indicating whether a household faced CHE on inpatient treatment.

2.2.2 Covariates

A gamut of household and individual level variables, drawn from Andersen’s behavioral health model ( 33 ), were incorporated into the study. The covariates were cogitated into legitimate/need and illegitimate/non-need variables to unravel the horizontal inequities underlying the CHE. The need for healthcare is considered an elusive concept, and the choice of variables is embedded in the normative categorization, which requires a potentially contestable value judgment ( 27 ). In general, the need sources of variation in health are ethically acceptable, whereas the non-need sources are ethically unjust or unfair ( 34 ). The variables underscoring the differential need for healthcare expenditure, viz. demographic characteristics, health status, and severity of ailments, such as age composition of household members, number of chronic members, hospitalization cases in households, and duration of stay in the hospital, were considered as the need-based variables in the study.

A myriad of factors impacted the choice of non-need variables, such as previous literature ( 35 – 37 ), relevance to explaining the inequality within the available dataset, and availability of periodic and routine monitoring of the indicators. A broad spectrum of household-level variables across the demographic characteristics such as age and gender of the household head, household size, and marital status of the household members; socio-economic characteristics, such as education, social group, religion, principal occupation of the household, monthly household consumption expenditure, and housing conditions (comprehensive indicator coalescing information on the drinking water source, cooking source, drainage type, and garbage disposal) ; enabling characteristics, such as insurance coverage and type of facility where care is sought; and contextual var iables such as the level of epidemiological transition level of the residential region and the geographical location (urban/rural) were chosen as the non-need variables. The monthly household consumption expenditure was adjusted to account for the economies of scale in household consumption stemming from the household size and demographic composition due to underlying differences in need among the household members using the Oxford equivalence scale ( 38 ). Furthermore, the monthly consumption household expenditure was converted to the annual expenditure to make it uniform with the expenses incurred on hospitalization with a recall period of 365 days.

2.3 Statistical analysis

2.3.1 incidence of catastrophic health expenditure.

The incidence of catastrophic health expenditure was computed via a budget-share approach and elucidated as the share of out-of-pocket health expenditure and out of the total household expenditure:

Where, O O P E i is the out-of-pocket expenditure of household i , T H E i is the household’s total consumption expenditure of household i , and S i is the share of the total healthcare expenditure out of the total consumption expenditure of household i . Consider Z i is the threshold beyond which the household i incurs catastrophic expenditure if S i > 10 % , which can be represented as:

2.3.2 Concentration curve and index

The concentration curve was used to glean the inequities in the CHE on hospitalization care. Cumulative proportions of the catastrophic health payment (vertical axis) were plotted against the cumulative proportion of the households with hospitalization cases (horizontal axis), ranked by the equivalized household consumption expenditure. The concentration index, denoted by C, is estimated as twice the area between the concentration curve and diagonal, which is represented as:

where, C H E i is the variable of interest for the household; μ is the mean of C H E i ; and R i is the i t h ranked household in the socio-economic distribution from most disadvantaged (i.e., poorest) to the least disadvantaged (i.e., richest). The value of C I ranges between −1 and + 1, where a positive value indicates the distribution concentrated among the rich and a negative value represents a distribution concentrated among the poor.

2.3.3 Choice of index

The outcome variable chosen in our study is binary, which is not consonant with the standard concentration index that measures relative inequality and does not allow for the differences between the individuals to be compared. When the standard concentration index is applied to the binary variable, characterized by ordinal and bounded nature, erroneous estimates are produced due to the following reasons: (a) An increase in the binary measure is mirrored by the decrease in the measure; (b) An equi-proportionate increase in the binary measure does not translate to the equi-proportionate decrease in the measure; and (c) Bounds act as constraints to (proportionally) equal transformations of the binary measure. The standard concentration index violates the mirror condition and cardinal invariance property. Additionally, a scale-invariant and rank-dependent index, such as the standard concentration index, fails to account for mirror conditions while accounting for the relative differences simultaneously ( 39 , 40 ). These conditions, however, can be satisfied by the generalized version of the modified concentration index proposed by Wagstaff ( 41 ) or Erreygers corrected concentration index ( 39 ). The generalized concentration index departs from the Erreygers index based on value judgments related to the desirability of level independence ( 42 ). This study employed the Erreygers corrected concentration index to compute the wealth-related inequalities in incurring the CHE by the households. Erreygers corrected concentration index is an absolute rather than a relative measure and is only a rank-dependent measure, which is suitable for our binary outcome measure as it satisfies all the desirable properties for rank-dependent indices, i.e., mirror, transfer, cardinal invariance, and level independence. Furthermore, Erreygers has developed the notions of ‘quasi-absoluteness’ and ‘quasi-relativity’ best suited for the bounded variables as they mitigate the infeasibility of equi-proportional change or equal additions in binary constructs. The index is represented as:

Where C I denotes the standard concentration index as represented in Equation 2 , μ is the mean of CHE in the population, and a n , b n are the upper and lower bounds of the outcome variables.

2.3.4 Need standardization

The differential role of need-based factors such as health conditions and demographics in driving health inequality is not considered in the unstandardized distribution of the outcome measures. However, the differential role of such factors can be observed by segregating the inequality into legitimate and illegitimate health inequality. As a result, the need-standardization was conducted to adjust for the legitimate factors impacting health inequality and to facilitate the comparison across groups. The need-standardization can be done via direct-standardization and indirect-standardization methods. The indirect standardization, reflecting the actual distribution of healthcare outcomes and the distribution that would be expected given the distribution of need, was adopted in this study. The indirect standardization exhibits greater accuracy when dealing with unit-level data. However, the evidence on standardization of equity procedures suggests that inequity measures do not digress significantly with the use of linear methods vis-a-vis non-linear methods ( 43 , 44 ). Thus, a linear regression model for standardization was employed first, which is depicted as follows:

