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Pattern Matching
Pattern matching is the comparison of two patterns to determine whether they match (i.e., that they are the same) or do not match (i.e., that they differ). Pattern matching is the core procedure of theory-testing with cases. Testing consists of matching an observed pattern (a pattern of measured values) with an expected pattern (a hypothesis) and deciding whether these patterns match (resulting in a confirmation of the hypothesis) or do not match (resulting in a disconfirmation). Essential to pattern matching (as opposed to pattern recognition, a procedure by which theory is built) is that the expected pattern is precisely specified before the matching takes place.
Conceptual Overview and Discussion
The Concept of Pattern Matching
A pattern is any arrangement of objects or entities. The term arrangement indicates that a pattern is nonrandom. Theories predict some pattern of values of variables. Such predictions are usually called hypotheses. The term expected pattern will be used here for specifications of the hypothesis that allow for a rigorous comparison with an observed pattern of values of variables in a test.
Donald T. Campbell coined the term pattern identification as a characteristic of qualitative analysis that he defined as holistic (i.e., analyzing the pattern) rather than atomistic (i.e., analyzing its constituents). He argued that the single case study design could provide for a strong test of a theory if an entire set of expectations deduced from that theory (which together would constitute an expected pattern) could be shown to be true in that case. Campbell also called this a configurational approach. He insisted that qualitative analysis in this design tends to disconfirm rather than confirm a prior belief because of the requirement that, in the test, each separate element of a pattern or configuration that is observed is exactly as expected. As noted by Thomas D. Cook and Donald T. Campbell, the strength of this nonequivalent, dependent variables design is precisely that the variables that constitute the pattern or configuration are nonequivalent, that is, not substitutable.
Yin's Approach to Pattern Matching
Robert Yin discussed pattern matching as the most desirable analytic strategy in case study research. He identified two main types of pattern matching in theory-testing: (1) the pattern in a nonequivalent dependent variables design (in which the initially predicted value must be found for each element of a pattern of dependent variables) and (2) the pattern in a nonequivalent independent variables design. An example of the latter is a pattern derived from a typological or configurational theory in management. Yin stated that pattern matching in the dependent variables design should be rigorous, such that the hypothesis is disconfirmed even if only one variable of the pattern does not behave as predicted. For the independent variables design, however, he recommended a different approach. Yin stated that one should formulate different expected patterns of independent variables, each based on a different and mutually exclusive (rival) theory, and that the concern of the case study would be to determine which of the rival patterns has the largest overlap with the observed one. An additional complication in this approach is that Yin presented some examples in which the rival pattern represents not a real (theoretical) explanation but rather a version of a null hypothesis.
Application
Independent Variable Designs
Campbell's and Yin's approaches to pattern matching are implicitly limited to the testing of propositions about characteristics of single cases (which can be tested in single cases) and not about differences between cases (see the Theory-Testing With Cases entry, this volume). Expected and observed patterns, therefore, consist of values of variables that all pertain to the single case. The simplest type of an independent variable pattern consists of the expected value of only one independent variable (rather than of a number of variables), given the value of a dependent variable. There are only two propositions on which such single-point expected patterns can be based: (1) necessary condition propositions and (2) sufficient condition propositions.
Necessary condition propositions state that an Outcome Y is possible only if Condition X is present. To test such a proposition, a case must be selected in which Outcome Y is present. The expected pattern is: X is present. (Note that the proposition does not entail any prediction about conditions of the absence of Y.) The researcher observes whether X is present in the selected case. The observed pattern is either that X is present or that X is absent. Pattern matching in this case consists of checking what the value of X is in the observed pattern.
Sufficient condition propositions state that an Outcome Y is always present when Condition X is present. To test such a proposition, a case must be selected in which Outcome Y is absent. (Note that the proposition does not entail any prediction about conditions of the presence of Y.) The expected pattern is: X is absent. Pattern matching in this case consists of checking what the value of X is in the observed pattern.
Configurational theories usually specify a number of conditions that together (i.e., in a configuration) must be present for an outcome to exist. If a configuration consists of, say, four elements, these can be seen as four separate necessary conditions. Four single-point independent variable patterns could be specified and tested as just described for the necessary condition proposition, but it is also possible to specify a single four-point pattern (e.g., [A+/B+/C+/D+]) that is expected to be observed in a case in which the outcome is present. The observed and the expected patterns do not match if any of the four variables is absent and, in such a case, the hypothesis is disconfirmed.
