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Flashcards in Midterm Review Deck (53)
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1
Q

claim

A

a statement about what is true/valid

2
Q

basis (of a claim)

A

the basis for a claim is the reason we should accept the truth or validity of that claim (evidence used to prove the claim is true)

3
Q

four criteria of scientific evidence

A
  1. transparent procedures
  2. systematic use of evidence
  3. consider alternatives
  4. acknowledge uncertainty
4
Q

appeal to authority

A

arguing that a claim is true because a person with authority says its true

5
Q

appeal to personal experience

A

a claim based on one’s own personal (non-systematic) observation or one’s own reaction to an observation

6
Q

appeal to common sense

A

unscientific evidence

7
Q

normative claims

A

a claim about what is desirable or undesirable (what should/should not be)

8
Q

basis/evidence for a normative claim

A

must assume a value judgement about what is desirable/undesirable

9
Q

value judgements

A

normative claims that state what goal is “right” or “good”, or provide criteria for judging what is better/worse

10
Q

prescriptive claims

A

are normative claims that assert what kinds of actions should be taken

11
Q

basis/evidence for a prescriptive claim

A

an empirical claim about the consequences of some action and assumption that some value judgement is correct

12
Q

empirical claims

A

a claim about what is/exists or how things that exist affect each other

13
Q

basis/evidence for empirical claims

A

consists of observation of the world, and no assumption about what is good/desirable

14
Q

causal claims

A

are claims about how one phenomena (X) affects or causes another phenomena (Y), state that X acts on Y in some way, not merely that they appear together in some pattern

15
Q

descriptive claims

A

claims about what exists (or has existed/will exist in the world)

16
Q

falsifiable

A

can prove an empirical claim wrong:

  • falsifiable if the claim can be shown wrong by empirical evidence
  • unfalsifiable if there is no empirical evidence that shows the claim is wrong
17
Q

verifiability

A

if we had an empirical claim, H1 (H for hypothesis) and, if H1 were true or valid, then it implies we should make certain empirical observations O1

18
Q

concepts

A

are abstract or general categories that we apply to particular cases using a set of rules/criteria that determine membership in the category

19
Q

rules for concepts

A

ontological, observable, relevant

20
Q

ontological concept

A

the traits we use for a concept are about what mean to be in this category

21
Q

observable concept

A

defining traits in a concept must be something we can observe (empirical)

22
Q

relevant concept

A

traits are relevant to predicting how cases belonging to the concept affect other things, are affected by other events, or are part of a causal process

23
Q

variable

A

a measurable property of case (phenomena, group, or individual) that corresponds to a concept or part of a concept (dimension) and can potentially take on different values across cases and time (it varies across cases)

24
Q

measures

A

a procedure for determining the value a variable takes for specific cases based on observation

25
Q

four levels of measurement

A
  1. nominal
  2. ordinal
  3. interval
  4. ratio
26
Q

nominal measurement

A

place cases into unranked categories

- ex. type of crime

27
Q

ordinal measurement

A

places cases into categories that are ranked

- ex. university rankings

28
Q

interval measurement

A

assign cases numbers that rank the cases

- ex. years (but not years since some event)

29
Q

ratio

A

assign cases numbers that rank the cases

- ex. rates (unemployment)

30
Q

variable value types

A

absolute and relative

31
Q

absolute values

A

variable values with counts given in raw units

32
Q

relative values

A

variable values that are given in fractions of rates or ranks

33
Q

validity

A

degree of fit between a variables concept and what the variable is intended to capture (link between variable and concept)

34
Q

threats to validity

A
  1. variable does not cover enough of the concept
  2. variable covers things outside the concept
  3. variable captures different things across units: non-comparability
35
Q

measurement error

A

is a difference between the true value of of a variable for a case and the observed value of the variable for that case produced by the measurement procedure

36
Q

two forms of measurement error

A
  1. systematic measurement error (bias)

2. random measurement error

37
Q

systematic measurement error (bias)

A

error produced when our measurement procedure obtains values that are, on average, too high or too low (or, incorrect) compared to the truth

38
Q

sources of systematic measurement error (bias)

A
  1. researcher subjectivity/interpretation

2. obstacles to observation

39
Q

random measurement error

A

errors that occur due to random features of measurement process or phenomenon and the values that we measure are, on average, correct

40
Q

sources of random measurement error

A
  • imperfect memory, random changes in mood/concern, researcher interpretation
41
Q

population

A

full set of cases (countries, individuals, etc.) we’re interested in describing

42
Q

sample

A

a subset of the population that we observe and measure

43
Q

inference

A

a subset of the population that we observe and measure

44
Q

sampling to work

A

we need to:

  1. ensure the sample is representative of the population (does not differ from the population)
  2. know the level of uncertainty associated with our inference
    - use random sampling
45
Q

random sampling

A

sampling cases from the population in a manner that gives all cases an equal probability of being chosen

46
Q

sampling error

A

the difference between the value of the measure for the sample and the true value of the measure for the population

47
Q

sampling bias

A

the procedure by which cases are chosen for the sample does not give every member of population an equal chance of being in sample

48
Q

random sampling error

A

by chance we get samples where there are too many/few certain types of people (compared to the population)

49
Q

two varieties of sampling error

A
  1. sampling bias

2. random sampling error

50
Q

measurement error

A

incorrectly describe the world because you incorrectly observe values for the case(s) you study

51
Q

sampling error

A

incorrectly describe the world because the sample cases that are different from the population you want to learn about

52
Q

when is sampling error = measurement error?

A

sampling error is measurement error when you are evaluating descriptive claims about the population you sample (the case we measure is the population)

53
Q

when is sampling error ≠ measurement error?

A

sampling error is not measurement error when you are evaluating claims about the cases you sample (the case we measure are, ex. the survey respondents)