what is research method example

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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Research Methods: What are research methods?

• What are research methods?
• Searching specific databases

What are research methods

Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic.

There are different types of research methods which use different tools for data collection.

Types of research

• Qualitative Research
• Quantitative Research
• Mixed Methods Research

Qualitative Research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Mixed Methods Research integrates both Q ualitative and Quantitative Research . It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables Triangulation,  or verification, of the data from two or more sources.

Finding Mixed Methods research in the Databases 

“mixed model*” OR “mixed design*” OR “multiple method*” OR multimethod* OR triangulat*

Data collection tools

Sage research methods.

• SAGE research methods online This link opens in a new window Research methods tool to help researchers gather full-text resources, design research projects, understand a particular method and write up their research. Includes access to collections of video, business cases and eBooks,

Help and Information

• Next: Finding qualitative research >>
• Last Updated: Apr 5, 2024 2:16 PM
• URL: https://libguides.newcastle.edu.au/researchmethods

• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Research Design & Method

Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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• Next: Survey Research >>
• Last Updated: Aug 21, 2023 10:42 AM

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Types of Research Methods: Examples and Tips

What are research methods?

Research methods are the techniques and procedures used to collect and analyze data in order to answer research questions and test a research hypothesis . There are several different types of research methods, each with its own strengths and weaknesses. 

Common Types of Research Methods

There are several main types of research methods that are employed in academic articles. The type of research method applied depends on the nature of the data to be collected and analyzed, as well as any restrictions or limitations that dictate the study’s resources and methodology. Surveying articles from your target journal and identifying the methods commonly used in these studies is also recommended before choosing a research method or methods.

It’s important to note that research methods can be combined for a more complete understanding of a research question or hypothesis. For example, an experiment can be followed by a survey to gather more information about participants’ attitudes and behaviors.

Overall, the choice of research method depends on the research question, the type of data needed, and the resources available to the researcher.

Data Collection Methods

Data is information collected in order to answer research questions . The kind of data you choose to collect will depend on the nature of your research question and the aims of your study. There are a few main category distinctions of data a researcher can collect.

Quantitative vs qualitative data

Qualitative and quantitative data are two types of data that are often used in research studies. They are different in terms of their characteristics, how they are collected, and how they are analyzed.

Quantitative data is numerical and is collected through methods such as surveys, polls, and experiments. It is often used to measure and describe the characteristics of a large group of people or objects. This data can be analyzed using statistical methods to find patterns and trends.

Qualitative data, on the other hand, is non-numerical and is collected through methods such as interviews, observations, and focus groups. It is often used to understand the experiences, attitudes, and perceptions of individuals or small groups. This data is analyzed using methods such as content analysis, thematic analysis, and discourse analysis to identify patterns and themes.

Overall, quantitative data provides a more objective and generalizable understanding of a phenomenon, while qualitative data provides a more subjective and in-depth understanding. Both types of data are important and can be used together to gain a more comprehensive understanding of a topic.

You can also make use of both qualitative and quantitative research methods in your study.

Primary vs secondary data

Primary and secondary research are two different types of research methods that are used in the field of academia and market research. Both primary and secondary sources can be applied in most studies.

Primary research is research that is conducted by the individual or organization themselves. It involves collecting original data through methods such as surveys, interviews, or experiments. The data collected through primary research is specific to the research question and objectives, and is not typically available through other sources.

Secondary research, on the other hand, involves the use of existing data that has already been collected by someone else. This can include data from government reports, academic journals, or industry publications. The advantage of secondary research is that it is typically less time-consuming and less expensive than primary research, as the data has already been collected. However, the data may not be as specific or relevant to the research question and objectives.

The choice between using primary and secondary research will depend on the research question, study budget, and time constraints of the project, as well as the target journal to which you are submitting your manuscript.

Experimental vs descriptive data collection

Experimental data is collected through a controlled experiment, in which the researcher manipulates one or more variables to observe the effect on another variable. The goal of experimental data is to determine cause-and-effect relationships. For example, in a study on the effectiveness of a new drug for treating a certain condition, the researchers would randomly assign participants to either a group that receives the drug or a group that receives a placebo, and then compare the outcomes between the two groups. The data collected in this study would be considered experimental data.

Descriptive data, on the other hand, is data that is collected through observation or surveys and is used to describe the characteristics of a population or phenomenon. The goal of descriptive data is to provide a snapshot of the current state of a certain population or phenomenon, rather than to determine cause-and-effect relationships. For example, in a study on the dietary habits of a certain population, the researchers would collect data on what types of food the participants typically eat and how often they eat them. This data would be considered descriptive data.

In summary, experimental data is collected through a controlled experiment to determine cause-and-effect relationships, while descriptive data is collected through observation or surveys to describe the characteristics of a population or phenomenon.

Descriptive data examples:

• A survey that asks people about their favorite type of music
• A census that counts the number of people living in a certain area
• A poll that asks people about their political affiliation

Experimental data examples:

• A study comparing the effectiveness of two different medications for treating a certain condition
• An experiment measuring the effect of different levels of a certain chemical on plant growth
• A clinical trial comparing the side effects of a new treatment to a standard treatment for a disease

Examples of Difference Data Collection Methods

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods.

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base. This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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What Is Desk Research? Meaning, Methodology, Examples

Apr 4, 2024

10 min. read

Research in the digital age takes many shapes and forms. There are traditional methods that collect first-hand data via testing, focus groups, interviews, and proprietary data. And then there are ways to tap into the time and effort others have put into research, playing “armchair detective” by conducting desk research .

