Block 1: Introduction to X-centered research designs (Lesson 1) Flashcards

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1
Q

Method

A

Specific procedure for gathering and analyzing data

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2
Q

Methodology

A

Tasks, strategies and criteria governing scientific inquiry

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3
Q

Research design

A

Plan for carrying out a given study, commonly involving a sequence of research steps

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4
Q

X-centred methodology

A

Population / variable oriented research Main book: Gerring Main article: Ross (2006): “Is democracy good for the poor?”

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5
Q

Y-centred methodology

A

Case oriented research Main book: Blatter & Haverland Main article: Beland & Hacker (2004): “Ideas, private institutions and American welfare state ‘exceptionalism’: the case of health and old-age insurance, 1915–1965

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6
Q

Between X and Y-centered methodologies

A

Interpretive (critical) research Main book: Schwartz-Shea & Yanow Main article: Best (2010): “The IMF’s Constructivist Strategy in Critical Perspective”

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7
Q

What is the difference between the social and natural sciences?

A

Focus of social sciences is mankind, not as part of nature, but as creator and product of history

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8
Q

What is the difference between social science and the humanities?

A

Social science studies human actions in a systematic and falsifiable way

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9
Q

Experiments

A

Focus on the treatment effect (x-centered/ variable) Extrapolate to a bigger polulation than the analyzed sampe (population-oriented reserach, not case, not interpretive)

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10
Q

Issues with experiments

A

How representative is the sample of the overall population? Was the correct methodology used?

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11
Q

Gerring’s conceptualization of a case study

A

Study of a single unit for the purpose of understanding a class of similar units (Gerring’s conceptualization).

This is actually a sample!

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12
Q

Definition of a unit (case study)

A

A spatially limited phenomenon (e.g. nation- state) observed at a single point in time or over some limited time period.

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13
Q

Population in a case study, definition and what does it consist of?

A

The empirical boundaries of the set of cases relevant to an investigation A population is comprised of a sample as well as unstudied cases

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14
Q

Sample in a case study

A

The sample is all the studied cases

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15
Q

What does a case consist of?

A

Variables (relevant dimensions), built on observation(s)

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16
Q

Definition of a variable

A

Unidimensional factor that can have various values

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17
Q

Data cell (table)

A

[Image]

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18
Q

Variable (table)

A

[Image]

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19
Q

Observation (table)

A

[Image]

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20
Q

Case (table)

A

[Image]

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21
Q

Case study/ sample (table)

A

[Image]

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22
Q

Comparative study sample (table)

A

[Image]

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23
Q

Population (table)

A

[Image]

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24
Q

Single-outcome study

A

Case which does not belong to a larger category of cases Note: Case selection is NOT a problem Example: The French Revolution

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25
Q

Aim of a single-outcome (case) study

A

Investigate a bounded unit in an attempt to explain a single outcome within that unit

26
Q

Critique of single-outcome (case) studies

A

Population-oriented researchers say you cannot learn much from such studies

27
Q

Why is the choice of population important in population-oriented research?

A

Due to missing negative cases (cases which don’t show the outcome of interest)

28
Q

What does the research question determine (in population-oriented research)

A

Size of population and which variables/ observations are included

29
Q

Scope conditions

A

Criteria specifying the appropriate range of cases based on theory OR scope conditions = define the population Based on a condition/ variable, BUT can also be both a causal effect (causal condition)

30
Q

Causal condition

A

Causal effect put on the population

31
Q

Should a defined population be changed?

A

King, Keohane & Verba: No, too easy to omit unfavorable cases Ragin, Yes, but only with very good reasons

32
Q

Ragin’s reasons for changing a defined population

A

Unrecognized heterogeneity = There are sub-groups in the population Inflexibility = Never changing the population means that you assume “causal homogeneity” (same causal relationship for all cases) without checking for appropriateness

33
Q

Why is case selection important in x-centered research?

A

Affects the answers you get

34
Q

Main problems of case selection (x-centered)

A

1) Defining the population (stretching vs relevance) 2) How to select the case which is worth analyzing

35
Q

What is the goal of sampling? (x-centered)

A

Make a representative sample for the population so that you can make an (unbiased) inference from the sample to the population

36
Q

Definition of sampling bias

A

Distortion in the representativeness of the sample, where some members of the population have a lower chance of being selected for inclusion in the sample

37
Q

Main distinction in sampling strategies

A

Random and non-random selection

38
Q

Random selection sampling strategies

A

SSSM

  • Simple random sample: Complete randomization
  • Systematic sample: First pick randomly and then follow a selection rule
  • Stratified random sampling: Independent sampling in each level
  • Multi-stage cluster sampling: Hierarchical structure of data, with sampling at each level
39
Q

Non-random selection sampling strategies

A

Convenient Snow Quotas

  • Convenience sampling
  • Snowball sampling (ask interviewees to create a contact or to forward survey)
  • Quota sampling (interviewer needs to satisfy quotas, determined by research design)
40
Q

Why do we use random sampling?

