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• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, 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 the underlying assumptions of 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 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 the research design in your paper can vary considerably, but any well-developed description 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 information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or 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 of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop 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.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

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.
• 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.
• Not all relationships are causal! 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, rather 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; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

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.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

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.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

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.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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3.2: Overview of the Research Process

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• Anol Bhattacherjee
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So how do our mental paradigms shape social science research? At its core, all scientific research is an iterative process of observation, rationalization, and validation. In the observation phase, we observe a natural or social phenomenon, event, or behavior that interests us. In the rationalization phase, we try to make sense of or the observed phenomenon, event, or behavior by logically connecting the different pieces of the puzzle that we observe, which in some cases, may lead to the construction of a theory. Finally, in the validation phase, we test our theories using a scientific method through a process of data collection and analysis, and in doing so, possibly modify or extend our initial theory. However, research designs vary based on whether the researcher starts at observation and attempts to rationalize the observations (inductive research), or whether the researcher starts at an ex ante rationalization or a theory and attempts to validate the theory (deductive research). Hence, the observation-rationalization-validation cycle is very similar to the induction-deduction cycle of research discussed in Chapter 1.

Most traditional research tends to be deductive and functionalistic in nature. Figure 3.2 provides a schematic view of such a research project. This figure depicts a series of activities to be performed in functionalist research, categorized into three phases: exploration, research design, and research execution. Note that this generalized design is not a roadmap or flowchart for all research. It applies only to functionalistic research, and it can and should be modified to fit the needs of a specific project.

The first phase of research is exploration . This phase includes exploring and selecting research questions for further investigation, examining the published literature in the area of inquiry to understand the current state of knowledge in that area, and identifying theories that may help answer the research questions of interest.

The first step in the exploration phase is identifying one or more research questions dealing with a specific behavior, event, or phenomena of interest. Research questions are specific questions about a behavior, event, or phenomena of interest that you wish to seek answers for in your research. Examples include what factors motivate consumers to purchase goods and services online without knowing the vendors of these goods or services, how can we make high school students more creative, and why do some people commit terrorist acts. Research questions can delve into issues of what, why, how, when, and so forth. More interesting research questions are those that appeal to a broader population (e.g., “how can firms innovate” is a more interesting research question than “how can Chinese firms innovate in the service-sector”), address real and complex problems (in contrast to hypothetical or “toy” problems), and where the answers are not obvious. Narrowly focused research questions (often with a binary yes/no answer) tend to be less useful and less interesting and less suited to capturing the subtle nuances of social phenomena. Uninteresting research questions generally lead to uninteresting and unpublishable research findings.

The next step is to conduct a literature review of the domain of interest. The purpose of a literature review is three-fold: (1) to survey the current state of knowledge in the area of inquiry, (2) to identify key authors, articles, theories, and findings in that area, and (3) to identify gaps in knowledge in that research area. Literature review is commonly done today using computerized keyword searches in online databases. Keywords can be combined using “and” and “or” operations to narrow down or expand the search results. Once a shortlist of relevant articles is generated from the keyword search, the researcher must then manually browse through each article, or at least its abstract section, to determine the suitability of that article for a detailed review. Literature reviews should be reasonably complete, and not restricted to a few journals, a few years, or a specific methodology. Reviewed articles may be summarized in the form of tables, and can be further structured using organizing frameworks such as a concept matrix. A well-conducted literature review should indicate whether the initial research questions have already been addressed in the literature (which would obviate the need to study them again), whether there are newer or more interesting research questions available, and whether the original research questions should be modified or changed in light of findings of the literature review. The review can also provide some intuitions or potential answers to the questions of interest and/or help identify theories that have previously been used to address similar questions.

Since functionalist (deductive) research involves theory-testing, the third step is to identify one or more theories can help address the desired research questions. While the literature review may uncover a wide range of concepts or constructs potentially related to the phenomenon of interest, a theory will help identify which of these constructs is logically relevant to the target phenomenon and how. Forgoing theories may result in measuring a wide range of less relevant, marginally relevant, or irrelevant constructs, while also minimizing the chances of obtaining results that are meaningful and not by pure chance. In functionalist research, theories can be used as the logical basis for postulating hypotheses for empirical testing. Obviously, not all theories are well-suited for studying all social phenomena. Theories must be carefully selected based on their fit with the target problem and the extent to which their assumptions are consistent with that of the target problem. We will examine theories and the process of theorizing in detail in the next chapter.

The next phase in the research process is research design . This process is concerned with creating a blueprint of the activities to take in order to satisfactorily answer the research questions identified in the exploration phase. This includes selecting a research method, operationalizing constructs of interest, and devising an appropriate sampling strategy.

Operationalization is the process of designing precise measures for abstract theoretical constructs. This is a major problem in social science research, given that many of the constructs, such as prejudice, alienation, and liberalism are hard to define, let alone measure accurately. Operationalization starts with specifying an “operational definition” (or “conceptualization”) of the constructs of interest. Next, the researcher can search the literature to see if there are existing prevalidated measures matching their operational definition that can be used directly or modified to measure their constructs of interest. If such measures are not available or if existing measures are poor or reflect a different conceptualization than that intended by the researcher, new instruments may have to be designed for measuring those constructs. This means specifying exactly how exactly the desired construct will be measured (e.g., how many items, what items, and so forth). This can easily be a long and laborious process, with multiple rounds of pretests and modifications before the newly designed instrument can be accepted as “scientifically valid.” We will discuss operationalization of constructs in a future chapter on measurement.