Desk research gives you a shortcut to insights by pulling data from other resources, which is crucial for understanding the customer journey . It takes less time and is more cost-effective compared to conducting primary market research . Most importantly, it can give you the consumer insights you need to make important business decisions.

Let’s explore the official desk research definition along with types of desk research, methodologies, examples, and how to do desk research effectively.

Desk Research Meaning: What is Desk Research?

Advantages and limitations of desk research, desk research methodology and methods, how to conduct desk research effectively, best practices for desk research, applications of desk research, how to conduct desk research with meltwater.

Desk Research definition: Desk research, also known as secondary research, involves gathering information and data from existing sources, such as books, journals, articles, websites, reports, and other published materials. Users analyze and synthesize information from already available information.

Companies use desk research at the onset of a project to gain a better understanding of a topic, identify knowledge gaps, and inform the next stages of research. It can also supplement original findings and provide context and background information.

Desk research gives marketers attractive advantages over traditional primary research, but it’s not without its shortcomings. Let’s explore these in more detail.

Desk research advantages

• Quick insights. Conducting interviews, focus groups, panels, and tests can take weeks or even months, along with additional time to analyze your findings. With desk research, you can pull from existing information to gain similar results in less time.
• Cost-effectiveness. Desk market research is usually less expensive than primary research because it requires less time and fewer resources. You don’t have to recruit participants or administer surveys, for example.
• Accessibility. There’s a world of data out there ready for you to leverage, including online databases, research studies, libraries, and archives.
• Diverse sources. Desk market research doesn’t limit you to one information source. You can use a combination of sources to gain a comprehensive overview of a topic.

Want to see how Meltwater can supercharge your market research efforts? Simply fill out the form at the bottom of this post and we'll be in touch.

Desk research limitations 

• Data quality. Marketers don’t know how reliable or valid the data is, which is why it’s important to choose your sources carefully. Only use data from credible sources, ideally ones that do not have a financial interest in the data’s findings.
• Less control. Users are at the mercy of the data that’s available and cannot tailor it to their needs. There’s no opportunity to ask follow-up questions or address specific research needs.
• Potential bias. Some sources may include biased findings and/or outdated information, which can lead to inaccurate conclusions. Users can mitigate the risk of bias by relying only on credible sources or corroborating evidence with multiple sources.

Desk research typically involves multiple sources and processes to gain a comprehensive understanding of an idea. There are two main desk methodologies: qualitative research and quantitative research .

• Qualitative research refers to analyzing existing data (e.g., interviews, surveys, observations) to gain insights into people's behaviors, motivations, and opinions. This method delves deeper into the context and meaning behind the data.
• Quantitative research refers to analyzing and interpreting numerical data to draw conclusions and make predictions. This involves quantifying patterns and trends to find relationships between variables.

Both desk research methodologies use a variety of methods to find and analyze data and make decisions.

Examples of desk research methods include but are not limited to:

• Literature review. Analyze findings from various types of literature, including medical journals, studies, academic papers, books, articles, online publications, and government agencies.
• Competitor analysis . Learn more about the products, services, and strategies of your competitors, including identifying their strengths and weaknesses, market gaps, and overall sentiment.
• Social listening . Discover trending topics and sentiments on social media channels to learn more about your target audience and brand health.
• Consumer intelligence . Understand your audience based on digital behaviors, triggers, web usage patterns, and interests.
• Market research . Analyze market reports, industry trends, demographics, and consumer buying patterns to identify market opportunities and strengthen your positioning.

Now let’s look at how to use these methods to their full potential.

While desk research techniques can vary, they all follow a similar formula. Here’s how you can conduct desk research effectively, even if it’s your first time.

1. Define your objective

Desk research starts with a specific question you want to answer. 

In marketing , your objective might be to:

• Learn about Gen Z buying behaviors for home goods
• Gauge the effectiveness of influencer marketing for food brands
• Understand the pain points of your competitor’s customers

These questions can help you find credible sources that can provide answers.

2. Choose reliable data sources

Based on your objectives, start collecting secondary data sources that have done the heavy lifting for you. Examples include:

• Market reports (often available as gated assets from research companies)
• Trade publications
• Academic journals
• Company websites
• Government publications and data
• Online databases and resources, such as Google Scholar 
• Secondary market research tools like Meltwater and Linkfluence
• Online blogs, articles, case studies, and white papers from credible sources

In many cases, you’ll use a combination of these source types to gain a thorough answer to your question.

3. Start gathering evidence

Go through your source materials to start answering your question. This is usually the most time-intensive part of desk research; you’ll need to extract insights and do some fact-checking to trust those insights.

One of your top priorities in this step is to use reliable sources. Here are some ways you can evaluate sources to use in your desk research:

• Consider the authority and reputation of the source (e.g., do they have expertise in your subject)
• Check whether the content is sponsored, which could indicate bias
• Assess whether the data is current
• Evaluate the publisher’s peer review processes , if applicable
• Review the content’s citations and references
• Seek consensus among multiple sources
• Use sources with built-in credibility, such as .gov or .edu sites or well-known medical and academic journals

If your source materials have supporting elements, such as infographics, charts, or graphs, include those with your desk research.

4. Cross-reference your findings with other sources

For desk research to be effective, you need to be able to trust the data you find. One way to build trust is to cross-reference your findings with other sources. 

For instance, you might see who else is citing the same sources you are in their research. If there are reputable companies using those same sources, you might feel they’re more credible compared to a random internet fact that lacks supporting evidence. 

5. Draw your conclusions & document the results

Organize and synthesize your findings in a way that makes sense for your objectives. Consider your stakeholders and why the information is important.

For example, the way you share your research with an internal team might have a different structure and tone compared to a client-facing document.

