Search Results

This book investigates the relationship between the quantitative and qualitative research traditions in the social sciences, with a particular focus on political science and sociology. It argues that the two traditions are alternative cultures with distinctive research procedures and practices, each having its own values, beliefs, and norms. The book considers the ways in which the traditions differ in terms of methodology, such as type of research question, mode of data analysis, and method of inference. It suggests that the two traditions draw on alternative mathematical foundations: quantitative research is grounded in inferential statistics (that is, probability and statistical theory), whereas qualitative research is (often implicitly) rooted in logic and set theory. This chapter discusses the book's approach to characterizing and comparing the two cultures of social science research and explains what is distinctive about qualitative research.

This chapter describes ethical issues in social research including discussion of the NASW Code of Ethics, Institutional Review Board (IRB) processes, and requirements for the protection of human subjects. In addition, quantitative research methods; qualitative research methods; mixed-methods research designs; experimental, quasi-experimental, explanatory, exploratory, and descriptive research; program evaluation; and the relative merits of disparate models of research are also presented. The requirements of rigor in both quantitative and qualitative studies and evaluating the degree of fit between research strategies and problems under investigation are also discussed.

This chapter discusses quantitative and qualitative practices of case-study selection when the goal of the analysis is to evaluate causal hypotheses. More specifically, it considers how the different causal models used in the qualitative and quantitative research cultures shape the kind of cases that provide the most leverage for hypothesis testing. The chapter examines whether one should select cases based on their value on the dependent variable. It also evaluates the kinds of cases that provide the most leverage for causal inference when conducting case-study research. It shows that differences in research goals between quantitative and qualitative scholars yield distinct ideas about best strategies of case selection. Qualitative research places emphasis on explaining particular cases; quantitative research does not.

The introduction chapter of the book explores the evolution of qualitative research within the framework of evidence-based practice and its inclusion in the family of systematic reviews. This chapter briefly recounts the history of qualitative systematic reviews across disciplines and within systematic review enterprises, including the Cochrane and the Campbell collaborations. Although qualitative approaches have traditionally been excluded from systematic reviews, the tides are slowly turning. There is a growing interest devoted to improving the methods for including qualitative reviews within the family of systematic reviews. Numerous approaches are now available for conducting qualitative synthesis. Methods have been developed specifically for including qualitative primary studies, whereas other methods have been created to combine quantitative and qualitative studies.

This chapter examines how the qualitative and quantitative research traditions treat within-case analysis versus cross-case analysis for causal inference. In qualitative research, the primary focus is on specific events and processes taking place within each individual case. Leading qualitative methodologies of hypothesis testing, such as process tracing and counterfactual analysis, are fundamentally methods of within-case analysis. By contrast, quantitative research traditionally involves exclusively cross-case comparison. The chapter begins with a comparison of the typical roles (or nonroles) of within-case and cross-case analysis in case studies versus experiments. It then considers how causal inference in quantitative and qualitative research is linked to the use of “data-set observations” and “causal-process observations,” respectively. It also explains the differences between process-tracing tests and statistical tests and concludes by suggesting that cross-case analysis and within-case analysis can and often should be combined.

This book concludes by reemphasizing important differences in the nature of qualitative and quantitative research—differences that extend across research design, data analysis, and causal inference. While their differences are considerable, the book argues that both research cultures can complement one another in terms of explaining the social and political world. However, a fruitful collaboration between quantitative and qualitative research—one built around mutual respect and appreciation—is possible only if scholars of both traditions understand and acknowledge their differences. These differences, summarized in tables, come in the areas of individual cases, causality and causal models, populations and data, concepts and measurement, and asymmetry. The book also contends that mixing the qualitative and quantitative cultures will contribute to methodological pluralism in the social sciences.

This chapter examines two approaches used in social science research: the “causes-of-effects” approach and the “effects-of-causes” approach. The quantitative and qualitative cultures differ in the extent to which and the ways in which they address causes-of-effects and effects-of-causes questions. Quantitative scholars, who favor the effects-of-causes approach, focus on estimating the average effects of particular variables within populations or samples. By contrast, qualitative scholars employ individual case analysis to explain outcomes as well as the effects of particular causal factors. The chapter first considers the type of research question addressed by both quantitative and qualitative researchers before discussing the use of within-case analysis by the latter to investigate individual cases versus cross-case analysis by the former to elucidate central tendencies in populations. It also describes the complementarities between qualitative and quantitative research that make mixed-method research possible.

