inta 2010 emperical methods midterm

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79 Terms

1

emperical evidence

measured systematic trends

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2

anectdotal evidence

personal experience

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3

building theories

not data, theory, data, confirm/deny theory

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4

a theory contains

answer and why

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5

a hypothesis contains

guess statement, no why

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6

powners 4 parts of theory

expectation, causal mechanism, assumptions, scope conditions

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7

in P. 4 parts of theory, what is the expectation

what answer does the theory provide to the question

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8

in P. 4 parts of theory, what is a causal mech

why causes x to y, why?

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9

in P. 4 parts of theory, what is assumptions

what has to be true for the theory to hold

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10

outcome we want to explain

dependent variable

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11

what we measure, what is the explanatory concept

independent variable

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12

what are K&W 4 hurdles for causality

credible causal mechanism, could y lead to x, covariation between x&y, are confounding vars being controlled

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13

in k&w hurdles, what are the options for covariation

do the variables move together in a positive relationship, or apart in a negative relationship

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14

cross sectional variation

change over space

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15

longitudinal variation

change over time

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16

hypothesis contains what

null hypothesis

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17

what is a null hypothesis

expect no change or no patterns

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18

causality

is there a causal relationship and whats the substantive effect

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19

what do we worry about for causality

biased causal effects

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20

what are the 3 biases for causality

simultaneity bias, common cause bias, selection bias

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21

what is simultaneity bias

comes from reverse causality, x to y/y to x

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22

what is common cause bias

comes from confounding variables where z makes it seem like x and y have a relationship

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23

what is selection bias

comes from selection effects; only looking at certain values of DV or not being thorough with causal relationship

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24

counterfactual theory of causation

were x to be different than y would also be different

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25

fundamental problem of causal inference

cannot assign the same unit to both treatment and control at the same time

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26

iv should be exogenous or endogenous

exogenous

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27

what does exogenous mean

no reverse causality, doesn't depend on other things that effect DV

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28

what are control variables

part of IV, potential confounding vars

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29

3 measurement metrics

categorical, ordinal, continuous

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30

what are categorical vars

no universal ranking; ex) is a country democratic

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31

what are ordinal vars

has rankings but not equal unit diffs; ex) how democratic is a country

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32

what are continuous vars

equal unit diff and universally held rankings; ex) income levels

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33

reliable

consistent when repeated

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34

measurement bias

systematic under/over reporting of values for var

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35

three types of validity

face, content, construct

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36

what is face validity

does it make sense?

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37

what is content validity

is the measure complete?

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38

what is construct validity

is it reasonable given measures of related concepts

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39

steps of operationalization

1) conceptual clarity
2) measurement metric
3) reliability
4) measurement bias
5) validity

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40

what are the types of research design

experiments, small N, large N

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41

what are the types of experiments

randomized control trials, natural

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42

tools to identify causality

randomization, control

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43

why is causality important

extract implicit biases, change outcomes of interest, identify policy impacts

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44

what are the two types of research designs

experiments and observational

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45

basic steps of experiments

sample subjects, randomly divide subjects into groups, measure and compare values of DV between groups

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46

diff types of sampling

convenience, random, representative

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47

what is the gold standard of research

experiments

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48

2 problems when establishing causality for experiments

confounding factors, endogeneity

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49

necessary assumptions for experiments

randomization, excludability, non interference

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50

pros for experiments

highly transparent, replicable, allows for tests of statistical significance

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51

cons for experiments

ethics, external validity, not all variables manipulable

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52

what is an observational design

design where researches does not control administration of treatment

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53

steps for observational design

decide on type of study, gather data on IV, DV, and controls; model relationships between variables

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54

steps for deciding on type of study for observational design

type of variation, units of observation, type of data

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55

steps for gathering data for observational design

population and sampling technique, primary or secondary data, variables

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56

what is the most similar (method of difference)

for small N studies, look at diff dv, diff iv, and same controls; then IV must be cause of DV

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57

what is most different (method of direct agreement)

for small N studies, look at same DV, same IV, different controls then one IV same must cause DV

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58

pros to small N studies

answers how; high plausibility; eliminate endogeneity; high internal validity

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59

cons to small N studies

external validity, selection effects; no randomization; ethical concerns

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60

large N observational designs

collect as much data as possible and use stats to identify patterns;

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61

pros to large N design

feasible, cheap, external validity

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62

cons to large N design

low internal validity, hard to identify causality;

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63

what is variation ratio

V = 1 - (number of modal cases) / (total number of cases)

Shows percentage of cases outside modal category

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64

what is variance

measure of dispersion of variable around mean; SD^2

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65

what descriptive statistics needed for categorical

mode, var ratio

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66

what descriptive statistics needed for ordinal

mean, median, mode, variation ratio, and IQR

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67

what descriptive statistics needed for continuous

mean, median, mode, range, IQR, variance, standard deviation

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68

gyst of CLT

keep taking samples, getting means, and plotting them, the means would eventually form a normal distribution with a mean = true population mean and standard error = standard deviation of population

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69

equation for SEx

SEx = SDx/sqrt(n)

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70

what gets us from what we have to what we want

CLT

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71

confidence intervals definition

gives set range of where we think true population value is likey to be located

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72

confidence interval

1-(fish figure)

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73

margin of error =

t(crit) * SEx

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74

if confidence % is outside MEx

capturing systematic pattern

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75

if confidence % is statistical tie

capturing random pattern

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76

type 1 error

we say smth occured but smth did not occur

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77

type 2 error

we say smth did not occur but smth did occur

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if tcrit is less than tx

reject our null

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79

if tcris is greater than tx

fail to reject our null

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