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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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• v.37(16); 2022 Apr 25

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.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Quantitative Methods
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Statistics & Data Research Guide

Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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13 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

This post is really helpful.

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

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Quantitative Research Design: Four Common Ways to Collect Your Data Efficiently

What is the quantitative research design? Why is the research design needed? What are the four main types of quantitative research designs used by researchers?

This article illustrates research design using an analogy, explains why a research design is needed, describes four main types of research designs, and gives examples of each research design’s application.

In doing your research on whatever goals you have in mind, you make a plan to reach those goals. You spell out the specific items that you want to pursue in your research objectives.

An Analogy of Research Design

You need to engage the architect’s help to draw what you have in mind (your concept), estimate the cost to build it, and list the steps to follow to bring that plan into reality. The architect comes up with a blueprint of the house, detailing the size and quantity of reinforced steel bars, the floor plan, dimensions of the house, and aesthetics.

If your house comprises not only one floor but two, or even three, and you want the house to be sturdy, that could last decades or generations; you will need to engage a structural engineer. He makes sure that the home maintains its integrity and can handle the loads and forces they encounter through time.

And, of course, the electrical connections require the expertise of an electrical engineer. He plans how the electrical circuits are arranged in the entire house to make it convenient for you to access electricity.

Why is a Research Design Needed?

As pointed out earlier, the main reasons for coming up with a research design relate to efficiency and effectiveness. If you have a good research design, you will save time, energy, and cost in doing your research. You have a plan to get the data that you want to answer the research objectives.

Thus, before conducting research, you already have in mind what to expect. And of course, you will know how much that would cost you. If you cannot afford it, then you revise your plan.

Defining the Research Objectives

Thus, you will need to define your research objectives based on your topic of interest. What do you want to achieve in your research? Will you be dealing with people, animals, plants, or things?

Will you manipulate some variables? Will you compare different groups? Would you want to know which  variable  causes an effect on other variables? Or will you describe what is there?

It all boils down to  what you want . Be very clear if you’re going to describe things, correlate them, find out if one causes the other, or put up an experiment to test if manipulating one variable can effect a change to another variable.

The Four Main Types of Quantitative Research Design

The four quantitative research designs are distinguished from each other in Figure 1. Please note that as you go from left to right; the approach becomes more manipulative. The descriptive research design studies the existing situation, whereas the researcher manipulates variables at the other end, using the experimental method.

Examples of the Application of the Different Research Design on the Same Subject

Descriptive research design, correlational research design.

The same study on air quality may be conducted as in Example 1, but this time, the respondent’s awareness about air quality is correlated with their attitude towards emission testing.

Causal-Comparative Research Design

Still, on the air quality study, you might want to know what causes the respondents to behave positively or negatively towards emission testing. Does attitude have something to do with a person’s educational background? Or perhaps, their capacity to pay for emission testing?

The following video explains this research design further with two examples.

Experimental Research Design

The two groups’ members were randomly assigned, and all other variables were kept constant, meaning the respondents have similar characteristics where only attendance at the seminar is the difference.

You are interested in finding the difference between a person’s attitude towards emission testing. And what discriminates them from the other is that one group attended a seminar on air pollution while the other group did not.

Final Notes

There are still other types of research designs out there. What is important here is that you are clear about what you want to investigate.

Related Posts

Technical writing tips: interpreting graphs with two variables, a research on in-service training activities, teaching efficacy, job satisfaction and attitude, faculty-directed fieldwork: eight important benefits, about the author, patrick regoniel.

Dr. Regoniel, a hobbyist writer, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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Designing Research Proposal in Quantitative Approach

• First Online: 27 October 2022

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• Md. Rezaul Karim 4  

This chapter provides a comprehensive guideline for writing a research proposal in quantitative approach. It starts with the definition and purpose of writing a research proposal followed by a description of essential parts of a research proposal and subjects included in each part, organization of a research proposal, and guidelines for writing different parts of a research proposal including practical considerations and aims of a proposal that facilitate the acceptance of the proposal. Finally, an example of a quantitative research proposal has been presented. It is expected that research students and other interested researchers will be able to write their research proposal(s) using the guidelines presented in the chapter.

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Karim, M.R. (2022). Designing Research Proposal in Quantitative Approach. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_10

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

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• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

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A Complete Guide to Quantitative Research Methods

Numbers are everywhere and drive our day-to-day lives. We take decisions based on numbers, both at work and in our personal lives. For example, an organization may rely on sales numbers to see if it’s succeeding or failing, and a group of friends planning a vacation may look at ticket prices to pick a place.

In the social domain, numbers are just as important. They help identify what interventions are needed, whether ongoing projects are effective, and more. But how do organizations in the social domain get the numbers they need?

This is where quantitative research comes in. Quantitative research is the process of collecting numerical data through standardized techniques, then applying statistical methods to derive insights from it.

When is quantitative research useful?

The goal of quantitative research methods is to collect numerical data from a group of people, then generalize those results to a larger group of people to explain a phenomenon. Researchers generally use quantitative research when they want get objective, conclusive answers.

For example, a chocolate brand may run a survey among a sample of their target group (teenagers in the United States) to check whether they like the taste of the chocolate. The result of this survey would reveal how all teenagers in the U.S. feel about the chocolate.

Similarly, an organization running a project to improve a village’s literacy rate may look at how many people came to their program, how many people dropped out, and each person’s literacy score before and after the program. They can use these metrics to evaluate the overall success of their program.

Unlike  qualitative research , quantitative research is generally not used in the early stages of research for exploring a question or scoping out a problem. It is generally used to answer clear, pre-defined questions in the advanced stages of a research study.

How can you plan a quantitative research exercise?

• Identify the research problem . An example would be, how well do New Delhi’s government schools ensure that students complete their education?
• Prepare the research questions that need to be answered to address the research problem. For example, what percentage of students drop out of government schools in New Delhi?
• Review existing literature on the research problem and questions to ensure that there is no duplication. If someone has already answered this, you can rely on their results.
• Develop a research plan . This includes identifying the target group, sample , and method of data collection ; conducting data analysis; collating recommendations; and arriving at a conclusion.

What are the advantages of quantitative research methods?

• Quantitative research methods provide an relatively conclusive answer to the research questions.
• When the data is collected and analyzed in accordance with standardized, reputable methodology, the results are usually trustworthy.
• With statistically significant sample sizes, the results can be generalized to an entire target group.

Samples have to be carefully designed and chosen, else their results can’t be generalized. Learn how to choose the right sampling technique for your survey.

What are the limitations of quantitative research methods?

• Does not account for people’s thoughts or perceptions about what you’re evaluating.
• Does not explore the “why” and “how” behind a phenomenon.

What quantitative research methods can you use?

Here are four quantitative research methods that you can use to collect data for a quantitative research study:

Questionnaires

This is the most common way to collect quantitative data. A questionnaire (also called a survey) is a series of questions, usually written on paper or a digital form. Researchers give the questionnaire to their sample, and each participant answers the questions. The questions are designed to gather data that will help researchers answer their research questions.

Typically, a questionnaire has closed-ended questions — that is, the participant chooses an answer from the given options. However, a questionnaire may also have quantitative open-ended questions. In the open-ended example above, the participants could write a simple number like “4”, a range like “I usually go one or two times per week” or a more complex response like “Most weeks I go twice, but this week I went 4 times because I kept forgetting my grocery list. During the winter, I only go once a week.”

Understanding closed and open-ended questions is crucial to designing a great survey and collecting high quality data. Learn more with our complete guide about when and how to use closed and open-ended questions.

A good questionnaire should have clear language, correct grammar and spelling, and a clear objective.

Advantages:

• Questionnaires are often less time consuming than interviews or other in-person quantitative research methods.
• They’re a common, fairly simple way to collect data.
• They can be a cost-effective option for gathering data from a large sample.

Limitations:

• Responses may lack depth and provide limited information.
• Respondents may lose interest or quit if the questionnaire is long.
• Respondents may not understand all questions, which would lead to inaccurate responses.

Response bias — a set of factors that lead participants answer a question incorrectly — can be deadly for data quality. Learn how it happens and how to avoid it.

An interview for quantitative research involves verbal communication between the participant and researcher, whose goal is to gather numerical data. The interview can be conducted face-to-face or over the phone, and it can be structured or unstructured.

In a structured interview, the researcher asks a fixed set of questions to every participant. The questions and their order are pre-decided by the researcher. The interview follows a formal pattern. Structured interviews are more cost efficient and can be less time consuming.

In an unstructured interview, the researcher thinks of his/her questions as the interview proceeds. This type of interview is conversational in nature and can last a few hours. This type of interview allows the researcher to be flexible and ask questions depending on the participant’s responses. This quantitative research method can provide more in-depth information, since it allows researchers to delve deeper into a participant’s response.

• Interviews can provide more in-depth information.
• Interviews are more flexible than questionnaires, since interviewers can adapt their questions to each participant or ask follow-up questions.
• Interviewers can clarify participants’ questions, which will help them get clearer, more accurate data.
• Interviewing one person at a time can be time-consuming.
• Travel, interviewer salaries and other expenses can make interviews an expensive data collection tool.
• With unstructured interviews, it can be difficult to quantify some responses.

One way to speed up interviews is to conduct them with multiple people at one time in a focus group discussion. Learn more about how to conduct a great FGD.

Observation

Observation is a systematic way to collect data by observing people in natural situations or settings. Though it is mostly used for collecting qualitative data, observation can also be used to collect quantitative data.

Observation can be simple or behavioral. Simple observations are usually numerical, like how many cars pass through a given intersection each hour or how many students are asleep during a class. Behavioral observation, on the other hand, observes and interprets people’s behavior, like how many cars are driving dangerously or how engaging a lecturer is.