Where, y i is the CHE for the household i ; x j i and Z k i are the vectors of need and non-need factors driving the inequality; α , β j , and θ k are the parameters, while the ε i is the error term. Additionally, the predicted values of the outcome measure ( y ^ i x ) was obtained using the OLS parameter estimates ( a ^ , β ^ j , and θ ^ k ), individual values of the need-variables ( x j i ), and sampled means of the controlled non-need variables ( z ¯ j i ). In the next step, the estimates for indirect standardization of outcome measure ( y ^ i IS ) was obtained by subtracting the predicted values from actual values and adding the overall sample mean ( y ¯ ). The subsequent procedure is depicted as follows:

2.3.5 Decomposition of concentration index

The Erreygers concentration index was decomposed to estimate the relative contribution of covariates to explain the inequality in the outcome measure and other unexplained residual variations. A linear approximation of the model, which is based on the partial effects of each covariate evaluated at the sample means, was employed to perform the decomposition. The linear decomposition of inequalities in outcome measure is illustrated as:

Where, x ¯ j and z ¯ j denotes the means of need and non-need factors, respectively, whereas, C I j and C I k are representative of the respective concentration indices. G C I ε is the generalized concentration index for ε i (residual term), which corresponds to the inequality in the outcome measure that cannot be explained by the systematic variation in other variables. The representation is depicted below:

The modified form of decomposition of Erreyger’s index is thus, given as ( 44 ):

The horizontal inequity (HI) in the CHE was thus estimated by subtracting the absolute contributions made by the need-based factors from the unadjusted value of the Erreygers index. A positive value of HI indicates the inequities concentrated among the better-off, whereas a negative value indicates the inequities concentrated among the worse-off.

2.3.6 Determinants of catastrophic health expenditure

The determinants of CHE were gleaned using a gamut of variables that were embedded within Andersen’s behavioral health model ( 45 ). As per the Andersen framework, the choice variables were prorated into (a) Predisposing components reflecting the demographic and socio-structural characteristics of the household; (b) Enabling components subsuming standard of living and insurance coverage for the households; (c) Need components underscoring the severity of disease, frequency, and duration of hospitalization episodes; and (d) Contextual components comprising the regional aspects such as spatial location and burden of the NCD’s in the region.

A multivariate logistic regression model was employed to unravel the determinants of CHE, represented as:

where, the S i , which is the share of out-of-pocket health expenditure ( O O P E i ) out of the total health expenditure ( T H E i ), is dichotomous, i.e., S i assumes the value of 1 if the out-of-pocket health expenditure ( O O P E i ) exceeds the 10% threshold of the total health expenditure T H E i and 0 otherwise. The notation X 1 , X 2 ….. X n represents the socio-economic-demographic-contextual variables driving the CHE. The analysis was conducted using the STATA 15.0 statistical package. The estimates were weighted to account for the complex multistage sample design and confidence intervals for the horizontal inequity index were computed using Bootstrap with 1,000 replications.

The unstandardized and need-standardized distribution of CHE on Hospitalization care in India is illustrated in Figure 1 . Overall, 27% of the ailing treated as inpatients (except for childbirth) incurred CHE during 2017–2018 in India. The incidence of CHE, however, exhibited an inverse relationship with the relative ranking of the expenditure quintile groups. An extensive gradient in the levels of CHE was found between the lowest and highest quintile groups. The incidence of CHE for the population hospitalized in the poorest quintile (41%) was more than twice as compared to the richest quintile (19%). Furthermore, the estimates of the need-standardized CHE were found to be higher than the unstandardized CHE estimates for poor- and middle-income groups (need-standardized CHE greater than unstandardized by 4, 2, and 1% points for poorest, poor, and middle quintile groups); whereas, standardized CHE levels were less than the unstandardized estimates for the wealthier groups (need-standardized CHE lesser than unstandardized estimates by 1 and 7% for rich and richest quintile groups, respectively).

Figure 1 . Distribution of actual and need-standardized levels of CHE on inpatient care in India.

3.1 Inequality and inequities in the catastrophic health expenditure on hospitalization care

The concentration curve eliciting the inequalities and inequities in the CHE on hospitalization care is plotted in Figure 2 . The concentration curve (unstandardized) was found to be above (dominates) the line of equality, indicating that the burden of CHE on inpatient care was concentrated among the poor. Furthermore, the standardized curve (adjusted for differential needs) dominated the unstandardized curve, which denoted that for equal need, the concentration of inequality among the poor was more pronounced vis-a-vis the inequality in CHE, which is not adjusted by the need-based confounding factors. The dominance testing to test the difference between estimated concentration curve ordinates and diagonal via the Multiple Comparison Approach and Intersection Union Principle rejected the null of no wealth-related inequality and established that concentration curves significantly dominated the line of equality. Correspondingly, the estimated value of the Erreyger’s corrected concentration index ( Table 1 ) was negative and significant (EI: -0.191; p  < 0.05), underscoring the disproportionate incidence of CHE among the poor in India. Moreover, the estimates of the need-adjusted concentration index (EI: -0.258; p  < 0.01) corroborated the wider inequities when accounting for the differential needs.

Figure 2 . Concentration curves depicting the inequalities in CHE on inpatient care in India.

Table 1 . Concentration indices depicting the inequality in CHE for hospitalization care.

3.2 Inter-state differentials in the inequities in CHE on hospitalization

The extent of the need-adjusted wealth inequities in incurring the CHE on inpatient care is exhibited in Figure 3 . The measure of inequity was perceptibly concentrated among the poor in most of the Indian states. However, substantial heterogeneities were found in the degree of the inequities among the states. Wealth-related inequities (concentrated among the poor) were found to be high in the states such as Goa (EI: −0.18) and Jharkhand (EI: −0.13). A few states, such as Uttar Pradesh and Maharashtra, with just approximately one-fourth of the total health spending financed by the government, also exhibited significantly high inequities concentrated among the poor. Conversely, no inequities (EI: 0.00) were estimated for the states of Bihar, Chhattisgarh, and Kerala. Furthermore, the states of Assam and Jammu and Kashmir with the highest level of government spending as a proportion of total health spending (55.2 and 51.3% for Assam and Jammu and Kashmir, respectively) evinced relatively less wealth-related inequities. However, the need-adjusted inequalities were concentrated among the rich in the North-Eastern states of Sikkim (EI: 0.07) and Manipur (0.03) in India.

