what type of research design is comparative study

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• > The Research Imagination
• > COMPARATIVE RESEARCH METHODS

Book contents

• Frontmatter
• 1 RESEARCH PROCESS
• 2 THEORY AND METHOD
• 3 RESEARCH DESIGN
• 4 MEASUREMENT
• 5 ETHICAL AND POLITICAL ISSUES
• 7 SURVEY RESEARCH
• 8 INTENSIVE INTERVIEWING
• 9 OBSERVATIONAL FIELD RESEARCH
• 10 FEMINIST METHODS
• 11 HISTORICAL ANALYSIS
• 12 EXPERIMENTAL RESEARCH
• 13 CONTENT ANALYSIS
• 14 AGGREGATE DATA ANALYSIS
• 15 COMPARATIVE RESEARCH METHODS
• 16 EVALUATION RESEARCH
• 17 INDEXES AND SCALES
• 18 BASIC STATISTICAL ANALYSIS
• 19 MULTIVARIATE ANALYSIS AND STATISTICAL SIGNIFICANCE
• EPILOGUE: THE VALUE AND LIMITS OF SOCIAL SCIENCE KNOWLEDGE
• Appendix A A Precoded Questionnaire
• Appendix B Excerpt from a Codebook
• Author Index
• Subject Index

15 - COMPARATIVE RESEARCH METHODS

Published online by Cambridge University Press:  05 June 2012

INTRODUCTION

In contrast to the chapters on survey research, experimentation, or content analysis that described a distinct set of skills, in this chapter, a variety of comparative research techniques are discussed. What makes a study comparative is not the particular techniques employed but the theoretical orientation and the sources of data. All the tools of the social scientist, including historical analysis, fieldwork, surveys, and aggregate data analysis, can be used to achieve the goals of comparative research. So, there is plenty of room for the research imagination in the choice of data collection strategies. There is a wide divide between quantitative and qualitative approaches in comparative work. Most studies are either exclusively qualitative (e.g., individual case studies of a small number of countries) or exclusively quantitative, most often using many cases and a cross-national focus (Ragin, 1991:7). Ideally, increasing numbers of studies in the future will use both traditions, as the skills, tools, and quality of data in comparative research continue to improve.

In almost all social research, we look at how social processes vary and are experienced in different settings to develop our knowledge of the causes and effects of human behavior. This holds true if we are trying to explain the behavior of nations or individuals. So, it may then seem redundant to include a chapter in this book specifically dedicated to comparative research methods when all the other methods discussed are ultimately comparative.

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• COMPARATIVE RESEARCH METHODS
• Paul S. Gray , Boston College, Massachusetts , John B. Williamson , Boston College, Massachusetts , David A. Karp , Boston College, Massachusetts , John R. Dalphin
• Book: The Research Imagination
• Online publication: 05 June 2012
• Chapter DOI: https://doi.org/10.1017/CBO9780511819391.016

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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• 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 objectives 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, other interesting articles, frequently asked questions about research design.

• 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
• 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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.

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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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

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

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

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

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

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Characteristics of a Comparative Research Design

Hannah richardson, 28 jun 2018.

Comparative research essentially compares two groups in an attempt to draw a conclusion about them. Researchers attempt to identify and analyze similarities and differences between groups, and these studies are most often cross-national, comparing two separate people groups. Comparative studies can be used to increase understanding between cultures and societies and create a foundation for compromise and collaboration. These studies contain both quantitative and qualitative research methods.

Explore this article

• Comparative Quantitative
• Comparative Qualitative
• When to Use It
• When Not to Use It

1 Comparative Quantitative

Quantitative, or experimental, research is characterized by the manipulation of an independent variable to measure and explain its influence on a dependent variable. Because comparative research studies analyze two different groups -- which may have very different social contexts -- it is difficult to establish the parameters of research. Such studies might seek to compare, for example, large amounts of demographic or employment data from different nations that define or measure relevant research elements differently.

However, the methods for statistical analysis of data inherent in quantitative research are still helpful in establishing correlations in comparative studies. Also, the need for a specific research question in quantitative research helps comparative researchers narrow down and establish a more specific comparative research question.

2 Comparative Qualitative

Qualitative, or nonexperimental, is characterized by observation and recording outcomes without manipulation. In comparative research, data are collected primarily by observation, and the goal is to determine similarities and differences that are related to the particular situation or environment of the two groups. These similarities and differences are identified through qualitative observation methods. Additionally, some researchers have favored designing comparative studies around a variety of case studies in which individuals are observed and behaviors are recorded. The results of each case are then compared across people groups.

3 When to Use It

Comparative research studies should be used when comparing two people groups, often cross-nationally. These studies analyze the similarities and differences between these two groups in an attempt to better understand both groups. Comparisons lead to new insights and better understanding of all participants involved. These studies also require collaboration, strong teams, advanced technologies and access to international databases, making them more expensive. Use comparative research design when the necessary funding and resources are available.

4 When Not to Use It

Do not use comparative research design with little funding, limited access to necessary technology and few team members. Because of the larger scale of these studies, they should be conducted only if adequate population samples are available. Additionally, data within these studies require extensive measurement analysis; if the necessary organizational and technological resources are not available, a comparative study should not be used. Do not use a comparative design if data are not able to be measured accurately and analyzed with fidelity and validity.

• 1 San Jose State University: Selected Issues in Study Design
• 2 University of Surrey: Social Research Update 13: Comparative Research Methods

About the Author

Hannah Richardson has a Master's degree in Special Education from Vanderbilt University and a Bacheor of Arts in English. She has been a writer since 2004 and wrote regularly for the sports and features sections of "The Technician" newspaper, as well as "Coastwach" magazine. Richardson also served as the co-editor-in-chief of "Windhover," an award-winning literary and arts magazine. She is currently teaching at a middle school.

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Scientific Research and Methodology

3.5 comparing study designs.

In experimental studies, researchers create differences in the explanatory variable through allocation, and note the effect of this on the response variable. In observational studies, researchers observe differences in the explanatory variable, and note the values in the response variable. Different RQs require different study designs (Table 3.3 ).

Importantly, only well-designed true experiments can show cause-and-effect . Well-designed true experiments provide stronger evidence than quasi-experiments, which produce stronger evidence than observational studies.

However, experimental studies are often not possible for ethical, financial, practical or logistical reasons. The animation below compares observational, quasi-experimental and true experimental designs.

Well-designed quasi-experiments and observational studies can still produce strong conclusions, but cannot be used by themselves to establish cause-and-effect conclusions.

The three main study designs

Clinical Research: Research Design Comparison/Contrast

• Publication Cycle
• Searching PubMed
• Case Report
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• Research Design Comparison/Contrast
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Types of Research Design

The following are just a few highlights of several clinical research types (including observational and experimental). For details on each of them and other types of research design, please consult books on research design/clinical epidemiology/biostatistics or articles discussing research design.  

Web Resources on Research Design

• Research Manual: A Primer for Basic Research Competencies and Research Projects By des Anges Cruser, Ph.D. from the University of North Texas Health Science Center
• Medical Students and Research By Michelle Biros, MS, MD, Editor in Chief; James Adams, MD, Senior Associate Editor; Academic Emergency Medicine

  Finding Statistical Data

• Finding health statistics generated by governmental and nongovernmental entities An excellent resource guide created by Janice Flahiff

Featured Books on Clinical Research from the Library

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Organizing Your Social Sciences Research Paper: Types of Research Designs

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base . 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations far too early, before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing research designs in your paper can vary considerably, but any well-developed design will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide an outline to follow for your own paper.

NOTE : Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

• Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation ; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base . 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, r ather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101 . Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study . Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design, Application, Strengths and Weaknesses of Cross-Sectional Studies . Healthknowledge, 2009. Cross-Sectional Study . Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies . Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design , September 26, 2008. Explorable.com website.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs . School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design . Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research . Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research . Wikipedia.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study . Wikipedia.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research . Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

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Causal Comparative Research: Methods And Examples

Ritu was in charge of marketing a new protein drink about to be launched. The client wanted a causal-comparative study…

Ritu was in charge of marketing a new protein drink about to be launched. The client wanted a causal-comparative study highlighting the drink’s benefits. They demanded that comparative analysis be made the main campaign design strategy. After carefully analyzing the project requirements, Ritu decided to follow a causal-comparative research design. She realized that causal-comparative research emphasizing physical development in different groups of people would lay a good foundation to establish the product.