Bonus tip: Include a list of sources with your documentation to build credibility in your findings. 

When conducting desk research, follow these best practices to ensure a reliable and helpful outcome.

Organize and manage your research data

It’s helpful to have a system to organize your research data. This way, you can easily go back to review sources or share information with others. Spreadsheets, databases, and platforms like Meltwater for market research are great options to keep your desk research in one place.

Create actionable recommendations

It’s not enough to state your findings; make sure others know why the data matters. Share the data along with your conclusions and recommendations for what to do next.

Remember, desk research is about decision-making, not the data itself.

Document your sources

Whether you choose to share your sources or not, it’s best practice to document your sources for your own records. This makes it easier to provide evidence if someone asks for it or to look back at your research if you have additional questions.

Now for the big question: How can marketers apply desk research to their day-to-day tasks?

Try these desk research examples to power your marketing efforts.

Use desk research for market intelligence

Markets, preferences, and buying habits change over time, and marketers need to stay up to date on their industries. Desk research can provide market intelligence insights, including new competitors, trends, and audience segments that may impact your business.

Apply desk research in competitive analysis

Desk research can help you identify your true competitors and provide more context about their strengths and weaknesses. Marketers can use this intel to improve their positioning and messaging. For instance, a competitor’s weak spot might be something your company does well, and you can emphasize this area in your messaging.

Include desk research in content strategy and audience analysis

Desk research can support consumer intelligence by helping you define various audience segments and how to market to them. These insights can help you develop content and creative assets on the right topics and in the right formats, as well as share them in the best channels to reach your audience.

Emerging technologies like Meltwater's integrated suite of solutions have a strong impact on desk research, helping you streamline how you find and vet data to support your desired topics.

Using a combination of data science, AI, and market research expertise, Meltwater offers the largest media database of its kind to help marketers learn more about their audience and how to connect with them. Millions of real-time data points cover all niches, topics, and industries, giving you the on-demand insights you need.

Our clients use Meltwater for desk research to measure audience sentiment and identify audience segments as well as to conduct competitor analysis , social listening , and brand monitoring , all of which benefit from real-time data. 

Learn more about how you can leverage Meltwater as a research solution when you request a demo by filling out the form below:

Continue Reading

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Plan-Do-Study-Act Directions and Examples

The Plan-Do-Study-Act (PDSA) method is a way to test a change that is implemented. Going through the prescribed four steps guides the thinking process into breaking down the task into steps and then evaluating the outcome, improving on it, and testing again. Most of us go through some or all of these steps when we implement change in our lives, and we don't even think about it. Having them written down often helps people focus and learn more. For more information on the PDSA, go to the IHI (Institute for Healthcare Improvement) website .

Keep the following in mind when using the PDSA cycles to implement the health literacy tools:

• Single Step— Each PDSA often contains only a segment or single step of the entire tool implementation.
• Short Duration— Each PDSA cycle should be as brief as possible for you to gain knowledge that it is working or not (some can be as short as 1 hour).
• Small Sample Size— A PDSA will likely involve only a portion of the practice (maybe 1 or 2 doctors). Once that feedback is obtained and the process refined, the implementation can be broadened to include the whole practice.

Filling Out The Worksheet

Tool: Fill in the tool name you are implementing.

Step: Fill in the smaller step within that tool you are trying to implement.

Cycle: Fill in the cycle number of this PDSA. As you work though a strategy for implementation, you will often go back and adjust something and want to test whether the change you made is better or not. Each time you make an adjustment and test it again, you will do another cycle.

I plan to: Here you will write a concise statement of what you plan to do in this testing. This will be much more focused and smaller than the implementation of the tool. It will be a small portion of the implementation of the tool.

I hope this produces: Here you can put a measurement or an outcome that you hope to achieve. You may have quantitative data like a certain number of doctors performed teach-back, or qualitative data such as nurses noticed less congestion in the lobby.

Steps to execute: Here is where you will write the steps that you are going to take in this cycle. You will want to include the following:

• The population you are working with—are you going to study the doctors' behavior or the patients' or the nurses'?
• The time limit that you are going to do this study—remember, it does not have to be long, just long enough to get your results. And, you may set a time limit of 1 week but find out after 4 hours that it doesn't work. You can terminate the cycle at that point because you got your results.

After you have your plan, you will execute it or set it in motion. During this implementation, you will be keen to watch what happens once you do this.

What did you observe? Here you will write down observations you have during your implementation. This may include how the patients react, how the doctors react, how the nurses react, how it fit in with your system or flow of the patient visit. You will ask, "Did everything go as planned?" "Did I have to modify the plan?"

After implementation you will study the results.

What did you learn? Did you meet your measurement goal? Here you will record how well it worked, if you meet your goal.

What did you conclude from this cycle? Here you will write what you came away with for this implementation, whether it worked or not. And if it did not work, what you can do differently in your next cycle to address that. If it did work, are you ready to spread it across your entire practice?

Below are 2 examples of how to fill out the PDSA worksheet for 2 different tools, Tool 17: Get Patient Feedback and Tool 5: Use the Teach-Back Method . Each contain 3 PDSA cycles. Each one has short cycles and works through a different option on how to disseminate the survey to patient ( Tool 17: Patient Feedback ) and how to introduce teach-back and have clinicians try it. ( Tool 5: Use the Teach-Back Method ).

PDSA (plan-do-study-act) Worksheet

Tool: Patient Feedback

Step: Dissemination of surveys

Cycle: 1st Try

I plan to: test a process of giving out satisfaction surveys and getting them filled out and back to us.

I hope this produces: at least 25 completed surveys per week during this campaign.