Simple observation can be a good way to collect numerical data. This can be done by pre-defining clear numerical variables that can be collected during observation — for example, what time employees leave the office. This data can be collected by observing employees over a period of time and recording when each person leaves.

• Observation is often an inexpensive way to collect data.
• Since researchers are recording the data themselves (rather than participants reporting the data), most of the collected data will generally be usable.
• Data collection can be stopped and started by researchers at any time, making it a flexible data collection tool.
• Researchers need to be extensively trained to undertake observation and record data correctly.
• Sometimes the environment or research may bias the data, like when participants know they’re being observed.
• If the situation to be observed sometimes doesn’t happen, researchers may waste a lot of time during data collection.

Simple vs. behavioral is just one type of observation. Learn more about the 5 different types of observation and when you should use each to collect different types of data.

Since quantitative research depends on numerical data, records (also known as external data) can provide critical information to answer research questions. Records are numbers and statistics that institutions use to track activities, like attendance in a school or the number of patients admitted in a hospital.

For example, the Government of India conducts the Census every 10 years, which is a record of the country’s population. This data can be used by a researcher who is addressing a population-related research problem.

• Records often include comprehensive data captured over a long period of time.
• Data collection time is minimal, since the data has already been collected and recorded by someone else.
• Records often only provide numerical data, not the reason or cause behind the data.
• Cleaning badly structured or formatted records can take a long time.
• If a record is incomplete or inaccurate, there is often no way to fix it.

Summing it up

Quantitative research methods are one of the best tools to identify a problem or phenomenon, how widespread it is, and how it is changing over time. After identifying a problem, quantitative research can also be used to come up with a trustworthy solution, identified using numerical data collected through standardized techniques.

Image credits:  Curtis MacNewton ,  Brijesh Nirmal ,  Charles Deluvio , and Atlan.

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14 comments.

Very useful for research

Very easy to read and informative book. Well written. Thany thanks for the download.

It is concise and practical as well as easy to understand.

Nice book but I kind find a way to download it. Kindly let me know how to download it. Thanks

Hello Micah Nalianya Greetings! Kindly tell me how to download the book. Simeon

Hi Micah and Simeon! You can download our data collection ebook here: https://socialcops.com/ebooks/data-collection/

I have loved reviewing the brief write up. Good revision for me. Thanks

The text contains concise and important tips on data collection techniques.

Thanks for an explicit and precise outline of data collection methods.

thank you very much, this guide is really useful and easy to understand. Specially for students that just have started research.

Thank you so much for sharing me this very important material.

I am highly impressed with the simply ways you explain methods of collecting data. I am a Monitoring and Evaluation Specialist and I will like to be receiving your regular publications.

i have benefited from the work. well organized .thank you

interview is a qualitative method not quantitative.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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• Translation

How to design a Quantitative research study

By charlesworth author services.

• Charlesworth Author Services
• 10 December, 2021

Beginning the design of a quantitative research project can feel like stepping foot into a maze; there are lots of different potential routes you can take, and it can be hard to know which the right one is. Due to the potential complexity of designing a study like this, knowing which first step to take can be confusing. To help clarify the process and make it easier for you, we’ve split up the decision making into several distinct points that you can address separately as you plan your quantitative research project.

1. Decide what your key question(s) is/are

First, the most important thing to do is to work out why you’re designing and conducting this experiment in the first place; in other words…

What is the key question that you’re trying to answer? 

Focus on what interests you and use this to guide some of your reading in the area. Read relevant articles and concentrate on the other experiments that they reference. This will help you work out what gaps in knowledge there are in the field and how your own project can make a novel contribution. 

2. Identify the methods you will use

Once you know what the question is that you’re trying to answer, your next step is to work out how you will answer it. In other words…

What will your methodology be?

Reading other papers in the area will be helpful at this stage too. You might find that you can adapt a paradigm from another experiment, or that there are commonly used measures in your area. 

3. Narrow in on your variables

A good thing to do after identifying the method that you will use is to decide exactly what the independent and dependent variables will be in your experiment(s). 

• Independent variable (IV) is the factor that you will manipulate in your experiment. For example, this might be which stimuli a participant is shown or which treatment they are given.
• Dependent variable (DV) is what you are measuring. This could be reaction time, score on a particular measure or ratings that the participants give.

4. Formulate your hypothesis

Now that you’ve identified your question, methodology and variables, you can begin to formulate the hypothesis for your experiment(s). In other words…

What do you expect to happen? 

A hypothesis should be clear and directional, for example:

In this experiment, we expect that participants who see the colourful stimuli will give higher ratings than those who see the black and white stimuli.

Your hypothesis should always be based in evidence, using findings from other previous studies and research to guide what you expect to see. Again, reading relevant papers will help you to arrive at better hypotheses.

Now that you have more clarity on designing your research project, you can proceed to actually put those plans into action. Start preparing for and conducting your experiments to collect the data, then analyse those results to find out if your hypothesis is correct.

Read next (third/ final ) in series: How to design a qualitative research study

Read previous (first) in series: Deciding between a quantitative design and a qualitative design for your study

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• Cookies & Privacy
• GETTING STARTED
• Introduction
• FUNDAMENTALS
• Acknowledgements
• Research questions & hypotheses
• Concepts, constructs & variables
• Research limitations
• Getting started
• Sampling Strategy
• Research Quality
• Research Ethics
• Data Analysis

How to structure quantitative research questions

There is no "one best way" to structure a quantitative research question. However, to create a well-structured quantitative research question, we recommend an approach that is based on four steps : (1) Choosing the type of quantitative research question you are trying to create (i.e., descriptive, comparative or relationship-based); (2) Identifying the different types of variables you are trying to measure, manipulate and/or control, as well as any groups you may be interested in; (3) Selecting the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved; and (4) Writing out the problem or issues you are trying to address in the form of a complete research question. In this article, we discuss each of these four steps , as well as providing examples for the three types of quantitative research question you may want to create: descriptive , comparative and relationship-based research questions .

• STEP ONE: Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create
• STEP TWO: Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in
• STEP THREE: Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved
• STEP FOUR: Write out the problem or issues you are trying to address in the form of a complete research question

STEP ONE Choose the type of quantitative research question (i.e., descriptive, comparative or relationship) you are trying to create

The type of quantitative research question that you use in your dissertation (i.e., descriptive , comparative and/or relationship-based ) needs to be reflected in the way that you write out the research question; that is, the word choice and phrasing that you use when constructing a research question tells the reader whether it is a descriptive, comparative or relationship-based research question. Therefore, in order to know how to structure your quantitative research question, you need to start by selecting the type of quantitative research question you are trying to create: descriptive, comparative and/or relationship-based.

STEP TWO Identify the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in

Whether you are trying to create a descriptive, comparative or relationship-based research question, you will need to identify the different types of variable that you are trying to measure , manipulate and/or control . If you are unfamiliar with the different types of variable that may be part of your study, the article, Types of variable , should get you up to speed. It explains the two main types of variables: categorical variables (i.e., nominal , dichotomous and ordinal variables) and continuous variables (i.e., interval and ratio variables). It also explains the difference between independent and dependent variables , which you need to understand to create quantitative research questions.

To provide a brief explanation; a variable is not only something that you measure , but also something that you can manipulate and control for. In most undergraduate and master's level dissertations, you are only likely to measure and manipulate variables. You are unlikely to carry out research that requires you to control for variables, although some supervisors will expect this additional level of complexity. If you plan to only create descriptive research questions , you may simply have a number of dependent variables that you need to measure. However, where you plan to create comparative and/or relationship-based research questions , you will deal with both dependent and independent variables . An independent variable (sometimes called an experimental or predictor variable ) is a variable that is being manipulated in an experiment in order to observe the effect this has on a dependent variable (sometimes called an outcome variable ). For example, if we were interested in investigating the relationship between gender and attitudes towards music piracy amongst adolescents , the independent variable would be gender and the dependent variable attitudes towards music piracy . This example also highlights the need to identify the group(s) you are interested in. In this example, the group of interest are adolescents .

Once you identifying the different types of variable you are trying to measure, manipulate and/or control, as well as any groups you may be interested in, it is possible to start thinking about the way that the three types of quantitative research question can be structured . This is discussed next.

STEP THREE Select the appropriate structure for the chosen type of quantitative research question, based on the variables and/or groups involved

The structure of the three types of quantitative research question differs, reflecting the goals of the question, the types of variables, and the number of variables and groups involved. By structure , we mean the components of a research question (i.e., the types of variables, groups of interest), the number of these different components (i.e., how many variables and groups are being investigated), and the order that these should be presented (e.g., independent variables before dependent variables). The appropriate structure for each of these quantitative research questions is set out below:

Structure of descriptive research questions

• Structure of comparative research questions
• Structure of relationship-based research questions

There are six steps required to construct a descriptive research question: (1) choose your starting phrase; (2) identify and name the dependent variable; (3) identify the group(s) you are interested in; (4) decide whether dependent variable or group(s) should be included first, last or in two parts; (5) include any words that provide greater context to your question; and (6) write out the descriptive research question. Each of these steps is discussed in turn:

Choose your starting phrase

Identify and name the dependent variable

Identify the group(s) you are interested in

Decide whether the dependent variable or group(s) should be included first, last or in two parts

Include any words that provide greater context to your question

Write out the descriptive research question

FIRST Choose your starting phrase

You can start descriptive research questions with any of the following phrases:

How many? How often? How frequently? How much? What percentage? What proportion? To what extent? What is? What are?

Some of these starting phrases are highlighted in blue text in the examples below:

How many calories do American men and women consume per day?