What Is Causal Comparative Research?

Examples of causal comparative research variables.

Causal-comparative research is a method used to identify the cause–effect relationship between a dependent and independent variable. This relationship is usually a suggested relationship because we can’t control an independent variable completely. Unlike correlation research, this doesn’t rely on relationships. In a causal-comparative research design, the researcher compares two groups to find out whether the independent variable affected the outcome or the dependent variable.

A causal-comparative method determines whether one variable has a direct influence on the other and why. It identifies the causes of certain occurrences (or non-occurrences). It makes a study descriptive rather than experimental by scrutinizing the relationships among different variables in which the independent variable has already occurred. Variables can’t be manipulated sometimes, but a link between dependent and independent variables is established and the implications of possible causes are used to draw conclusions.

In a causal-comparative design, researchers study cause and effect in retrospect and determine consequences or causes of differences already existing among or between groups of people.

Let’s look at some characteristics of causal-comparative research:

• This method tries to identify cause and effect relationships.
• Two or more groups are included as variables.
• Individuals aren’t selected randomly.
• Independent variables can’t be manipulated.
• It helps save time and money.

The main purpose of a causal-comparative study is to explore effects, consequences and causes. There are two types of causal-comparative research design. They are:

Retrospective Causal Comparative Research

For this type of research, a researcher has to investigate a particular question after the effects have occurred. They attempt to determine whether or not a variable influences another variable.

Prospective Causal Comparative Research

The researcher initiates a study, beginning with the causes and determined to analyze the effects of a given condition. This is not as common as retrospective causal-comparative research.

Usually, it’s easier to compare a variable with the known than the unknown.

Researchers use causal-comparative research to achieve research goals by comparing two variables that represent two groups. This data can include differences in opportunities, privileges exclusive to certain groups or developments with respect to gender, race, nationality or ability.

For example, to find out the difference in wages between men and women, researchers have to make a comparative study of wages earned by both genders across various professions, hierarchies and locations. None of the variables can be influenced and cause-effect relationship has to be established with a persuasive logical argument. Some common variables investigated in this type of research are:

• Achievement and other ability variables
• Family-related variables
• Organismic variables such as age, sex and ethnicity
• Variables related to schools
• Personality variables

While raw test scores, assessments and other measures (such as grade point averages) are used as data in this research, sources, standardized tests, structured interviews and surveys are popular research tools.

However, there are drawbacks of causal-comparative research too, such as its inability to manipulate or control an independent variable and the lack of randomization. Subject-selection bias always remains a possibility and poses a threat to the internal validity of a study. Researchers can control it with statistical matching or by creating identical subgroups. Executives have to look out for loss of subjects, location influences, poor attitude of subjects and testing threats to produce a valid research study.

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Research Design

Ai generator.

From broad assumptions to comprehensive methods of data collection, analysis, and interpretation, research plans and procedures involve various decisions and approaches which are essential in order to carefully study a specific topic. That’s why researchers should use the suitable procedures of inquiry or research designs and certain research methods of data collection, analysis, and interpretation. However, what is a research design? In this post, we will explain the main purpose of research designs, different types of research designs, steps on how to effectively write a systematic research design, the research design format and research design examples.

Research Design Definition

Research design is a crucial element when conducting a research work. Along with research approaches and research methods, research designs represent a clear perspective about research. So, these components demonstrate information in a successive way: from extensive constructions of research to the narrow procedures of methods. 

What Is a Research Design?

A research design is a type of inquiry within wide-ranging approaches in the research field such as qualitative, quantitative and mixed methods approaches. It significantly provides a certain direction for procedures in a specific research study. Also known as strategies of inquiry, there are numerous research designs accessible to many researchers that significantly guide them towards advanced data analysis and assist them in examining complex models. 

Research Design Examples

1. Experimental Design

• Example : A pharmaceutical company tests a new drug by giving it to one group and a placebo to another under controlled conditions to observe the effects on illness recovery rates.

2. Quasi-Experimental Design

• Example : A school implements a new teaching method in some classes but not others and compares the academic performance of students across these classes to assess the method’s effectiveness.

3. Cross-Sectional Design

• Example : A market research company surveys 1,000 smartphone users at one point in time to determine consumer preferences for mobile phone brands.

4. Longitudinal Study

• Example : A university research project tracks the same group of students from enrollment through graduation to study changes in their academic performance and social behaviors over the years.

5. Case Study

• Example : A business analyst conducts a detailed study on a single company that successfully pivoted its business model during a financial downturn, to understand the strategies and factors that led to its recovery.

6. Comparative Study

• Example : A researcher compares the healthcare systems of two countries to evaluate the impact of policy differences on patient outcomes.

7. Correlational Study

• Example : A psychologist studies the relationship between social media usage and self-esteem by measuring both variables among a group of teenagers.

8. Ethnography

• Example : An anthropologist lives within a remote tribe for a year to observe and report on their cultural practices and social interactions.

9. Phenomenology

• Example : A study focuses on a group of survivors from a natural disaster, exploring their personal experiences and emotional responses to understand their coping mechanisms.

10. Grounded Theory

• Example : Researchers collect data from various startups to develop a theory about the key factors that contribute to entrepreneurial success in the tech industry.

11. Content Analysis

• Example : A media studies student analyzes the portrayal of gender roles in a decade’s worth of TV commercials to track changing societal attitudes.

12. Action Research

• Example : A community development organization collaborates with residents to identify and address urgent neighborhood problems, using feedback to guide project adjustments.

13. Narrative Research

• Example : A historian interviews WWII veterans to compile their war experiences into a book that explores personal narratives from the conflict.

14. Survey Research

• Example : A non-profit organization conducts a nationwide survey to gather data on public opinion regarding climate change.

15. Experimental Auction

• Example : An economist uses an experimental auction to determine how much consumers are willing to pay for organic versus non-organic produce.

16. Simulation

• Example : Engineers use computer simulations to predict the impacts of earthquake stress on building structures.

17. Field Experiment

• Example : A biologist observes behavioral changes in wildlife introduced to a newly established nature reserve compared to those in an undisturbed control area.

18. Meta-Analysis

• Example : A medical researcher combines data from several studies on drug efficacy to provide stronger evidence of its benefits and side effects.

19. Cohort Study

• Example : Public health officials follow a cohort of smokers over 20 years to study the long-term health outcomes compared to non-smokers.

20. Archival Research

• Example : A scholar accesses old political documents and speeches to analyze patterns of rhetoric used by leaders during critical historical events.

Main Purpose of Research Designs

The main purpose of research designs is to guide you in terms of analyzing various complex models and articulating new procedures for conducting any types of research fields like in social science research. Medical researchers, field researchers, academic researchers, scientific researchers, academic  researchers and other kinds of researchers use research designs to properly conduct their research projects as they consciously structure their research work in order to answer the key research questions which guide the overall research study and the appropriate hypothesis. Additionally, a research design provides essential information about the parts of the research study methods like data collection, instrumentation selection, participant recruitment and analysis.

Types of Research Designs

Case study research design.

As an in-depth study of a specific research issue, a case study research design is commonly used to narrow down a very far-reaching field of research into one or a few easily researchable examples. It is a beneficial type of research design  for testing whether a certain theory and model really applies to phenomena in the real world. So, it means that researchers  who are using a case study design can implement a variety of research methodologies and depend on multiple collections of sources to examine a research problem.

Descriptive Research Design

A descriptive research design is a type of research design that assists in providing answers to the key questions of what, when, who, where, and how related with a  specific research problem. However, it does not conclusively ensure answers to why questions. Being used to acquire important details about the current status of the phenomena, this research design clearly describes what exists based on the variables or conditions in a particular situation. So, this means researchers use this research design to observe a certain subject matter in a completely natural and constant natural environment. Additionally, it acts as a pre-cursor towards more quantitative research designs.

Causal Research Design

Researchers use a type of research design called causal design to measure what kind of impact a certain change will have on current norms and assumptions.  It is used to narrow down the cause and effect relationship easily by ensuring that both variables are not influenced by any force other than each other. A causal research design is used to maintain accuracy in the variables and determine the exact impact that a particular variable has on another variable. Applying this research design also explores the connection between two matters. 