Steps to execute:

• We will display the surveys at the checkout desk.
• The checkout attendant will encourage the patient to fill out a survey and put it in the box next to the surveys.
• We will try this for 1 week.

What did you observe?

• We noticed that patients often had other things to attend to at this time, like making an appointment or paying for services and did not feel they could take on another task at this time.
• The checkout area can get busy and backed up at times.
• The checkout attendant often remembered to ask the patient if they would like to fill out a survey.

What did you learn? Did you meet your measurement goal?

We only had 8 surveys returned at the end of the week. This process did not work well.

What did you conclude from this cycle?

Patients did not want to stay to fill out the survey once their visit was over. We need to give patients a way to fill out the survey when they have time.

We will encourage them to fill it out when they get home and offer a stamped envelope to mail the survey back to us.

Cycle: 2nd Try

• The checkout attendant will encourage the patient to take a survey and an envelope. They will be asked to fill the survey out at home and mail it back to us.
• We will try this for 2 weeks.
• The checkout attendant successfully worked the request of the survey into the checkout procedure.
• We noticed that the patient had other papers to manage at this time as well.
• Per Checkout attendant only about 30% actually took a survey and envelope.

We only had 3 surveys returned at the end of 2 weeks. This process did not work well.

Some patients did not want to be bothered at this point in the visit; they were more interested in getting checked out and on their way.

Once the patient steps out of the building, they will likely not remember to do the survey.

We need to approach them at a different point in their visit when they are still with us—maybe at a point where they are waiting for the doctor and have nothing to do.

Cycle: 3rd Try

• We will leave the surveys in the exam room next to a survey box with pens/pencils.
• We will ask the nurse to point the surveys out/hand them out after vitals and suggest that while they are waiting they could fill out our survey and put it in box.
• We will see after 1 week how many surveys we collected.
• Upon self report, most nurses reported they were good with pointing out or handing the patient the survey.
• Some patients may need help reading survey, but nurses are too busy to help.
• On a few occasions, the doctor came in while patient filling out survey so survey was not complete.

We had 24 surveys in the boxes at the end of 1 week. This process worked better.

Approaching patients while they are still in the clinic was more successful.

Most patients had time while waiting for the doctor to fill out the survey.

We need to figure out how to help people who may need help reading the survey.

Tool: Teach-back

Step: MDs initially performing Teach-back

I plan to: ask the physicians in Wednesday PM to perform teach-back with the last person they see that day.

I hope this produces: physicians performing teach-back and that they find that it was useful, did not take that much more time, and they will continue the practice.

• We will ask the 5 physicians who hold clinic on Wednesday PM to perform teach-back with their last patient of the day.
• We will show these physicians the teach-back video.
• It was useful?
• It was time consuming?
• They will do it again?

All physicians found the teach-back video informative and seemed eager to try this new tool.

4 out of 5 physicians performed teach-back on at least one patient in the afternoon. The 1 physician who did not indicated she did not quite know how to integrate it into her visit.

4 out of 5 felt comfortable with it and said they would continue using it.

For the 1 who was not sure how to integrate it, we will look for other teach-back resources to help address this.

Ready to introduce to entire clinical staff.

Step: MDs continuing to perform Teach-back

Cycle: modified 2nd try

I plan to: see if the physicians in Wednesday PM clinic are still performing teach-back by asking them after their last patient leaves. (3 weeks have gone by since initial introduction.)

I hope this produces: confirmation that each of the physicians will have performed teach-back on at least 3 of their afternoon patients.

• We will approach the 5 physicians on Wednesday PM after their last patient leaves and ask them to count the number of patients they performed teach-back on this afternoon.

Some physicians could not find appropriate situations for teach-back.

All still felt it was a worthy tool during their patient visits but feel they need to remember it and practice it more.

3 out of 5 physicians said they did perform teach-back on 3 of their patients.

1 performed it in one instance.

1 did not perform it at all (same one as before).

Teach-back is being used, maybe not as readily as I had anticipated. 

Maybe the goals of '3 out of 6 patient encounters should contain teach-back' is unrealistic. We may put a sign in the clinic rooms, in view of the physicians, to remind them about teach-back.

Will measure again in 6 months.

Step: MDs continuing performing Teach-back

I plan to: see if the signs put up in the exam rooms help physicians remember to do teach-back and increased its utilization.

I hope this produces: physicians will perform teach-back 3 out of 6 times.

• We will put signs reading "Teach it Back" taped on the exam room desk/work area to remind physicians to use the technique.
• We will ask physicians if they notice the signs and if they reminded them to perform teach-back.
• We will see if Wednesday PM clinic had increased use of teach-back.

Nurses felt the sign will get in the way.

4 out of 5 physicians did teach-back on 3 patients Wednesday afternoon. 1 did it on 1 patient.

4 out of 5 said they did see the sign and that it was a reminder to do teach-back.

That a reminder is needed (especially initially) to help physicians use this tool in their visit.

No further intervention needed at this point.

Return to Contents

Internet Citation: Plan-Do-Study-Act Worksheet, Directions, and Examples. Content last reviewed March 2024. Agency for Healthcare Research and Quality, Rockville, MD. https://www.ahrq.gov/health-literacy/improve/precautions/tool2b.html

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• Knowledge Base
• Methodology
• 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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• Knowledge Base

Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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 sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Sister Cells Reveal Cancer’s Fate

A new method traces treatment resistant cells and predicts drugs that can make them more susceptible to cancer therapy..

Aparna is a freelance science writer with a PhD in bioinformatics and genomics at Harvard University. Her writing has also appeared in The Philadelphia Inquirer, Popular Science, PBS NOVA, and more.