A

To ensure that the sample average represents the population

41
Q

Strategies to use if there are too few cases?

A
  • Select all cases (sample = population) - Convenience sampling (e.g. data availability) - Deliberate (theory-driven) selection
42
Q

Deliberate (theory-driven) selection methods

A

“PC Teddy” PC TEDDI

pathway,

crucial (least likely and most likely),

typical,

extreme,

deviant,

diverse,

influential

43
Q

Typical selection method

A
  • Goal:* How want to study the mechanism linking X to Y / hypothesis testing
  • Case selection*: A low-residual case (on-lier)
  • Desired outcome:* Your cross-case argument can be observed also within-case
44
Q

Diverse cases selection method

A
  • Goal:* Analysis of variation of X and Y (hypothesis generating)
  • Case selection:* Combine values of X and Y
  • Desired outcome*: Improve understanding of how X and Y are connected
45
Q

Extreme case selection method

A
  • Goal:* (Explorative) study by looking at extreme examples
  • Case selection:* (Very) high scores on X and Y
  • Desired outcome:* Hypothesis generation
46
Q

Deviant case selection method

A
  • Goal:* Identify the missing element of a valid X-Y relationship
  • Case selection:* Find an outlier
  • Desired outcome:* Complements the X-Y relationship and does not disprove it
47
Q

Influential case selection method

A
  • Goal:* Check robustness of X-Y relationship by studying cases that heavily influence the relationship (shifts it)
  • Case selection*: Statistical
  • Desired outcome*: X-Y relationship holds
48
Q

Least likely case selection method

A
  • Goal:* You want to show that a theory is correct
  • Case selection:* You pick the case where your theory is least likely to hold
  • Desired outcome*: Your theory holds even in the least likely case
49
Q

Most likely case selection method

A
  • Goal:* You want to show that an (established) theory is wrong
  • Case selection:* You pick the case for which the theory is most likely to hold
  • Desired outcome*: The theory does not even work in the most likely case
50
Q

Pathway case selection method

A
  • Goal:* You know that X causes Y, but you are not sure about the causal mechanism linking them together
  • Case selection:* You pick a case in which X and Y are present, but all other potential causes of Y are absent
  • Desired outcome:* You can find the mechanism linking X to Y
51
Q

On what do you base selection on for necessary conditions?

A

The dependent variable (y)

52
Q

When using deliberate (theory-driven) selection, what do you base the selection on?

A

The independent variable (x), unless you have used random sampling

53
Q

Describe Gerring’s criterial approach

A

Based on hypothetical ideal types and not real world features. Stress certain elements to further understanding. Ideational constructs that help us better understand the world

54
Q

Implications of Gerring’s criterial approach

A

Criteria are true, under ceteris paribus conditions Tradoffs are necessary Experimental template is the ideal

55
Q

Goals of causal inference in social science

A
  • CAD*
  • Causality: To say that a factor, X, is a cause of an outcome, Y
  • Appraisal: Is it falsifiable?
  • Discovery: Is it new?
56
Q

Gerring’s criteria for a good argument (general, NOT causal)

A

BCCG PPRT

Boundedness: Scope conditions?

Coherence: How consistence is it?

Commensurability: How well does it cumulate with other theories?

Generality: How broad is its scope?

Parsimony: How concise is it? Number of assumptions?

Precise: Is it specific?

Relevance: Everyday importance?

Truth: Is it true?

57
Q

Gerring’s criteria for a good analysis

A

CAST Cumulation Accuracy Sampling Theoretical fit

58
Q

Criteria for a good analysis: Accuracy

A

Are the results (a) valid, (b) Reliable and ( c) accompanied by an estimate of uncertainty with respect to (d) internal validity and (e) external validity

59
Q

Criteria for a good analysis: Sampling

A

Are the chosen observations (a) representative of the population, (b) sufficiently large in number and (c) at the principal level of analysis?

60
Q

Criteria for a good analysis: Cumulation

A

(a) Is the research design standardized with other research? (b) Does it replicate current findings? (c) Is it transparent?

61
Q

Criteria for a good analysis: Theoretical fit

A

(a) Does the research design provide an appropriate test for the inference (construct validity)? (b) Is the test hard (severity)? (c) Is the test segregated from the argument under investigation (partition)?