Case study Study Guide

📖 Core Concepts Case Study – an in‑depth, detailed examination of one (or a few) real‑world cases, emphasizing qualitative data, thick description, and naturalistic settings. Idiographic vs. Nomothetic – case studies use an idiographic (single‑case) approach; quantitative work seeks nomothetic (law‑like) generalizations. Within‑case vs. Cross‑case – within‑case focuses on a single case; cross‑case compares multiple cases to identify patterns. Information‑Rich Cases – outlier, extreme, deviant, or influential cases that reveal more about causal mechanisms than typical cases. Design Purpose – case studies can be atheoretical, interpretative, hypothesis‑generating, theory‑testing, plausibility‑probe, or building‑block, each with a different analytic aim. 📌 Must Remember Key distinguishing feature: evidence drawn from a single (or few) cases is used to illuminate broader phenomena. Selection priority: choose cases with high expected information gain (e.g., extreme, deviant, influential). Strengths: high conceptual validity, rich description, ability to uncover causal mechanisms, handle causal complexity (equifinality, path dependency). Limitations: limited causal inference, selection bias, poor generalizability, difficulty estimating effect magnitude. 🔄 Key Processes Define Research Question & Purpose Determine if the goal is description, theory testing, hypothesis generation, etc. Select Cases Use formal typology: typical, diverse, extreme, deviant, influential, most‑similar, most‑different. Prefer information‑rich cases over random selection in small‑N settings. Choose Design Single‑case vs. multiple‑case; match design (e.g., Stake’s social‑construction, Burawoy’s anomaly‑identification) to purpose. Collect Data Gather thick, qualitative evidence (interviews, documents, observations) in naturalistic settings. Analyze Conduct idiographic analysis → identify causal mechanisms, scope conditions, necessary/insufficient conditions. For cross‑case work, conduct pattern‑matching or configurational analysis. Validate Check internal validity via triangulation, member checks; assess external validity through theoretical generalization. 🔍 Key Comparisons Single‑case vs. Multiple‑case Single‑case: deep dive, ideal for rare or extreme phenomena. Multiple‑case: allows comparison, pattern detection, stronger external claims. Typical vs. Extreme/Deviant Cases Typical: representative of a stable cross‑case relationship. Extreme/Deviant: highlight unusual causal pathways; richer theoretical insight. Interpretative vs. Hypothesis‑Generating Designs Interpretative: uses existing theory to explain a case. Hypothesis‑Generating: inductively builds new variables/hypotheses from the data. ⚠️ Common Misunderstandings “Case studies can’t be generalizable.” – They achieve theoretical generalization, not statistical; they illuminate mechanisms that apply elsewhere. “Random sampling is always best.” – In small‑N research random sampling often yields uninformative cases; purposeful, information‑rich selection is superior. “Qualitative data lack rigor.” – Rigor comes from thick description, triangulation, and systematic analytic procedures, not from sample size alone. 🧠 Mental Models / Intuition “Information‑Rich Lens” – Imagine each case as a flashlight; an extreme or deviant case shines light on hidden corners of theory that a typical case leaves in shadow. “Puzzle Piece Fit” – Treat each case as a puzzle piece; the goal is to see how its unique shape (context) fits into the larger picture of theory (the whole puzzle). 🚩 Exceptions & Edge Cases Random selection may be justified when the researcher aims to estimate population parameters and can secure a sufficiently large N (rare in case‑study work). Multiple‑case designs can still be “single‑case” in spirit if each case is examined in depth and the cross‑case analysis remains idiographic. Quantitative triangulation (e.g., embedding surveys) can bolster causal inference but may dilute the qualitative depth if not carefully balanced. 📍 When to Use Which Use single‑case when the phenomenon is rare, unique, or you have an extreme/deviant case that can reveal novel mechanisms. Use multiple‑case when you need to compare across contexts or test the consistency of a mechanism. Select most‑similar cases to isolate the effect of one variable (all else equal). Select most‑different cases to test whether a hypothesized mechanism holds under varied conditions. Choose atheoretical design for pure description (e.g., exploratory ethnography). Choose interpretative design when you have a theory you want to apply and deepen. Choose hypothesis‑generating design when you lack a clear theory and need to inductively build one. 👀 Patterns to Recognize Repeated “outlier” narratives → likely a deviant or extreme case; look for new causal mechanisms. Consistent “most‑similar” contrasts → indicates the researcher is isolating a single variable’s impact. Cross‑case “pattern‑matching” tables → signals building‑block or theory‑testing designs. References to “scope conditions” → the study is mapping where a theory applies or fails. 🗂️ Exam Traps Distractor: “Case studies provide statistical generalization.” – Wrong; they provide theoretical generalization. Distractor: “Random sampling is required for validity.” – Wrong for small‑N qualitative work; purposeful selection is often superior. Distractor: “All case studies are purely descriptive.” – Wrong; many are interpretative, hypothesis‑generating, or theory‑testing. Distractor: “Extreme cases are less useful because they are not typical.” – Wrong; they are precisely valuable for uncovering hidden mechanisms. Distractor: “Causal inference is impossible in case studies.” – Wrong; while harder, careful design (e.g., most‑similar/different) can support credible causal claims.