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ABOVE: Sister cells have similar molecular profiles, which researchers leveraged to measure different cellular traits in parallel. © iStock, Rost-9D

C ancer is notoriously hard to treat. In part, this is because the cells making up a tumor are heterogeneous, expressing different genes and molecules that determine their response to treatment. Even if a treatment kills most cancer cells, one survivor is enough for the cancer to persist.

As scientists struggled to find these treatment-resistant cells, they turned to an unexpected tool: sister cells. While human sisters may share clothes or toys, sister cells share their gene expression profiles, which could hint at whether the cells are treatment resistant.

In a study published in Nature Communications , researchers at the University of Helsinki presented a new method called ReSisTrace that utilizes sister cells to identify the molecular states driving treatment resistance in cancer cell lines. 1 Guided by these resistance signatures, the researchers devised a method to predict drugs that would sensitize the cells to treatment. 

“We can have data [on] both drug sensitivity and transcriptomics at the single-cell level,” said Jing Tang , a bioinformatician at the University of Helsinki and coauthor of the study. “This is unique and novel, and not available by using other techniques.”

Tang and Anna Vähärautio , a cancer biologist at the University of Helsinki and coauthor of the study, wanted to develop a method that combined lineage tracing—the process of tracking cell fate and offspring—with the ability to profile gene expression in individual cells. However, measuring gene expression in a cell typically destroys it, so scientists cannot trace its lineage at the same time. Enter sister cells: a way to achieve both goals in parallel.

Vähärautio's team devised a method to insert unique DNA barcodes into an ovarian cancer cell line using lentiviral transduction. Then, they allowed the cells to undergo a single division to each produce two sister cells, which they found had similar gene expression profiles. The researchers split the pool of cells in half: in one half, they measured gene expression by single cell RNA-sequencing (scRNA-seq) to construct a picture of each cell’s state, and in the other half, they tested whether the cells responded to certain common cancer treatments.

Using the treatment-resistant cells’ barcodes, the researchers matched them with their sister cells in the pre-treatment pool and analyzed their gene expression profiles. This comparison helped them identify genes that might have caused the cell to evade being killed. 

At first, the researchers tried to focus on individual genes, but they soon realized this approach might not be enough. “We don't know if [the genes] are really driving the resistance or if they are secondary effects,” Vähärautio said. This inspired the team to search the whole transcriptome for broader gene expression signatures of treatment sensitivity or resistance. Vähärautio and Tang suspected that these signatures could even help predict additional drugs that could sensitize the cells to a subsequent treatment.

Using published gene expression data collected from cell lines treated with a variety of compounds, Tang’s team identified potential drugs that could push treatment-resistant cells’ gene expression toward that of treatment-responsive cells. 2 By doing so, the added drug could prime the cells to respond to cancer treatment. Using computational models, the researchers predicted that administering pevonedistat—a drug that inhibits an enzyme involved in protein degradation—before carboplatin chemotherapy would make the cancer cell line that they were studying easier to kill. They tested their predictions and found that pevonedistat pretreatment, and many other predicted compounds, worked synergistically with common cancer therapies to kill the cancer cells. 

These findings came as a pleasant surprise to Vähärautio, and they convinced Tang that this could be a new approach for developing more effective cancer treatments to overcome drug resistance. 

Amy Brock , a bioengineer at the University of Texas at Austin who was not involved in this study, noted that the authors defined gene expression signatures by comparing all resistant cells to all sensitive cells, but that there might be even more patterns hidden in individual resistant cells. “It would be interesting to further examine whether sister cells become resistant via common or distinct mechanisms,” Brock said.

Brock hopes that, with a slew of similar methods to track cell lineages and single-cell gene expression , researchers will now focus on applying these tools to better understand how cells evade specific treatments. 3,4 Vähärautio and Tang are now applying their method to more sample types, including cancer organoids and acute myeloid leukemia cell lines. But Vähärautio thinks this method could even be useful for studying how cells’ states influence their fates in other contexts, such as development or responses to chemicals. With the computational models for drug prediction, ReSisTrace could even identify ways to change these fates.

“I think the method is really widely applicable and can be used to study many different cell state and fate connections,” Vähärautio said.

• Dai J, et al. Tracing back primed resistance in cancer via sister cells . Nat Commun . 2024;15(1):1158.
• Subramanian A, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles . Cell . 2017;171(6):1437-1452.
• Oren Y, et al. Cycling cancer persister cells arise from lineages with distinct programs . Nature . 2021;596(7873):576-582.
• Gutierrez C, et al. Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment . Nat Cancer . 2021;2(7):758-772.

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Home » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

Definition:

Research refers to the process of investigating a particular topic or question in order to discover new information , develop new insights, or confirm or refute existing knowledge. It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail.

History of Research

The history of research can be traced back to ancient times when early humans observed and experimented with the natural world around them. Over time, research evolved and became more systematic as people sought to better understand the world and solve problems.

In ancient civilizations such as those in Greece, Egypt, and China, scholars pursued knowledge through observation, experimentation, and the development of theories. They explored various fields, including medicine, astronomy, and mathematics.

During the Middle Ages, research was often conducted by religious scholars who sought to reconcile scientific discoveries with their faith. The Renaissance brought about a renewed interest in science and the scientific method, and the Enlightenment period marked a major shift towards empirical observation and experimentation as the primary means of acquiring knowledge.

The 19th and 20th centuries saw significant advancements in research, with the development of new scientific disciplines and fields such as psychology, sociology, and computer science. Advances in technology and communication also greatly facilitated research efforts.

Today, research is conducted in a wide range of fields and is a critical component of many industries, including healthcare, technology, and academia. The process of research continues to evolve as new methods and technologies emerge, but the fundamental principles of observation, experimentation, and hypothesis testing remain at its core.