How often do British university students use Facebook each week?

What are the most important factors that influence the career choices of Australian university students?

What proportion of British male and female university students use the top 5 social networks?

What percentage of American men and women exceed their daily calorific allowance?

SECOND Identify and name the dependent variable

All descriptive research questions have a dependent variable. You need to identify what this is. However, how the dependent variable is written out in a research question and what you call it are often two different things. In the examples below, we have illustrated the name of the dependent variable and highlighted how it would be written out in the blue text .

The first two examples highlight that while the name of the dependent variable is the same, namely daily calorific intake , the way that this dependent variable is written out differs in each case.

THIRD Identify the group(s) you are interested in

All descriptive research questions have at least one group , but can have multiple groups . You need to identify this group(s). In the examples below, we have identified the group(s) in the green text .

What are the most important factors that influence the career choices of Australian university students ?

The examples illustrate the difference between the use of a single group (e.g., British university students ) and multiple groups (e.g., American men and women ).

FOURTH Decide whether the dependent variable or group(s) should be included first, last or in two parts

Sometimes it makes more sense for the dependent variable to appear before the group(s) you are interested in, but sometimes it is the opposite way around. The following examples illustrate this, with the group(s) in green text and the dependent variable in blue text :

Group 1st; dependent variable 2nd:

How often do British university students use Facebook each week ?

Dependent variable 1st; group 2nd:

Sometimes, the dependent variable needs to be broken into two parts around the group(s) you are interested in so that the research question flows. Again, the group(s) are in green text and the dependent variable is in blue text :

How many calories do American men and women consume per day ?

Of course, you could choose to restructure the question above so that you do not have to split the dependent variable into two parts. For example:

How many calories are consumed per day by American men and women ?

When deciding whether the dependent variable or group(s) should be included first or last, and whether the dependent variable should be broken into two parts, the main thing you need to think about is flow : Does the question flow? Is it easy to read?

FIFTH Include any words that provide greater context to your question

Sometimes the name of the dependent variable provides all the explanation we need to know what we are trying to measure. Take the following examples:

In the first example, the dependent variable is daily calorific intake (i.e., calories consumed per day). Clearly, this descriptive research question is asking us to measure the number of calories American men and women consume per day. In the second example, the dependent variable is Facebook usage per week. Again, the name of this dependent variable makes it easy for us to understand that we are trying to measure the often (i.e., how frequently; e.g., 16 times per week) British university students use Facebook.

However, sometimes a descriptive research question is not simply interested in measuring the dependent variable in its entirety, but a particular component of the dependent variable. Take the following examples in red text :

In the first example, the research question is not simply interested in the daily calorific intake of American men and women, but what percentage of these American men and women exceeded their daily calorific allowance. So the dependent variable is still daily calorific intake, but the research question aims to understand a particular component of that dependent variable (i.e., the percentage of American men and women exceeding the recommend daily calorific allowance). In the second example, the research question is not only interested in what the factors influencing career choices are, but which of these factors are the most important.

Therefore, when you think about constructing your descriptive research question, make sure you have included any words that provide greater context to your question.

SIXTH Write out the descriptive research question

Once you have these details ? (1) the starting phrase, (2) the name of the dependent variable, (3) the name of the group(s) you are interested in, and (4) any potential joining words ? you can write out the descriptive research question in full. The example descriptive research questions discussed above are written out in full below:

In the section that follows, the structure of comparative research questions is discussed.

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Basics of Research Design: A Guide to selecting appropriate research design

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Quantitative Data Analysis Methods. Applications, Methods, and Case Studies

August 29th, 2024

The ability to properly analyze and understand numbers has become very valuable, especially in today’s time.

Analyzing numerical data systematically involves thoughtfully collecting, organizing, and studying data to discover patterns, trends, and connections that can guide important choices.  

Key Highlights

• Analyzing data numerically involves gathering info, organizing it neatly, and examining the numbers to gain insights and make choices informed by data.
• It involves various methods like descriptive statistics, predictive modeling, machine learning, and other statistical techniques. These help make sense of everything.
• For businesses, researchers, and organizations, it’s important to analyze numbers to spot patterns, relationships, and how things change over time within their info.
• Doing analyses allows for data-driven decision-making, projecting outcomes, assessing risks intelligently, and refining strategies and workflows. Finding meaning in the metrics helps optimize processes.

What is Quantitative Data Analysis?

Analyzing numbers is useful for learning from information. It applies stats methods and computational processes to study and make sense of data so you can spot patterns, connections, and how things change over time – giving insight to guide decisions.

At the core, quantitative analysis builds on math and stats fundamentals to turn raw figures into meaningful knowledge.

The process usually starts with gathering related numbers and organizing them neatly. Then analysts use different statistical techniques like descriptive stats, predictive modeling, and more to pull out valuable lessons.

Descriptive stats provide a summary of the key details, like averages and how spread out the numbers are. This helps analysts understand the basics and find any weird outliers.

Inferential stats allow analysts to predict broader trends based on a sample. Things like hypothesis testing , regression analysis, and correlation investigations help identify significant relationships.

Machine learning and predictive modeling have also enhanced working with numbers. These sophisticated methods let analysts create models that can forecast outcomes, recognize patterns across huge datasets, and uncover hidden insights beyond basic stats alone.

Leveraging data-based evidence supports more informed management of resources.

Data Collection and Preparation

The first step in any quantitative data analysis is collecting the relevant data. This involves determining what data is needed to answer the research question or business objective.

Data can come from a variety of sources such as surveys, experiments, observational studies, transactions, sensors, and more. 

Once the data is obtained, it typically needs to go through a data preprocessing or data cleaning phase.

Real-world data is often messy, containing missing values, errors, inconsistencies, and outliers that can negatively impact the analysis if not handled properly. Common data cleaning tasks include:

• Handling missing data through imputation or case deletion
• Identifying and treating outliers 
• Transforming variables (e.g. log transformations)
• Encoding categorical variables
• Removing duplicate observations

The goal of data cleaning is to ensure that quantitative data analysis techniques can be applied accurately to high-quality data. Proper data collection and preparation lays the foundation for reliable results.

In addition to cleaning, the data may need to be structured or formatted in a way that statistical software and data analysis tools can read it properly.

For large datasets, data management principles like establishing data pipelines become important.

Descriptive Statistics of Quantitative Data Analysis

Descriptive statistics is a crucial aspect of quantitative data analysis that involves summarizing and describing the main characteristics of a dataset.

This branch of statistics aims to provide a clear and concise representation of the data, making it easier to understand and interpret.

Descriptive statistics are typically the first step in analyzing data, as they provide a foundation for further statistical analyses and help identify patterns, trends, and potential outliers.

The most common descriptive statistics measures include:

• Mean : The arithmetic average of the data points.
• Median : The middle value in a sorted dataset.
• Mode : The value that occurs most frequently in the dataset.
• Range : The difference between the highest and lowest values in the dataset.
• Variance : The average of the squared deviations from the mean.
• Standard Deviation : The square root of the variance, providing a measure of the spread of data around the mean.
• Histograms : Visual representations of the distribution of data using bars.
• Box Plots : Graphical displays that depict the distribution’s median, quartiles, and outliers.
• Scatter Plots : Displays the relationship between two quantitative variables.

Descriptive statistics play a vital role in data exploration and understanding the initial characteristics of a dataset.

They provide a summary of the data, allowing researchers and analysts to identify patterns, detect potential outliers, and make informed decisions about further analyses.

However, it’s important to note that descriptive statistics alone do not provide insights into the underlying relationships or causal mechanisms within the data.

To draw meaningful conclusions and make inferences about the population, inferential statistics and advanced analytical techniques are required.

Inferential Statistics

While descriptive statistics provide a summary of data, inferential statistics allow you to make inferences and draw conclusions from that data.

Inferential statistics involve taking findings from a sample and generalizing them to a larger population. This is crucial when it is impractical or impossible to study an entire population.

The core of inferential statistics revolves around hypothesis testing . A hypothesis is a statement about a population parameter that needs to be evaluated based on sample data.

The process involves formulating a null and alternative hypothesis, calculating an appropriate test statistic, determining the p-value, and making a decision whether to reject or fail to reject the null hypothesis.

Some common inferential techniques include:

T-tests – Used to determine if the mean of a population differs significantly from a hypothesized value or if the means of two populations differ significantly.

ANOVA ( Analysis of Variance ) – Used to determine if the means of three or more groups are different.  

Regression analysis – Used to model the relationship between a dependent variable and one or more independent variables. This allows you to understand drivers and make predictions.

Correlation analysis – Used to measure the strength and direction of the relationship between two variables.

Inferential statistics are critical for quantitative research, allowing you to test hypotheses, establish causality, and make data-driven decisions with confidence in the findings.

However, the validity depends on meeting the assumptions of the statistical tests and having a properly designed study with adequate sample sizes.

The interpretation of inferential statistics requires care. P-values indicate the probability of obtaining the observed data assuming the null hypothesis is true – they do not confirm or deny the hypothesis directly. Effect sizes are also crucial for assessing the practical significance beyond just statistical significance.

Predictive Modeling and Machine Learning

Quantitative data analysis goes beyond just describing and making inferences about data – it can also be used to build predictive models that forecast future events or behaviors.

Predictive modeling uses statistical techniques to analyze current and historical data to predict unknown future values. 

Some of the key techniques used in predictive modeling include regression analysis , decision trees , neural networks, and other machine learning algorithms.

Regression analysis is used to understand the relationship between a dependent variable and one or more independent variables.

It allows you to model that relationship and make predictions. More advanced techniques like decision trees and neural networks can capture highly complex, non-linear relationships in data.