Simultaneously with operationalization, the researcher must also decide what research method they wish to employ for collecting data to address their research questions of interest. Such methods may include quantitative methods such as experiments or survey research or qualitative methods such as case research or action research, or possibly a combination of both. If an experiment is desired, then what is the experimental design? If survey, do you plan a mail survey, telephone survey, web survey, or a combination? For complex, uncertain, and multifaceted social phenomena, multi-method approaches may be more suitable, which may help leverage the unique strengths of each research method and generate insights that may not be obtained using a single method.

Researchers must also carefully choose the target population from which they wish to collect data, and a sampling strategy to select a sample from that population. For instance, should they survey individuals or firms or workgroups within firms? What types of individuals or firms they wish to target? Sampling strategy is closely related to the unit of analysis in a research problem. While selecting a sample, reasonable care should be taken to avoid a biased sample (e.g., sample based on convenience) that may generate biased observations. Sampling is covered in depth in a later chapter.

At this stage, it is often a good idea to write a research proposal detailing all of the decisions made in the preceding stages of the research process and the rationale behind each decision. This multi-part proposal should address what research questions you wish to study and why, the prior state of knowledge in this area, theories you wish to employ along with hypotheses to be tested, how to measure constructs, what research method to be employed and why, and desired sampling strategy. Funding agencies typically require such a proposal in order to select the best proposals for funding. Even if funding is not sought for a research project, a proposal may serve as a useful vehicle for seeking feedback from other researchers and identifying potential problems with the research project (e.g., whether some important constructs were missing from the study) before starting data collection. This initial feedback is invaluable because it is often too late to correct critical problems after data is collected in a research study.

Having decided who to study (subjects), what to measure (concepts), and how to collect data (research method), the researcher is now ready to proceed to the research execution phase. This includes pilot testing the measurement instruments, data collection, and data analysis.

Pilot testing is an often overlooked but extremely important part of the research process. It helps detect potential problems in your research design and/or instrumentation (e.g., whether the questions asked is intelligible to the targeted sample), and to ensure that the measurement instruments used in the study are reliable and valid measures of the constructs of interest. The pilot sample is usually a small subset of the target population. After a successful pilot testing, the researcher may then proceed with data collection using the sampled population. The data collected may be quantitative or qualitative, depending on the research method employed.

Following data collection, the data is analyzed and interpreted for the purpose of drawing conclusions regarding the research questions of interest. Depending on the type of data collected (quantitative or qualitative), data analysis may be quantitative (e.g., employ statistical techniques such as regression or structural equation modeling) or qualitative (e.g., coding or content analysis).

The final phase of research involves preparing the final research report documenting the entire research process and its findings in the form of a research paper, dissertation, or monograph. This report should outline in detail all the choices made during the research process (e.g., theory used, constructs selected, measures used, research methods, sampling, etc.) and why, as well as the outcomes of each phase of the research process. The research process must be described in sufficient detail so as to allow other researchers to replicate your study, test the findings, or assess whether the inferences derived are scientifically acceptable. Of course, having a ready research proposal will greatly simplify and quicken the process of writing the finished report. Note that research is of no value unless the research process and outcomes are documented for future generations; such documentation is essential for the incremental progress of science.

Research Design

• First Online: 01 January 2013

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• Pradip Kumar Sahu 2  

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A research design is the blueprint of the different steps to be undertaken starting with the formulation of the hypothesis to drawing inference during a research process. The research design clearly explains the different steps to be taken during a research program to reach the objective of a particular research. This is nothing but advance planning of the methods to be adopted at various steps in the research, keeping in view the objective of the research, the availability of the resources, time, etc. As such, the research design raises various questions before it is meticulously formed. The following questions need to be clarified before the formulation of the research design:

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Sahu, P.K. (2013). Research Design. In: Research Methodology: A Guide for Researchers In Agricultural Science, Social Science and Other Related Fields. Springer, India. https://doi.org/10.1007/978-81-322-1020-7_4

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A Research Blueprint

25 January 2014

You’re a researcher—perhaps a research professional, a student, or a family historian—who regularly tackles a variety of subjects. How do you approach each new assignment or each new geographic area? Do you, as someone asked recently in an online forum, use a checklist of source types that you keep handy so you won’t forget to search for any critical type?

EE would suggest a different tack, one with a long track record of delivering the best results. Rather than a generic list of record types, we need a targeted resource list for that specific subject or geographic area. Numerous catalogs and guides exist online to help us, materials that are not buried behind the paywall of a commercial provider. For example:

• The catalog of the Salt Lake City–based Family History Library, which houses microfilmed copies of original documents from around the world, is an ideal place to start building a geographic-based list.
• The Library of Congress , WorldCat.org, and their counterparts in other nations—even Amazon.com and Google Books —offer catalogs to millions of books for subject-area, geographic, and biographical studies.
• LOC also offers several essential catalogs to manuscript collections, including NUCMC ( National Union Catalog of Manuscript Collection s, which covers archives across America), as well as catalogs to its own vast manuscript holdings.
• Newspaper archives, which can be found online at LOC and Google , have many of the same papers offered by commercial providers—and some those providers do not have.
• The archives of USGenWeb.com and RootsWeb.com offer many localized records and record abstracts.
• Cyndi’sList.com provides a vast catalog of links, arranged by subject and geographic areas.
• National, state-level, and university archives are also putting their internal catalogs online for public use.
• Other research guides focus on states, counties, and large metropolitan areas, pointing us to many resources not included in any of the above—including online guides such as those  provided by the Family History Library’s wiki at FamilySearch and the print- and e-book publications of the National Genealogical Society, the New England Historic Genealogical Society, and other regional and state-level counterparts.

Whatever project we work, we cannot consider our research to be "thorough" until we have done two things: (1) used these resources to build a research plan; and (2) actually studied the materials we gleaned from these catalogs and guides.