Figure 3 . Need-adjusted inequality indices for CHE on hospitalization in Indian states.

3.3 Descriptive statistics of the variables

The descriptive statistics of the households with hospitalization episodes in the survey period are presented in Table 2 . Most households were headed by adults aged 25–75 years (95.6%) and were men (88.6%). The demographic structure consisted of small (47.5%) and middle (50.2%)-sized households, and more than half of the households (53.5%) lived with children and older adult dependents. Furthermore, one-fourth of the households had a vulnerable widowed population. Approximately 24% of households were headed by household heads who were not literate, and a majority of the households were not employed in activities with regular sources of income. Most of the targeted surveyed households prescribed the religion of Hinduism (75.8%), followed by Islam (13.6%). Socially, a vast proportion of households belonged to the marginal communities, viz. scheduled caste/scheduled tribes (27.9%) and other backward castes (40.2%). Additionally, the housing conditions for most of the households were good (82.3%). However, the access to healthcare services for the household members was considerably low as more than three-fourths of the households were bereft of insurance coverage. Government-sponsored insurance coverage (14%) constituted the highest financial risk protection cover, followed by employer-sponsored coverage (4.4%). Health-seeking behavior divulged that a colossal 50.8% of households sought care from only private facilities, whereas less than half of the households (43.1%) sought care from only public facilities (43.1%). The need for healthcare was more for certain households, as approximately one-fourth of households had at least two or more members suffering from chronic ailments and had more than one hospitalization episode. The majority of the households (63.2%) accounted for a total duration of ≤7 days stay in the hospital, while 32% of households reported a hospital stay of between 7 and 14 days. Spatially, approximately 50.9% of households were residing in the states/UT’s with a higher-middle and high epidemiological transition level. Furthermore, 55.7% of households were in rural areas, while 44.3% of sampled households were residing in urban areas.

Table 2 . Descriptive statistics of the variables.

3.4 Factors impacting the CHE on hospitalization care among households

The wider socio-economic-contextual predictors of the CHE on hospitalization care among households in India are presented in Table 3 . The estimates revealed that among the predisposing demographic factors, the age mix in the household significantly impacted the CHE. Households that were composed of only older adult members and older adult, but no children, were 9% (significant at 1% level) and 4.7% (significant at 1% level), respectively, more likely to incur the CHE vis-a-vis households with a mixed composition of both children and older adult. The structural factor of household size strongly influenced the outcome, as smaller households with less than 5 members and 5–10 members had 16.3 and 10.7%, respectively, more probability than larger households to get impacted by the CHE on inpatient care. Additionally, those households that are principally unemployed/engaged in unpaid work were less likely to be subjected to the CHE vis-a-vis households that were self-employed or receiving pensions post-retirement. Among the social characteristics, households that are ascribed to the other backward castes were more likely to suffer the catastrophic impacts of health payments compared to the households that are classified as scheduled caste/scheduled tribes. Furthermore, practicing Hinduism or other religions, such as Sikhism and Judaism, was positively associated with the CHE incidence as Hindus and other religious groups were 4 and 7.3% more likely vis-a-vis households practicing Islam to face the CHE. The results also underscored the significance of enabling factors in driving the CHE. The evidence indicated an inverse relationship of the CHE with the wealth of households, as richer households were significantly less likely to incur the CHE than their poorer counterparts. The poor, middle, rich, and richest had 11.2, 18.7, 24.1, and 30.5%, respectively, less probability of facing CHE than the poorest household. Analogously, households with government-sponsored insurance cover (6.6%), employer-sponsored cover (10.9%), and private insurance/other covers (12.9%) were less likely to incur CHE vis-a-vis households that are not covered under any financial risk protection scheme. Conversely, households that sought inpatient treatment from private facilities had significantly more likelihood of spending a catastrophic amount on treatment (24.7% for households who sought treatment in a mix of public and private facilities and 32.7% for households who sought treatment in private facilities alone) than those households which sought treatment in just the public hospitals. With respect to the need-based factors, longer duration of hospital stay was associated with more CHE; the probability of incurring CHE was lesser for shorter admission time of fewer than 2 weeks (18.9%), 4–7 days (35.9%), and 3 or fewer days (50.7%) in comparison with the households with longer inpatient days. Finally, the contextual factor of geographical (spatial) location impacted the CHE, as households residing in the regions at higher levels of epidemiological transition level were less likely (7, 4.8, and 6.8% lesser probability for lower-middle, higher-middle, and high epidemiological transition level) to face the CHE on hospital stay as compared to the households residing in the regions having low epidemiolocal level.

Table 3 . Determinants of the CHE on hospitalization care among households in India.

3.5 Decomposition of the inequalities in the CHE on hospitalization care in India

The results ascertaining the contribution of various determinants in driving the wealth-related inequality in CHE on hospitalization care in India is encapsulated in Table 4 , which presents the estimates of coefficients, Erreyger’s concentration indices, absolute contributions (computing the product of elasticity and regressor’s concentration index), and relative contributions (denoting the percentage of inequality in CHE attributable to the inequality in the contributing factor). A positive (negative) value of the absolute contribution of a correlate demonstrates that if the inequality in the CHE was determined by that correlate alone, then it would be concentrated toward the worse-off (better off). The relative contribution of a correlate is computed by dividing the absolute contribution of correlates by total inequality in the outcome variable and multiplying it by 100. The aggregate relative contributions of covariates in driving the inequality are also illustrated in Figure 4 . Overall, the relative contribution of need-based variables was exhibited to be negative, connoting that if the CHE were determined by need alone, it would be more concentrated among the poor. Aggregately, the need factors accounted for 35.3% of the unstandardized concentration index, and most of this contribution was attributed to the duration of stay (30.6% of the unstandardized concentration index) in the hospital. However, the inequality push toward the poor was offset to a degree by the effect of the non-need/illegitimate factors. The majority of the inequality in the CHE was driven by illegitimate/non-need factors, with most of the contributions from the enabling factors such as inequality in the wealth of households (expenditure quintiles) and health utilization pattern (facility mix for hospitalization) in conjunction with socio-structural variables such as the size of the household. Additionally, the decomposition results enable the estimation of horizontal inequity, which is obtained by subtracting the absolute need contributions (0.068) from the unstandardized index (−0.19), thus yielding an index value of −0.26.