Machine learning has become an integral part of quantitative data analysis and predictive modeling. Machine learning algorithms can automatically learn and improve from experience without being explicitly programmed.

They can identify hidden insights and patterns in large, complex datasets that would be extremely difficult or impossible for humans to find manually.

Some popular machine learning techniques used for predictive modeling include:

• Supervised learning (decision trees, random forests, support vector machines)
• Unsupervised learning ( k-means clustering , hierarchical clustering) 
• Neural networks and deep learning
• Ensemble methods (boosting, bagging)

Predictive models have a wide range of applications across industries, from forecasting product demand and sales to identifying risk of customer churn to detecting fraud.

With the rise of big data , machine learning is becoming increasingly important for building accurate predictive models from large, varied data sources.

Quantitative Data Analysis Tools and Software

To effectively perform quantitative data analysis, having the right tools and software is essential. There are numerous options available, ranging from open-source solutions to commercial platforms.

The choice depends on factors such as the size and complexity of the data, the specific analysis techniques required, and the budget.

Statistical Software Packages

• R : A powerful open-source programming language and software environment for statistical computing and graphics. It offers a vast collection of packages for various data analysis tasks.
• Python : Another popular open-source programming language with excellent data analysis capabilities through libraries like NumPy, Pandas, Matplotlib, and sci-kit-learn.
• SPSS : A commercial software package widely used in academic and research settings for statistical analysis, data management, and data documentation.
• SAS : A comprehensive software suite for advanced analytics, business intelligence, data management, and predictive analytics.
• STATA : A general-purpose statistical software package commonly used in research, especially in the fields of economics, sociology, and political science.

Spreadsheet Applications

• Microsoft Excel : A widely used spreadsheet application that offers built-in statistical functions and data visualization tools, making it suitable for basic data analysis tasks.
• Google Sheets : A free, web-based alternative to Excel, offering similar functionality and collaboration features.

Data Visualization Tools

• Tableau : A powerful data visualization tool that allows users to create interactive dashboards and reports, enabling effective communication of quantitative data.
• Power BI : Microsoft’s business intelligence platform that combines data visualization capabilities with data preparation and data modeling features.
• Plotly : A high-level, declarative charting library that can be used with Python, R, and other programming languages to create interactive, publication-quality graphs.

Business Intelligence (BI) and Analytics Platforms

• Microsoft Power BI : A cloud-based business analytics service that provides data visualization, data preparation, and data discovery capabilities.
• Tableau Server/Online : A platform that enables sharing and collaboration around data visualizations and dashboards created with Tableau Desktop.
• Qlik Sense : A data analytics platform that combines data integration, data visualization, and guided analytics capabilities.

Cloud-based Data Analysis Platforms

• Amazon Web Services (AWS) Analytics Services : A suite of cloud-based services for data analysis, including Amazon Athena, Amazon EMR, and Amazon Redshift.
• Google Cloud Platform (GCP) Data Analytics : GCP offers various data analytics tools and services, such as BigQuery, Dataflow, and Dataprep.
• Microsoft Azure Analytics Services : Azure provides a range of analytics services, including Azure Synapse Analytics, Azure Data Explorer, and Azure Machine Learning.

Applications of Quantitative Data Analysis

Quantitative data analysis techniques find widespread applications across numerous domains and industries. Here are some notable examples:

Business Analytics

Businesses rely heavily on quantitative methods to gain insights from customer data, sales figures, market trends, and operational metrics.

Techniques like regression analysis help model customer behavior, while clustering algorithms enable customer segmentation. Forecasting models allow businesses to predict future demand, inventory needs, and revenue projections.

Healthcare and Biomedical Research with Quantitative Data Analysis

Analysis of clinical trial data, disease prevalence statistics, and patient outcomes employs quantitative methods extensively.

Hypothesis testing determines the efficacy of new drugs or treatments. Survival analysis models patient longevity. Data mining techniques identify risk factors and detect anomalies in healthcare data.

Marketing and Consumer Research

Marketing teams use quantitative data from surveys, A/B tests, and online behavior tracking to optimize campaigns. Regression models predict customer churn or likelihood to purchase.

Sentiment analysis derives insights from social media data and product reviews. Conjoint analysis determines which product features impact consumer preferences.

Finance and Risk Management with Quantitative Data Analysis

Quantitative finance relies on statistical models for portfolio optimization, derivative pricing, risk quantification, and trading strategy formulation. Value at Risk (VaR) models assess potential losses. Monte Carlo simulations evaluate the risk of complex financial instruments.

Social and Opinion Research

From political polls to consumer surveys, quantitative data analysis techniques like weighting, sampling, and survey data adjustment are critical. Researchers employ methods like factor analysis, cluster analysis, and structural equation modeling .

Case Studies

Case study 1: netflix’s data-driven recommendations.

Netflix extensively uses quantitative data analysis, particularly machine learning, to drive its recommendation engine.

By mining user behavior data and combining it with metadata about movies and shows, they build predictive models to accurately forecast what a user would enjoy watching next.

Case Study 2: Moneyball – Analytics in Sports

The adoption of sabermetrics and analytics by baseball teams like the Oakland Athletics, as depicted in the movie Moneyball, revolutionized player scouting and strategy.

By quantifying player performance through new statistical metrics, teams could identify undervalued talent and gain a competitive edge.

Quantitative data analysis is a powerful toolset that allows organizations to derive valuable insights from their data to make informed decisions.

By applying the various techniques and methods discussed, such as descriptive statistics, inferential statistics , predictive modeling , and machine learning, businesses can gain a competitive edge by uncovering patterns, trends, and relationships hidden within their data.

However, it’s important to note that quantitative data analysis is not a one-time exercise. As businesses continue to generate and collect more data, the analysis process should be an ongoing, iterative cycle.

If you’re looking to further enhance their quantitative data analysis capabilities, there are several potential next steps to consider:

• Continuous learning and skill development : The field of data analysis is constantly evolving, with new statistical methods, modeling techniques, and software tools emerging regularly. Investing in ongoing training and education can help analysts stay up-to-date with the latest advancements and best practices.
• Investing in specialized tools and infrastructure : As data volumes continue to grow, organizations may need to invest in more powerful data analysis tools, such as big data platforms, cloud-based solutions, or specialized software packages tailored to their specific industry or use case.
• Collaboration and knowledge sharing : Fostering a culture of collaboration and knowledge sharing within the organization can help analysts learn from each other’s experiences, share best practices, and collectively improve the organization’s analytical capabilities.
• Integrating qualitative data : While this article has focused primarily on quantitative data analysis, incorporating qualitative data sources, such as customer feedback, social media data, or expert opinions, can provide additional context and enrich the analysis process.
• Ethical considerations and data governance : As data analysis becomes more prevalent, it’s crucial to address ethical concerns related to data privacy, bias, and responsible use of analytics.

Implementing robust data governance policies and adhering to ethical guidelines can help organizations maintain trust and accountability.

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• Open access
• Published: 02 September 2024

Clinical supervisor’s experiences of peer group clinical supervision during COVID-19: a mixed methods study

• Owen Doody   ORCID: orcid.org/0000-0002-3708-1647 1 ,
• Kathleen Markey   ORCID: orcid.org/0000-0002-3024-0828 1 ,
• James Turner   ORCID: orcid.org/0000-0002-8360-1420 2 ,
• Claire O. Donnell   ORCID: orcid.org/0000-0003-2386-7048 1 &
• Louise Murphy   ORCID: orcid.org/0000-0003-2381-3963 1  

BMC Nursing volume  23 , Article number:  612 ( 2024 ) Cite this article

Metrics details

Providing positive and supportive environments for nurses and midwives working in ever-changing and complex healthcare services is paramount. Clinical supervision is one approach that nurtures and supports professional guidance, ethical practice, and personal development, which impacts positively on staff morale and standards of care delivery. In the context of this study, peer group clinical supervision provides allocated time to reflect and discuss care provided and facilitated by clinical supervisors who are at the same grade/level as the supervisees.

To explore the clinical supervisor’s experiences of peer group clinical supervision a mixed methods study design was utilised within Irish health services (midwifery, intellectual disability, general, mental health). The Manchester Clinical Supervision Scale was used to survey clinical supervisors ( n  = 36) and semi-structured interviews ( n  = 10) with clinical supervisors were conducted. Survey data were analysed through SPSS and interview data were analysed utilising content analysis. The qualitative and quantitative data’s reporting rigour was guided by the CROSS and SRQR guidelines.

Participants generally had a positive encounter when providing clinical supervision. They highly appreciated the value of clinical supervision and expressed a considerable degree of contentment with the supervision they provided to supervisees. The advantages of peer group clinical supervision encompass aspects related to self (such as confidence, leadership, personal development, and resilience), service and organisation (including a positive working environment, employee retention, and safety), and patient care (involving critical thinking and evaluation, patient safety, adherence to quality standards, and elevated levels of care).

There are many benefits of peer group clinical supervision at an individual, service, organisation, and patient level. Nevertheless, there is a need to address a lack of awareness and misconceptions surrounding clinical supervision to create an environment and culture conducive to realising its full potential. It is crucial that clinical supervision be accessible to nurses and midwives of all grades across all healthcare services, with national planning to address capacity and sustainability.