Correlational Research Design

When it comes to setting up the statistical pattern between two clearly interconnected variables, researchers use a type of research design called correlational research design as it refers to a non-experimental method in research work that conducts studies on the relationships between two variables by utilizing statistical analysis. This is a fundamental research design in order to test specific relationships between categorical or quantitative variables without the manipulation of an independent variable. Simply, correlational research aims at observing and measuring historical patterns between two variables. 

Cross-Sectional Research Design

A cross-sectional research design is used by researchers to collect data only once and examine a certain population at a single point in time by having a slice or cross-section of a particular group and variables being documented for each participant. Researchers and other investigators measure the outcome and the exposures in the participants of the research study at similar time. The participants in a cross-sectional research study are simply chosen according to the exclusion and inclusion criteria being established for the study. Also, this type of research design is important for carrying out population-based surveys and assessing the prevalence of certain matters like diseases in clinic-based samples. 

Diagnostic Research Design

Composed of major research phases such as problem inception, problem diagnosis and problem solution, a diagnostic research design is a type of research design used by researchers to make a clear evaluation of a certain problem or phenomenon’s cause. If the researchers need to fully understand the factors and other essential aspects that are generating concerns and issues inside the company or organization in detail, they should use a diagnostic research design. Carrying out a diagnostic research design allows them to know exactly the time when the issue appears, the underlying cause of the issues, potential influences of the issue which lead to its worsening, and the effective solutions for the issue. 

Factorial Research Design

Researchers use a factorial research design to investigate the major effects of two or more individual  independent variables in a simultaneous way, and to allow them to recognize interactions among variables. When the effects of one variable differ based on the levels of another variable, an interaction is made and these interactions can only be recognized when the variables are combined and investigated. If you need to yield valid conclusions over a wide array of experimental conditions, use a factorial research design to estimate the effects of a factor based on various levels of the other factors.

Historical Research Design

A historical research design is a type of research design that provides a fundamental context for understanding our modern society while informing global concepts like foregin policy development. Researchers use this research design to guide them when it comes to analyzing the past events, developing new concepts, examining the previous information or events to test their validity, and formulating logical decisions that impact our society, economy, and culture. Typically, they collect, verify and synthesize evidence from the past to build facts that defend or refute a hypothesis. Thus, a historical research design involves the comprehensive study and analysis of data about past events, developments and other experiences. 

Action Research Design

In order to promote iterative learning, comprehensive evaluation and improvement, many researchers and other professionals use action research design especially teachers, professors and other key individuals working in schools or in the education sector. With this design, they can collect sufficient information about current programs and outcomes so that they are able to analyze the collected information, develop a cohesive plan to improve it, collect changes after a new plan is carried out, and produce conclusions based on the improvements. So, professionals who use an action research design focus on operational or technical, collaboration, critical reflection, and transformative change of their own process of taking action and conducting research. 

Legal Research Design

A legal research design is commonly used by researchers working in the legal sector as they carefully identify and retrieve information which are crucial to support in their legal decision-making process. Legal researchers develop a research plan, consult primary and secondary sources, expand and update primary law and analyze and organize results. There are two types of legal research: doctrinal or non-empirical research and non-doctrinal or empirical methods. 

Longitudinal Research Design

Use a longitudinal research design if you need to investigate similar individuals repeatedly so that you can determine any changes that might happen over a period of time. Researchers apply this type of research design in order to observe and gather adequate data on a number of variables without trying to affect those variables. Most generally used in economics, epidemiology and medicine, longitudinal research design is also used in social sciences and other scientific fields. It is also the opposite of a cross-sectional research design. Implementing this design can help researchers to follow their subjects in real time and allow repeated observations of the same individual over time.

Marketing Research Design

In marketing research design, business professionals such as project managers, content marketing specialists, sales and marketing experts and brand managers use marketing research questionnaires to collect information and clearly understand the intended audience or target market of a business firm or an organization. This type of research design will significantly assist them in developing industry and market analysis and designing worthwhile products, enhancing user experience, and designing an effective marketing strategy that fully engages quality leads and elevates conversion rates.

Narrative Research Design

If you need to focus on studying a specific person, you may use a narrative research design which refers to writing narratives about the experiences of individuals, telling a life experience, and explaining the meaning of the individual’s experience. Several types of narrative research design are analysis of narrative projects, collecting background information from narrative interview report , interviews and re-storying, oral history and journals and storytelling, and letter writing. To conduct narrative research, researchers need to code narrative blocks, group and read by live event, create nested story structure codes, examine the structure of the story, make comparisons and tell the main idea of the narrative research.

Experimental Research Design

As a blueprint of the research procedure, an experimental research design is used by researchers to allow them to manage and control over all aspects that may influence the outcome of an experiment. Performing a research work with this type of design helps researchers to determine or predict what may happen. Often used where there exists a time priority in a cause and effect relationship, an experimental research design is also applied when there is a consistency in a cause and effect relationship, and if there is a great magnitude of correlation. Plus, it enables researchers to provide the highest level of evidence for single studies.

Observational Research Design

In several cases where the researchers have no control over the experiment being conducted, they use an observational research design to draw a conclusion after making a comparison of subjects against a control group. With this type of research design, you can gather a depth of information about a specific behavior, show interrelationships among multidimensional aspects of group interactions, and generalize your results to real life situations. If you need to discover what kind of variables may be crucial before utilizing other research methods, use an observational research design.

Exploratory Research Design

An exploratory research design is a type of research design which is integral when it comes to investigating a specific and unclear research issue. Researchers use this research design to have an in-depth understanding of a research problem and its context prior to the further development and execution of the research process. So, an exploratory research design acts as a groundwork to facilitate research work while it manages other research concerns which have not been sufficiently investigated in the last years.  

Retrospective Research Design

When the outcome of interest has already taken place at the period the research study is started, researchers use a type of research design called retrospective research design which enables them to formulate ideas about potential associations and thoroughly examine possible relationships without causal statements. It is a very feasible research design in terms of scope, resources, and time. However, it cannot yield causal effects due to the absence of random assignment and random selection. Still, researchers can use this design because it is less expensive to conduct and can be used immediately.

Cohort Research Design

If you need to conduct a study over a time period which involves members of a population that the subject originated from, and united by some similarity, you must use a cohort research design as it guides you in analyzing the statistical occurrence within a specialized subgroup which is united by similar characteristics linked to the research problem. Researchers are able to measure possible causes prior to the result having taken place and show that these causes preceded the result. Also, it can provide clear insight into effects over time and is linked to a wide range of diverse cultural, economic, social, and political changes. 

Meta-Analysis Research Design

Considered as an evidence-based resource with confirmatory data analysis, a meta-analysis research design is used by researchers to create statistical significance with studies that have conflicting outcomes, to generate a more appropriate estimate of effect magnitude, to bring a more in-depth analysis of risks, safety data and advantages, and to analyze subgroups with individual members that are not significant statistically. Researchers systematically integrate essential qualitative and quantitative study data from various selected research studies to draw out a single conclusion that provides greater statistical effect.

Quantitative Research Design

A quantitative research design is a type of research design used by researchers to explore and investigate how many people act, feel, think or feel in a specific manner. As the major research design in the social sciences and other fields, it is generally aimed at developing strategies, and techniques with the use of numeric patterns or a range of numeric data. Social scientists, communication researchers and other professionals bring knowledge and set up a clear understanding about certain matters in the social environment and other fields. Simply, this type of research design depends on data that are being observed or measured.

Qualitative Research Design

When it comes to understanding various concepts, experiences or opinions, researchers use a qualitative research design through a collection and in-depth analysis of non-numerical data like a, text or video. Also, they use this type of research design to collect comprehensive insights into a problem or form new ideas for their research study. Generally used in the humanities and social sciences like anthropology, education, health sciences and others, qualitative research design is used to clearly understand people’s experiences and focus on meaningful data interpretation. 

Mixed Method Research Design

A mixed methods research design is a type of research design when the researchers and other professionals collect, analyze, and mix both quantitative and qualitative research and methods in a single study so that they can easily understand a certain research problem. To execute this design properly, you need to understand both quantitative and qualitative research. Some major types of mixed method research design are triangulation design, embedded design, and explanatory design. 