Process theories are a type of configurational theory in which not only the presence of a number of conditions is specified but also their temporal order. The expected pattern in a case in which Y is present is, for instance, [A+ → B+ → C+ → D+]. The observed pattern must reflect the temporal order in which Conditions A, B, C, and D occurred (if at all), and a match is confirmed only if A, B, C, and D have the same temporal place in the expected and the observed pattern.
Dependent Variable Designs
Similar to independent variable designs, the simplest type of a pattern in a dependent variable design consists of the expected value of only one dependent variable, given the value of an independent variable. Here also, such single-point expected patterns can only be based on a necessary condition proposition or a sufficient condition proposition. To test a necessary condition proposition in a dependent variable design, a case must be selected in which Condition X is absent. The expected pattern is: Y is absent. Pattern matching in this case consists of checking whether Outcome Y is absent in the observed pattern. To test a sufficient condition proposition in a dependent variable design, a case must be selected in which Condition X is present. Pattern matching in this case consists of checking whether Outcome Y is present in the observed pattern.
As mentioned earlier, Campbell used the term configuration for a pattern of dependent variables. Pattern matching with a configuration of dependent variables in a case in which Condition X is present consists of checking whether each of the dependent variables has the expected value. If a temporal order is expected in the configuration of dependent variables, then pattern matching additionally consists of checking whether the observed outcomes have occurred in the expected order.
However, dependent variable single case study designs are relatively rare in practice because most theorists and researchers, even those who adhere to necessary condition hypotheses (i.e., those who believe in constraints on outcomes that can be theorized and tested), do not believe that sufficient conditions are credible in the social sciences. Most researchers frame the outcomes that follow certain conditions as (more) likely rather than as inevitable. The dependent variable designs discussed so far cannot be used for testing probabilistic propositions. However, pattern matching can also be used as a testing procedure in a sample case study design (see the Theory-Testing With Cases entry, this volume).
Critical Summary
Every hypothesis derived from a proposition can be formulated as an expected pattern, which specifies the values of one or more variables (either independent or dependent) that should be observed in a case (or a sample) if the hypothesis is true. Pattern matching, therefore, is the core procedure in every theory-testing study.
Further Readings
- Case Study Research in Anthropology
- Before-and-After Case Study Design
- Agency
- Abduction
- Action-Based Data Collection
- Activity Theory
- Case Study and Theoretical Science
- Analytic Generalization
- ANTi-History
- Case Study Research in Business and Management
- Blended Research Design
- Alienation
- Bayesian Inference and Boolean Logic
- Analysis of Visual Data
- Actor-Network Theory
- Chicago School
- Audience
- Case Study as a Teaching Tool
- Case Study Research in Business Ethics
- Bounding the Case
- Authenticity and Bad Faith
- Bricoleur
- Anonymity and Confidentiality
- ANTi-History
- Colonialism
- Authenticity
- Case Study in Creativity Research
- Case Study Research in Education
- Case Selection
- Author Intentionality
- Case-to-Case Synthesis
- Anonymizing Data for Secondary Use
- Autoethnography
- Constructivism
- Concatenated Theory
- Case Study Research in Tourism
- Case Study Research in Feminism
- Case-to-Case Synthesis
- Case Study and Theoretical Science
- Causal Case Study: Explanatory Theories
- Archival Records as Evidence
- Base and Superstructure
- Critical Realism
- Conceptual Argument
- Case Study With the Elderly
- Case Study Research in Medicine
- Case Within a Case
- Contentious Issues in Case Study Research