The best blueprint for research, of course, is a flexible document that can never be complete. As we proceed to study published works, their reference notes and bibliographies will expose us to new materials. The manuscripts we use will point us to other documents. New record collections, long in private hands, continue to surface. Obviously, our research can never be exhaustive. But our results will be far sounder if we start each project with a well-focused blueprint.

• Research plan
• Source catalogs

I would also recommend

I would also recommend ArchiveGrid (http://beta.worldcat.org/archivegrid/), which is tied to WorldCat, but only searches manuscript collections and finding aids. I have found it to make locating these important records a bit easier than when searching WorldCat itself.

Thanks, Michael, for the

Thanks, Michael, for the addition. I definitely should have included it.

What a great suggestion. I

What a great suggestion. I has inspired me to start compiling a New Netherland research guide, with a range of sources that I use when working on New Netherland lines. I know it will be helpful for me to have all this infomration conveniently in one place and hope others will enjoy it too. I expect to publish it in the form of a blog post in the next month or so. That way, I can easily add new sources when I discover them. 

It's sorely needed, Yvette,

It's sorely needed, Yvette, and will be greatly valued by many.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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NIH Blueprint Overview

The blueprint mission .

The NIH Blueprint for Neuroscience Research aims to accelerate transformative discoveries in brain function in health, aging, and disease. Blueprint is a collaborative framework that includes the NIH Office of the Director together with NIH Institutes and Centers that support research on the nervous system. By pooling resources and expertise, Blueprint identifies cross-cutting areas of research and confronts challenges too large for any single Institute or Center. Since its inception in 2004, Blueprint has supported the development of new research tools, training opportunities, and resources to assist neuroscientists. 

In addition to supporting cross-cutting neuroscience activities like research training , workforce diversity , and  therapeutic development , Blueprint also funds research initiatives. Topics have ranged from transforming our understanding of dynamic neuroimmune interactions to enhancing our fundamental knowledge of interoception, supporting the development of innovative tools and technologies to monitor and manipulate biomolecular condensates, and more. To learn about both current and past areas of research, visit the Blueprint Research Initiatives page . 

Blueprint Grand Challenges

In 2009, the Blueprint Grand Challenges were launched to catalyze research with the potential to transform our basic understanding of the brain and our approaches to treating brain disorders.

The Human Connectome Project (HCP) is an ambitious effort to map all the connections within the human brain. Beginning in 2010, Blueprint awarded $40 million to two major research consortia which took complementary approaches to deciphering the brain’s complex wiring diagram. In five years, this highly coordinated effort mapped the connections of 1,200 healthy adults paired with behavioral assessments and GWAS results, resulting in the publication of over 100 papers. The MRI scanner system developed by  HCP scientists was 4-8 times more powerful than conventional systems, providing ten-fold faster imaging times and better spatial resolution than ever before. Building on the success of the Connectome Project, in 2014 Blueprint authorized funds to expand the age range of normal subjects to include both young people and older adults. The Connectome Coordination Facility, funded by Blueprint in 2015, maintains a central data repository for HCP data and offers advice to the research community regarding data collection strategies and harmonization. 

The Grand Challenge on Chronic Neuropathic Pain supported research to understand the changes in the nervous system that cause acute, temporary pain to become chronic. The initiative has supported multi-investigator projects partnering researchers in the pain field with researchers in the neuroplasticity field. Starting in 2010, Blueprint funded 10 R01 grants investigating various models, mechanisms, and plasticity in the transition to chronic pain, resulting in more than 80 publications related to pain and neural plasticity.

The  Blueprint Neurotherapeutics Network (BPN)  helps small labs develop new drugs for nervous system disorders. BPN provides research funding, plus access to millions of dollars’ worth of services and expertise to assist in every step of the drug development process, from laboratory studies to preparation for clinical trials. Since 2010, project teams across the U.S. have received funding to pursue drugs for conditions from vision loss toneurodegenerative disease to depression.  A hallmark of the program is  the research institution retains the intellectual property rights. Now in its eighth year, BPN has awarded 22 grants resulting in 1 Phase 1 clinical trial, 5 licensed programs, and several successful partnerships with industry. 

The BRAIN Initiative ®

April 2013 marked the beginning of the  Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative , a coordinated effort among public and private institutions and agencies aimed at revolutionizing our understanding of the human brain. NIH has a large role in this effort, and Blueprint was one of the inaugural sponsors of the BRAIN Initiative by investing $10 million in 2014 on initial high priority research areas. Blueprint invested an additional $19 million in BRAIN Initiative research in 2015 and 2016 and continues to support and collaborate with the BRAIN Initiative on various projects. 

Historic Blueprint Resources 

Since 2004, Blueprint has supported the development of new resources , tools, and opportunities for neuroscientists. From fiscal years 2007 to 2009, Blueprint focused on three major themes of neuroscience - neurodegeneration, neurodevelopment, and neuroplasticity. These efforts enabled unique funding opportunities and training programs, and helped establish new resources that continue to be available to researchers and the public. Some of these resources include:

• The  Gene Expression Nervous System Atlas (GENSAT)  and the  Cre Driver Network  are projects that have developed, characterized and continue to distribute transgenic mouse lines (GFP reporters and Cre drivers) to serve as tools for research on the central nervous system. Over 100 lines are available from the Cre driver network and over 1400 (GFP and Cre) lines are available from GENSAT.
• The  Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC)  triad of services include a resources registry, data commons, and cloud-based virtual machine with popular neuroimaging software pre-installed. These services help researchers save time, meet data sharing requirements, and leverage cloud-based computing on increasingly larger data sets. 
• The  Neuroscience Information Framework (NIF)  is an online portal to neuroscience information that includes a customized search engine, a curated registry of resources and direct access to more than 100 databases.
• The  NIH Toolbox for Assessment of Neurological and Behavioral Function  is a set of integrated tools for measuring neurologic and behavioral function, and for generating data that can be used and compared across diverse clinical studies.
• The  NIH Blueprint Enhancing Neuroscience Diversity through Undergraduate Research Experiences (ENDURE)  supports undergraduates from underrepresented groups in a two-year neuroscience research program and encourages matriculation into PhD programs.