Research Design Writing

Looking at the long list of types of research designs in this post may be overwhelming for you. It is possible to get lost from these details because these classifications are made up from various disciplines with highlighted diverse elements of research designs and many other aspects in research. Your research questions might lead you to try creating a theory and then selecting the right research design for your study. What research study would you use in that case? How will you outline your research design? 

Research Design Elements

Hypotheses and objectives.

• Hypotheses are testable predictions about the relationships between variables.
• Objectives define the purpose of the study and what the research aims to achieve.
• Independent variables are manipulated to observe their effect on dependent variables.
• Dependent variables are the outcomes measured in the experiment.
• Control variables are kept constant to ensure that any changes in the dependent variable are due to the independent variable.
• Population and Sample : The population is the entire set of individuals relevant to the research question, while the sample is a subset of the population that is studied.
• Sampling Methods : Methods like random sampling, stratified sampling, or convenience sampling dictate how participants are chosen from the population.

Data Collection Methods

• Qualitative methods such as interviews, observations, and focus groups gather non-numerical data.
• Quantitative methods such as surveys, experiments, and secondary data analysis gather numerical data.

Study Design Types

• Descriptive studies describe characteristics of the population or phenomena being studied.
• Analytical studies investigate the relationships between variables.
• Experimental designs manipulate variables to determine cause-and-effect relationships, often using control and experimental groups.

Data Analysis Techniques

• Statistical Analysis : Techniques vary depending on the nature of the data and may include descriptive statistics, inferential statistics, regression analysis, etc.
• Qualitative Analysis : Methods like thematic analysis or content analysis are used to interpret textual data.

Ethics and Reliability

• Ethical Considerations : Ensuring the confidentiality, consent, and welfare of participants.
• Reliability and Validity : Strategies to ensure that the study can be replicated and that the results truly represent what they are supposed to measure.

Research Design in Research Methodology

Research design in research methodology refers to the blueprint or framework that guides how a research project is conducted, aiming to ensure the validity and reliability of the findings. It encompasses the overall strategy and methods chosen to integrate the different components of the study in a coherent and logical manner, effectively addressing the research questions. Research design outlines the procedures for collecting, measuring, and analyzing data. It is pivotal in determining the type of evidence gathered and how it is interpreted. Types of research design include experimental, correlational, descriptive, and qualitative designs, each suited to different kinds of research questions and objectives, influencing how researchers select participants, define variables, and structure the overall study. This design process is crucial for aligning the methodology with the study’s goals, thereby enhancing the robustness and integrity of the results.

Research Design in Qualitative Research

Research design in qualitative research involves structuring the approach to explore complex phenomena by focusing on the meanings, concepts, characteristics, and descriptions of the subject matter. Unlike quantitative research, which seeks to quantify variables, qualitative research design is more flexible and adaptive, often evolving as the study progresses. It typically includes methods such as interviews, focus groups, observations, and content analysis, which allow for a deep, narrative understanding of participants’ experiences and social contexts. This type of design is oriented towards understanding “how” and “why” things happen, aiming to provide insights into human behavior, social processes, and cultural phenomena. The design in qualitative research is crucial for ensuring depth, richness, and relevance in the data collected, allowing researchers to capture the complexities of the phenomena in question. This approach requires a thoughtful integration of various elements like the research questions, the nature of the participants, the settings, and the researcher’s philosophical standpoint, all of which influence the data collection and analysis procedures.

How to Write a Research Design

Once the researchers formulate their research questions, they need to work on designing their overall research work and research investigation reports while using research designs appropriate for their respective work. When should you use a survey? Conduct experiments or perform participant observation? Need to combine several research designs? Structuring a well-coordinated research design will guide you in developing the right methods for your research goals. Here are some steps that you need to follow while writing a suitable research design for your research project:

1. Think about your specific aims and research approach.

First of all, have a clear understanding of what your research project will investigate. This will help you to properly think about what you really want to accomplish in your study.

2. Select a type of research design

There are wide-ranging types of research designs that you can select based on your research goals and objectives. Each research design gives you a framework for the overall structure of your research work.

3. Define your intended audience and sampling method

Make sure that you fully define who or what your research study will aim on, and what specific sampling method that you will use when you select your participants or subjects. Some examples of sampling methods are probability sampling and non-probability sampling.

4. Select your data collection methods

In order to effectively measure variables and gather sufficient information, you must select the one data collection method or several data collection methods like survey methods to enable you in acquiring original knowledge and comprehensive insights into your research problem. 

5. Develop a cohesive plan for your data collection methods

Next, you need to develop a systematic plan for your data collection methods so that you can accurately define your variables and make sure that you have credible and trustworthy measurements.

6. Choose the suitable data analysis strategies for your study

Lastly, you need to determine what specific data analysis strategies you will use in your research study. Read some research papers related to your research study so that you can choose the suitable data analysis strategies. 

Characteristics of Research Design

Research design is fundamental in conducting a reliable and valid study. Here are the key characteristics that define a strong research even further

• Research designs are tailored to address specific research questions or hypotheses. The design guides the methodology to ensure that the data collected is appropriate and sufficient to answer the research questions effectively.

Rigorous and Methodical

• A well-designed study follows a systematic, structured approach to ensure the integrity and quality of the research. This includes detailed planning of procedures like data collection and analysis to minimize errors and biases.

Feasibility

• The chosen design must be practical and manageable within the given resources and time constraints. It should also consider ethical issues, ensuring that the study can be conducted without undue risk to participants.

Flexibility

• While research designs must be structured, they should also allow for adjustments as new insights and conditions arise during the study, provided these changes do not compromise the study’s objectives.

Replicability

• A robust research design can be replicated by other researchers, which helps in validating the findings through repeated studies in similar or varying contexts.

Specificity

• Research designs should be specific enough to clearly define the population, variables, methods of data collection, and methods of analysis. This clarity is crucial for the validity and reliability of the study.
• Research designs often include mechanisms to control for variables that could influence the outcomes. In experimental designs, for example, this could mean controlling the environment or randomizing subjects to different groups to ensure that the results are due to the intervention and not other factors.

Validity and Reliability

• Ensuring the research measures what it intends to measure (validity) and can produce consistent results under consistent conditions (reliability) are critical aspects of research design.
• All research designs must incorporate ethical considerations to protect participants from harm, ensure confidentiality, and promote integrity in the research process.

Resource Efficient

• Effective research designs make optimal use of available resources, including time, money, and personnel, to achieve the research objectives without unnecessary expenditure.

Research Design Format

Research Goals and Purpose Statement: While formulating your research question, set your specific research goals and purpose while highlighting your priorities for your research design. Every research study has diverse priorities that’s why you need to clarify your exact aims and purpose in your research study.

Research Data Type: Indicate what specific type of research data essential for your research study. Consider your research questions and hypotheses so that you can choose the right research data type. Some examples of research data types are primary data, secondary data, qualitative data, and quantitative data.

Data Collection Methods: Determine the research data collection method that you will use in your study so that you are able to address your research problem. Research methods such as procedures, materials, tools, and techniques are commonly used for research studies. 

Data Analysis Procedure: Select the proper data analysis procedure for the design of your research study. You can use a quantitative data analysis or qualitative data analysis based on your needs and preferences.

Benefits of Research Design

A well-crafted research design is crucial for the success of any scientific study. It provides a structured approach to investigate research questions and ensures that the findings are valid and applicable. Here are the key benefits:

Enhances Validity

• Internal Validity : Good research design controls for confounding variables, ensuring that the observed effects are due to the independent variables.
• External Validity : It allows findings to be generalized to other settings or populations, enhancing the broader applicability of the research.

Increases Reliability

• Consistency : A structured design helps ensure that the study can be reliably reproduced under similar conditions, which is fundamental for building trust in the findings.
• Accuracy : Precision in the design helps in minimizing errors and biases, providing more accurate results.

Facilitates Data Collection

• Efficiency : Efficient design reduces the resources (time, cost, effort) required to conduct the study.
• Appropriateness : It ensures that the chosen methods and techniques are suitable for the research question and objectives, thereby optimizing data collection.

