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Comparative Qualitative Research Methods Oxford University, Hilary Term 2006 Wk 6: Case Selection and Research Design

Case Studies and Qualitative Methods . Week Six : Case Selection and Research Design The ?small-N" problem : the KKV approach to qualitative research Case selection : maximizing v. minimizing variation Means of increasing N : counterfactuals, within-case observationsWeek Seven : Single Ca

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Comparative Qualitative Research Methods Oxford University, Hilary Term 2006 Wk 6: Case Selection and Research Design

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    1. Comparative Qualitative Research Methods Oxford University, Hilary Term 2006 Wk 6: Case Selection and Research Design Adrienne LeBas Nuffield College, Oxford Email: adrienne.lebas@nuffield.ox.ac.uk Phone: 01865 278518

    2. Case Studies and Qualitative Methods

    3. Mill’s Methods and the Value of Individual Observations

    4. Mill’s Methods and the Value of Individual Observations

    5. Assumptions behind Mill and KKV Causal homogeneity: variables’ causal effects are deterministic Independence of observations: observations are not affected by one another “Conditional independence” (RSI: 31-36): variation within groups with different DV values otherwise similar; if not, researcher introduces controls Non-endogeneity: variables not affected by one another

    6. Selection Bias and Causal Inference

    7. Selection Bias and Causal Inference

    8. Selection Bias and Causal Inference

    9. Selection Bias and Causal Inference: Truncation

    10. KKV Advice on Case Selection Increase number of observations Full universe of cases, full range of variation Avoid selection on the dependent variable Limit number of explanatory variables: “most similar” research design According to KKV, Geddes and others, small-N research designs are particularly vulnerable to selection bias / sampling error

    11. Qualitative Research and Case Selection Selection on DV is common Research designs based on “extreme values” of particular variables often useful Attempts to boost N may lead to other problems, poor estimation of causal effects Using full universe of cases imposes heavy burden for data collection, especially if question is new or understudied

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