Definition and Purpose

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).

What do these studies tell you?

1. 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.
2. 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.
3. 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.].
4. Either original data or secondary data can be used in this design.

What these studies don’t tell you?

1. 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.
2. 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.
3. 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.

Definition and Purpose

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.

What do these studies tell you?

1. 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].
2. 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.
3. If the limitations are understood, they can be a useful tool in developing a more focused study.
4. Descriptive studies can yield rich data that lead to important recommendations in practice.
5. Appoach collects a large amount of data for detailed analysis.

What these studies don’t tell you?

1. The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
2. Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
3. The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

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

Definition and Purpose

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.

What do these studies tell you?

1. Design is a useful approach for gaining background information on a particular topic.
2. Exploratory research is flexible and can address research questions of all types (what, why, how).
3. Provides an opportunity to define new terms and clarify existing concepts.
4. Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
5. In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.

What these studies don’t tell you?

1. Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
2. The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
3. The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
4. 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.

Definition and Purpose

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.

What do these studies tell you?

1. The historical research design is unobtrusive; the act of research does not affect the results of the study.
2. The historical approach is well suited for trend analysis.
3. Historical records can add important contextual background required to more fully understand and interpret a research problem.
4. There is often no possibility of researcher-subject interaction that could affect the findings.
5. Historical sources can be used over and over to study different research problems or to replicate a previous study.

What these studies don’t tell you?

1. 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.
2. Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
3. Interpreting historical sources can be very time consuming.
4. The sources of historical materials must be archived consistentally to ensure access. This may especially challenging for digital or online-only sources.
5. 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.
6. Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
7. 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.

Definition and Purpose

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.

What do these studies tell you?

1. Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
2. Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
3. 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].
4. Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.

What these studies don’t tell you?

1. The data collection method may change over time.
2. Maintaining the integrity of the original sample can be difficult over an extended period of time.
3. It can be difficult to show more than one variable at a time.
4. This design often needs qualitative research data to explain fluctuations in the results.
5. A longitudinal research design assumes present trends will continue unchanged.
6. It can take a long period of time to gather results.
7. 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.

Definition and Purpose

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 analyses 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.

What do these studies tell you?

1. Can be an effective strategy for determining gaps in the literature.
2. Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
3. Is useful in clarifying what policy or programmitic actions can be justified on the basis of analyzing research results from multiple studies.
4. Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
5. Can be used to generate new hypotheses or highlight research problems for future studies.

What these studies don’t tell you?

1. Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
2. A large sample size can yield reliable, but not necessarily valid, results.
3. 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.
4. Depending on the sample size, the process of reviewing and synthesizing multple studies can be very time consuming.

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.

Definition and Purpose

What do these studies tell you?

1. Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
2. Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
3. A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
4. The strengths of one method can be used to overcome the inherent weaknesses of another method.
5. Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
6. May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
7. 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.

What these studies don’t tell you?

1. 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.
2. 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].
3. Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
4. 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.
5. 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.
6. 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.

Definition and Purpose

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.

What do these studies tell you?

1. 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].
2. The researcher is able to collect in-depth information about a particular behavior.
3. Can reveal interrelationships among multifaceted dimensions of group interactions.
4. You can generalize your results to real life situations.
5. Observational research is useful for discovering what variables may be important before applying other methods like experiments.
6. Observation research designs account for the complexity of group behaviors.

What these studies don’t tell you?

1. Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
2. In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
3. There can be problems with bias as the researcher may only “see what they want to see.”
4. There is no possiblility to determine “cause and effect” relationships since nothing is manipulated.
5. Sources or subjects may not all be equally credible.
6. Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentionally 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.

Definition and Purpose

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?

What do these studies tell you?

1. Can provide a basis for applying ethical decision-making to practice.
2. Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
3. Brings clarity to general guiding practices and principles of an individual or group.
4. Philosophy informs methodology.
5. Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
6. Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
7. Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.

What these studies don’t tell you?

1. Limited application to specific research problems [answering the “So What?” question in social science research].
2. Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
3. 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.
4. There are limitations in the use of metaphor as a vehicle of philosophical analysis.
5. 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.

Definition and Purpose

What do these studies tell you?

1. The researcher has a limitless option when it comes to sample size and the sampling schedule.
2. 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.
3. This is a useful design for exploratory studies.
4. 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.
5. 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.

What these studies don’t tell you?

1. 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.
2. Because the sampling technique is not randomized, the design cannot be used to create conclusions and interpretations that pertain to an entire population. Generalizability from findings is limited.
3. 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.