Peer Review reports

Within a dynamic healthcare system, nurses and midwives face growing demands, underscoring the necessity for ongoing personal and professional development. This is essential to improve the effectiveness and efficiency of care delivery for patients, families, and societies. Despite the increased emphasis on increasing the quality and safety of healthcare services and delivery, there is evidence highlighting declining standards of nursing and midwifery care [ 1 ]. The recent focus on re-affirming and re-committing to core values guiding nursing and midwifery practice is encouraging such as compassion, care and commitment [ 2 ], competence, communication, and courage [ 3 ]. However, imposing value statements in isolation is unlikely to change behaviours and greater consideration needs to be given to ways in which compassion, care, and commitment are nurtured and ultimately applied in daily practice. Furthermore, concerns have been raised about global staff shortages [ 4 ], the evidence suggesting several contributing factors such as poor workforce planning [ 5 ], job dissatisfaction [ 6 ], and healthcare migration [ 7 ]. Without adequate resources and staffing, compromising standards of care and threats to patient safety will be imminent therefore the importance of developing effective strategies for retaining competent registered nurses and midwives is paramount in today’s climate of increased staff shortages [ 4 ]. Clinical supervision serves as a means to facilitate these advancements and has been linked to heightened job satisfaction, enhanced staff retention, improved staff effectiveness, and effective clinical governance, by aiding in quality improvements, risk management, and heightened accountability [ 8 ].

Clinical supervision is a key component of professional practice and while the aim is largely known, there is no universally accepted definition of clinical supervision [ 8 ]. Clinical supervision is a structured process where clinicians are allowed protected time to reflect on their practice within a supportive environment and with the purpose of developing high-quality clinical care [ 9 ]. Recent literature published on clinical supervision [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] highlights the advantages and merits of clinical supervision. However, there are challenges also identified such as a lack of consensus regarding the meaning and goal, implementation issues, variations in approaches in its operationalisation, and an absence of research evidence on its effectiveness. Duration and experience in clinical supervision link to positive benefits [ 8 ], but there is little evidence of how clinical supervision altered individual behaviours and practices. This is reinforced by Kuhne et al., [ 15 ] who emphasise that satisfaction rather than effectiveness is more commonly examined. It is crucial to emphasise that reviews have pinpointed that clinical supervision lowers the risks of adverse patient outcomes [ 9 ] and demonstrates enhancements in the execution of certain care processes. Peer group clinical supervision is a form of clinical supervision whereby two or more practitioners engage in a supervision or consultation process to improve their professional practice [ 17 ]. There is limited evidence regarding peer group clinical supervision and research on the experiences of peer clinical supervision and stakeholders is needed [ 13 ]. In Ireland, peer group clinical supervision has been recommended and guidelines have been developed [ 18 ]. In the Irish context, peer clinical supervision is where both clinical supervisees and clinical supervisors are peers at the same level/grade. However, greater evidence is required to inform future decisions on the implementation of peer group clinical supervision and the purpose of this study is to explore clinical supervisors’ experiences of peer group clinical supervision. As the focus is on peer group supervisors and utilising mixed methods the experiences of the other stakeholders were investigated and reported separately.

A mixed methods approach was used (survey and semi-structured interviews) to capture clinical supervisor’s experiences of clinical supervision. The study adhered to the Consensus-Based Checklist for Reporting of Survey Studies guidelines [ 19 ] (Supplementary File S1 ) and Standards for Reporting Qualitative Research guidelines [ 20 ] (Supplementary File S2 ).

Participants

This study was conducted with participants who successfully completed a professionally credited award: clinical supervision module run by a university in Ireland (74 clinical supervisors across 5 programmes over 3 years). The specific selection criteria for participants were that they were registered nurses/midwives delivering peer group clinical supervision within the West region of Ireland. The specific exclusion criteria were as follows: (1) nurses and midwives who haven’t finished the clinical supervision module at the University, (2) newly appointed peer group clinical supervisors who have yet to establish their groups and initiate the delivery of peer group clinical supervision.

Measures and procedures

The Manchester Clinical Supervision Scale-26 was used to survey participants in February/March 2022 and measure the peer group clinical supervisors’ overall experiences of facilitating peer group clinical supervision. The Manchester Clinical Supervision Scale-26 is a validated 26-item self-report questionnaire with a Likert-type (1–5) scale ranging from strongly disagree (1) to strongly agree (5) [ 21 ]. The Manchester Clinical Supervision Scale-26 measures the efficiency of and satisfaction with supervision, to investigate the skills acquisition aspect of clinical supervision and its effect on the quality of clinical care [ 21 ]. The instrument consists of two main sections to measure three (normative, restorative, and formative) dimensions of clinical supervision utilising six sub-scales: (1) trust and rapport, (2) supervisor advice/support, (3) improved care/skills, (4) importance/value of clinical supervision, (5) finding time, (6) personal issues/reflections and a total score for the Manchester Clinical Supervision Scale-26 is also calculated. Section two consisted of the demographic section of the questionnaire and was tailored to include eight demographic questions concerning the supervisor’s demographics, supervisee characteristics, and characteristics of clinical supervision sessions. There were also two open field questions on the Manchester Clinical Supervision Scale-26 (model of clinical supervision used and any other comments about experience of peer group clinical supervision). The main question about participants’ experiences with peer clinical supervision was “What was your experience of peer clinical supervision?” This was gathered through individual semi-structured interviews lasting between 20 and 45 min, in March/April 2022 (Supplementary file 3 ).

Ethical considerations

Health service institutional review boards of two University hospitals approved this study (Ref: 091/19 and Ref: C.A. 2199). Participants were recruited after receiving a full explanation of the study’s purpose and procedure and all relevant information. Participants were aware of potential risks and benefits and could withdraw from the study, or the survey could be stopped at any time. Informed consent was recorded, and participant identities were protected by using a pseudonym to protect anonymity.

Data analysis method

Survey data was analysed using the data analysis software package Statistical Package for the Social Sciences, version 26 (SPSS Inc., Chicago, Il, USA). Descriptive analysis was undertaken to summarise responses to all items and categorical variables (nominal and ordinal) were analysed using frequencies to detail the number and percentage of responses to each question. Scores on the Manchester Clinical Supervision Scale-26 were reverse scored for 9 items (Q1-Q6, Q8, Q20,21) and total scores for each of the six sub-scales were calculated by adding the scores for each item. Raw scores for the individual sub-scales varied in range from 0 to 20 and these raw scores were then converted to percentages which were used in addition to the raw scores for each sub-scale to describe and summarise the results of the Manchester Clinical Supervision Scale-26. Cronbach’s alpha coefficient was undertaken with the 26 questions included within the Manchester Clinical Supervision Scale-26 and more importantly with each of the dimensions in the Manchester Clinical Supervision Scale-26. The open-ended questions on the Manchester Clinical Supervision Scale-26 and interviews were analysed using content analysis guided by Colorafi and Evans [ 22 ] and categories were generated using their eight steps, (1) creating a coding framework, (2) adding codes and memos, (3) applying the first level of coding, (4) categorising codes and applying the second level of coding, (5) revising and redefining the codes, (6) adding memos, (7) visualising data and (8) representing the data.

Research rigour

To ensure the validity and rigour of this study the researchers utilised the Manchester Clinical Supervision Scale-26 a recognised clinical supervision tool with good reliability and wide usage. Interviews were recorded, transcribed, and verified by four participants, data were collected until no new components appeared, data collection methods and analysis procedures were described, and the authors’ biases were minimised throughout the research process. The Manchester Clinical Supervision Scale-26 instrument internal consistency reliability was assessed which was overall good (α = 0.878) with individual subscale also good e.g., normative domain 0.765, restorative domain 0.864, and formative domain 0.900. Reporting rigour was demonstrated using the Consensus-Based Checklist for Reporting of Survey Studies guidelines [ 19 ] and Standards for Reporting Qualitative Research guidelines [ 20 ].

Quantitative data

Participant and clinical supervision characteristics.

Thirty-six of the fifty-two (69.2%) peer group clinical supervisors working across a particular region of Ireland responded to the Manchester Clinical Supervision Scale-26 survey online via Qualtrics. Table 1 identifies the demographics of the sample who were predominantly female (94.4%) with a mean age of 44.7 years (SD. 7.63).

Peer group clinical supervision session characteristics (Table  2 ) highlight over half of peer group clinical supervisors ( n  = 20, 55.6%) had been delivering peer group clinical supervision for less than one year and were mainly delivered to female supervisees ( n  = 28, 77.8%). Most peer group clinical supervision sessions took place monthly ( n  = 32, 88.9%) for 31–60 min ( n  = 27, 75%).

Manchester Clinical Supervision Scale-26 results

Participants generally viewed peer group clinical supervision as effective (Table  3 ), the total mean Manchester Clinical Supervision Scale-26 score among all peer group clinical supervisors was 76.47 (SD. 12.801) out of 104, Surpassing the clinical supervision threshold score of 73, which was established by the developers of the Manchester Clinical Supervision Scale-26 as the benchmark indicating proficient clinical supervision provision [ 21 ]. Of the three domains; normative, formative, and restorative, the restorative domain scored the highest (mean 28.56, SD. 6.67). The mean scores compare favourably to that of the Manchester Clinical Supervision Scale-26 benchmark data and suggest that the peer group clinical supervisors were satisfied with both the level of support, encouragement, and guidance they provided and the level of trust/rapport they had developed during the peer group clinical supervision sessions. 83.3% ( n  = 30) of peer group clinical supervisors reported being either very satisfied ( n  = 12, 33.3%) or moderately satisfied ( n  = 18, 50%) with the peer group clinical supervision they currently delivered. Within the peer group clinical supervisor’s supervisee related issues ( n  = 17, 47.2%), work environment-related issues ( n  = 16, 44.4%), staff-related issues ( n  = 15, 41.7%) were reported as the most frequent issues, with patient/client related issues being less frequent ( n  = 8, 22.2%). The most identified model used to facilitate peer group clinical supervision was the Proctors model ( n  = 8, 22.22%), which was followed by group ( n  = 2, 5.55%), peer ( n  = 2, 5.55%), and a combination of the seven-eyed model of clinical supervision and Proctors model ( n  = 1, 2.77%) with some not sure what model they used ( n  = 2, 5.553%) and 58.33% ( n  = 21) did not report what model they used.