Types of Research

Types of Research are as follows:

• Applied Research : This type of research aims to solve practical problems or answer specific questions, often in a real-world context.
• Basic Research : This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.
• Experimental Research : This type of research involves manipulating one or more variables to determine their effects on another variable, while controlling all other variables.
• Descriptive Research : This type of research aims to describe and measure phenomena or characteristics, without attempting to manipulate or control any variables.
• Correlational Research: This type of research examines the relationships between two or more variables, without manipulating any variables.
• Qualitative Research : This type of research focuses on exploring and understanding the meaning and experience of individuals or groups, often through methods such as interviews, focus groups, and observation.
• Quantitative Research : This type of research uses numerical data and statistical analysis to draw conclusions about phenomena or populations.
• Action Research: This type of research is often used in education, healthcare, and other fields, and involves collaborating with practitioners or participants to identify and solve problems in real-world settings.
• Mixed Methods Research : This type of research combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a phenomenon or problem.
• Case Study Research: This type of research involves in-depth examination of a specific individual, group, or situation, often using multiple data sources.
• Longitudinal Research: This type of research follows a group of individuals over an extended period of time, often to study changes in behavior, attitudes, or health outcomes.
• Cross-Sectional Research : This type of research examines a population at a single point in time, often to study differences or similarities among individuals or groups.
• Survey Research: This type of research uses questionnaires or interviews to gather information from a sample of individuals about their attitudes, beliefs, behaviors, or experiences.
• Ethnographic Research : This type of research involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices.
• Historical Research : This type of research investigates events or phenomena from the past using primary sources, such as archival records, newspapers, and diaries.
• Content Analysis Research : This type of research involves analyzing written, spoken, or visual material to identify patterns, themes, or messages.
• Participatory Research : This type of research involves collaboration between researchers and participants throughout the research process, often to promote empowerment, social justice, or community development.
• Comparative Research: This type of research compares two or more groups or phenomena to identify similarities and differences, often across different countries or cultures.
• Exploratory Research : This type of research is used to gain a preliminary understanding of a topic or phenomenon, often in the absence of prior research or theories.
• Explanatory Research: This type of research aims to identify the causes or reasons behind a particular phenomenon, often through the testing of theories or hypotheses.
• Evaluative Research: This type of research assesses the effectiveness or impact of an intervention, program, or policy, often through the use of outcome measures.
• Simulation Research : This type of research involves creating a model or simulation of a phenomenon or process, often to predict outcomes or test theories.

Data Collection Methods

• Surveys : Surveys are used to collect data from a sample of individuals using questionnaires or interviews. Surveys can be conducted face-to-face, by phone, mail, email, or online.
• Experiments : Experiments involve manipulating one or more variables to measure their effects on another variable, while controlling for other factors. Experiments can be conducted in a laboratory or in a natural setting.
• Case studies : Case studies involve in-depth analysis of a single case, such as an individual, group, organization, or event. Case studies can use a variety of data collection methods, including interviews, observation, and document analysis.
• Observational research : Observational research involves observing and recording the behavior of individuals or groups in a natural setting. Observational research can be conducted covertly or overtly.
• Content analysis : Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Ethnography : Ethnography involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices. Ethnographic research can use a range of data collection methods, including observation, interviews, and document analysis.
• Secondary data analysis : Secondary data analysis involves using existing data from sources such as government agencies, research institutions, or commercial organizations. Secondary data can be used to answer research questions, without collecting new data.
• Focus groups: Focus groups involve gathering a small group of people together to discuss a topic or issue. The discussions are usually guided by a moderator who asks questions and encourages discussion.
• Interviews : Interviews involve one-on-one conversations between a researcher and a participant. Interviews can be structured, semi-structured, or unstructured, and can be conducted in person, by phone, or online.
• Document analysis : Document analysis involves collecting and analyzing written documents, such as reports, memos, and emails. Document analysis can be used to study organizational communication, policy documents, and other forms of written material.

Data Analysis Methods

Data Analysis Methods in Research are as follows:

• Descriptive statistics : Descriptive statistics involve summarizing and describing the characteristics of a dataset, such as mean, median, mode, standard deviation, and frequency distributions.
• Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data, using methods such as hypothesis testing, confidence intervals, and regression analysis.
• Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as text, images, or audio, to identify patterns, themes, or meanings. Qualitative analysis can be used to study subjective experiences, social norms, and cultural practices.
• Content analysis: Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Grounded theory: Grounded theory involves developing a theory or model based on empirical data, using methods such as constant comparison, memo writing, and theoretical sampling.
• Discourse analysis : Discourse analysis involves analyzing language use, including the structure, function, and meaning of words and phrases, to understand how language reflects and shapes social relationships and power dynamics.
• Network analysis: Network analysis involves analyzing the structure and dynamics of social networks, including the relationships between individuals and groups, to understand social processes and outcomes.

Research Methodology

Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or test hypotheses. The main components of research methodology include:

• Research design : Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods.
• Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units from the population of interest. The choice of sampling strategy will depend on the research question and the nature of the population being studied.
• Data collection methods : Data collection methods refer to the techniques used to collect data from study participants or sources, such as surveys, interviews, observations, or secondary data sources.
• Data analysis methods: Data analysis methods refer to the techniques used to analyze and interpret the data collected in the study, such as descriptive statistics, inferential statistics, qualitative analysis, or content analysis.
• Ethical considerations: Ethical considerations refer to the principles and guidelines that govern the treatment of human participants or the use of sensitive data in the research study.
• Validity and reliability : Validity and reliability refer to the extent to which the study measures what it is intended to measure and the degree to which the study produces consistent and accurate results.

