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exam prep Flashcards

1
Q

ontology

A

nature of the social world

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2
Q

epistemology

A

what can we know about social phonomena

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3
Q

does positivism use induction or deduction?

A

induction
observation => theory

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4
Q

does logical positivism use induction or deduction?

A

deduction
theory => observation

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5
Q

abduction: what is it? which approach?

A

abduction is selecting the most simple explanation that best explains something, and is used by scientific realism.

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6
Q

analytic review

A
  1. summarize
  2. evaluate
  3. conceptualize
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7
Q

grounded theory process

A
  1. coding
  2. sorting
  3. memo writing
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8
Q

key elements of causality

A
  1. non spurious
  2. temporal ordering
  3. spatial and temporal contiguity
  4. covariance
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9
Q

measurement validity - content validity

A

does the conceptualization and operationalization match, does the concept cover what it’s. necessary for the theoretical definition?

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10
Q

measurement validity - criterion/ construct validity

A

does the measurement actually measure what it seeks to measure?

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11
Q

convergent validity

A

how closely a test is related to other tests that measure the same (or similar) constructs.

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12
Q

concurrent validity

A

does it correlate with something that’s happening at the same time?

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13
Q

challenges of selection

5

A
  1. selection bias
  2. outliers
  3. non-equal size: heterogeneity
  4. historical contingency (joint history)
  5. path dependency (stable trends)
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14
Q

rolling cross section (longtidunial)

A

dynamic changes and trends 1 huge sample divided into groups, and interviewed at different times

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15
Q

methodological issues in surveys (3)

A
  1. surveys usually focus on predicting, rather than explaining
  2. surveys cannot identify causation, but can only imply causation. However, in panel studies making causal explanations is more possible.
  3. It’s important to not make inferences accross levels. For example, “individualistic fallacy” is generalizing from the micro (individual) level to the macro (group, general) level, and “ecological fallacy” is making inferences from the macro (group, general) level to the micro (individual) level. These types of fallacies should be considered while conducting survey research.
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16
Q

solutions to methodological issues in surveys (4)

A
  1. randomizing questions
  2. writing balanced questions (like giving both pro and con statements)
  3. pre-testing (to see if questions are easy to understand)
  4. monitoring and verifying to see if there’s problems
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17
Q

types of probability sampling (3)

A
  1. simple random sampling
  2. strafied sampling
  3. multistage cluster
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18
Q

types of non probability sampling (4)

A
  1. convenience
  2. snowball
  3. theoretical
  4. purposive
19
Q

types of response biases (2)

A

self selection
non response

20
Q

conditions of probability sampling (3)

A
  1. every unit has equal chance of being chosen
  2. an observer can’t predict which units will be chosen
  3. sample must include all possible combinations of units
21
Q

difference between stratified random sampling and systematic random sampling?

A
  • In systematic sampling, the list of elements is “counted off”. That is, every kth element is taken.
  • Stratified sampling also divides the population into groups called strata. However, this time it is by some characteristic.
22
Q

multistage cluster sampling

A

random sampling in several stages from big to small.

23
Q

is quota sampling probabilistic, or not probabilistic?

A

not probabilistic

24
Q

quota sampling

A

having quotas of subgroups so you can compare them
(but doesn’t represent the actual distribution)

  • if the quota is ARBITRARY and DOESN’T REFLECT REALITY => NOT STRATIFIED
  • if quota is REPRESENTATIVE and REFLECTS REALITY => STRATIFIED
25
Q

three types of comparative historical analysis

A
  1. parallel demonstration of theory
  2. contrast of contexts
  3. macro causal analysis
26
Q

the article by ulriksen and dadaulari is an example of

A

indepth process tracing and single case study for theory testing.

27
Q

influential cases are used for..

A

theory testing
(influential cases should confirm a theory)

28
Q

pathway case selection

A

cases that allow the researcher to isolate certain causal mechanisms- to prove that this and nothing else leads to the effect.

29
Q

difference between cohort and panel

A

in a panel study, random selection is done once, and we can track individual-level changes, and control for the differences that may occur within different samples.

in a cohort study, the researcher does random selection multiple times in different time periods. This alllows the researcher to observe changes at the aggregate level. in a cohort study, you can see how age affects certain variables, or if a critical event influenced all segments of the sample, or if attitudes are tied to specific birth cohorts like milennials etc.

30
Q

when is qualitative comparative analysis useful?

A

medium to large n comparative research
- too many cases to study in depth, but not enough cases to study qualitatively

31
Q

what’s data reduction?

A

related to: semi/un-structured interviews: systematically summarizing and interpreting responses

32
Q

assumptions of experiments

A
  1. same unit different treatment (counterfactual-unit homogeneity)
  2. conditional independence (iv is independent of dv)
    - no endogeneity, yes exogeneity
    - no selection bias
    - no omitted variable bias
33
Q

what’s matching? (under random assignment)

A

random assignment that’s sensitive to the demographics of participants

34
Q

solomon four group design

A

four groups:
2 pretest- posttest
2 posttest only
=> to both verify that random asignment was able to create equivalent groups while having the advantage of posstest only.

35
Q

what’s the interaction effect examined in mintz?

A

participants x certainty

36
Q

syntactical unit of content

A

words, sentences, paragraphs

37
Q

referential unit of content

A

events, people

38
Q

thematic unit of content

A

topics

39
Q

3 V’s of big data

A

volume
velocity
variety

40
Q

data mining

A

focusing on data correlations instead of theory by analyzing large volumes of information.

41
Q

key assumptions of inferential statistics

A
  1. sampling from complete target population
  2. simple random sampling with perfect response rate (or non response completely random)
  3. no nonsampling erorr
42
Q

sampling distribution

A

if you draw an infinite number of samples of the same size, you will get a sampling distirbution.

43
Q

necessary conditions for causality (4)

A
  1. covariance
  2. temporal ordering
  3. spatial and temporal contiguity
  4. non spurious

methods (24 decks)

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