Survey open-ended question

‘Please enter any additional comments , which are related to your current experience of delivering Peer Group Clinical Supervision.’ There were 22 response comments to this question, which represented 61.1% of the 36 survey respondents, which were analysed using content analysis guided by Colorafi & Evans [ 22 ]. Three categories were generated. These included: personal value/benefit of peer group clinical supervision, challenges with facilitating peer group clinical supervision, and new to peer group clinical supervision.

The first category ‘personal value/benefit of peer group clinical supervision’ highlighted positive experiences of both receiving and providing peer group clinical supervision. Peer group clinical supervisors reported that they enjoyed the sessions and found them both worthwhile and beneficial for both the group and them as peer group clinical supervisors in terms of creating a trusted supportive group environment and motivation to develop. Peer group clinical supervision was highlighted as very important for the peer group clinical supervisors working lives and they hoped that there would be more uptake from all staff. One peer group clinical supervisor expressed that external clinical supervision was a ‘lifeline’ to shaping their supervisory journey to date.

The second category ‘challenges with facilitating peer group clinical supervision’, identified time constraints, lack of buy-in/support from management, staff shortages, lack of commitment by supervisees, and COVID-19 pandemic restrictions and related sick leave, as potential barriers to facilitating peer group clinical supervision. COVID-19 was perceived to have a negative impact on peer group clinical supervision sessions due to staff shortages, which resulted in difficulties for supervisees attending the sessions during work time. Peer group clinical supervisors felt that peer group clinical supervision was not supported by management and there was limited ‘buy-in’ at times. There was also a feeling expressed that peer group clinical supervision was in its infancy, as COVID-19 and its related restrictions impacted on this by either slowing down the process of commencing peer group clinical supervision in certain areas or having to move online. However, more recently improvements in managerial support and supervisee engagement with the peer group clinical supervision process are noted.

The final category ‘new to peer group clinical supervision’ highlighted that some peer group clinical supervisors were new to the process of providing peer group clinical supervision and some felt that this survey was not a true reflection of their experience of delivering peer group clinical supervision, as they were not fully established yet as clinical supervisors due to the impact of COVID-19. Peer group clinical supervisors identified that while they were new to providing peer group clinical supervision, they were enjoying it and that it was a learning curve for them.

Qualitative data

The qualitative phase explored peer group clinical supervisors’ ( n  = 10) own experiences of preparation received and experiences of being a peer group clinical supervisor. Three themes were identified through data analysis, building the foundations, enacting engagement and actions, and realities (Table  4 ).

Building the foundations

This theme highlights the importance of prior knowledge, awareness, and training but also the recruitment process and education in preparing peer group clinical supervisors.

Knowledge and awareness

Participant’s prior knowledge and awareness of peer group clinical supervision was mixed with some reporting having little or no knowledge of clinical supervision.

I’m 20 years plus trained as a nurse , and I had no awareness of clinical supervision beforehand , I really hadn’t got a clue what all of this was about , so it was a very new concept to me (Bernie) .

Others were excited about peer group clinical supervision and while they could see the need they were aware that there may be limited awareness of the value and process of clinical supervision among peers.

I find that there’s great enthusiasm and passion for clinical supervision as it’s a great support mechanism for staff in practice , however , there’s a lack of awareness of clinical supervision (Jane) .

Recruitment

Some participants highlighted that the recruitment process to become a peer group clinical supervisor was vague in some organisations with an unclear and non-transparent process evident where people were chosen by the organisation’s management rather than self-selecting interested parties.

It was just the way the training was put to the people , they were kind of nominated and told they were going and there was a lot of upset over that , so they ended up in some not going at all (Ailbhe) .

In addition, the recruitment process was seen as top loaded where senior grades of staff were chosen, and this limited staff nurse grade opportunities where there was a clear need for peer group clinical supervisors and support.

We haven’t got down to the ground level like you know we’ve done the directors , we’ve done the CNM3s the CNM2s we are at the CNM1s , so we need to get down to the staff nurse level so the nurses at the direct frontline are left out and aren’t receiving supervision because we don’t have them trained (Bernie) .

Training and education

Participants valued the training and education provided but there was a clear sense of ‘imposter syndrome’ for some peer group clinical supervisors starting out. Participants questioned their qualifications, training duration, and confidence to undertake the role of peer group clinical supervisor.

Because it is group supervision and I know that you know they say that we are qualified to do supervision and you know we’re now qualified clinical supervisors but I’m not sure that a three-month module qualifies you to be at the top of your game (Maria) .

Participants when engaged in the peer group clinical supervisor educational programme did find it beneficial and the true benefit was the actual re-engagement in education and published evidence along with the mix of nursing and midwifery practice areas.

I found it very beneficial , I mean I hadn’t been engaged in education here in a while , so it was great to be back in that field and you know with the literature that’s big (Claire) .

Enacting engagement and actions

This theme highlights the importance of forming the groups, getting a clear message out, setting the scene, and grounding the group.

Forming the groups

Recruitment for the group was of key importance to the peer group clinical supervisor and they all sent out a general invitation to form their group. Some supervisors used invitation letters or posters in addition to a general email and this was effective in recruiting supervisees.

You’re reaching out to people , I linked in with the ADoN and I put together a poster and circulated that I wasn’t ‘cherry picking , and I set up a meeting through Webex so people could get a sense of what it was if they were on the fence about it or unsure if it was for them (Karen) .

In forming the peer clinical supervision groups consideration needs to be given to the actual number of supervisees and participants reported four to six supervisees as ideal but that number can alter due to attendance.

The ideal is having five or six consistent people and that they all come on board and that you get the dynamics of the group and everything working (Claire) .

Getting a clear message out

Within the recruitment process, it was evident that there was a limited and often misguided understanding or perception of peer group clinical supervision.

Greater awareness of what actually clinical supervision is , people misjudge it as a supervision where someone is appraising you , when in fact it is more of a support mechanism , I think peer support is the key element that needs to be brought out (Jane) .

Given the lack of clarity and understanding regarding peer group clinical supervision, the participants felt strongly that further clarity is needed and that the focus needs to be on the support it offers to self, practice, and the profession.

Clinical supervision to me is clinical leadership (Jane) .

Setting the scene and grounding the group

In the initial phase of the group coming together the aspect of setting the scene and grounding the group was seen as important. A key aspect of this process was establishing the ground rules which not only set the boundaries and gave structure but also ensured the adoption of principles of trust, confidentiality, and safety.

We start with the ground rules , they give us structure it’s our contract setting out the commitment the expectation for us all , and the confidentiality as that’s so important to the trust and safety and building the relationships (Brid) .

Awareness of group dynamics is important in this process along with awareness of the group members (supervisees) as to their role and expectations.

I reiterate the role of each person in relation to confidentiality and the relationship that they would have with each other within the group and the group is very much aware that it is based on respect for each person’s point of view people may have a fear of contributing to the group and setting the ground rules is important (Jane) .

To ground the group, peer group clinical supervisors saw the importance of being present and allowing oneself to be in the room. This was evident in the time allocated at the start of each session to allow ‘grounding’ to occur in the form of techniques such as a short meditation, relaxation, or deep breathing.

At the start , I do a bit of relaxation and deep breathing , and I saw that with our own external supervisor how she settled us into place so very much about connecting with your body and you’ve arrived , then always come in with the contract in my first sentence , remember today you know we’re in a confidential space , of course , you can take away information , but the only information you will take from today is your own information and then the respect aspect (Mary Rose) .

This settling in and grounding was seen as necessary for people to feel comfortable and engage in the peer group clinical supervision process where they could focus, be open, converse, and be aware of their role and the role of peer group clinical supervision.

People have to be open, open about their practice and be willing to learn and this can only occur by sharing, clinical supervision gives us the space to do it in a space where we know we will be respected, and we can trust (Claire) .

This theme highlights the importance of the peer group clinical supervisors’ past experiences, delivering peer group clinical supervision sessions, responding to COVID-19, personal and professional development, and future opportunities.

Past experiences

Past experiences of peer group clinical supervisors were not always positive and for one participant this related to the lack of ground rules or focus of the sessions and the fact it was facilitated by a non-nurse.

In the past , I suppose I would have found it very frustrating as a participant because I just found that it was going round in circles , people moaning and you know it wasn’t very solution focused so I came from my situation where I was very frustrated with clinical supervision , it was facilitated by somebody that was non-nursing then it wasn’t very , there wasn’t the ground rules , it was very loose (Caroline) .

However, many did not have prior experience of peer group clinical supervision. Nonetheless, through the education and preparation received, there was a sense of commitment to embrace the concept, practice, and philosophy.

I did not really have any exposure or really much information on clinical supervision , but it has opened my eyes , and as one might say I am now a believer (Brid) .

Delivering peer group clinical supervision

In delivering peer group clinical supervision, participants felt supervisees were wary, as they did not know what peer group clinical supervision was, and they had focused more on the word supervision which was misleading to them. Nonetheless, the process was challenging, and buy-in was questioned at an individual and managerial level.

Buy-in wasn’t great I think now of course people will blame the pandemic , but this all happened before the pandemic , there didn’t seem to be you know , the same support from management that I would have expected so I kind of understood it in a way because then there wasn’t the same real respect from the practitioners either (Mary Rose) .