Applications of Research

Research has a wide range of applications across various fields and industries. Some of the key applications of research include:

• Advancing scientific knowledge : Research plays a critical role in advancing our understanding of the world around us. Through research, scientists are able to discover new knowledge, uncover patterns and relationships, and develop new theories and models.
• Improving healthcare: Research is instrumental in advancing medical knowledge and developing new treatments and therapies. Clinical trials and studies help to identify the effectiveness and safety of new drugs and medical devices, while basic research helps to uncover the underlying causes of diseases and conditions.
• Enhancing education: Research helps to improve the quality of education by identifying effective teaching methods, developing new educational tools and technologies, and assessing the impact of various educational interventions.
• Driving innovation: Research is a key driver of innovation, helping to develop new products, services, and technologies. By conducting research, businesses and organizations can identify new market opportunities, gain a competitive advantage, and improve their operations.
• Informing public policy : Research plays an important role in informing public policy decisions. Policy makers rely on research to develop evidence-based policies that address societal challenges, such as healthcare, education, and environmental issues.
• Understanding human behavior : Research helps us to better understand human behavior, including social, cognitive, and emotional processes. This understanding can be applied in a variety of settings, such as marketing, organizational management, and public policy.

Importance of Research

Research plays a crucial role in advancing human knowledge and understanding in various fields of study. It is the foundation upon which new discoveries, innovations, and technologies are built. Here are some of the key reasons why research is essential:

• Advancing knowledge: Research helps to expand our understanding of the world around us, including the natural world, social structures, and human behavior.
• Problem-solving: Research can help to identify problems, develop solutions, and assess the effectiveness of interventions in various fields, including medicine, engineering, and social sciences.
• Innovation : Research is the driving force behind the development of new technologies, products, and processes. It helps to identify new possibilities and opportunities for improvement.
• Evidence-based decision making: Research provides the evidence needed to make informed decisions in various fields, including policy making, business, and healthcare.
• Education and training : Research provides the foundation for education and training in various fields, helping to prepare individuals for careers and advancing their knowledge.
• Economic growth: Research can drive economic growth by facilitating the development of new technologies and innovations, creating new markets and job opportunities.

When to use Research

Research is typically used when seeking to answer questions or solve problems that require a systematic approach to gathering and analyzing information. Here are some examples of when research may be appropriate:

• To explore a new area of knowledge : Research can be used to investigate a new area of knowledge and gain a better understanding of a topic.
• To identify problems and find solutions: Research can be used to identify problems and develop solutions to address them.
• To evaluate the effectiveness of programs or interventions : Research can be used to evaluate the effectiveness of programs or interventions in various fields, such as healthcare, education, and social services.
• To inform policy decisions: Research can be used to provide evidence to inform policy decisions in areas such as economics, politics, and environmental issues.
• To develop new products or technologies : Research can be used to develop new products or technologies and improve existing ones.
• To understand human behavior : Research can be used to better understand human behavior and social structures, such as in psychology, sociology, and anthropology.

Characteristics of Research

The following are some of the characteristics of research:

• Purpose : Research is conducted to address a specific problem or question and to generate new knowledge or insights.
• Systematic : Research is conducted in a systematic and organized manner, following a set of procedures and guidelines.
• Empirical : Research is based on evidence and data, rather than personal opinion or intuition.
• Objective: Research is conducted with an objective and impartial perspective, avoiding biases and personal beliefs.
• Rigorous : Research involves a rigorous and critical examination of the evidence and data, using reliable and valid methods of data collection and analysis.
• Logical : Research is based on logical and rational thinking, following a well-defined and logical structure.
• Generalizable : Research findings are often generalized to broader populations or contexts, based on a representative sample of the population.
• Replicable : Research is conducted in a way that allows others to replicate the study and obtain similar results.
• Ethical : Research is conducted in an ethical manner, following established ethical guidelines and principles, to ensure the protection of participants’ rights and well-being.
• Cumulative : Research builds on previous studies and contributes to the overall body of knowledge in a particular field.

Advantages of Research

Research has several advantages, including:

• Generates new knowledge: Research is conducted to generate new knowledge and understanding of a particular topic or phenomenon, which can be used to inform policy, practice, and decision-making.
• Provides evidence-based solutions : Research provides evidence-based solutions to problems and issues, which can be used to develop effective interventions and strategies.
• Improves quality : Research can improve the quality of products, services, and programs by identifying areas for improvement and developing solutions to address them.
• Enhances credibility : Research enhances the credibility of an organization or individual by providing evidence to support claims and assertions.
• Enables innovation: Research can lead to innovation by identifying new ideas, approaches, and technologies.
• Informs decision-making : Research provides information that can inform decision-making, helping individuals and organizations make more informed and effective choices.
• Facilitates progress: Research can facilitate progress by identifying challenges and opportunities and developing solutions to address them.
• Enhances understanding: Research can enhance understanding of complex issues and phenomena, helping individuals and organizations navigate challenges and opportunities more effectively.
• Promotes accountability : Research promotes accountability by providing a basis for evaluating the effectiveness of policies, programs, and interventions.
• Fosters collaboration: Research can foster collaboration by bringing together individuals and organizations with diverse perspectives and expertise to address complex issues and problems.