Table 4 . Regression coefficients (B), absolute contribution and relative contribution of determinants to income-related inequality in catastrophic health expenditure on hospitalization in India.

Figure 4 . Decomposition analysis of income-related inequalities in CHE on hospitalization.

4 Discussion and conclusion

Our study revealed significant wealth-related inequalities in the CHE for hospitalization care in India, with a pervasive gap between the poorest and richest income quintiles. The CHE was concentrated more among the poor, with the incidence of CHE being more than twice for the poorest quintile vis-a-vis the richest quintile group. The findings were corroborated by the negative value of the Erreygers concentration index, denoting the inequalities that are disadvantageous to the poor. Furthermore, need-adjusted inequalities also underscored the systemic inequalities (caused by the factors amenable to the policy change) to be concentrated among the poor. Globally, the evidence on the relationship between CHE and socio-economic status has been mixed, and few findings suggest that the better-off experience more CHE in low- and middle-income settings (LMIC) due to the higher propensity of the rich to consume more health services ( 46 ). However, our findings were consonant with the studies conducted in other LMIC settings such as Iran ( 47 ), China ( 48 ), Malawi ( 49 ), Columbia ( 50 ), and Sub-Saharan Africa ( 46 ), where inequality gradients indicated the poor getting afflicted by the CHE disproportionately. The higher incidence of CHE among the poor can be understood by the fact that for households with low income, even a small proportion of healthcare costs can be catastrophic.

The relatively higher incidence of CHE among the poor is pertinent from a policy perspective as it also connotes the intrinsic disparities in healthcare access and finance. India has launched various programs targeted toward the poor to move along the trajectory of Universal Health Coverage (UHC). To achieve the goal of equitable financial risk protection for the marginalized, India launched flagship initiatives such as the National Rural Health Mission (NRHM) in 2005, providing free cost care to the poor and Rashtriya Swasthya Bima Yojana (RSBY) in 2008, covering the poor population with cashless insurance on hospitalization. However, the relatively higher incidence of CHE among the poor alludes to the inefficacy of these programs in providing financial risk protection to the poor. Furthermore, the empirical evidence on the impact of schemes such as RSBY has concurred with its ineffectiveness in reducing the inpatient out-of-pocket spending and catastrophic inpatient spending ( 51 , 52 ). However, India recently revamped and bolstered these schemes further for expanded coverage by launching the Ayushman Bharat (AB) Program (National Health Protection Mission) for integrated healthcare. The scheme has two components: (a) AB-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which provides cashless cover up to INR 5 lakh per family for hospitalization in secondary and tertiary care to over 10 crore poor and vulnerable families; and (b) AB-Health and Wellness Centers (AB-HWCs) providing comprehensive primary and community-based services free of cost to the population. Furthermore, India has launched other initiatives such as free drugs and diagnostics services and financial assistance to patients living below the poverty line for life-threatening diseases under schemes such as Rashtriya Arogya Nidhi (RAN), Health Minister’s Cancer Patient Fund (HMCPF), and Health Minister’s Discretionary Grant (HMDG). Furthermore, affordable medicines and reliable implants for treatment (AMRIT) deendayal outlets have been opened to make available drugs and implants for cardiovascular diseases (CVDs), cancer, and diabetes at discounted prices to patients ( 53 ). Although a legion of health initiatives providing free healthcare to different marginalized sections of society have been launched recently, the impact evaluation of these interventions in reducing the burden of OOP on hospitalization among the poor in India needs to be undertaken.

Our findings indicated that members of more than half of the poor households were hospitalized in private facilities with a disproportionately higher incidence of CHE (38.5% in private facilities vis-a-vis 11.5% in public facilities). A myriad of reasons for the preference for private provider(s) in India has been expounded in literature, such as poor readiness and quality of care, higher waiting times, inconvenient facility timings, long distances, absence of healthcare personnel, and lack of acceptability and trust in public providers ( 54 – 57 ). Hence, it is recommended to strengthen the public healthcare system to encompass NCD care (with a disproportionately higher incidence of CHE) ( 58 ) and improve the quality of care in terms of infrastructure, equipment, drugs, and diagnostics. A legion of guidelines and standards to ensure the quality of care has been enforced in India, such as Indian Public Health Standards (IPHS), Mera Aspataal (My hospital), and National Quality Assurance Standards (NQAS). However, the non-compliance of quality protocols and standards has hampered the readiness of public health facilities. Thus, the objective periodic monitoring and evaluation of the quality parameters along the continuum of care is suggested to ensure readiness. Concomitantly, surveillance measures such as record keeping, frequent monitoring of employee absence behavior, detection of absence via biometric attendance, and management-oriented punitive action measures for dereliction of duties can be introduced to minimize absenteeism. Simultaneously, to mitigate the low acceptability and poor confidence in public provider, knowledge dissemination, advocacy, and public engagement activities should be promoted at an individual, household, and community and regional level as a confidence-building measure.