- Chronological Order
- Audiovisual Recording
- Case Study as a Methodological Approach
- Critical Theory
- Conceptual Model: Causal Model
- Collective Case Study
- Case Study Research in Political Science
- Comparative Case Study
- Cultural Sensitivity and Case Study
- Coding: Axial Coding
- Autobiography
- Character
- Dialectical Materialism
- Conceptual Model: Operationalization
- Configurative-Ideographic Case Study
- Case Study Research in Psychology
- Critical Incident Case Study
- Dissertation Proposal
- Coding: Open Coding
- Case Study Database
- Class Analysis
- Epistemology
- Conceptual Model in a Qualitative Research Project
- Critical Pedagogy and Digital Technology
- Case Study Research in Public Policy
- Cross-Sectional Design
- Ecological Perspectives
- Coding: Selective Coding
- Case Study Protocol
- Closure
- Existentialism
- Conceptual Model in a Quantitative Research Project
- Diagnostic Case Study Research
- Decision Making Under Uncertainty
- Ideology
- Cognitive Biases
- Case Study Surveys
- Codifying Social Practices
- Families
- Contribution, Theoretical
- Explanatory Case Study
- Case Study Research in Tourism
- Deductive-Nomological Model of Explanation
- Masculinity and Femininity
- Cognitive Mapping
- Consent, Obtaining Participant
- Communicative Action
- Formative Context
- Credibility
- Exploratory Case Study
- Case Study With the Elderly
- Deviant Case Analysis
- Objectivism
- Communicative Framing Analysis
- Contextualization
- Community of Practice
- Frame Analysis
- Docile Bodies
- Inductivism
- Ecological Perspectives
- Discursive Frame
- Othering
- Complexity
- Critical Pedagogy and Digital Technology
- Comparing the Case Study With Other Methodologies
- Historical Materialism
- Equifinality
- Institutional Ethnography
- Healthcare Practice Guidelines
- Dissertation Proposal
- Patriarchy
- Computer-Based Analysis of Qualitative Data: ATLAS.ti
- Cultural Sensitivity and Case Study
- Consciousness Raising
- Interpretivism
- Experience
- Instrumental Case Study
- Pedagogy and Case Study
- Ethics
- Pluralism and Case Study
- Computer-Based Analysis of Qualitative Data: CAITA (Computer-Assisted Interpretive Textual Analysis)
- Data Resources
- Contradiction
- Liberal Feminism
- Explanation Building
- Intercultural Performance
- Event-Driven Research
- Power
- Computer-Based Analysis of Qualitative Data: Kwalitan
- Depth of Data
- Critical Discourse Analysis
- Managerialism
- Extension of Theory
- Intrinsic Case Study
- Exemplary Case Design
- Power/Knowledge
- Computer-Based Analysis of Qualitative Data: MAXQDA 2007
- Diaries and Journals
- Critical Sensemaking
- Modernity
- Falsification
- Limited-Depth Case Study
- Extended Case Method
- Pragmatism
- Computer-Based Analysis of Qualitative Data: NVIVO
- Direct Observation as Evidence
- Dasein
- North American Case Research Association
- Functionalism
- Multimedia Case Studies
- Extreme Cases
- Researcher as Research Tool
- Concept Mapping
- Discourse Analysis
- Decentering Texts
- Ontology
- Generalizability
- Participatory Action Research
- Healthcare Practice Guidelines
- Terroir
- Congruence Analysis
- Documentation as Evidence
- Deconstruction
- Paradigm Plurality in Case Study Research
- Genericization
- Participatory Case Study
- Holistic Designs
- Utilitarianism
- Constant Causal Effects Assumption
- Ethnostatistics
- Dialogic Inquiry
- Philosophy of Science
- Indeterminacy
- Pluralism and Case Study
- Hypothesis
- Verstehen
- Content Analysis
- Fiction Analysis
- Discourse Ethics
- Pluralism and Case Study
- Indexicality
- Pracademics
- Integrating Independent Case Studies
- Conversation Analysis
- Field Notes
- Double Hermeneutic
- Postcolonialism
- Instrumental Case Study
- Processual Case Research
- Juncture
- Cross-Case Synthesis and Analysis
- Field Work
- Dramaturgy
- Postmodernism
- Macrolevel Social Mechanisms
- Program Evaluation and Case Study
- Longitudinal Research
- Decision Making Under Uncertainty
- Going Native
- Ethnographic Memoir
- Postpositivism
- Middle-Range Theory
- Program-Logic Model
- Mental Framework
- Document Analysis
- Informant Bias
- Ethnography
- Poststructuralism
- Naturalistic Generalization