Download the NIH Blueprint Overview Flyer (pdf, 1215 KB) .

Blueprint of a Proposal

Trying to make sense of proposal preparation, review and submission at the UW?

This course introduces participants to the UW processes, concepts and terminology that will help get you started in the right direction.

Through discussion, hands-on exercises, annotated online resources and in class handouts, we will cover:

• Proposal Process Policies and Procedures
• Roles & Responsibilities
• Where to find critical information needed for proposal preparation
• Proposal best practices

Anyone involved in the preparation or review of sponsored programs proposals to external sponsors, especially those new to the process.

Research Administration Certificate

Last updated, course materials.

Class Slides , Reading the FOA (online exercise)

Related Learning

SAGE: Creating and Submitting eGC1s

Introduction to Sponsored Project Budgets

Workshop: Preparing Sponsored Project Budgets

SAGE:  Budget

SAGE:  Creating NIH Proposals in Grant Runner

CORE [email protected] 206.616.0804

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How Blueprints Work

A blueprint is a paper-based reproduction of a drawing, usually a technical drawing, such as an architect or engineer would use. Blueprints use the cyanotype process that was invented by the astronomer John Herschel in 1842. The paper (or vellum or plastic) is coated with a solution of two soluble iron(III) salts – potassium hexacyanoferrate(III) (potassium ferricyanide) and iron(III) ammonium citrate. The two iron salts do not react with each other in the dark, but when they are exposed to ultraviolet light the iron(III) ammonium citrate becomes an iron(II) salt. The iron(II) ion reacts with the potassium ferricyanide to form an insoluble blue compound, KFeFe(CN) 6 ·H 2 O. This compound is blue ferric ferrocyanide, also known as Prussian blue.

How Blueprints Are Made

A blueprint starts out as a black ink sketch on clear plastic or translucent tracing paper. The ink sketch is laid on top of a sheet of blueprint paper and exposed to ultraviolet light (e.g., placed in sunlight). Where the light strikes the paper, it turns blue. The black ink prevents the area under the drawing from turning blue. After exposure to UV light, the water-soluble chemicals are washed off the blueprint, leaving a white (or whatever color the paper is) drawing on a blue background. The resulting print is light-stable and as permanent as the substrate upon which it is printed.

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Making and using blueprint paper

Blueprints use the cyanotype process invented by the astronomer John Herschel in 1842. The paper is coated with a solution of two soluble iron(III) salts. The two iron salts do not react with each other in the dark, but when they are exposed to ultraviolet light the iron(III) ammonium citrate becomes an iron(II) salt. The iron(II) ion reacts with the potassium ferricyanide to form an insoluble blue compound, blue iron(III) ferrocyanide, also known as Prussian blue.

Student Sheet

In this practical I will be:

• Carrying out an experiment to produce Blueprint paper.
• Producing an image or diagram on my Blueprint paper.
• Investigating the process of producing Blueprints and the role UV light plays.

Introduction:

While on a school trip, you saw that some renovation work was being carried out by some builders. On a table were the Blueprints for the building. You realise that the shades of white and blue would be perfect for a piece of art you are currently working on. However, before you can use these shades, you need to understand how they are made. You decide to investigate further…   

• 1 beaker (250 cm 3  )
• 2 beakers (100 cm 3 )
• 1 measuring cylinder (100 cm 3 )
• 1 glass stirring rod
• 1 plastic tray
• 1 wash bottle containing distilled water
• 20 sheets (or access to) plain A4 paper avoid shiny or very absorbent papers
• 2 weighing boats (or gallipots)
• Potassium hexacyanoferrate(III) – labelled “Substance A – Irritant ” (low hazard)
• Ammonium iron(III) citrate – labelled “Substance B” (low hazard)
• 1 drying line with 2 bulldog clips (or string and pegs)
• Digital balance
• Drying line (string and pegs)
• Paper towelling
• Disposable gloves
• Newspaper (to cover the work area)

Making the blueprint paper

Wear gloves and goggles. 

• Get two 100 cm 3 beakers, a measuring cylinder and a stirring rod. Mark one beaker A and the other B.
• Weigh 5 g of Substance A into the beaker marked A.
• Now weigh 9 g of Substance B into the beaker marked B. 

Use the measuring cylinder to measure 50 cm 3 of water and pour the water into beaker A. 

• Stir carefully until all the crystals have dissolved.
• Now measure out another 50 cm 3 of water and pour into beaker B.
• Stir carefully with a clean glass rod until all the crystals have dissolved.
• Do steps 10–12 in a dark part of the lab. 
• Mix the two liquids together, and pour them into a tray. Move the tray gently to get the liquid to cover the base of the tray properly.
• Put a piece of white A4 paper into the liquid just long enough to get it damp - not wet! Place a piece of A4 paper onto the liquid in the tray, then lift the paper out of the tray by the two corners nearest to you. Allow the excess solution to drip into the tray before placing it wet side up onto some newspaper on a desk. 

Your paper will turn greenish blue. Hang it up to dry out in a dark part of the laboratory or store it lying flat in a dark drawer. Hang your paper up using the string line and pegs in a darkened area to dry.

• Why do you think you have to wear gloves and goggles?
• What does dissolve mean?
• Why do you think the mixing has to be carried out in a dark place?
• Why do think the experiment will not work if the paper is wet?