Our findings found a legion of factors influencing CHE on hospitalization care. The role of demographic factors was accentuated in the study, and it was found that households comprising only older adult members incur significantly high CHE on hospitalization, which is in tandem with other studies conducted in India ( 59 ). Analogously, our estimates revealed that larger size households experience more CHE, which is conflated by other research conducted in LMICs ( 60 , 61 ). Additionally, other predisposing socio-structural factors, such as affiliation with the marginalized social group and practicing the religion of Hinduism, are associated with higher CHE, which is consonant with the other studies conducted in India ( 62 – 64 ). Although equity has been a primary goal of the flagship programs launched by the Government of India, the related policy discourse has been focused on the praxis of wealth-related inequalities and has precluded other social disparities, such as religion and caste, as a potential axis of healthcare marginalization ( 65 ). The multivariate regression estimates also underscored the role of enabling factors such as the absence of insurance coverage and treatment-seeking in private facilities to increase the CHE significantly. The role of these enabling factors, such as the type of health facility and insurance coverage, in influencing the CHE has also been accentuated in many other studies from similar settings ( 66 , 67 ).

In the LMIC context, the policy discourse has given impetus to the establishment/extension of national/social health insurance in which service providers are paid from designated government funds, which are partly funded through taxes. India via AB-PMJAY provides such insurance coverage for hospitalization to the poor and vulnerable; however, evidence from rural India suggests that around one-fourth of the eligible participants are still unaware of the AB-PMJAY scheme; moreover, the level of utilization of the scheme has been found to be abysmally low at 1.3% ( 68 ). The low level of utilization can be explained via complex enrollment or reimbursement process, which acts as a significant barrier to take up. The findings on PMJAY in India also suggest that this scheme shifted the use of health facilities from public providers to privately empaneled hospitals where the cost of care is higher ( 69 ). Thus, a gamut of strategies can be employed to increase the penetration and uptake of Public Funded Health Insurance (PHFI) schemes in India, such as an increase in the awareness of benefits and community engagement via appropriate training for competencies of the community health workers, such as Accredited social health activists (ASHA) and Anganwadi workers (AWW); easing the process of enrollment and reimbursement and streamlining other hospital-based processes for effective implementation of the scheme ( 70 ) and establishing a robust referral linkage between the primary healthcare facilities with secondary and tertiary hospitals with the help of digital interventions and infrastructure. However, in regions where the institutional capacity to organize mandatory nationwide risk-pooling is weak, community-based health insurance schemes can be effective in protecting poor households from unpredictably high medical expenses ( 31 ).

The findings also demonstrated the role of contextual factors such as the region in influencing the CHE as the households belonging to the states with higher levels of the epidemiological transition level (defined based on the ratio of disability-adjusted life years and computed as the sum of years of potential life lost due to the premature mortality and the years of productive life lost due to disability from communicable disease to those from non-communicable and injuries combined) incurred lesser CHE as compared to their counterparts residing in the states at a lower level of ETL. These inter-region heterogeneities can be explained by the inverse relationship between the epidemiological transition ratio and socio-economic development of the states ( 71 ). A higher burden of CHE on the states with a lower level of epidemiological transition is a pertinent finding from the policy perspective as these states are associated with the lower per capita expenditure on healthcare, thus lacking financial risk protection vis-a-vis other states. Thus, there is a need to increase public spending on healthcare to reach the targeted level of 4% of GDP by 2025. However, realistically, the state governments can set a target to allocate at least 2.5% of the state’s gross domestic product (SGDP) to healthcare, which is the recommended level by the World Health Organization (WHO). It is further suggested that the government explore new and innovative financing mechanisms to generate the fiscal space, such as the public–private partnership to fund the sector; simultaneously, other fiscal space measures, such as the collection of health-specific tax, goods, and services tax reform, higher excise duty on tobacco products, tax administration reform and direct beneficiary transfer of health services could be employed as the alternative revenue mobilization channels for fiscal space in health ( 72 ).

The decomposition analysis revealed that the contribution of non-need/illegitimate factors in driving the inequality was relatively high vis-à-vis need/legitimate factors, as most of the inequality in CHE was driven by the non-need factors amenable to the policy change. Most of the unfair inequalities arose from socio-structural factors such as the size of the household and enabling factors such as income (expenditure) and type of facility (public or private) utilized. The relative contribution of these determinants in influencing inequalities in CHE is found in other LMICs. A study on the decomposition of inequalities in CHE in Iran ( 47 ) demonstrated that most of the illegitimate inequalities emanated from household economic status (64%), followed by household size (40%). Other studies in China have also accounted for household size as the largest contributor to CHE inequality ( 73 , 74 ). Furthermore, evidence from Sierra Leonne suggested that the distributional effect of the type of facility significantly impacted the inequalities in the CHE ( 75 ). Thus, from the policy perspective, it is imperative to invest more in public health facilities, providing significant financial risk protection to the poor. From the Indian perspective, the burden of CHE was found to be disproportionately higher for the poor and middle-population groups as well. Thus, it is suggested that the state and central governments expand the PFHI coverage to the missing middle population as well.

The study has a few caveats due to the nature of the dataset and the methodological approach. First , the same weights are assigned to the catastrophic payments incurred by poor and non-poor households and, thus, ignore the differentials in the opportunity cost in the health spending between rich and poor, thereby rendering the measure non-normative, which does not allow for distributional sensitivity. Second , health expenditures are not adjusted for coping mechanisms such as distressed financing or adjustment in the consumption pattern to pay for the health expenditure, thus understating CHE. Third , the data on expenditure used in the survey is self-reported and is susceptible to recall and information bias. Fourth, in the multivariate regression, the information on outcome measures and covariates was collected concurrently due to the cross-sectional design; thus, associations rather than causal relationships are defined in the study. Fifth, the information on self-reported monthly household consumer expenditure is a one-shot open-ended with no parallel validation, and thus can lead to the underestimation of the household’s income.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

SS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. VV: Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. PG: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing. MA: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research was funded by the internal funding support made available to the first and corresponding author Dr. Shyamkumar Sriram from Ohio University College of Health Sciences and Professions, Ohio University, USA.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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Keywords: out-of-pocket healthcare expenditures, hospitalization care, catastrophic health expenditures, inequality, need-adjusted inequities, decomposition of inequality

Citation: Sriram S, Verma VR, Gollapalli PK and Albadrani M (2024) Decomposing the inequalities in the catastrophic health expenditures on the hospitalization in India: empirical evidence from national sample survey data. Front. Public Health . 12:1329447. doi: 10.3389/fpubh.2024.1329447

Received: 29 October 2023; Accepted: 18 March 2024; Published: 04 April 2024.