Supports Objective Analysis

• Reduces Bias : A good design minimizes the researcher’s biases by using blinded assessments, randomized allocations, etc.
• Statistical Power : Proper design increases the likelihood that the study will detect any true effects of the variables being tested, thereby preventing false negatives.

Enhances Ethical Integrity

• Protects Participants : Ensures that the research adheres to ethical standards, protecting participants’ rights and well-being.
• Moral Responsibility : Promotes transparency and accountability in research, fostering trust among participants and the public.

Improves Decision Making

• Informed Decisions : The findings from a well-designed study provide robust evidence that can inform policy-making, clinical practices, and other decision-making processes.
• Problem Solving : Helps identify effective interventions and solutions by clearly demonstrating what works, what doesn’t, and under what conditions.

Guides Future Research

• Foundation for Further Studies : Establishes a solid basis for future research, indicating potential new areas to explore or methodological improvements to consider.
• Contributes to Theory : Helps in building or testing theoretical frameworks, contributing to the overall knowledge and understanding of a particular discipline.

What is research design?

Research design is a structured framework that guides the collection and analysis of data for a research project.

Why is research data design important?

Effective research design ensures accurate, reliable data collection and analysis, leading to valid conclusions.

What are the types of research designs?

Common types include experimental, correlational, and observational research designs.

How does research design affect reliability?

A well-structured research design enhances the reliability of the findings by minimizing biases and errors.

What is the difference between qualitative and quantitative research designs?

Qualitative research designs explore phenomena in-depth, while quantitative designs quantify data and often involve statistical analysis.

How do you choose a research design?

Choose based on the research question, objectives, and the nature of the data required.

What is a case study in research design?

A case yet study involves an in-depth investigation of a single subject or entity to uncover unique insights.

How does a cohort study design work?

A cohort study design follows a group sharing a common characteristic over time to assess outcomes.

What is the significance of a cross-sectional study design?

Cross-sectional studies analyze data from a population at a specific point in time to identify patterns and correlations.

How can a research design be ethical?

Ensure informed consent, confidentiality, and transparency to uphold the ethical standards of research.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

• Open access
• Published: 18 June 2024

Use of Self-Efficacy Scale in Mass Casualty Incidents During Drill Exercises

• María Carmen Cardós-Alonso   ORCID: orcid.org/0000-0003-1541-214X 1 , 2 ,
• Miguel Inzunza 3 ,
• Lina Gyllencreutz 4 , 5 ,
• Salvador Espinosa 1 ,
• Tatiana Vázquez 1 ,
• Maria Aranzazu Fernandez 1 ,
• Alberto Blanco 1 &
• Ana María Cintora-Sanz   ORCID: orcid.org/0000-0003-2654-0235 1  

BMC Health Services Research volume  24 , Article number:  745 ( 2024 ) Cite this article

Metrics details

Introduction

Medical First Responders (MFRs) in the emergency department SUMMA 112 are tasked with handling the initial management of Mass Casualty Incidents (MCI) and building response capabilities. Training plays a crucial role in preparing these responders for effective disaster management. Yet, evaluating the impact of such training poses challenges since true competency can only be proven amid a major event. As a substitute gauge for training effectiveness, self-efficacy has been suggested.

The purpose of this study is to employ a pre- and post-test assessment of changes in perceived self-efficacy among MFRs following an intervention focused on the initial management of MCI. It also aimed to evaluate a self-efficacy instrument for its validity and reliability in this type of training.

In this study, we used a pretest (time 1 = T1) – post-test (time 2 = T2) design to evaluate how self-efficacy changed after a training intervention with 201 MFRs in initial MCI management. ANOVA within-subjects and between subjects analyses were used.

The findings reveal a noteworthy change in self-efficacy before and after training among the 201 participants. This suggests that the training intervention positively affected participants’ perceived capabilities to handle complex situations like MCI.

The results allow us to recommend a training program with theory components together with practical workshops and live, large-scale simulation exercises for the training of medical first responders in MCI, as it significantly increases their perception of the level of self-efficacy for developing competencies associated with disaster response.

Peer Review reports

Mass Casualty Incidents (MCI) are defined by the local health system’s ability to address the initial health needs of victims, influenced by the ratio between victims and available resources. These incidents, involving multiple patients, can temporarily overwhelm and collapse the Emergency Medical Response (EMR) [ 1 , 2 ]. MCI’s inherent complexity, influenced by factors like incident type, casualty numbers, resources availability, timing and weather conditions, and triage system used [ 3 ], makes developing a curriculum challenging for teaching Medical First Responders (MFRs) how to handle victims in this environment [ 4 ].

The chaos of MCIs creates stressful situations for professionals, which can lead to loss of situational awareness, fixation errors, and hindered communication, all affecting decision-making and patient outcomes. Experts often struggle to explain their decision-making process during MCI care [ 5 ]. Studies suggest that stress levels among emergency professionals vary and impact them differently [ 6 ]. Additionally, training on-site can improve their subsequent performance [ 7 , 8 ].

Training in safe scenarios improves care outcomes, reduces coping stress, results in better decision-making and decreases emotional impact, all of which increases the quality of care and patient safety [ 9 ], as well as changing the perception of their work [ 10 ].

Simulation for training healthcare workers in emergencies is based on the ability to reproduce rare events in a safe environment for patients and professionals ([ 11 , 12 ], Kim et al. 2020). In addition, it is also a significant advantage in the possibility of working on technical and non-technical or relational skills, with results being superior to passive learning such as reading or lectures [ 13 ]. Simulation-based training is increasingly used in emergency and disaster management to acquire the necessary knowledge, skills and experience [ 14 ].

Kolb’s Experiential Learning Theory for adult learning argues that acquired skills, if not practised, decrease in effectiveness after 24 months [ 15 ]. Thus, it is important that the skills acquired and trained are becoming automated over time and embedded in the individual, as one of the key elements is the subsequent analysis, generating new concepts that can be put into practice in the next situation [ 5 , 12 ]. This characteristic makes it difficult for practitioners to describe the specific behaviours that make crisis resolution successful [ 16 ]. More specific related to acute situations, decision-making in emergencies is very difficult to study as it is an internal process that occurs rapidly and carries a great deal of responsibility [ 17 ].

Until recently, MCI training was carried out through table-top exercises and large-scale drills, demanding considerable human resources and equipment [ 18 ]. However, frequent repetition of such training is both unsafe and expensive. Due to the infrequent provision of training in disaster response, it is not surprising that many healthcare professionals, including MFRs, perceive their preparedness as inadequate [ 19 , 20 ]. Research indicates that higher frequency and quality of training directly correlate with better disaster preparedness [ 21 ].

Two models of interactive (learning-by-doing) training in emergency preparedness have been developed in Sweden: practical field exercises or “tabletop” exercises. In this study [ 22 ], they used the standardized MAss Casualty SIMulation (MACSIM®) training model, a scientifically validated simulation system of “tabletop exercises” for the training of hospital, prehospital and collaborating agency medical personnel. The study demonstrates that this type of simulation exercise is useful for training healthcare personnel, as well as helping to develop emergency plans or revealing deficiencies in existing ones, although field exercises should also be conducted. Both models have certain advantages or disadvantages. A challenge for emergency planners is to choose the most appropriate model to achieve the best learning outcomes for participants. Drills should be conducted to ensure the feasibility of the evacuation plan.

Several models for evaluating training programs are available [ 23 ]. The Kirkpatrick model is widely used and consists of four levels: reaction, learning, behavior, and results (Annex I). Each level builds upon the previous one, with higher levels of evaluation recommended only after success is demonstrated at lower levels. However, this progressive evaluation process can become increasingly complex and resource-intensive [ 24 , 25 ].

Another model to evaluate preparedness is the self-concept that is a concept that is frequently used in research [ 25 ] and “refers to a person’s self-perceptions concerning important aspects of life” [ 26 ]. The process of transitioning from self-concept to self-efficacy involves identifying and recognizing particular skills and abilities within one’s overall self-perception. Subsequently, it focuses on cultivating a belief in the ability to employ those skills effectively to achieve specific goals. A positive self-efficacy is the key for emergency professionals to manage the different phases of multi-victim incident resolution. Measuring participants’ self-efficacy is one approach to evaluating the impact of a training intervention. Self-efficacy indirectly measures the training’s effect on improving healthcare skills, providing insights into the potential impact of an educational intervention on subsequent clinical practice [ 27 , 28 ].