Making the blueprints

Wear disposable plastic gloves

• When dry place your prepared paper under another piece of paper to keep it away from the sun.
• Place the package by the window so the light can fall on it.
• Remove the protecting piece of paper and place an object on the surface.
• Leave it in the light for about 1–5 minutes. Longer exposure leaves a shadow; shorter exposure times produce a sharper image. 
• When you think it has gone blue enough, take the object off the paper. The covered parts will still be green.
• Wash the paper with water to wash away the green chemicals and leave the blue behind.
• Hang your blueprint up to dry out.
• Why does your prepared blueprint paper need to be kept in the dark?
• Does the paper change colour quickly when it is exposed to the light?
• What does the washing do to the paper?
• Why do you have to wash your hands at the end?

Going further:

Try a range of different types of paper to see if the paper type makes a difference to exposure time, depth of exposure, etc.

If you can get some old black and white negatives try using those on the blueprint paper. You will have to experiment with exposure times.

Describe how the blueprint paper is similar and how different it is to photographic developing with a film. Research the chemicals used in photography.

Blueprints use the cyanotype process invented by the astronomer John Herschel in 1842. The paper is coated with a solution of two soluble iron(III) salts - potassium hexacyanoferrate(III) (potassium ferricyanide) and iron(III) ammonium citrate.

The two iron salts do not react with each other in the dark, but when they are exposed to ultraviolet light the iron(III) ammonium citrate becomes an iron(II) salt. The iron(II) ion reacts with the potassium ferricyanide to form an insoluble blue compound, blue iron(III) ferrocyanide, also known as Prussian blue.

A blueprint starts out as a black ink sketch on clear plastic or tracing paper. The ink sketch is laid on top of a sheet of blueprint paper and exposed to ultraviolet light or sunlight. Where the light strikes the paper, it turns blue. The black ink prevents the area under the drawing from turning blue. After exposure to UV light, the water-soluble chemicals are washed off the blueprint, leaving a white (or whatever colour the paper is) drawing on a blue background. The resulting blueprint is light-stable and as permanent as the substrate upon which it is printed.

Teacher and Technician Sheet

In this practical students will:

• Produce Blueprint paper.
• Create an image or diagram on Blueprint paper.
• Investigate the process of producing Blueprints and the role UV light plays.

Introduction: 

(The topic could start with a group discussion during which teachers introduce the following ideas, especially the words in bold.)

A blueprint is an old term used for a reproduction of a technical drawing of an object such as an architectural or engineering design. They were made by a contact process using light-sensitive sheets. It was important because it allowed the rapid and accurate reproduction of design documents. It was called a blueprint because of the light lines on a blue background, forming a negative of the original. 

Paper was frequently used but for more durable prints linen was sometimes used. Sadly, over time the linen prints would shrink slightly, so later imitation vellum and polyester film were used instead. Nowadays drawings are produced on computer, printed, and then photocopied.

These blueprint papers have absorbed certain chemicals that are changed when visible light or ultraviolet (UV) light falls on them. Hence objects put onto the dried blueprinting paper will block visible or UV light from getting to the chemicals and those areas, untouched by the visible or UV light, stay unchanged.

Where the visible or UV light can get to the paper, an intense blue colour develops. The blue colour will not wash out of the paper, but the greenish colour left under the object will. This leaves a white image of the object on a blue background. It is possible to investigate the effects of differing exposure times , screening with certain materials.

(To make the process easier for the students and safer the two solutions can be made up in the dark and stored in dark bottles.)

(This practical can be done with pupils working as individuals or in groups of two. Groups of two allows for good discussion between the pupils. Teachers can use the questions set as the stimulus for discussion and the answers can be used as a group report, article, presentation, poster or talk.)

Curriculum range:

Suitable for middle school or lower secondary students; it links with:

• ask questions and develop a line of enquiry based on observations of the real world, alongside prior knowledge and experience; 
• use appropriate techniques, apparatus, and materials during fieldwork and laboratory work, paying attention to health and safety; 
• make and record observations and measurements using a range of methods for different investigations; and evaluate the reliability of methods and suggest possible improvements; 
• present observations and data using appropriate methods, including tables and graphs; 
• interpret observations and data, including identifying patterns and using observations, measurements and data to draw conclusions; 
• present reasoned explanations, including explaining data in relation to predictions and hypotheses; 
• the concept of a pure substance; 
• mixtures, including dissolving. 

Hazard warnings: 

Potassium hexacyanoferrate(III) – Skin/eye irritant, (Cat 2)  Respiratory irritant (STOT SE3) Ammonium iron(III) citrate –  Skin/eye irritant, (Cat 2)  

Good practice would require exposure to be kept to a minimum and suitable gloves be used by the students. Students with impaired respiratory function may incur further disability if excessive concentrations of particulate are inhaled so good ventilation is required. 

In addition, contact with strong acids causes the release of highly toxic hydrogen cyanide. This is not likely to be an issue but care should be taken on disposal to ensure that the drain/sink does not have acid already present.

Ammonium iron(III) citrate is slightly hazardous as an irritant through skin or eye contact.

Wear safety glasses. Wear disposable gloves.

For a group of students:

• 1 beaker (250 cm 3 )
• 2 glass stirring rods
• 20 sheets (or access to) plain A4 paper (avoid shiny or very absorbent papers)
• 10 g potassium hexacyanoferrate(III) – labelled “Substance A – Irritant”
• 15 g ammonium iron(III) citrate – labelled “Substance B – Irritant”
• 1 digital balance

Technical notes:

If available use a fume cupboard to hang the string lines up in ready to peg the paper to dry and close any blinds near it.

It is possible to dry the prepared sheets more quickly by using radiators and/or hairdryers if available, but otherwise this practical would have to be carried out over two lessons to allow for drying time.

The amount of pages that can be hung out to dry is limited by the amount of space available.

