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Small-n

iainajniven English

Mind map on Small-n comparative method: case selection, strengths and weaknesses, MDSD and MSSD

Mind map: Small-n comparative method

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Small-n

  1. Analysis of a small number of cases, entailing at least two, yet to permit the application of conventional statistical analysis - Lijphart

  2. Intentionally select cases - usually between 2 - 12

  3. Why compare few cases?

    • Goals of researcher - focus on cases rather than variables
    • Intensive rather than extensive knowledge
    • Advantages in relation to global statistical analysis
    • Less likely concept stretching
    • Large data set difficult to collect
    • Easier hypothesis testing
  4. Too Many Variables too few cases

    • Can choose cases matched on many variables not central to the study
    • Can combine variables on a single measure
    • Develop theory that focuses on a smaller number of explanatory variables
  5. Evaluation

    • Intentional case selection to control for potential confounding factors
    • More intensive and less extensive since they encompass more of the nuances specific to each country
    • Case - orientated, more similar and differences among countries rather than the analytical relationship between variables
    • Cannot be used to assess theories that assume probalistic causation
    • Coding of variables crude - dichotomous rather than continuous
      • But coding may be more reliable as more knowledge of cases
    • Lack of precisions about causal effects
  6. Strengths

    • Control by selecting either MSSD or MDSD, Good for theory building, intensive, less variable oreintated
      • Avoid conceptual stretching, thick description, area studies, configurative analysis, macro-history
    • SNA provides more opportunity to identify causal process and mechanisms
  7. Weaknessess

    • Less secure inferences, selection bias, language training, field reserach
  8. MDSD AND MSSD

    • MSSD - perspectives that are similar for one researcher may be different to another, lending little value to the approach
      • Possible over determination
      • Design fails to eliminate rival explanations
      • Different understandings of concepts lead to different measures being used for he same concept
    • Reduce number of variables by focusing on the key explanatory factors, this is achieved by MDSD
      • MDSD inferences relate only to the conformation of the importance of the presence of a certain explanatory factor
        • MDSD can rule out cases more confidently than rule in
          • Solution - Multiple and conjuctional causation
          • Solution - Deepen within case analysis and use of historical anaylsis

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Mind Map on Small-n, created by iainajniven on 01/05/2013.
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