This chapter examines how the qualitative and quantitative research traditions empirically assess theories about mechanisms when making causal inferences. In the qualitative paradigm, researchers carry out this assessment by attempting to observe causal mechanisms through process tracing and through the analysis of causal-process observations. In the qualitative paradigm, the within-case analysis of specific cases is combined with the effort to observe mechanisms. By contrast, statistical methods are not designed to observe mechanisms within particular cases. The chapter considers the importance of mechanisms in causal inference as well as the use of process tracing in multimethod vs. qualitative research. It shows that multimethod research, which integrates regression and case study analysis, is increasingly regarded as a best practice.

We argue for a nonhierarchical approach to systematic reviews, where each method within the broad family of systematic reviews (e.g., meta-analysis, qualitative synthesis, rapid evidence assessments) is understood to have a complementary, unique approach based on the type of question being asked and the needs of the researcher. In this chapter, current methods for conducting qualitative syntheses are explored by presenting various models of aggregation, integration, and interpretation of qualitative findings across studies. The common link among these different models is that they all attempt to draw out findings across qualitative studies to generate new insights and understandings. Within the presentation of these models, we draw out the epistemological frameworks and underlying assumptions of each of these models to provide the reader with some direction for choosing methods for specific types of questions and purposes.

This chapter considers some key ideas from logic and set theory as they relate to qualitative research in the social sciences, including ideas concerning necessary and sufficient conditions. It also highlights a major contrast between qualitative and quantitative research: whereas quantitative research draws on mathematical tools associated with statistics and probability theory, qualitative research is often based on set theory and logic. The chapter first compares the natural language of logic in the qualitative culture with the language of probability and statistics in the quantitative culture. It then considers the necessary conditions and sufficient conditions as basis for qualitative methods, focusing on set theory and Venn diagrams, two-by-two tables, and truth tables. It also discusses the use of qualitative and quantitative aggregation techniques and concludes by explaining the criteria for assessing the “fit” of the model or the “importance” of a given causal factor.

This chapter compares two causal models used in qualitative and quantitative research: an additive-linear model and a set-theoretic model. The additive-linear causal model is common in the statistical culture, whereas the set-theoretic model is often used (implicitly) in the qualitative culture. After providing an overview of the two causal models, the chapter considers the main differences between them. It then gives an example to illustrate how a set-theoretic causal model is implicitly used in the within-case analysis of a specific outcome. It also explains how the form of causal complexity varies across the quantitative and qualitative paradigms. Finally, it examines another difference between the causal models used in quantitative and qualitative research, one that revolves around the concept of “equifinality” or “multiple causation.” The chapter suggests that while the two causal models are quite different, neither is a priori correct.

This chapter considers two fundamental differences between the quantitative and qualitative research traditions with respect to conceptualization and measurement: these differences are related to the relative emphasis placed on definitions versus indicators in the two cultures. The first difference concerns the relative importance assigned to issues of concept definition versus issues of concept measurement. Qualitative researchers are centrally concerned with definitional issues and the meaning of their concepts, whereas their quantitative counterparts are more interested in the quantitative measurement of latent variables. The second difference concerns error and the coding of cases. The chapter examines how characteristics versus indicators are defined in the qualitative and quantitative research paradigms. It also discusses the relationship between “error,” which is central to all statistics, and “fuzziness,” which is important in qualitative research.

This chapter discusses other ethical and practical issues in palliative care research from the perspective of qualitative research, and serves as a complementary discussion to Chapter 2. The discussion also aims to help researchers think through some of the challenges posed in qualitative research projects, so that they are not defeated by these challenges and are able to work from a clear ethical base.

This chapter discusses how qualitative research should be evaluated. It presents a number of quantitative research reviews, which reinforce the view that evidence from quantitative research, particularly randomized controlled trials, represents the highest level of research evidence. It also highlights the aims and objectives of certain perspectives, which differ from quantitative approaches, and articulates the criteria relevant to an assessment of their quality.

This chapter focuses on scope conditions in qualitative and quantitative research. It begins with a simple example, Hooke's law from physics, to illustrate the concept of “scope.” It then considers some of the most popular “within-model” responses to causal heterogeneity problems, showing that the option of changing the causal model to address causal heterogeneity issues is more attractive to quantitative researchers than to qualitative researchers. It also examines how the existence of causal complexity and concerns about fit with data can lead scholars to use scope conditions. Finally, it discusses the relationship between empirical testing and the proposed scope of theories and suggests that issues of scope raise Fundamental Tradeoffs in social science research, including tradeoffs concerning the tension between generality and parsimony, and between generality and issues of model fit.