Reviewed by:

Copyright © 2024 Sriram, Verma, Gollapalli and Albadrani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shyamkumar Sriram, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Project Management

10 executive summary examples and how to write one yourself (with ai).

February 14, 2024

In a world where people have the attention span of a goldfish (or less), we don’t make time to read long, detailed documents unless they are valuable to us. So, how do we convince the reader that the document is valuable? That’s where the executive summary comes in.

What is an Executive Summary?

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An executive summary is a shorter version of a longer corporate document. It summarizes the salient points of a business plan, proposal, or report so executives can get the gist and read further about what matters to them.

In other words, the tl;dr (too long; didn’t read) version.

A typical executive summary includes:

• Problem statement
• Proposed solution
• Expected outcomes

This might vary depending on what you write an executive summary for. Let’s take the example of a project report. You might have to replace the proposed solution and expected outcomes with execution solutions and actual outcomes achieved, respectively. Or, if you’re writing a business plan, research proposal, or market analysis, you might include your methodology, too.

Now that you know the purpose of an executive summary, let’s see how to write one.

How to Write Executive Summaries and Examples

While an executive summary is just a condensed version of a longer report, it isn’t easy to write. It needs to capture the essence of the report, outline the salient points, and tell a story as compelling as the full report. Here are some ways you can achieve that.

Just stating facts and data wouldn’t be a compelling read for anyone. So, identify the story that really impacts people’s lives. While industry terms like workflow optimization or cost control capture people’s attention, they don’t tell the real story behind your efforts. Focus on the latter.

If you’re writing the project executive summary in software development, you might begin with what matters to the reader as follows.

In 2020, the retail major was managing its inventory on spreadsheets. So, whenever a customer asked whether a product was in stock, a staff member had to walk across the 5000 sq. ft. store to check, often with the customer in tow. The new ABC digital inventory management system records stock in and out online in real time. The staff member can check and confirm in a flash. More pertinently, the customers themselves can check at any of the 25 kiosks throughout the store.

While the story is more important, data isn’t useless. Accurate and relevant data helps establish credibility. Your next section might say the following in the ABC digital inventory management system example.

Since the implementation of the ABC inventory management system, the retail major has seen: 85% decrease in time taken to check stock 75% decrease in time taken to find where stock is placed

The data demonstrates that there has been real improvement. However, for the reader to understand its impact, you must explain the benefits. This can be done with real-life scenarios or even quotes. For example,

Adrian, the customer service manager at the Central Park store, says, “Now, from anywhere—a kiosk, the checkout counter, or my mobile phone—I can quickly check stock and confirm we have the products the customer needs. I see that customers are delighted at getting their answers instantly.”

You can also use data to do this. For example, you can explain how the decreased time taken to check stock has increased staff productivity, customer satisfaction, or company revenue. Or you can include your suggestions here. Based on your observations, explain the process improvement methodologies you recommend.

This is the time to complete the story. Here, talk about how your project has delivered the changes in the present and sets up for an even more prosperous future. This could be something like:

The ABC inventory management system marks the first step in the retail major’s digital transformation journey. By Q2 next year, we will link the store solution to the e-commerce inventory platform to give 360-degree visibility into the stock situation. This would also enable a new sales channel in the form of Buy Online, Pick Up in Store (BOPIS), enabling same-day fulfillment.

While you write your executive summary, here are some best practices to remember.

Keep it short and simple : The length might depend on the report you’re summarizing, but it’s best to keep it under one page for quick reading. Also, avoid cliches and jargon; make it easy to read. A quick business plan under one page is the best first impression you can make.

Focus on the target audience : Not all executive summaries are read by business executives. Often, you might want to address your summary to peers, vendors, partners, or even teens. Know your target audience and customize your executive summary accordingly.

Use the right tool : You can, of course, use Notepad or Word doc to write your executive summaries. But give it a boost with modern document software like ClickUp Docs .

• Use rich formatting features without jumping through hoops
• Style the critical information with color-coded banners, buttons, and more
• Collaborate in real time with comments, action items, and trackable tasks
• Securely share with anyone with appropriate access controls

Pick a suitable template : If it’s your first time writing an executive summary, we’ve got your back. Fire up one of ClickUp’s executive summary templates or content writing templates , and kickstart your work.

Get the AI boost : If you’ve thoughtfully created your report, you can write your executive summary much quicker with one of the many AI writing tools . For instance, ClickUp AI offers a single-click summarize option right on ClickUp Docs.

What’s more? ClickUp AI supports you in brainstorming new ideas, writing the first drafts of your executive summaries, and proofreading them for good measure.

10 Executive Summary Examples

Now that we have discussed the theory of executive summary writing, let’s look at some examples to see what it looks like in practice. Here are ten to learn from or emulate.

Periodically, the board would expect to see a report on the organization’s performance. Various departments typically write their reports, which are consolidated into a board report. An effective executive summary of this would include the following.

• Revenue and expenditure
• Key areas of focus
• Critical success factors
• Financial information
• Challenges and roadblocks

This ClickUp Board Report Summary Template brings all these aspects together to get you started on your executive summary right away. You can customize this free executive summary template to suit your needs and fill in the data as appropriate.

McKinsey, one of the world’s leading consulting firms, publishes dozens of research reports annually. For every one of them, they write executive summaries, often called ‘in brief.’

In this report titled, ‘ Performance through people: Transforming human capital into competitive advantage ,’ the executive summary takes a two-pronged approach. It presents key insights in text on one page and data in infographics on the next.