Laminating sheets can be drawn on and placed onto the prepared sheets before placing in bright light to leave an imprint on the paper.

An alternative is to get pupils to use image editing software to produce a negative of their choice that can then be printed out on transparency film.

The paper may stain yellow and dry yellow, but it will still change colour when exposed to bright light and develop a blueprint when washed with water.

This practical works well in normal daylight with the internal lights switched off. 

The amount of space to dry the papers directs how many sheets can be used in a practical.

Good results can be obtained using ordinary A4 paper and using laminating sheets to draw on.

Any shadows will also be processed on the paper so try to place the paper where it is in direct light and is laid flat.

The amount of chemicals used and solution produced could be halved and still cover about 10 sides of A4.

The hazards are minimal assuming the expected level of behaviour from students.

Making and using blueprint paper: student sheet

Making and using blueprint paper: teacher sheet.

• 11-14 years
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• Cross-curriculum
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Top 20 Research Design MCQ With Answers

Below given are top 20 important Research Design MCQ with answers. These updated multiple choice questions on research design are helpful for BBA, B Com, MBA, MMS, BMS, B Sc, Engineering, PGDM, M Phil and Ph D students and researchers. These MCQs will help for UGC NET, SET, MPSC, UPSC and other competitive entrance exams.

_______research is based on the measurement of quantity or amount.

A. Qualitative

B. Descriptive

C. Quantitative

D. Numerical

______ describes the present state of affairs as it exists without having any control over variables.

A. Analytical research

B. Descriptive research

C. Applied research

D. Distinctive research

In the _______research, the researcher has to use facts or information already available .

A. Analytical

D. Distinctive

__ ___ research is concerned with qualitative phenomena.

______ is related to some abstract ideas or theory.

A. Contextual research

B. Conceptual research

C. Ideal research

D. Empirical research

______ is data-based, coming up with conclusions that are capable of being verified, by observation or by experiment.

The objective of ______ is the development of hypotheses rather than their testing .

A. Laboratory research

B. Diagnostic research

C. Exploratory research

A ________ refers to some difficulty that a researcher experiences in either a theoretical or practical situation

A. research hypothesis

B. research experience

C. research problem

D. research crisis

_______ as a testable statement of a potential relationship between two or more variables.

Research design is a _________for conducting the marketing research project.

A. strategy

B. framework

C. blueprint

D. both B & C

______ is a hypothetical statements denying what are explicitly indicated in working hypotheses.

A. Null hypotheses

B. Working hypotheses

C. Descriptive hypotheses

D. Relational hypotheses

A Blue print of Research work is known as _______

A. sampling design

B. research design

C. research hypotheses

D. research approach

Research design is a blue print, outline and a _________

A. guidance

D. strategy

The choice of research design is influenced by the ________

A. the nature of the research problem

B. the audiences for the study

C. the researchers’ personal experiences

D. all of the above

A Blue print of Research work is called ____

A. Research design

B. Research Problem

C. Research methods

D. Research tools

_______ affect the choice of research methods .

A. Whether the research is ethical or not

B. Time and money available

C. Aims of the researcher

________ is the name of the conceptual framework in which the research is carried out.

A. Research paradigm

B. Synopsis of Research

C. Research design

D. Research hypothesis

The longitudinal research approach mainly deal with _____

A. Horizontal research

B. Vertical Research

C. Short-term research

D. Long-term research

Authenticity of a research finding is its ____

A. Objectivity

B. Tangibility

C. Originality

D. Validity

Research design is a blue print, outline and a ______

A. Strategy

This is all about solved MCQ on Research Design and related concepts.

You’ll also like Business Research Methods MCQ With Answers .

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Over one third of working americans more uninspired than ever, new study reveals.

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More than a third of Americans have never felt more uninspired at work than they do now (38%), according to new research.

A survey of 2,000 employed Americans found that this lull is translating to their productivity, too, as 38% of respondents are only feeling “somewhat” productive at work.

Four in 10 suburban survey-takers are also experiencing this productivity slump (41%).

Results showed that their work setting plays a role in their productivity and found many are looking to refresh their work routines with breaks and jump-start productivity with community and exercise.

Conducted by Talker Research for Life Time Work, the coworking company for Life Time, the survey found that 37% of those who have a work routine consider it to be stale, especially those who work fully in person (39%).

According to respondents, surroundings matter. Sixty-two percent said that the people and community around their workspace have a positive effect on their productivity.

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“Today more than ever, both employers and employees are acknowledging the link between physical activity and mental acuity, and the ability to perform at our best,” said Brooks Hagen, senior director of Life Time Work. “As a result, we’re seeing demand from both team leaders as well as team members for workspaces that are near health and athletic facilities to prioritize being able to incorporate more into their regular wellness routine.”

When they’re in a rut during the workday, respondents said their mood can be improved by getting active and going for a walk (33%) or exercising (19%).

However, a fifth of employed Americans admit they exercise once a week or less (17%).

Nearly two-thirds of all respondents said that being active would make a difference in their ability to be more productive at work (64%).

“The idea of an activity-based workspace design is more relevant than even five years ago,” Hagen said. “Working in a space that actively encourages physical movement throughout the day is healthier and allows for occupants to do their best work. Being part of a community at work also increases satisfaction levels and that passes through to accountability — knowing the people you work out with means you’re more likely to show up.”

But how much can respondents veer from being tied to their work? Thirty-nine percent “always” or “often” struggle to find time for themselves during the workday.

To maximize employee productivity, those surveyed said that offices should have unique areas available like a quiet room (36%) and gym or fitness space (23%).

Similarly, 70% said the environment they work in affects the quality of their work-related ideas and have had a good work-related idea in non-work areas like their bed (33%), a coffee shop (21%) or the gym (14%).