Limitations of Research

Some Limitations of Research are as follows:

• Cost : Research can be expensive, particularly when large-scale studies are required. This can limit the number of studies that can be conducted and the amount of data that can be collected.
• Time : Research can be time-consuming, particularly when longitudinal studies are required. This can limit the speed at which research findings can be generated and disseminated.
• Sample size: The size of the sample used in research can limit the generalizability of the findings to larger populations.
• Bias : Research can be affected by bias, both in the design and implementation of the study, as well as in the analysis and interpretation of the data.
• Ethics : Research can present ethical challenges, particularly when human or animal subjects are involved. This can limit the types of research that can be conducted and the methods that can be used.
• Data quality: The quality of the data collected in research can be affected by a range of factors, including the reliability and validity of the measures used, as well as the accuracy of the data entry and analysis.
• Subjectivity : Research can be subjective, particularly when qualitative methods are used. This can limit the objectivity and reliability of the findings.
• Accessibility : Research findings may not be accessible to all stakeholders, particularly those who are not part of the academic or research community.
• Interpretation : Research findings can be open to interpretation, particularly when the data is complex or contradictory. This can limit the ability of researchers to draw firm conclusions.
• Unforeseen events : Unexpected events, such as changes in the environment or the emergence of new technologies, can limit the relevance and applicability of research findings.

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If you've heard of or read about mindfulness meditation — also known as mindfulness — you might be curious about how to practice it. Find out how to do mindfulness exercises and how they might benefit you.

What is mindfulness?

Mindfulness is a type of meditation in which you focus on being intensely aware of what you're sensing and feeling in the moment, without interpretation or judgment. Practicing mindfulness involves breathing methods, guided imagery, and other practices to relax the body and mind and help reduce stress.

Spending too much time planning, problem-solving, daydreaming, or thinking negative or random thoughts can be draining. It can also make you more likely to experience stress, anxiety and symptoms of depression. Practicing mindfulness exercises can help you direct your attention away from this kind of thinking and engage with the world around you.

What are the benefits of meditation?

Meditation has been studied in many clinical trials. The overall evidence supports the effectiveness of meditation for various conditions, including:

• High blood pressure (hypertension)

Preliminary research indicates that meditation can also help people with asthma and fibromyalgia.

Meditation can help you experience thoughts and emotions with greater balance and acceptance. Meditation also has been shown to:

• Improve attention
• Decrease job burnout
• Improve sleep
• Improve diabetes control

What are some examples of mindfulness exercises?

There are many simple ways to practice mindfulness. Some examples include:

• Pay attention. It's hard to slow down and notice things in a busy world. Try to take the time to experience your environment with all of your senses — touch, sound, sight, smell and taste. For example, when you eat a favorite food, take the time to smell, taste and truly enjoy it.
• Live in the moment. Try to intentionally bring an open, accepting and discerning attention to everything you do. Find joy in simple pleasures.
• Accept yourself. Treat yourself the way you would treat a good friend.
• Focus on your breathing. When you have negative thoughts, try to sit down, take a deep breath and close your eyes. Focus on your breath as it moves in and out of your body. Sitting and breathing for even just a minute can help.

You can also try more structured mindfulness exercises, such as:

• Body scan meditation. Lie on your back with your legs extended and arms at your sides, palms facing up. Focus your attention slowly and deliberately on each part of your body, in order, from toe to head or head to toe. Be aware of any sensations, emotions or thoughts associated with each part of your body.
• Sitting meditation. Sit comfortably with your back straight, feet flat on the floor and hands in your lap. Breathing through your nose, focus on your breath moving in and out of your body. If physical sensations or thoughts interrupt your meditation, note the experience and then return your focus to your breath.
• Walking meditation. Find a quiet place 10 to 20 feet in length, and begin to walk slowly. Focus on the experience of walking, being aware of the sensations of standing and the subtle movements that keep your balance. When you reach the end of your path, turn and continue walking, maintaining awareness of your sensations.

When and how often should I practice mindfulness exercises?

It depends on what kind of mindfulness exercise you plan to do.

Simple mindfulness exercises can be practiced anywhere and anytime. Research indicates that engaging your senses outdoors is especially beneficial.

For more structured mindfulness exercises, such as body scan meditation or sitting meditation, you'll need to set aside time when you can be in a quiet place without distractions or interruptions. You might choose to practice this type of exercise early in the morning before you begin your daily routine.

Aim to practice mindfulness every day for about six months. Over time, you might find that mindfulness becomes effortless. Think of it as a commitment to reconnecting with and nurturing yourself.

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• Bystritsky A. Complementary and alternative treatments for anxiety symptoms and disorders: Physical, cognitive, and spiritual interventions. https://uptodate.com/contents/search. Accessed June 14, 2018.
• Seaward BL. Meditation and mindfulness. In: Managing Stress: Principles and Strategies for Health and Well-being. 9th ed. Burlington, Mass.: Jones & Bartlett Learning; 2018.
• Shapiro SL, et al. The Art and Science of Mindfulness: Integrating Mindfulness into Psychology and the Helping Professions. 2nd ed. Washington, D.C.: American Psychological Association; 2017.
• Lymeus F, et al. Building mindfulness bottom-up: Meditation in natural settings supports open monitoring and attention restoration. Consciousness and Cognition. 2018;59:40.
• Blanck P, et al. Effects of mindfulness exercises as stand-alone interventions on symptoms of anxiety and depression: Systematic review and meta-analysis. Behaviour Research and Therapy. 2018;102:25.
• AskMayoExpert. Meditation. Rochester, Minn.: Mayo Foundation for Medical Education and Research; 2018.
• Khoury B, et al. Mindfulness-based stress reduction for healthy individuals: A meta-analysis. Journal of Psychosomatic Research. 2015;78:519.
• Practice mindfulness and relaxation. Springboard Beyond Cancer. https://survivorship.cancer.gov/springboard/stress-mood/practice-mindfulness. Accessed June 14, 2018.

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