Each individual’s behavioural choices are based on his or her self-efficacy expectations. Training in technical skills is necessary, but not sufficient, to perform well. Technique is a means to achieve certain outcomes, but in itself does not constitute an outcome expectation. In fact, “effectiveness in behaviour requires continuous improvisation of skills to master the continually changing circumstances of the environment, most of which are made up of ambiguous, unpredictable and often stressful elements” ([ 29 ], p. 416 [ 30 ]). Self-efficacy is a dynamic and context-specific construct considering individuals’ perceptions of their capabilities in a particular situation.

The self-efficacy belief mediates the impact of environmental conditions on the person’s behavior; that is, those who possess a high level of self-efficacy expectations can cope more successfully with these conditions, while generating behavior that in one way or another can also modify these conditions. In MCIs, individuals with high self-efficacy are more likely to engage in problem-solving behaviours, persist in adversity, and collaborate effectively. In contrast, those with low self-efficacy may experience heightened anxiety, struggle with decision-making, and exhibit decreased resilience [ 31 ]. According to these statements students with a higher degree of self-efficacy following Kolb’s experiential model would have greater growth in each of the turns of the circle, with its four phases: experiencing, reflecting, theorizing and acting [ 32 ]. Self-efficacy does not tell us how many of these lives have been saved thanks to the simulation, but it speaks to us about a change in the learner’s behavior, which would correspond to level 3 of evaluation, within Kirkpatrick’s [ 33 , 34 ] evaluative model (Annex I).

Research of this kind is fundamental because academic self-efficacy has been shown to predict cognitive engagement and academic achievement in various educational contexts [ 30 ].

This study aims to employ a pre-and post-test to assess the alterations in self-efficacy among MFRs after a training intervention focused on the initial management of an MCI. The purpose was also to determine a self-efficacy instrument regarding validity and reliability.

This study used a pretest (time 1 = T1) – post-test (time 2 = T2) design to evaluate how self-efficacy changed after a training intervention with MFRs in initial MCI management. This scale was previously analyzed for validity and reliability through a Confirmatory Factor Analysis (CFA) with the maximum likelihood method. Subsequently, Cronbach’s alpha was calculated to obtain the reliability of the factors and the entire scale.

Training intervention

During the year 2023, between February and October, eight iterations of an MCI training program were conducted. This program is designed for employees working at the Prehospital Emergency Medical Service of Madrid Community (SUMMA 112) and stands as an integral component within the training framework for MFRs.

061 was created by the Special Emergency Service of Madrid (SEU) in January 1964. Its birth 40 years ago constituted one of the pioneering experiences in Europe in the implementation of out-of-hospital emergency medical care services. It has undergone changes and mergers until today’s SUMMA 112.

SUMMA 112 is the Emergency Medical Service of the Community of Madrid, handling out-of-hospital emergencies with a vast network of resources. Its functions include:

Managing health-related phone calls.

Providing healthcare in MCI.

Coordinating critical patient transfers.

SUMMA 112 is not just a reactive service. It takes a proactive approach, engaging in epidemiological alerts, international health missions, and organ transplant activities. The organization conducts external training and research in emergency and disaster management, always staying ahead of the curve. Staffed by emergency physicians, nurses, and technicians focused on first responder needs, SUMMA 112 supports end-user testing, identifies training gaps, and evaluates technologies with health experts in various scenarios. This proactive stance ensures that SUMMA 112 is always prepared to handle any emergency situation.

The program comprises both theoretical instruction and practical features.

The theoretical part focuses on theoretical content delivered through online modalities, which participants must complete and pass before the practical component. The practical component includes practical workshops and culminates in live drill exercises (Annex I).

MCI trainers with the necessary training and accreditation deliver this theoretical and practical component.

The course is divided into two days. The first day, as can be seen in Annex II, consists of a five-hour theoretical session divided into several lessons: review of the procedure for multiple victim incidents of the Madrid emergency service (definition of MCI and the criteria for its activation), roles of the different responders, colors of the vests they must wear (to be differentiated during the action), communications between interveners (communication channels to be used, radio language, communications procedure), triage (types of triage, triage backpack and its contents, triage cards, life-saving measures using tourniquets, oropharyngeal cannulas, hemostats, etc.

At the end of the day, doubts will be answered and finally a theoretical exam will be performed. The second day consists of carrying out 3 simultaneous workshops, which the students must go through. After the workshops, two MCI exercises will take place, where students will be able to practice what they have learned. At the beginning of the day, the researchers explain the self-efficacy study to the trainees and collect the consent inform. In the first workshop, the procedure for an MCI action in the SUMMA 112 service is reviewed. It consists of a dynamic using a whiteboard and magnetic markers that represent the different participants in a MCI. The zoning of the incident will be carried out in three areas: intervention zone (firefighters and rescue teams), relief zone (triage zone, advanced health post, stretcher wheel) and base zone (advanced command post, waiting area of resources).

In the second workshop, the different roles that the MFRs will take when arriving at the MCI are explained, as well as the different vests they will have to put on to be differentiated during the action. The roles in the MCI will be: head commander (in charge of all the medical teams) (red vest), triage commander (blue vest), commander in charge of resources (orange vest),commander of the advanced medical post (green vest), responsible for affiliation (green vest), responsible for evacuation and charge (green vest), responsible of the communications of the advance medical post (black vest), and commander in charge of light or green casualties (green vest). In addition to this, in this workshop the triage card and the triage backpack are shown (how to fill the triage card, life saving maneuvers, contents of the backpack).. Our service uses the START method for the first triage and the revised trauma score for the second triage.

The last workshop emphasises using communication, using the correct channel, and providing clear and concise information. After the workshops, the drill exercise is based on two scenarios, each lasting approximately 45 min. The first is a bus accident with 15 victims, and the second is a building explosion with 20 victims. In this exercises, the trainees simulate everything that would happen from the beginning in a MCI, putting on the safety equipment, using the communications devices, triaging patients, transferring them to the advanced medical post, prioritizing their evacuation and so on…Before each exercise, there will be a debriefing. At the end of the second day, the researchers will give the questionaries of the self-efficacy study to the trainees.

Participants

Employees enrolled in the MCI training program were selected for participation. The sampling approach was convenience sampling from the SUMMA 112 Emergency Medical Service. The study successfully recruited a comprehensive cohort comprising 201 participants. The age variable was transformed to have three categories (40 or below, 41 to 55, 56, and above), gender (female, male), occupation (doctor, nurse, and technician), and experience transformed to have three categories (10 years or less, 11 to 20 years, and 21 years or more). The sample description is composed of 42.8% women and 57.2% men. Among them we find 18.4% of physicians, 26.9% of nurses and 54.7% of technicians. In relation to age, 25.4% were 40 years of age or younger, 46.3% were between 41–50 years of age, and 28.4% were 51 years of age or older. Finally, 40.5% had 10 years of work experience in emergencies or less, 38% had between 11 and 20 years and 21.5% had more than 20 years.

The instrument utilized in this study is the “Self-efficacy scale for first responders in MCI” (SESMCI), developed and validated by Cardós et al. [ 35 ], depicted in Table  1 . The SESMCI is produced from the existing scales “The Disaster Preparedness Perception Scale in Nurses” (DPPSN) [ 33 ] and “The Disaster Response Self-Efficacy Scale” (DRSES) Li et al. [ 36 ]. These scales have been validated by Kim [ 6 ], Cruz et al. [ 37 ], and Toraman et al. [ 38 ] in various countries and with different MCI training methods [ 39 ]. Additionally, the SESMCI instrument has been influenced by the “Learner Evaluation Questionnaire (LEQ)”, originally designed to assess medical students’ attitudes toward the curriculum [ 40 ]. The SESMCI instrument employs a 6-point Likert scale to measure self-efficacy before and after the simulation exercise, with the scale ranging from 1 (No Trust) to 6 (Total Confidence).

