Midterm Review

Question 1

claim

Answer 1

a statement about what is true/valid

Question 2

basis (of a claim)

Answer 2

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)

Question 3

four criteria of scientific evidence

Answer 3

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

Question 4

appeal to authority

Answer 4

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

Question 5

appeal to personal experience

Answer 5

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

Question 6

appeal to common sense

Answer 6

unscientific evidence

Question 7

normative claims

Answer 7

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

Question 8

basis/evidence for a normative claim

Answer 8

must assume a value judgement about what is desirable/undesirable

Question 9

value judgements

Answer 9

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

Question 10

prescriptive claims

Answer 10

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

Question 11

basis/evidence for a prescriptive claim

Answer 11

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

Question 12

empirical claims

Answer 12

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

Question 13

basis/evidence for empirical claims

Answer 13

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

Question 14

causal claims

Answer 14

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

Question 15

descriptive claims

Answer 15

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

Question 16

falsifiable

Answer 16

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

Question 17

verifiability

Answer 17

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

Question 18

concepts

Answer 18

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

Question 19

rules for concepts

Answer 19

ontological, observable, relevant

Question 20

ontological concept

Answer 20

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

Question 21

observable concept

Answer 21

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

Question 22

relevant concept

Answer 22

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

Question 23

variable

Answer 23

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)

Question 24

measures

Answer 24

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

Question 25

four levels of measurement

Answer 25

1. nominal 2. ordinal 3. interval 4. ratio

Question 26

nominal measurement

Answer 26

place cases into unranked categories | - ex. type of crime

Question 27

ordinal measurement

Answer 27

places cases into categories that are ranked | - ex. university rankings

Question 28

interval measurement

Answer 28

assign cases numbers that rank the cases | - ex. years (but not years since some event)

Question 29

ratio

Answer 29

assign cases numbers that rank the cases | - ex. rates (unemployment)

Question 30

variable value types

Answer 30

absolute and relative

Question 31

absolute values

Answer 31

variable values with counts given in raw units

Question 32

relative values

Answer 32

variable values that are given in fractions of rates or ranks

Question 33

validity

Answer 33

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

Question 34

threats to validity

Answer 34

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

Question 35

measurement error

Answer 35

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

Question 36

two forms of measurement error

Answer 36

1. systematic measurement error (bias) | 2. random measurement error

Question 37

systematic measurement error (bias)

Answer 37

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

Question 38

sources of systematic measurement error (bias)

Answer 38

1. researcher subjectivity/interpretation | 2. obstacles to observation

Question 39

random measurement error

Answer 39

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

Question 40

sources of random measurement error

Answer 40

- imperfect memory, random changes in mood/concern, researcher interpretation

Question 41

population

Answer 41

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

Question 42

sample

Answer 42

a subset of the population that we observe and measure

Question 43

inference

Answer 43

a subset of the population that we observe and measure

Question 44

sampling to work

Answer 44

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

Question 45

random sampling

Answer 45

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

Question 46

sampling error

Answer 46

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

Question 47

sampling bias

Answer 47

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

Question 48

random sampling error

Answer 48

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

Question 49

two varieties of sampling error

Answer 49

1. sampling bias | 2. random sampling error

Question 50

measurement error

Answer 50

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

Question 51

sampling error

Answer 51

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

Question 52

when is sampling error = measurement error?

Answer 52

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

Question 53

when is sampling error ≠ measurement error?

Answer 53

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)