From the peer group clinical supervisor’s perspective, they were all novices in delivering/facilitating peer group clinical supervision sessions, and the support of the external clinical supervisors, and their own peer group clinical supervision sessions were invaluable along with a clinical supervision model.

Having supervision myself was key and something that is vital and needed , we all need to look at our practice and how we work it’s no good just facilitating others without being part of the process yourself but for me I would say the three principles of clinical supervision , you know the normative , formative and restorative , I keep hammering that home and bring that in regularly and revisit the contract and I have to do that often you know (Claire) .

All peer group clinical supervisors commented on the preparation for their peer group clinical supervision sessions and the importance of them having the right frame of mind and that often they needed to read over their course work and published evidence.

I want everybody to have a shared voice and you know that if one person , there is something that somebody feels very strongly and wants to talk about it that they e-mail in advance like we don’t have a set agenda but that’s agreed from the participant at the start (Caroline) .

To assist this, the peer group clinical supervisors noted the importance of their own peer group clinical supervision, the support of their peers, and external clinical supervisors. This preparation in an unpredictable situation can be difficult but drawing on one’s experience and the experience within the group can assist in navigating beyond unexpected situations.

I utilise the models of clinical supervision and this helps guide me , I am more of a facilitator of the group we are experts in our own area and our own role but you can only be an expert if you take the time to examine your practice and how you operate in your role (Brid) .

All clinical supervisors noted that the early sessions can be superficial, and the focus can be on other practice or management issues, but as time moves on and people become more engaged and involved it becomes easier as their understanding of supervision becomes clearer. In addition, there may be hesitancy and people may have difficulty opening up with certain people in the group and this is a reality that can put people off.

Initially there was so much managerial bashing and I think through supervision , I began to kind of think , I need the pillars of supervision , the governance , bringing more knowledge and it shifted everything in the room , trying to marry it with all the tensions that people have (Mary Rose) .

For some clinical supervisors, there were expected and unexpected challenges for them as clinical supervisors in terms of the discussions veering off course and expectations of their own ability.

The other big challenge is when they go off , how do you bring him back , you know when they veer off and you’re expected to be a peer , but you have to try and recoil that you have to get the balance with that right (Mary Rose) .

While peer group clinical supervision is accepted and seen as a valuable process by the peer group clinical supervisors, facilitating peer group supervision with people known to you can be difficult and may affect the process.

I’d love to supervise a group where I actually don’t know the people , I don’t know the dynamics within the group , and I’d love to see what it would be like in a group (Bernie) .

Of concern to clinical supervisors was the aspect of non-attendance and while there may be valid reasons such as COVID-19 the absence of a supervisee for several sessions can affect the group dynamics, especially if the supervisee has only engaged with early group sessions.

One of the ones that couldn’t attend because of COVID and whatever , but she’s coming to the next one and I just feel there’s a lot of issues in her area and I suppose I’m mindful that I don’t want that sort of thing to seep in , so I suppose it’s just for me just to keep reiterating the ground rules and the boundaries , that’s something I just have to manage as a facilitator , but what if they don’t attend how far will the group have progressed before she attends (Caroline) .

Responding to COVID-19

The advent of COVID-19 forced peer group clinical supervisors to find alternative means of providing peer group clinical supervision sessions which saw the move from face-to-face to online sessions. The online transition was seen as seamless for many established groups while others struggled to deliver sessions.

With COVID we did online for us it was fine because we were already formed (Corina) .

While the transition may have been positive many clinical supervisors came across issues because they were using an online format that would not be present in the face-to-face session.

We did have a session where somebody was in the main office and they have a really loud booming voice and they were saying stuff that was not appropriate to say outside of clinical supervision and I was like are you in the office can you lower it down a bit can you put your headphones on (Maria) .

However, two peer group clinical supervisors ceased or hasted the progress of rolling out peer group clinical supervision sessions mainly due to redeployment and staff availability.

With COVID it just had to be canceled here , it’s just the whole thing was canceled so it was very , very difficult for people (Mary Rose) .

It was clear from clinical supervisors that online sessions were appropriate but that they felt they were only appropriate for existing established groups that have had the opportunity to build relationships, develop trust, embed the ground rules, and create the space for open communication and once established a combined approach would be appropriate.

Since we weren’t as established as a group , not everybody knew each other it would be difficult to establish that so we would hold off/reschedule , obviously COVID is a major one but also I suppose if you have an established group now , and again , you could go to a remote one , but I felt like since we weren’t established as a group it would be difficult to develop it in that way (Karen) .

Within practice COVID-19 took priority and other aspects such as peer group clinical supervision moved lower down on the priority list for managers but not for the clinical supervisors even where redeployment occurred.

With COVID all the practical side , if one of the managers is dealing with an outbreak , they won’t be attending clinical supervision , because that has to be prioritised , whereas we’ve prioritised clinical supervision (Maria) .

The valuing of peer group clinical supervision was seen as important by clinical supervisors, and they saw it as particularly needed during COVID-19 as staff were dealing with many personal and professional issues.

During the height of COVID , we had to take a bit of a break for four months as things were so demanding at work for people but then I realised that clinical supervision was needed and started back up and they all wanted to come back (Brid) .

Having peer group clinical supervision during COVID-19 supported staff and enabled the group to form supportive relationships.

COVID has impacted over the last two years in every shape and they needed the supervision and the opportunity to have a safe supportive space and it gelled the group I think as we all were there for each other (Claire) .

While COVID-19 posed many challenges it also afforded clinical supervisors and supervisees the opportunity for change and to consider alternative means of running peer group clinical supervision sessions. This change resulted in online delivery and in reflecting on both forms of delivery (face-to-face and online) clinical supervisors saw the benefit in both. Face-to-face was seen as being needed to form the group and then the group could move online once the group was established with an occasional periodic face-to-face session to maintain motivation commitment and reinforce relationships and support.

Online formats can be effective if the group is already established or the group has gone through the storming and forming phase and the ground rules have been set and trust built , then I don’t see any problem with a blended online version of clinical supervision , and I think it will be effective (Jane) .

Personal and professional development

Growth and development were evident from peer group clinical supervisors’ experiences and this growth and development occurred at a personal, professional, and patient/client level. This development also produced an awakening and valuing of one’s passion for self and their profession.

I suppose clinical supervision is about development I can see a lot of development for me and my supervisees , you know personally and professionally , it’s the support really , clinical supervision can reinvigorate it’s very exciting and a great opportunity for nursing to support each other and in care provision (Claire) .

A key to the peer group clinical supervisor’s development was the aspect of transferable skills and the confidence they gained in fulfilling their role.

All of these skills that you learn are transferable and I am a better manager because of clinical supervision (Maria) .

The confidence and skills gained translated into the clinical supervisor’s own practice as a clinical practitioner and clinical supervisor but they were also realistic in predicting the impact on others.

I have empowered my staff , I empower them to use their voice and I give my supervisees a voice and hope they take that with them (Corina) .

Fundamental to the development process was the impact on care itself and while this cannot always be measured or identified, the clinical supervisors could see that care and support of the individual practitioner (supervisee) translated into better care for the patient/client.

Care is only as good as the person delivering it and what they know , how they function and what energy and passion they have , and clinical supervision gives the person support to begin to understand their practice and how and why they do things in a certain way and when they do that they can begin to question and even change their way of doing something (Brid) .

Future opportunities

Based on the clinical supervisor’s experiences there was a clear need identified regarding valuing and embedded peer group clinical supervision within nursing/midwifery practice.

There has to be an emphasis placed on supervision it needs to be part of the fabric of a service and valued by all in that service , we should be asking why is it not available if it’s not there but there is some work first on promoting it and people knowing what it actually is and address the misconceptions (Claire) .

While such valuing and buy-in are important, it is not to say that all staff need to have peer group clinical supervision so as to allow for personal choice. In addition, to value peer group clinical supervision it needs to be evident across all staffing grades and one could question where the best starting point is.

While we should not mandate that all staff do clinical supervision it should become embedded within practice more and I suppose really to become part of our custom and practice and be across all levels of staff (Brid) .

When peer group clinical supervision is embedded within practice then it should be custom and practice, where it is included in all staff orientations and is nationally driven.

I suppose we need to be driving it forward at the coal face at induction , at orientation and any development for the future will have to be driven by the NMPDUs or nationally (Ailbhe) .

A formalised process needs to address the release of peer group clinical supervisors but also the necessity to consider the number of peer group clinical supervisors at a particular grade.

The issue is release and the timeframe as they have a group but they also have their external supervision so you have to really work out how much time you’re talking about (Maria) .

Vital within the process of peer group clinical supervision is receiving peer group clinical supervision and peer support and this needs to underpin good peer group clinical supervision practice.

Receiving peer group supervision helps me , there are times where I would doubt myself , it’s good to have the other group that I can go to and put it out there to my own group and say , look at this , this is what we did , or this is what came up and this is how (Bernie) .

For future roll out to staff nurse/midwife grade resourcing needs to be considered as peer group clinical supervisors who were managers could see the impact of having several peer group clinical supervisors in their practice area may have on care delivery.

Facilitating groups is an issue and needs to be looked at in terms of the bigger picture because while I might be able to do a second group the question is how I would be supported and released to do so (Maria) .

While there was ambiguity regarding peer group clinical supervision there was an awareness of other disciplines availing of peer group clinical supervision, raising questions about the equality of supports available for all disciplines.

I always heard other disciplines like social workers would always have been very good saying I can’t meet you I have supervision that day and I used to think my God what’s this fabulous hour that these disciplines are getting and as a nursing staff it just wasn’t there and available (Bernie) .

To address this equity issue and the aspect of low numbers of certain grades an interdisciplinary approach within nursing and midwifery could be used or a broader interdisciplinary approach across all healthcare professionals. An interdisciplinary or across-services approach was seen as potentially fruitful.