Data collection

Data was collected on two occasions, referred to as time 1 = T1 and time 2 = T2, and the same data collection method was employed on both occasions. The technique involved providing each participant, after informing them of the importance of their participation and the possibility of withdrawing from the study whenever they wish to do so, with a questionnaire to fill in before the practical workshops and drill exercises on day 2. The questionnaire included background questions and the 13 questions comprising the self-efficacy instrument. After completing the questionnaire (T1), the participant participated in the practical workshops and drill exercises. Immediately following the training, the participant completed the same questionnaire (T2). The questionnaires were coded with numbers to ensure they could be linked.

Data analysis

The tools used for analysis at item level, descriptive analysis and group differences was SPSS [ 41 ] version 28.0.1. The analysis concerning the latent variable models investigating construct validity according to fit indices were performed with Mplus version 8 (Muthén and Muthén, 1998–2017).

A global descriptive analysis was conducted at the item level between T1 and T2 using the Wilcoxon Signed Ranks Test. It was found that in all items, the mean score of the Likert scale increased by approximately one point and reported a p -value < 0.001, which confirms the very significant increase in the perception of self-efficacy after the practical workshop and brief exercises of MCI (Table  2 ).

The study encompasses the assessment of sociodemographic data, including age, gender, profession, and years of experience within the emergency services.

The validity and reliability of the dependent variable self-efficacy were investigated with latent variable modelling by conducting a confirmative factor analysis (CFA). The fit of the developed model was assessed according to several suggested fit indices with recommended thresholds. An insignificant chi square p  > 0.05; CFI with a minimum of 0.90 indicates an acceptable model; RMSEA of 0.08 or lower indicates a model with a satisfactory fit; SRMR of 0.08 or lower for a model with an adequate fit [ 42 , 43 , 44 ]. The omega coefficient was used to assess reliability following the recommendations for the reliability of latent variable models [ 45 ]. A repeated measures ANOVA within-subjects and between subjects was performed to investigate the effect of the intervention [ 46 , 47 ] and when studying group differences based on age, gender, occupation and experience with a significance level p set at < 0.05.

Mann-Whitney U-test for independent samples was applied to investigate if there were gender differences in their perception of self-efficacy.

Independent-Samples Kruskal-Wallis Test was performed to investigate the differences between doctors, nurses, and technicians concerning their self-efficacy.

Dependent variable

The 13-item measure of Self-efficacy (Fig.  1 ) was useful with the current sample of participants.

Standardized estimates for a 1-dimensional 13-item model at T1. Pre – post measurement

Within this cohort, individuals aged up to 40 years constituted 25.4%, those in the 41–50 age bracket accounted for 46.3%, showcasing the highest mean age frequency, and individuals aged > 50 years comprised 28.4%. Regarding gender distribution, the cohort encompassed 86 females and 115 males. Additionally, participants were stratified by job category, indicating that technicians constituted the majority at 54.7%, followed by nurses at 26.9%, and doctors at 18.4% (Table  3 ). The calculated mean work experience across all participants was 14.35 years.

CFA analyses of a 1-dimensional model showed a weak fit to the data at time 1 according to some of the selected fit indices, significant chi-square (65) = 252.46, p  < 0.01; CFI = 0.87; RMSEA = 0.12; SRMR = 0.06. At time 2 most fit indices showed an acceptable fit to the data, significant chi-square (65) = 155.17, p  < 0.01; CFI = 0.94; RMSEA = 0.08; SRMR = 0.04. Loadings were all significant and above 0.60 at both measurements. Reliability calculated as Omega was 0.94 at time 1 and 0.95 at time 2. Composite scores for time 1 and time 2 were developed from the 13 items and ranged at time 1 from 18–78 and at time 2 from 22–78, where a higher score indicated a higher level of Self-efficacy.

Our findings indicate a significant difference in Self-Efficacy pre-post training with a sample of n = 201 participants. A one-way within-subjects ANOVA test showed F (1,200) = 369.893, p  < 0.01, η2 = 0.65. The following observed means were noted at the different time points: T1 (M = 52.40, SD = 11.15) and T2 (M = 64.49, SD = 9.70).

When incorporating group variables, no significant interaction effect could be noted based on any of these variables (Figs. 2 , 3 , 4 and 5 ). A within and between-subjects design ANOVA test showed F (1,62) = 123.33, p  = 0.01, η2 = 0.43. The following observed marginal means were noted with the included variables (Figs. 2 , 3 , 4 and 5 ).

Estimated marginal means for group variable gender time 1 and time 2

Estimated marginal means for group variable occupation time 1 and time 2

Estimated marginal means for group variable experience time 1 and time 2

Estimated marginal means for group variable age time 1 and time 2

To conclude, there is a significant difference between time 1 and time 2 with a considerable effect, η2 = 0.65, η2 = 0.43. There were no significant interaction effects when including group variables in the model. Assumptions of equality of covariance matrices (homogeneity of intercorrelations) have been controlled with an insignificant Box’s test.

The findings show no significant differences between genders in improving perceived self-efficacy. However, when administering the prescribed treatment at the medical post, a p-value of 0.008 was obtained, indicating a more substantial increase in self-efficacy among men than women.

Concerning results about the differences between doctors, nurses, and technicians, our findings reveal statistically significant differences in 4 of the 13 items. Specifically, the self-efficacy of doctors and nurses significantly increases in performing/supervising the deployment of means and resources effectively and efficiently. In contrast, technicians perceive themselves as less self-effective in this task. Furthermore, variations were observed in applying the prescribed treatment at the advanced medical posts. Significant differences were observed among technicians who considered themselves more prepared after the simulations than the other professional categories. According to the MCI protocol, evacuating victims revealed significant differences between doctors and technicians. However, no notable distinctions were observed between nurses and the other two professional categories in this aspect.

Regarding the teaching methodology used, following the simulation zones [ 48 ], for the evaluation in behavioral change or in the perception of behavioral change that would be a level 3 of Kirkpatrick [ 34 ], simulation scenarios designed for zones 2 and 3 are used, which allow giving answers to how and why. But for this it is essential that the student has been able to acquire and practice the knowledge, and it is for this reason that the course had a theoretical part of knowledge acquisition, a practical part where the different skills are trained, individually (zone 1) or combining the different skills (zone 2) and a part of simulations where the human factors are also trained (zone 3) (Annex I).

In the dynamic realm of medical education, the concept of self-efficacy has garnered significant attention as a driving force behind learner motivation, academic success, and professional development. Stemming from Bandura’s social cognitive theory, self-efficacy reflects individuals’ beliefs in their capabilities to perform specific tasks [ 49 , 50 , 51 , 52 ].

The results indicate that the 13-item self-efficacy scale proved to be a valuable tool for assessing self-efficacy among participants in this study, where we used training methods using didactic tools simulating victims and scenarios without new technologies. This aligns with previous research in medical education that has explored various instruments to enhance self-efficacy (cf. [ 53 , 54 , 55 , 56 , 57 ]).

The result from this study, demonstrate that a comprehensive MCI training program, which includes theoretical instruction, practical workshops, and a simulation-based training exercise, significantly increased overall self-efficacy and management skills in the context of patient treatment, coordination, and communication skills during simulated MCI. This finding aligns with previous research that underscores the value of simulation-based training promising to influence self-efficacy beliefs positively (cf. [ 58 , 59 ]). Additionally, these results suggests that such a training method have the potential to boost the self-confidence of healthcare emergency professionals, bolstering their ability to perform tasks effectively, maintain resilience in challenging scenarios, and persevere through obstacles. This aligns with more than 30 years of self-efficacy research indicating that merely possessing knowledge and skills is insufficient for ensuring that students will apply them when needed. Instead, both “the skill and the will” are necessary for medical students to function successfully in dynamic clinical contexts [ 52 , 53 , 60 ]. Therefore, educators are encouraged to adopt instructional approaches that fosters competence and promote the growth of self-efficacy. Consequently, our findings contribute an effective training program to elevate self-efficacy, thereby enriching evidence-based educational strategies. Ultimately, this empowers trainees to navigate the complex and demanding landscape of disaster response medical training.