If they had the chance to take more breaks, those surveyed would especially want snack breaks (42%) or mental breaks (35%).

One in 10 respondents want more exercise breaks, with hybrid employees being the most likely to want them.

Survey methodology:

This random double-opt-in survey of 2,000 employed Americans was commissioned by Life Time between March 1 and March 6, 2024.

It was conducted by market research company Talker Research, whose team members are members of the Market Research Society (MRS) and the European Society for Opinion and Marketing Research (ESOMAR).

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Studying the role of postsecondary policies in shaping families’ financial health

A college degree provides substantial benefits, including lower unemployment, higher wages, and increased social mobility. However, as the price of a degree has risen, families increasingly use debt to finance a college education, with parents shouldering a larger share of the burden. For example, originations of federal “Parent PLUS” loans rose from $2.2 billion in 1991-1992 to $15.2 billion in 2017-2018 (Ma and Pender 2023). Parent PLUS loans are just one of many ways in which a child’s college decisions affect parents, leading to articles that declare parents as the “ Hidden Causalities of the Student Debt Crisis ” (Granville 2022). 

Motivated by these trends, Dr. Gurantz has begun to engage in a number of projects that look at how attending college affects family finances. Surprisingly there is very little research on this topic, in large part as there is rarely the ability to observe parent-student linked data at any reasonable scale. As a result, prior studies typically focus on how attending college impacts just the finances of the individual student (e.g., Scott-Clayton & Zafar, 2019 ), which paints an incomplete picture of how the costs of higher education are affecting society. 

With funding from the Spencer Foundation and Arnold Ventures, we have begun to answer these questions, with a research team that includes Palaash Bhargava and Dr. Sandra Black from Columbia University, Dr. Jeffrey Denning from Notre Dame, and Dr. Robert Fairlie from UCLA. These results rely on a newly-available, confidential, restricted-access administrative dataset that captures the universe of Free Application for Federal Student Aid (FAFSA) filers in California, which is then linked at the individual-level to detailed credit data records from a large credit bureau. These FAFSA data have an innovative feature: for students who are still dependents – which is most 18 and 19 year olds but can continue as long as students are not yet 26 – we can link applicants to their parents and consider how the child’s college experience affects their parent’s financial health. Although credit data do not capture many important outcomes, we can observe debt balances and default rates on a variety of loan types, such as educational loans, credit cards, and other forms of credit.  Impacts of state grant aid on financial health

The first project using these data focuses on how families react to changes in the price of college as a result of receiving state aid; we anticipate releasing this working paper in 2024. In our data we can observe families who just met the criteria to become eligible for state aid receipt and compare to them to essentially identical families who are just ineligible for state aid. As a result, these luckier families receive about $17,500 in additional aid over the following six years, which induces their children to be slightly more likely to enroll in a broad access, four-year California State University instead of a two-year community college. Even though state grant aid induces a small increase in more expensive, four-year college enrollment, we find that parents reduce the educational loan balances they take out on behalf of their students by about 10%. We also find reductions in HELOC balances (Home Equity Lines of Credit). These reductions vary by household wealth, with families that have mortgages showing larger reductions in HELOCs, whereas families who do not own a home having larger reductions in educational loan balances. Grant aid also reduces the chance that parents are delinquent on their debt but only for parents with a prior history of delinquency, with no effect on parents without prior delinquencies.

Interestingly, we do not find any evidence that receiving grant aid reduces student borrowing. This project demonstrates that, at least in this case, focusing just on student outcomes would have missed all of the positive financial effects of aid receipt on families.

Future Work

The paper described above is just the first in a sequence of topics that we hope to investigate over the coming years. A second paper, just underway, will examine the complex role of federal Parent PLUS loans. These loans are only available to parents if their child has exhausted their own available federal credit and have become an increasingly important source of financial support. There has been considerable controversy over the benefits of Parent PLUS loans and who should have access to them. As with other educational loans, the primary benefit is that they could enable students to attend college who otherwise could not. However, parents may become overextended by these sizeable loans, which could create adverse financial consequences if it causes them to miss mortgage, credit card, or other necessary payments.  

For most of recent history, PLUS loans could be originated through the Department of Education’s Direct Loan (DL) program or the Federal Family Education Loan (FFEL) program, which were bank-issued loans backed by the federal government. However, after the federal government became the sole provider of federal loans via the Direct Loan Program, they chose to synthesize policies and tighten loan standards, which had the effect of denying PLUS access to some families with adverse credit histories. This change led to significant public outcry, particularly from HBCUs and other colleges that enrolled more Black students who were negatively impacted by these adverse credit history standards (Stratford 2014). As a result, the federal government changed the eligibility criteria yet again in 2014, thus increasing access for a subset of parents. 

Using the same data listed above – FAFSA applicants linked to their credit histories – we will examine whether changes in access to Parent PLUS loans impacted whether students attended college. More importantly, we can examine whether access to Parent PLUS loans changed how parents finance their child’s education, potentially forcing them into riskier types of credit with higher interest payments, such as credit cards. Thus we will determine whether losing or gaining access to this program had financial implications for these families with worse credit histories. 

We hope the two papers described above are just the beginning of an avenue for producing policy relevant research that touches on some of the most important factors facing higher education today. 

School of Human Ecology

College of Behavioral and Social Sciences

GS4 honors work awarded

May 6, 2024

Congratulations Kennedy Johnson, Interior design student, mentored by Chris Smith won the CBSS honorable mention for undergraduate presentation at the 2024 Georgia Southern GS4 research summit. This Eagle Ambassador is an excellent representation of what can be accomplished with hard work and dedication. Excellent work Kennedy! #gasoutherncbss

Posted in General Announcements , Interior Design

Tags: Awards , CBSS , INDS , Scholarship , School of Human Ecology , SHE

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What workplace design can learn from higher education facilities, the future of resilient design, what you needed to know about the workplace in 2015, designing for neurodiversity and inclusion, chair of the month.

