Theory Main

Chapter 1: Introduction: This short chapter provides an overview of each chapter in the book.
Chapter 2: The Nature of Understanding: This chapter describes how people go about trying to understand the world in which they live. It develops the idea that social scientists often engage in the same processes when trying to explain phenomena through theories. It considers the nature of reality and conceptual systems more generally. It describes the core processes used to communicate processes to others.
Chapter 3: Science as an Approach to Understanding: This chapter describes what sets science apart from other "systems of knowing," such as theology, philosophy, jurisprudence, the arts, and literature. It describes what a theory is and how it differs from models and hypotheses. The chapter discusses the criteria against which theories are usually judged and then how journal editors and reviewers judge whether a research article "makes a theoretical contribution." Sixteen strategies social scientists often use to make a theoretical contribution are described.
Chapter 4: Creativity and the Generation of Ideas: This chapter briefly reviews research on creativity and ties it to the process of theory construction. How one chooses a topic about which to theorize is addressed as well as the timing of literature reviews in the theory construction process. The chapter presents 27 heuristics for generating ideas and for thinking outside the box. These heuristics apply to both qualitative and quantitative research as qualitative researchers often seek to move beyond mere description by generating novel insights into the descriptive data they have collected. Notable social scientists have written about how to generate ideas. The chapter also summarizes cognitive heuristics they have reported using. Finally, the chapter discusses how to build conceptual logic models for the ideas one generates, focusing on inductive reasoning, deductive reasoning, and reasoning by analogy.
Chapter 5: Focusing Concepts: Theories contain concepts and variables. It is important that theorists provide clear and unambiguous definitions of key contstructs in their theories. This chapter describes eight practical strategies for formulating clear conceptual defintions. The chapter discusses the idea of surplus meaning across theories and research studies. It articulates the process of instantiation, a method for making abstract concepts more concrete. The chapter also discusses multidimensional approaches to constructs and, in turn, strategies that social scientists use to literally create variables (since variables are, at essence, human inventions). The chapter also includes a historical note on the concept of operationism as a means of defining constructs.
Chapter 6: Thought Experiments and Variable Relationships: Thought experiments have been a rich source of idea generation for science in general. This chapter provides examples of the use of thought experiments and describes the mental processes involved when conducting a thought experiment. The bulk of the chapter focuses on a specific type of thought experiment, namely thought experiments to clarify the relationships between variables. Just as theories need to be clear with respect to how they define constructs in the theory (Chapter 5), they also need to be clear when charcaterizing the relationships between variables. This chapter describes how to use thought experiments to help one think through the possible relationships between variables.
Chapter 7: Causal Models: Causal thinking is a common feature of social science theorizing. This chapter describes strategies for building a causal theory. The chapter considers the nature of causality in general as well as the role of the concept of causality in grounded/emergent theories. Six types of relationships are identified that form the core of causal models in the social sciences. These include direct causes, indirect causes, moderated relationships, reciprocal causality, spurious effects, and unanalyzed relationships. Each of these relationships is elaborated in the context of a 10-step approach to constructing a causal theory. A second approach integrated with literature review methodology also is developed. The chapter includes extended discussions of mediation and moderation as well as longitudinal causal structures.
Chapter 8: Mathematical Modeling: Mathematical modeling is common in the physical sciences, but it is used less often in the social sciences. This chapter provides a sense of mathematical modeling for the social sciences. It describes basic concepts and terms you will encounter as you read about math models or pursue mathematical modeling. It differentiates axioms from theorems, introduces the notion of a function, uses linear functions to identify key features of functions, and describes the difference between deterministic and stochastic models. It also provides an intuitive overview of derivatives, differentiation, integrals, and integration in calculus. The chapter describes five commonly used functions in math models: logarithmic functions, exponential functions, power functions, polynomial functions, and trigonomic functions, as well as functions for categorical variables. It discusses ways of transforming and combining functions. Following the presentation of these concepts, the chapter describes the phases of building a mathematical model and then provides two examples of such models in the social sciences. It characterizes chaos theory and catastrophe theory as influential mathematical models.
Chapter 9: Simulation as a Theory Development Method: Simulations can be used to develop theory. This chapter describes simulation strategies and discusses some of the uses of research-based simulations. It describes different types of simulations as well as the core activity of analyzing criterion systems for purposes of designing simulations. It is at this stage where new ideas and theory clarification often occur. The chapter discusses the strategies of virtual experiments and agent-based modeling as theory construction devices and concludes by identifying resources that will facilitate the conduct of simulations.
Chapter 10: Emergent Theory: Qualitative/Mixed Method Approaches: This chapter discusses the role of grounded and emergent theorizing using qualitative data and mixed methods data. It first addresses the stereotype that associates quantitatively oriented approaches with positivism and grounded/emergent approaches with constructivism, arguing that the use of qualitative or quantitative data is not, by fiat, tied to a particular form of epistemology. It then discusses how grounded/emergent theorists frame problems and questions, and the role of literature reviews in this process. The chapter describes six different types of data upon which grounded/emergent theorists often rely. This overview conveys a sense of the richness of the data sources used when constructing a theory from qualitative data. They include archival records, direct observation, structured and unstructured interviews, focus groups, virtual ethnographies, and directive qualitative methods. The chapter discusses the concepts of memo writing, theoretical sampling, and the analysis and coding of data, drawing out their implications for theory construction. It concludes with a discussion of cognitive strategies for abstracting theoretical assertions from qualitative data (including the use of inductive reasoning, deductive reasoning, and abduction), a discussion of mixed methods research strategies, and, finally, a sampling of the range of potential products that qualitative research can produce.
