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emperical evidence
measured systematic trends
anectdotal evidence
personal experience
building theories
not data, theory, data, confirm/deny theory
a theory contains
answer and why
a hypothesis contains
guess statement, no why
powners 4 parts of theory
expectation, causal mechanism, assumptions, scope conditions
in P. 4 parts of theory, what is the expectation
what answer does the theory provide to the question
in P. 4 parts of theory, what is a causal mech
why causes x to y, why?
in P. 4 parts of theory, what is assumptions
what has to be true for the theory to hold
outcome we want to explain
dependent variable
what we measure, what is the explanatory concept
independent variable
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
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
cross sectional variation
change over space
longitudinal variation
change over time
hypothesis contains what
null hypothesis
what is a null hypothesis
expect no change or no patterns
causality
is there a causal relationship and whats the substantive effect
what do we worry about for causality
biased causal effects
what are the 3 biases for causality
simultaneity bias, common cause bias, selection bias
what is simultaneity bias
comes from reverse causality, x to y/y to x
what is common cause bias
comes from confounding variables where z makes it seem like x and y have a relationship
what is selection bias
comes from selection effects; only looking at certain values of DV or not being thorough with causal relationship
counterfactual theory of causation
were x to be different than y would also be different
fundamental problem of causal inference
cannot assign the same unit to both treatment and control at the same time
iv should be exogenous or endogenous
exogenous
what does exogenous mean
no reverse causality, doesn't depend on other things that effect DV
what are control variables
part of IV, potential confounding vars
3 measurement metrics
categorical, ordinal, continuous
what are categorical vars
no universal ranking; ex) is a country democratic
what are ordinal vars
has rankings but not equal unit diffs; ex) how democratic is a country
what are continuous vars
equal unit diff and universally held rankings; ex) income levels
reliable
consistent when repeated
measurement bias
systematic under/over reporting of values for var
three types of validity
face, content, construct
what is face validity
does it make sense?
what is content validity
is the measure complete?
what is construct validity
is it reasonable given measures of related concepts
steps of operationalization
1) conceptual clarity
2) measurement metric
3) reliability
4) measurement bias
5) validity
what are the types of research design
experiments, small N, large N
what are the types of experiments
randomized control trials, natural
tools to identify causality
randomization, control
why is causality important
extract implicit biases, change outcomes of interest, identify policy impacts
what are the two types of research designs
experiments and observational
basic steps of experiments
sample subjects, randomly divide subjects into groups, measure and compare values of DV between groups
diff types of sampling
convenience, random, representative
what is the gold standard of research
experiments
2 problems when establishing causality for experiments
confounding factors, endogeneity
necessary assumptions for experiments
randomization, excludability, non interference
pros for experiments
highly transparent, replicable, allows for tests of statistical significance
cons for experiments
ethics, external validity, not all variables manipulable
what is an observational design
design where researches does not control administration of treatment
steps for observational design
decide on type of study, gather data on IV, DV, and controls; model relationships between variables
steps for deciding on type of study for observational design
type of variation, units of observation, type of data
steps for gathering data for observational design
population and sampling technique, primary or secondary data, variables
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
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
pros to small N studies
answers how; high plausibility; eliminate endogeneity; high internal validity
cons to small N studies
external validity, selection effects; no randomization; ethical concerns
large N observational designs
collect as much data as possible and use stats to identify patterns;
pros to large N design
feasible, cheap, external validity
cons to large N design
low internal validity, hard to identify causality;
what is variation ratio
V = 1 - (number of modal cases) / (total number of cases)
Shows percentage of cases outside modal category
what is variance
measure of dispersion of variable around mean; SD^2
what descriptive statistics needed for categorical
mode, var ratio
what descriptive statistics needed for ordinal
mean, median, mode, variation ratio, and IQR
what descriptive statistics needed for continuous
mean, median, mode, range, IQR, variance, standard deviation
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
equation for SEx
SEx = SDx/sqrt(n)
what gets us from what we have to what we want
CLT
confidence intervals definition
gives set range of where we think true population value is likey to be located
confidence interval
1-(fish figure)
margin of error =
t(crit) * SEx
if confidence % is outside MEx
capturing systematic pattern
if confidence % is statistical tie
capturing random pattern
type 1 error
we say smth occured but smth did not occur
type 2 error
we say smth did not occur but smth did occur
if tcrit is less than tx
reject our null
if tcris is greater than tx
fail to reject our null
Note 
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