I think the value of interprofessional or interdisciplinary learning is key it addresses problem-solving from different perspectives that mix within the group is important for cross-fertilisation and embedding the learning and developing the experience for each participant within the group (Jane) .

As we move beyond COVID-19 and into the future there is a need to actively promote peer group clinical supervision and this would clarify what peer group clinical supervision actually is, its uptake and stimulate interest.

I’d say it’s like promoting vaccinations if you could do a roadshow with people , I think that would be very beneficial , and to launch it , like you have a launch an official launch behind it (Mary Rose) .

The advantages of peer group clinical supervision highlighted in this study pertain to self-enhancement (confidence, leadership, personal development, resilience), organisational and service-related aspects (positive work environment, staff retention, safety), and professional patient care (critical thinking and evaluation, patient safety, adherence to quality standards, elevated care standards). These findings align with broader literature that acknowledges various areas, including self-confidence and facilitation [ 23 ], leadership [ 24 ], personal development [ 25 ], resilience [ 26 ], positive/supportive working environment [ 27 ], staff retention [ 28 ], sense of safety [ 29 ], critical thinking and evaluation [ 30 ], patient safety [ 31 ], quality standards [ 32 ] and increased standards of care [ 33 ].

In this study, peer group clinical supervision appeared to contribute to the alleviation of stress and anxiety. Participants recognised the significance of these sessions, where they could openly discuss and reflect on professional situations both emotionally and rationally. Central to these discussions was the creation of a safe, trustworthy, and collegial environment, aligning with evidence in the literature [ 34 ]. Clinical supervision provided a platform to share resources (information, knowledge, and skills) and address issues while offering mutual support [ 35 ]. The emergence of COVID-19 has stressed the significance of peer group clinical supervision and support for the nursing/midwifery workforce [ 36 ], highlighting the need to help nurses/midwifes preserve their well-being and participate in collaborative problem-solving. COVID-19 impacted and disrupted clinical supervision frequency, duration and access [ 37 ]. What was evident during COVID-19 was the stress and need for support for staff and given the restorative or supportive functions of clinical supervision it is a mechanism of support. However, clinical supervisors need support themselves to be able to better meet the supervisee’s needs [ 38 ].

The value of peer group clinical supervision in nurturing a conducive working environment cannot be overstated, as it indorses the understanding and adherence to workplace policies by empowering supervisees to understand the importance and rationale behind these policies [ 39 ]. This becomes vital in a continuously changing healthcare landscape, where guidelines and policies may be subject to change, especially in response to situations such as COVID-19. In an era characterised by international workforce mobility and a shortage of healthcare professionals, a supportive and positive working environment through the provision of peer group clinical supervision can positively influence staff retention [ 40 ], enhance job satisfaction [ 41 ], and mitigate burnout [ 42 ]. A critical aspect of the peer group clinical supervision process concerns providing staff the opportunity to reflect, step back, problem-solve and generate solutions. This, in turn, ensures critical thinking and evaluation within clinical supervision, focusing on understanding the issues and context, and problem-solving to draw constructive lessons for the future [ 30 ]. Research has determined a link between clinical supervision and improvements in the quality and standards of care [ 31 ]. Therefore, peer group clinical supervision plays a critical role in enhancing patient safety by nurturing improved communication among staff, facilitating reflection, promoting greater self-awareness, promoting the exchange of ideas, problem-solving, and facilitating collective learning from shared experiences.

Starting a group arose as a foundational aspect emphasised in this study. The creation of the environment through establishing ground rules, building relationships, fostering trust, displaying respect, and upholding confidentiality was evident. Vital to this process is the recruitment of clinical supervisees and deciding the suitable group size, with a specific emphasis on addressing individuals’ inclination to engage, their knowledge and understanding of peer group clinical supervision, and dissipating any lack of awareness or misconceptions regarding peer group supervision. Furthermore, the educational training of peer group clinical supervisors and the support from external clinical supervisors played a vital role in the rollout and formation of peer group clinical supervision. The evidence stresses the significance of an open and safe environment, wherein supervisees feel secure and trust their supervisor. In such an environment, they can effectively reflect on practice and related issues [ 41 ]. This study emphasises that the effectiveness of peer group supervision is more influenced by the process than the content. Clinical supervisors utilised the process to structure their sessions, fostering energy and interest to support their peers and cultivate new insights. For peer group clinical supervision to be effective, regularity is essential. Meetings should be scheduled in advance, allocate protected time, and take place in a private space [ 35 ]. While it is widely acknowledged that clinical supervisors need to be experts in their professional field to be credible, this study highlights that the crucial aspects of supervision lie in the quality of the relationship with the supervisor. The clinical supervisor should be supportive, caring, open, collaborative, sensitive, flexible, helpful, non-judgmental, and focused on tacit knowledge, experiential learning, and providing real-time feedback.

Critical to the success of peer group clinical supervision is the endorsement and support from management, considering the organisational culture and attitudes towards the practice of clinical supervision as an essential factor [ 43 ]. This support and buy-in are necessary at both the management and individual levels [ 28 ]. The primary obstacles to effective supervision often revolve around a lack of time and heavy workloads [ 44 ]. Clinical supervisors frequently struggle to find time amidst busy environments, impacting the flexibility and quality of the sessions [ 45 ]. Time constraints also limit the opportunity for reflection within clinical supervision sessions, leaving supervisees feeling compelled to resolve issues on their own without adequate support [ 45 ]. Nevertheless, time-related challenges are not unexpected, prompting a crucial question about the value placed on clinical supervision and its integration into the culture and fabric of the organisation or profession to make it a customary practice. Learning from experiences like those during the COVID-19 pandemic has introduced alternative ways of working, and the use of technology (such as Zoom, Microsoft Teams, Skype) may serve as a means to address time, resource, and travel issues associated with clinical supervision.

Despite clinical supervision having a long international history, persistent misconceptions require attention. Some of these include not considering clinical supervision a priority [ 46 ], perceiving it as a luxury [ 41 ], deeming it self-indulgent [ 47 ], or viewing it as mere casual conversation during work hours [ 48 ]. A significant challenge lies in the lack of a shared understanding regarding the role and purpose of clinical supervision, with past perceptions associating it with surveillance and being monitored [ 48 ]. These negative connotations often result in a lack of engagement [ 41 ]. Without encouragement and recognition of the importance of clinical supervision from management or the organisation, it is unlikely to become embedded in the organisational culture, impeding its normalisation [ 39 ].

In this study, some peer group clinical supervisors expressed feelings of being impostors and believed they lacked the knowledge, skills, and training to effectively fulfil their roles. While a deficiency in skills and competence are possible obstacles to providing effective clinical supervision [ 49 ], the peer group clinical supervisors in this study did not report such issues. Instead, their concerns were more about questioning their ability to function in the role of a peer group clinical supervisor, especially after a brief training program. The literature acknowledges a lack of training where clinical supervisors may feel unprepared and ill-equipped for their role [ 41 ]. To address these challenges, clinical supervisors need to be well-versed in professional guidelines and ethical standards, have clear roles, and understand the scope of practice and responsibilities associated with being a clinical supervisor [ 41 ].

The support provided by external clinical supervisors and the peer group clinical supervision sessions played a pivotal role in helping peer group clinical supervisors ease into their roles, gain experiential learning, and enhance their facilitation skills within a supportive structure. Educating clinical supervisors is an investment, but it should not be a one-time occurrence. Ongoing external clinical supervision for clinical supervisors [ 50 ] and continuous professional development [ 51 ] are crucial, as they contribute to the likelihood of clinical supervisors remaining in their roles. However, it is important to interpret the results of this study with caution due to the small sample size in the survey. Generalising the study results should be approached with care, particularly as the study was limited to two regions in Ireland. However, the addition of qualitative data in this mixed-methods study may have helped offset this limitation.

This study highlights the numerous advantages of peer group clinical supervision at individual, service, organisational, and patient/client levels. Success hinges on addressing the initial lack of awareness and misconceptions about peer group clinical supervision by creating the right environment and establishing ground rules. To unlock the full potential of peer group clinical supervision, it is imperative to secure management and organisational support for staff release. More crucially, there is a need for valuing and integrating peer group clinical supervision into nursing and midwifery education and practice. Making peer group clinical supervision accessible to all grades of nurses and midwives across various healthcare services is essential, necessitating strategic planning to tackle capacity and sustainability challenges.

Data availability

Data are available from the corresponding author upon request owing to privacy or ethical restrictions.

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Acknowledgements

The research team would like to thank all participants for their collaboration, the HSE steering group members and Carmel Hoey, NMPDU Director, HSE West Mid West, Dr Patrick Glackin, NMPD Area Director, HSE West, Annette Cuddy, Director, Centre of Nurse and Midwifery Education Mayo/Roscommon; Ms Ruth Hoban, Assistant Director of Nursing and Midwifery (Prescribing), HSE West; Ms Annette Connolly, NMPD Officer, NMPDU HSE West Mid West.

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OD: Conceptualization, Methodology, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Project administration, Funding acquisition. COD: Methodology, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Funding acquisition. KM: Methodology, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Funding acquisition. JT: Methodology, Formal analysis, Writing - Original Draft, Writing - Review & Editing. LM: Methodology, Formal analysis, Investigation, Writing - Original Draft, Writing - Review & Editing, Funding acquisition.

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Doody, O., Markey, K., Turner, J. et al. Clinical supervisor’s experiences of peer group clinical supervision during COVID-19: a mixed methods study. BMC Nurs 23 , 612 (2024). https://doi.org/10.1186/s12912-024-02283-3

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