Chapter 11: Emergent Theory: Quantitative Approaches: Emergent theory is usually associated with qualitative data analysis but the fact is that quantitatively oriented social scientists often use exploratory data analysis to evolve theory. This chapter considers analytic approaches for doing so. The methods considered address a range of applications, from the use of exploratory analyses for a semi well-defined theory to broad exploratory analyses where one seeks to isolate relevant constructs from a large set of possible variables to theorize about. The initial focus is on concepts from Chapter 7 about causal frameworks, describing exploratory methods for the analysis of direct causal relationships and moderated relationships. It then moves to consideration of exploratory analyses in other contexts, such as cluster analysis, factor analysis, and data mining/machine learning. It concludes wih a discussion of chance effects in exploratory analyses.
Chapter 12: Historically Influential Systems of Thought: There are many ways of thinking about the world. This chapter describes historically influential systems of thought that large numbers of social scientists have used to theorize about diverse phenomena. It also considers some lesser known but still influential thinking strategies. The chapter advocates for what is sometimes known as meta-triangulation in theory construction, or the building of theories from the perspective of multiple paradigms. The frameworks are an eclectic group that can be organized in multiple ways. One set of frameworks fall under the general rubric of "grand theories." These include materialism, structuralism, functionalism, symbolic interactionism, and evolutionary perspectives, along with a critical commentary of them from the perspective of postmodernism. The chapter then discusses frameworks that draw heavily on metaphors. These include neural networks and systems theory. It next considers frameworks that emphasize the analysis of change followed by two influential psychological frameworks, reinforcement theory and positive psychology. The chapter concludes with the discussion of frameworks inspired by methodological innovations, namely multilevel modeling and person-centered theorizing.
Chapter 13: Theory and Measurement: General Frameworks: Measurement and observation are at the heart of science. When we measure a construct (e.g., depression), we specify a theory that links the observed measure to the properties or qualities of the underlying construct. When we confront the problem of measurement error in a study, we formulate theories about factors that create measurement error and then we seek to address them. As such, theory construction is directly relevant to measurement as we build theories of the sources of measurement error. This chapter elucidates theory construction principles to use when thinking about sources of measurement error. The theorizing is relevant for qualitative, quantitative, and mixed method research. The chapter seeks to provoke a mindset that to measure a construct well, you need to theorize about the population being studied, the structure/content of the questions asked of respondents, and the assessment context. It is within the context of such theories that one chooses the measures to use in a study.
Chapter 14: Theory and Measurement: Self Reports, Observer Reports and Objective Measures: This chapter complements and builds upon Chapter 13. It considers measurement theory as applied to self-reports, observer-reports, and "objective" measures, which collectively form the backbone of research in the social sciences. As with Chapter 13, this chapter encourages readers to adopt a theory construction mindset to measure construction and choosing measures for a study, be it quantitative or qualitative in focus. For example, self reports typically involve three processes. First, the individual must comprehend the question being asked. Second, the individual must formulate an answer in his or her mind. Third, the individual must translate that response onto a response format requested by the researcher. The processes of comprehension, judgment, and response translation are thus critical to formulating measurment strategies. The chapter illustrates how a theory construction mindset surrounding these three processes can improve measurement and better inform the choice of measures for research purposes.
Chapter 15: Theory Revision: When we collect data designed to test a theory, results can emerge that call the theory into question. We then need to revise the theory or abandon it all together. Faced with disconfirming or partially supportive data, there are critical thinking strategies one can use that impact decisions about whether and how to revise a theory. These strategies are the primary topic of this chapter. The chapter discusses Popper’s framework on theory disconfirmation. Within that context, it considers (a) the principle of falsification, (b) the identification of boundary conditions for a theory, and (c) replication of research results. It describes Bayesian perspectives on theory revision, providing a general appreciation of Bayesian epistemology. The chapter also discusses the topic of automated theory revision. Automated theory revision is in its infancy but the idea is to use computer algorithms to make decisions about theory revision in the face of disconfirming data, minimizing the role of humans in the process. Finally, if a theory is to be revised, there are desiderata that the revised theory should seek to satisfy. The chapter discusses these desiderata. It also briefly describes theory revision and rejection at the level of broad paradigm shifts via the work of Thomas Kuhn.
Chapter 16: Reading and Writing about Theories: The ways in which theories are written in professional reports differs by discipline. In disciplines that emphasize experimentation and empirical efforts to test theories, the theories appear as central elements of the introduction section. In disciplines that emphasize emergent/grounded theories, the theories usually are written in narrative form in a context that does not have the "tone" of a theory test. To be sure, the emergent theory is consistent with the collected data because, after all, it was derived from it after data collection and analysis. This chapter separates the two approaches, describing first how one will typically see theories presented in outlets emphasizing formal theory tests and then considering how theories are written in outlets emphasizing grounded and emergent theorizing. The chapter also discusses 12 writing principles when writing about theories.
Chapter 17: Epilogue: This is a short chapter that explores some of the career implications of pursuing truly creative/novel research. It also suggests a program of self-study for theory construction.