- Prospective Case Study
- Mixed Methods in Case Study Research
- Factor Analysis
- Institutional Ethnography
- Ethnomethodology
- Poststructuralist Feminism
- Overdetermination
- Real-Time Cases
- Most Different Systems Design
- Fiction Analysis
- Interviews
- Eurocentrism
- Radical Empiricism
- Plausibility
- Retrospective Case Study
- Multimedia Case Studies
- High-Quality Analysis
- Iterative Nodes
- Families
- Radical Feminism
- Probabilistic Explanation
- Re-Use of Qualitative Data
- Multiple-Case Designs
- Inductivism
- Language and Cultural Barriers
- Formative Context
- Reality
- Process Tracing
- Single-Case Designs
- Multi-Site Case Study
- Interactive Methodology, Feminist
- Multiple Sources of Evidence
- Frame Analysis
- Scientific Method
- Program Evaluation and Case Study
- Spiral Case Study
- Naturalistic Inquiry
- Interpreting Results
- Narrative Analysis
- Front Stage and Back Stage
- Scientific Realism
- Reporting Case Study Research
- Storyselling
- Natural Science Model
- Iterative
- Narratives
- Gendering
- Socialist Feminism
- Rhetoric in Research Reporting
- Number of Cases
- Iterative Nodes
- Naturalistic Context
- Genealogy
- Symbolic Interactionism
- Statistical Generalization
- Outcome-Driven Research
- Knowledge Production
- Nonparticipant Observation
- Governmentality
- Substantive Theory
- Paradigmatic Cases
- Method of Agreement
- Objectivity
- Grounded Theory
- Theory-Building With Cases
- Paradigm Plurality in Case Study Research
- Method of Difference
- Over-Rapport
- Hermeneutics
- Theory-Testing With Cases
- Participatory Action Research
- Multicollinearity
- Participant Observation
- Hybridity
- Underdetermination
- Participatory Case Study
- Multidimensional Scaling
- Participatory Action Research
- Imperialism
- Polar Types
- Over-Rapport
- Participatory Case Study
- Institutional Theory, Old and New
- Problem Formulation
- Pattern Matching
- Personality Tests
- Intertextuality
- Quantitative Single-Case Research Design
- Re-Analysis of Previous Data
- Problem Formulation
- Isomorphism
- Quasi-Experimental Design
- Regulating Group Mind
- Questionnaires
- Langue and Parôle
- Quick Start to Case Study Research
- Relational Analysis
- Reflexivity
- Layered Nature of Texts
- Random Assignment
- Replication
- Regulating Group Mind
- Life History
- Research Framework
- Re-Use of Qualitative Data
- Reliability
- Logocentrism
- Research Objectives
- Rival Explanations
- Repeated Observations
- Management of Impressions
- Research Proposals
- Secondary Data as Primary
- Researcher-Participant Relationship
- Means of Production
- Research Questions, Types of Retrospective Case Study
- Serendipity Pattern
- Re-Use of Qualitative Data
- Metaphor
- Rhetoric in Research Reporting
- Situational Analysis
- Sensitizing Concepts
- Modes of Production
- Sampling
- Standpoint Analysis
- Subjectivism
- Multimethod Research Program
- Socially Distributed Knowledge
- Statistical Analysis
- Subject Rights
- Multiple Selfing
- Spiral Case Study
- Storyselling
- Theoretical Saturation
- Native Points of View
- Statistics, Use of in Case Study
- Temporal Bracketing
- Triangulation
- Negotiated Order
- Storyselling
- Textual Analysis
- Use of Digital Data
- Network Analysis
- Temporal Bracketing
- Thematic Analysis
- Utilization
- One-Dimensional Culture
- Thematic Analysis
- Use of Digital Data
- Visual Research Methods
- Ordinary Troubles
- Theory, Role of
- Utilization
- Organizational Culture
- Theory-Testing With Cases
- Webs of Significance
- Paradigm Plurality in Case Study Research
- Utilization
- Within-Case Analysis
- Performativity
- Validity
- Phenomenology
- Practice-Oriented Research
- Praxis
- Primitivism
- Qualitative Analysis in Case Study
- Qualitative Comparative Analysis
- Quantitative Single-Case Research Design
- Quick Start to Case Study Research
- Self-Confrontation Method
- Self-Presentation
- Sensemaking
- Sexuality
- Signifier and Signified
- Sign System
- Simulacrum
- Social-Interaction Theory
- Storytelling
- Structuration
- Symbolic Value
- Symbolic Violence
- Thick Description
- Writing and Difference
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