The findings reveal a noteworthy change in self-efficacy before and after training among the 201 participants, as evident by Cohen effect sizes > 0.14 which suggest a large effect [ 61 ]. However, the absence of significant interaction effects for gender, occupation, experience, or age indicates that these factors did not significantly influence the observed outcomes. This suggests that the training intervention positively affected all participants’ perceived capabilities to handle complex situations like MCI. In contrast, another study [ 62 ] assessing attitudes toward VR training based on individual factors like gender, observed differing results from ours. For instance, while that study found a strong positive attitude in medical students toward VR-based teaching and assessment, female students exhibited comparatively lower positivity, indication potentially indicating gender differences that should be considered when implemented VR in the curriculum. Although the absence of significant impact at the item level in our study further underlines the generalisability of the positive influence at the group level, regardless of gender, occupation, experience and age, it remains crucial to address any potential disparities that may arise.

Contrary to the predominant focus on nursing staff in triage and management functions during MCI in reviewed studies [ 31 , 36 , 37 , 39 , 59 , 63 , 64 ], our results demonstrate that MFRs from various emergency health fields can acquire, train and manage MCI with positive impacts on their perception of self-efficacy. This broader applicability is crucial, as it indicates that any healthcare professionals activated in these situations can benefit from this type of training, enhancing overall response capability [ 19 ].

Hence, within the context of equipping healthcare professionals, particularly MFRs, to address the complexities presented in MCI, this study’s findings affirm the effectiveness of simulation-based training in enhancing self-efficacy, regardless of individual demographic variables. These findings emphasize the significance of recognizing self-efficacy as a pivotal metric, elucidating the training’s effectiveness in readying healthcare professionals for the intricate challenges associated with disaster scenarios.

In medical education, the focus often lies on teaching students’ essential knowledge and skills. However, more than 30 years of research on self-efficacy underscores that possessing knowledge and skills alone does not guarantee the motivation to apply them when needed [ 53 ]. From an educational perspective, assessing and positively influencing these self-efficacy trajectories is imperative, as individuals who persist through challenges actively strive for their self-efficacy trajectories throughout their clinical practice.

However, within the occupational context, an increase in the use of communications was noted between doctors and nurses. This observation is rationalized by their role in the SUMMA 112 service, where they assume responsibility for communication on ordinary devices during operational tasks. Regarding administering prescribed treatment at advanced medical posts, no differences in self-efficacy/perception were observed between doctors and nurses, as this skill aligns with their daily routine duties. Conversely, distinctions emerged among technicians, who exhibited heightened preparedness post-simulation. Our findings indicate that brief simulations contribute positively to self-efficacy, particularly for tasks less familiar in the routine scope of daily work. This underscores the potential of simulation-based training in enhancing self-efficacy, especially for healthcare professionals facing novel or less routine challenges.

Conclusions

The study demonstrates the effectiveness of a comprehensive MCI training program in significantly enhancing participants’ overall self-efficacy and management skills. Through theoretical instruction, practical workshops, and simulation-based exercises, participants improved their ability to handle patient treatment, coordination, and communication during simulated MCI. These findings align with prior research on simulation-based training’s positive impact on self-efficacy beliefs. The study suggests that such training methodologies can boost healthcare professionals’ self-confidence, regardless of demographic variables. Unlike previous studies focusing predominantly on nursing staff, our findings show that MFRs from various emergency health fields can effectively manage MCI, enhancing their self-efficacy. In summary, the study highlights the value of simulation-based training in preparing healthcare professionals for disaster scenarios and emphasizes its potential to enhance self-efficacy, especially for less routine tasks.

Limitations and future lines of research

With the implementation of this scale, we will be able to compare the levels of self-efficacy acquired using the different types of training:

To assess training using new technologies such as virtual, mixed and augmented reality (under development), for which work is underway, to discover whether it is useful for improving this type of MCI training and participants’ self-efficacy.

Evaluate the impact and effectiveness of training proposals specialized in self-efficacy to assess aspects related to developing competencies.

To identify factors related to self-efficacy in personnel involved in MCI.

Examine the relationship between self-efficacy and MCI performance.

Availability of data and materials

Raw data from this study can be made available at reasonable request from the authors. https://docs.google.com/spreadsheets/d/1mKfv-kVSs3lj9qrvojbfybU7Os3xddo6/edit?usp=drive_link&ouid=106466966080904205286&rtpof=true&sd=true

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European project MED1stMR (Medical First Responder with Mixed Reality) is funded by the European Union’s Horizon 2020 Research and Innovation Program. Grant agreement No 101021775.

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María Carmen Cardós-Alonso, Salvador Espinosa, Tatiana Vázquez, Maria Aranzazu Fernandez, Alberto Blanco & Ana María Cintora-Sanz

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María Carmen Cardós-Alonso

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Miguel Inzunza

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MCC, SE and AMC were involved in the conception and design of the work. MCC, AB, TV, MAF and AMC contributed with the collection data; MI made the data analysis and interpreted the results. MCC drafted the manuscript and was critically reviewed and enriched with medical expertise by LG. All authors read and approved the final manuscript.

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Correspondence to María Carmen Cardós-Alonso .

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The study was approved by the Ethics Committee of Cultural and Behavioural Studies of Heidelberg University (with a registration number. AZ Beu 2023 1/1.). Additionally, all local, regional, and national guidelines for ethical authorization were followed (World Medical Association, 2013), as well as the declaration of Helsinki. Ethical Principles for Medical Research Involving Human Subjects.

Participation in the study was voluntary. Participants were briefed on the purpose and conduct of the study. They were informed that the survey would assess their self-efficacy and not their performance during the training. All participants signed an informed consent form to ensure the processing of information and the confidentiality of the data. All data were anonymized by a reference number only the participants knew.

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12913_2024_11175_moesm1_esm.docx.

Supplementary Material 1: Annex I. Kirckpatrick levels of effective simulation training (Niemann, L. & Thielsch, M. (2020). Evaluation of Basic Trainings for Rescue Forces) [ 65 ]. Annex II. Timetable for the MCI Course Summa Procedure.

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Cardós-Alonso, M.C., Inzunza, M., Gyllencreutz, L. et al. Use of Self-Efficacy Scale in Mass Casualty Incidents During Drill Exercises. BMC Health Serv Res 24 , 745 (2024). https://doi.org/10.1186/s12913-024-11175-w

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Comparative study on the seismic performance of a typical low-rise building in Nepal using different seismic codes

• Published: 11 June 2024

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• Ashish Sapkota   ORCID: orcid.org/0000-0002-2570-8131 1 ,
• Binod Sapkota 1 ,
• Jhabindra Poudel 1 &
• Suyog Giri 1  

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The recently enacted Nepal National Building Code of 2020 necessitates an examination and contrast of the seismic performance of the most prevalent architectural typologies based on various building codes. However, previous research has primarily focused on comparing seismic building design codes with hypothetical building and various assumptions regarding seismic parameters. To address this gap, this study aims to investigate the performance of a reinforced concrete frame type building approved by the municipality for construction. The analysis was conducted using ETABS according to the linear equivalent lateral force method. The study building was analysed to compare the base shear, story displacement, and story drift in accordance with Nepal National Building Code , Indian Code, and American Society of Civil Engineers guidelines. The analysis results indicated that NBC 105:2020 showed a higher value of base shear compared to the other codes. Incorporating the impact of cracked sections, NBC 105:2020, IS 1893:2016, and ASCE 7–16 guidelines demonstrated a significant effect on displacement and period. In comparison to NBC 105:1994 and IS 1893:2002, the periods increased by approximately 35% for these codes. The investigation revealed that the revised NBC 105:2020 displayed higher values for all response parameters analysed. Based on the design outcomes, the IS codes exhibited the highest percentage of longitudinal rebar, while NBC 105:1994 depicted the lowest. Furthermore, the study emphasizes the adherence to building code regulations during construction. The findings of this study aim to equip designers with comprehensive knowledge regarding seismic provisions and standards to facilitate informed decision-making in their design endeavours.

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Acknowledgements

The authors would like to thank Dr. Umesh Puri (an international consultant and the Director of CivilPark International Co. Ltd, a global engineering firm) for his valuable contributions in discussions related to the research project.

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Sapkota, A., Sapkota, B., Poudel, J. et al. Comparative study on the seismic performance of a typical low-rise building in Nepal using different seismic codes. Asian J Civ Eng (2024). https://doi.org/10.1007/s42107-024-01053-5

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Received : 06 September 2023

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DOI : https://doi.org/10.1007/s42107-024-01053-5

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