Unispace’s Elise Cuneo shares three ways to spark cultural optimism and connect employees through change.

In today’s evolving world of work, employees expect a lot from their workplace environment — free snacks and games is an outdated approach and is no longer enough to entice people back into the office.

Creating a resilient and positive workplace environment through thoughtful design fosters a sense of belonging and purpose among employees, nurturing a culture of optimism and connection to mission and brand. By focusing on the integration of spaces and places that prioritize comfort and the mixed needs of a workforce, the workplace becomes more than just a series of physical spaces — it becomes a platform for connection and productivity.

When there is a strong alignment between employees’ personal values and the values of their organization, they are more engaged, motivated, and committed to their jobs. And now more than ever, business leaders are seeing the role that the workplace experience can play in shaping these positive feelings.

The key to unlocking value from the workplace, and in turn, the workforce, is to create an ecosystem that can adapt to evolving and varied employee needs and preferences over time.

Embedding workplace resilience in a hybrid era

There’s no one path to a resilient workplace, but Unispace’s Returning for Good report shows an inherent disconnect between what employers are providing, and what employees actually need and want in the workplace.

For example, according the Unispace report,

. . . the vast majority of U.S. employers (89%) are confident that their current office allows people to be productive, while more than half (52%) of employees say they struggle to carry out their core role in their current office environment because of regular interruptions and a high number of meetings.

The first step to unlocking the approach to any new workplace environment is through listening and understanding the needs of a company and their people — no two workplace environments are the same. With hybrid work here to stay, exactly how companies are bringing employees back into the office also varies from company to company, sector to sector.

Our research shows, for instance, that many of today’s employees enjoy the autonomy, privacy, and quietness of working from home. Knowing this, workplace strategy and design can play critical roles in creating responsive environments that bring together the elements of home that employees enjoy, while adding value where working from home may fall short, such as in areas where face-to-face interaction is more productive.

To support a hybrid workforce, the workplace needs to be designed purposefully to ensure it acts as a catalyst to empower, engage, inspire, and enable both remote and in-office workers to be productive and feel connected.

Here are three ways to drive that connection, from providing purposeful workplace choices and designing for all people to articulating brand culture.

1) Support employee purpose with flexible, tech-enabled design

How can companies create tailored, experiential work environments that evolve as their workers’ needs change?

A company can work with an experienced strategy and design team to create a tailored, experiential work environment by prioritizing flexibility and adaptability in their approach.

As no workforce is the same, this involves diving deep and understanding the diverse needs and preferences of an organization’s workforce and creating spaces that can evolve alongside them.

Integrating elements of modular furniture , flexible layouts, and adaptable technology infrastructure can allow for seamless adjustments to accommodate changing work styles and preferences. Additionally, integrating spaces such as designated quiet zones, collaborative areas, or wellness spaces empowers employees to tailor their work environment and choose environments to suit their individual needs.

By continually engaging and welcoming feedback from employees as well as monitoring evolving trends in workplace dynamics, companies can ensure that their environments remain responsive and relevant, fostering a sense of empowerment and ownership among employees while promoting productivity and well-being.

2) Deliver spaces where everyone belongs

One of the most important steps to supporting a culture where everyone feels welcome and belongs is understanding the diverse needs of a workforce and using inclusive and equitable design practices to accommodate them.

This means going above and beyond in developing a program and experience that supports all employees.

Some tactics that should be considered when designing an environment that supports belonging include (but are not limited to):

• Designing with inclusivity and equity in mind . At its core, the workplace is about people and their experiences. Everyone wants to feel they belong within their workplace, and when they do, they are more likely to contribute to their fullest potential. Using inclusive and equitable design principles that go beyond ‘checking a box’ can truly support all people, including their unique differences and abilities, ensuring they feel valued as a member of the company community.
• By connecting the physical environment with brand values and purpose , a company can reinforce its mission, whilst sparking employee connection and pride.
• Supporting a multigenerational workforce . The days of a one-size-fits-all workplace environment are long gone. As the workplace becomes more diverse, so are the range of priorities and expectations surrounding them. For example, Gen Zers prioritize mental health, diversity, and inclusion, whereas Millennials tend to focus on company reputation, purpose, and connection. One way to empower these and other generations at the same time is to encourage collaboration and communication across a mix of tailored office spaces.

3) Activate community, culture, and connection

For all companies, creating a strong culture in the workplace is a top priority. That means anchoring the workplace in community, fostered through deep connection and trust using a range of key workplace strategies, from authentic design and technology to well-being considerations.

Through thoughtful design, the physical workplace can bring community, culture, and connection to life in many ways.

For example, flexible or multi-use areas like cafes can support the serendipitous interactions and casual engagements that lead to new ideas as well as increased productivity.

Integrated technologies also support remote employees and ensure a parity of experience no matter where they might be located, while driving connection to people and place.

A creative approach to storytelling through intentional branding, art, and cultural programming can help support a feeling of ‘we’re in this together.’

Through it all prioritizing well-being is essential. Championing health and well-being goes beyond fitness centers and healthy food options but must also consider how the workplace environment impacts and supports the physical, cognitive, and emotional needs of people.

The future workplace is a resilient one

To achieve a future that can withstand the current pace of change, the design of a workplace environment must center around people and their needs, ensuring they feel valued and supported through both physical space and experience.

By reimagining the workplace, with design that is underpinned by purpose, belonging, and connection, business leaders will empower their teams to thrive now, and into the ever-changing future.

• corporate culture
• Elise Cuneo
• experiential work environment
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