Insights in text : The report begins by directly addressing the primary purpose of the research. Below are the first few sentences.

How does developing talent affect financial returns for firms? This research finds that companies with a dual focus on developing human capital and managing it well have a performance edge.

This section summarizes the key insights from the research. The headlines of each section are presented in bold, making it easy for the reader to skim.

Data in visuals : The text section is followed by an infographic of the key findings from the data. Within one page, it presents all the graphs relevant to the reader engagingly.

Within two pages, McKinsey gives the reader a bird’s eye view of what to expect, customized for the target market, from the 40-page document.

You can read the executive summary of this report on McKinsey’s website .

The Adaptation Gap Report 2023 by the United Nations Environment Programme is a 112-page report with a rather detailed executive summary, stretching eight pages. The depth of information and seriousness of the topics covered demand an extended executive summary.

Yet, the writers make every effort to make it engaging with a combination of typography, design, and graphs. It begins with the following.

Despite the clear signs of accelerating climate risks and impacts worldwide, the adaptation finance gap is widening and now stands at between US$194 billion and US$366 billion per year. Adaptation finance needs are 10–18 times as great as current international public adaptation finance flows – at least 50 percent higher than previously estimated.

In the following pages, it presents graphs to demonstrate the underpinnings of these key findings.

Every project manager creates performance reports at the end of each week, month, or quarter. This typically includes the tasks tracking , burn up, burn down, hours spent, etc.

While this can be written down in a list, presenting this information as a slide with visual elements is far more effective.

One way to achieve this is to use ClickUp’s project summary templates , which offer custom-designed templates for various project management purposes.

The other way is to use the dynamic reports on the ClickUp Dashboard , which brings together all the key metrics and keeps them updated in real time for you to share with anyone you’d like to.

Human resources or people management teams create payroll reports, typically in spreadsheets, for every payment period—bi-weekly or monthly. This data is also helpful for building financial projections. For the senior finance leaders, they often create an executive summary of critical information, such as:

• Total salaries paid
• Deductions across categories
• Year-to-date salary expenses
• Paid time off credits
• Net pay summary

ClickUp’s Payroll Summary Report Template can save time by automatically gathering all relevant data from the platform. When data is unavailable on ClickUp, you can highlight any text to @mention team members who can fill in the correct information.

Once complete, you can update the Doc’s settings for access control and share it with the management team instantly.

A company description or how it projects itself is often important to stand out in a crowded market. Mailchimp stood out with its style guide. The guide is comprehensive and widely used by smaller content teams that don’t yet have their own.

Mailchimp has made it public and available under a Creative Commons Attribution-NonCommercial 4.0 International license for anyone to adapt to their needs.

While every section in this style guide is engaging and valuable, for the purposes of this article, we want to draw your attention to the tl;dr section , which acts as a quasi-executive summary.

It is a bulleted list of seven sub-sections, highlighting the foundations of Mailchimp’s writing style.

The striking thing about this tl;dr version is its simplicity. Even without any visual elements, infographics, or charts, this page gives readers a real and actionable summary of the entire style guide.

When we speak of executive summary, we almost always think of a smaller version of an entire document. It need not be so.

For a software engineering team, the release notes are a kind of executive summary of all the changes/upgrades made in the latest version.

Take the example of ClickUp’s release notes 3.04 . Each release gets:

• An organized yet concise summary of all the changes that have been made
• “ClickTips” to help readers make the best use of new features
• Visuals and app images to show how the changes look
• Links to help pages of each of those features so the reader can learn more
• A list of bugs fixed
• And any other resources, such as on-demand webinars or training

These release notes inform users and developers of the latest upgrades to the ClickUp platform without overwhelming them with the details.

The New Yorker Magazine wrote a 10,000-word profile of Geoffrey Hinton , a computer scientist and cognitive psychologist, for their November 20, 2023 issue, titled ‘Metamorphosis.’ Even in podcast form, it’s over 60 minutes long.

When it was published online, they needed a title and description that summarized the article in a way that attracted a lay reader’s attention to click and read. The headline captures the primary conflict explored in the article. The description introduces the protagonist.

While this is typically not what we’d categorize as an executive summary, it is a fantastic example of capturing the essence of a long article in a few powerful words.

This executive summary serves as an inspiration for writers, irrespective of what you’re writing about, to summarize their main points not just briefly but also powerfully and attractively.

In the spring of 2019, Harvard University conducted its first-ever survey about campus culture. The executive summary of the report on these survey responses makes for great reading. It is also a great example of how to honestly and authentically present key findings, even unpleasant ones.

The executive summary is honest on multiple fronts. It admits that:

• 2019 was the first time in history that Harvard surveyed campus culture
• Three in ten of the Harvard community don’t feel like they belong
• 34% of students disagreed with the belief that Harvard will take appropriate action against incidents of harassment and discrimination
• Those from historically underrepresented and disadvantaged groups reported less positive views

At the end of this, the executive summary outlines the specific steps Harvard will take to address these responses from the community.

Project managers can use this as inspiration for handling executive summaries of projects that have gone awry. It helps leaders take responsibility for what has occurred and build systems to prevent future mistakes.

Not all executive summaries have to be written manually by you. A free executive summary template is also something to explore. Plenty of tools offer it. Dozens of AI tools for automation can summarize text in seconds. Here’s what ClickUp AI returned when we inserted the article above and asked for a summary.

The article discusses the purpose and importance of an executive summary, which provides a brief overview of detailed documents, making them more palatable for readers with limited time. Executive summaries typically include problem statements, proposed solutions, expected outcomes, and a conclusion. To create a compelling summary, it’s crucial to identify the main story, incorporate relevant data, expand on benefits, and conclude powerfully. The use of modern document software like ClickUp Docs and AI tools like ClickUp AI can enhance the quality and efficiency of writing executive summaries. The article also provides practical examples of executive summaries across different fields, showcasing their versatility and applicability. This provides a great starting point for those who fear the blank page. You can now edit this to add details, add images, or insert a quote.

With ClickUp AI, you can choose the tone (from professional, straightforward, inspirational, optimistic, casual, confident, friendly, or humorous) and creativity (low, medium, and high) to customize the summary to your needs.

That’s not all! For project managers and business leaders, ClickUp AI offers a wide range of writing and summarizing tools for scope documents, project briefs, meeting agendas, statements of work, survey questions, and more.

You can tag people to invite input or feedback. You can also convert comments into tasks and manage them effortlessly, all in one place.

Never used AI for writing before? No worries there, too. Here are AI prompt templates that will get you started instantly.

With a custom-built AI assistant tailored to your role, you can work faster, write better, spark creativity, and be significantly more productive.

Questions? Comments? Visit our Help Center for support.

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Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released.

Volume 30, Number 5—May 2024

Epidemiologic survey of crimean-congo hemorrhagic fever virus in suids, spain.

Main Article

Figure . Spatial distribution of samples and human cases notified from epidemiologic survey of Crimean-Congo hemorrhagic fever virus in suids, Spain.

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Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

About 1 in 4 u.s. teachers say their school went into a gun-related lockdown in the last school year.

Twenty-five years after the mass shooting at Columbine High School in Colorado , a majority of public K-12 teachers (59%) say they are at least somewhat worried about the possibility of a shooting ever happening at their school. This includes 18% who say they’re extremely or very worried, according to a new Pew Research Center survey.

Pew Research Center conducted this analysis to better understand public K-12 teachers’ views on school shootings, how prepared they feel for a potential active shooter, and how they feel about policies that could help prevent future shootings.

To do this, we surveyed 2,531 U.S. public K-12 teachers from Oct. 17 to Nov. 14, 2023. The teachers are members of RAND’s American Teacher Panel, a nationally representative panel of public school K-12 teachers recruited through MDR Education. Survey data is weighted to state and national teacher characteristics to account for differences in sampling and response to ensure they are representative of the target population.

We also used data from our 2022 survey of U.S. parents. For that project, we surveyed 3,757 U.S. parents with at least one child younger than 18 from Sept. 20 to Oct. 2, 2022. Find more details about the survey of parents here .

Here are the questions used for this analysis , along with responses, and the survey methodology .

Another 31% of teachers say they are not too worried about a shooting occurring at their school. Only 7% of teachers say they are not at all worried.

This survey comes at a time when school shootings are at a record high (82 in 2023) and gun safety continues to be a topic in 2024 election campaigns .

Teachers’ experiences with lockdowns

About a quarter of teachers (23%) say they experienced a lockdown in the 2022-23 school year because of a gun or suspicion of a gun at their school. Some 15% say this happened once during the year, and 8% say this happened more than once.

High school teachers are most likely to report experiencing these lockdowns: 34% say their school went on at least one gun-related lockdown in the last school year. This compares with 22% of middle school teachers and 16% of elementary school teachers.

Teachers in urban schools are also more likely to say that their school had a gun-related lockdown. About a third of these teachers (31%) say this, compared with 19% of teachers in suburban schools and 20% in rural schools.

Do teachers feel their school has prepared them for an active shooter?

About four-in-ten teachers (39%) say their school has done a fair or poor job providing them with the training and resources they need to deal with a potential active shooter.

A smaller share (30%) give their school an excellent or very good rating, and another 30% say their school has done a good job preparing them.

Teachers in urban schools are the least likely to say their school has done an excellent or very good job preparing them for a potential active shooter. About one-in-five (21%) say this, compared with 32% of teachers in suburban schools and 35% in rural schools.

Teachers who have police officers or armed security stationed in their school are more likely than those who don’t to say their school has done an excellent or very good job preparing them for a potential active shooter (36% vs. 22%).

Overall, 56% of teachers say they have police officers or armed security stationed at their school. Majorities in rural schools (64%) and suburban schools (56%) say this, compared with 48% in urban schools.

Only 3% of teachers say teachers and administrators at their school are allowed to carry guns in school. This is slightly more common in school districts where a majority of voters cast ballots for Donald Trump in 2020 than in school districts where a majority of voters cast ballots for Joe Biden (5% vs. 1%).

What strategies do teachers think could help prevent school shootings?

The survey also asked teachers how effective some measures would be at preventing school shootings.

Most teachers (69%) say improving mental health screening and treatment for children and adults would be extremely or very effective.

About half (49%) say having police officers or armed security in schools would be highly effective, while 33% say the same about metal detectors in schools.

Just 13% say allowing teachers and school administrators to carry guns in schools would be extremely or very effective at preventing school shootings. Seven-in-ten teachers say this would be not too or not at all effective.

How teachers’ views differ by party

Republican and Republican-leaning teachers are more likely than Democratic and Democratic-leaning teachers to say each of the following would be highly effective:

• Having police officers or armed security in schools (69% vs. 37%)
• Having metal detectors in schools (43% vs. 27%)
• Allowing teachers and school administrators to carry guns in schools (28% vs. 3%)

And while majorities in both parties say improving mental health screening and treatment would be highly effective at preventing school shootings, Democratic teachers are more likely than Republican teachers to say this (73% vs. 66%).

Parents’ views on school shootings and prevention strategies

In fall 2022, we asked parents a similar set of questions about school shootings.

Roughly a third of parents with K-12 students (32%) said they were extremely or very worried about a shooting ever happening at their child’s school. An additional 37% said they were somewhat worried.

As is the case among teachers, improving mental health screening and treatment was the only strategy most parents (63%) said would be extremely or very effective at preventing school shootings. And allowing teachers and school administrators to carry guns in schools was seen as the least effective – in fact, half of parents said this would be not too or not at all effective. This question was asked of all parents with a child younger than 18, regardless of whether they have a child in K-12 schools.

Like teachers, parents’ views on strategies for preventing school shootings differed by party. 

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

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‘Back to school’ means anytime from late July to after Labor Day, depending on where in the U.S. you live

Among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .