Download STUDY GUIDE MIDTERM 2: CAUSALITY:

STUDY GUIDE MIDTERM 2:
CAUSALITY:
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example: butterfly ballots in 2000 election in Palm Beach county. Some claim
that ballots caused Pat Buchanan to receive an excessive amount of votes.
o Regularity Approach: did areas where ballot was used have higher
Buchanan votes
o Conterfactual Approach: what happened in areas without butterfly ballot?
o Manipulation Approach: were the results similar in counties like Palm
Beach that did not use the butterfly ballots.
o Mechanisms Approach: how do butterfly ballots record votes that were not
voters’ intentions
RULES FOR CAUSAL THEORIES:
o Must create falsifiable theories…must have real world
evidence…cannot be definitional (ex. “all bachelors are unmarried men”)
o Must make theories internally consistent…cannot be contradictory (ex.
conservatives support individual freedom but oppose abortion and gay
marriage) … often produces a clear hypothesis, as is the case in Downs’
spatial model
o Choose dependent variable carefully… dependent variable should not
cause changes in explanatory variable (reverse causation)
o Maximize concreteness by choosing observable rather than unobservable
concepts…culture, identity and utility may be useful for formulating
theories but difficult to measure
o State theories as encompassing as possible…consider the domain of
applicability and how it forces us to think about the features of a
theory…stating theories encompassingly may run against the maxim to be
concrete, but must strike a balance between concreteness and generality
FOUR CAUSAL HURDLES
o 1) credible causal mechanism connecting X and Y
o 2) could Y cause X (reverse causality)
o 3) is there covariation between X and Y
o 4) is there a confounding variable “Z” that is related to both X and Y and
makes the observed association between X and Y spurious
when evaluating another’s work, most frequent objection is that the researcher
failed to control for some potentially important case of the dependant variable
if credible cause can be made that an uncontrolled for “Z” might be related to
both X and Y, we cannot conclude with full confidence that X indeed causes Y
example: House spent in child care kids have problems in school
 credible causal mechanism?... Maybe kids are too young to be put
into class with other kids and they learn aggressive behavior
 reverse causation?...Could behavior in school lead to problems in
child care
 covariation?...is there covariation between time a child is in child
care and aggression in kindergarten
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 confounder?
Simple bivariate comparisons can be very misleading despite initial appeal… if
the comparisons we make are faulty, then our conclusions about causal
relationships will also be faulty
Experimental Designs= control and randomly assign values of independent
variables to subject
o Randomization
o Researcher must have control over control groups
Control= values subjects receive are NOT determined either by subjects or
nature…must randomly assign treatment to subjects, treatment group and control
group.
Observational Studies= look out at world and observe something
o No randomization
TERMS:
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Unit of analysis= sort of phenomena that constitute cases
Population= all cases that an inference is said to apply to
Sample= the cases chosen for study referred to collectively
Case= any observation intended to provide independent evidence of a proposition
Observation= element of a case
N/n= total number of observations in a given context
Cross sectional research design… examines a cross section of social reality,
focusing on variation across individual units and explaining the variation in
dependent variable across them
o Ex.) unit of analysis countries
 Population all countries
 Sample N (163 countries)
 Case mean turnout in 1990s
Time series research design…examines variation with one unit over time
o Ex.) unit of analysis US elections
 Populationall presidential elections
 Sample 20th century presidential elections (N=25)
 Case aggregate turnout in a single presidential election
Comparability= how comparable are the cases to one another? Unit
homogeneity. When comparing countries to one another, we assume that
countries are alike in some ways.
Independence= in comparing turnout across countries, we assume that one
country is not influencing another country’s turnout
Representativeness= is it straightforward to generalize from the sample to the
population
Variation= do cases offer variation in Xs and Ys? (If not, broaden study
temporarily or geographically or make Y less specific)
Replicability= can the research design be replicated? (reliable /replicable results
are desirable)
EXPERIMENTS: random assignment of treatment and researcher must have
control
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o Randomization ensures (or tries to) that no confounding variables are there
o To check causality must check for the hurdles
 Causal mechanism
 Reverse causality
 Covariation
 Confounding variables
Laboratory Experiment: researcher has control over entire experiment, except for
the subject’s behavior
Field Experiment: researcher only controls treatment…no control over subjects
(but more control then observational studies)
o Ex.) Get Out the Vote- canvassing was more influential then fliers or
phone…BUT only certain people respond to surveysbiased sample
Natural Experiment: nature intervenes in a way that the researcher wanted it
to....enough randomization occurs (without the researcher’s intervention)…nature
had an intervention “as if random”…could be an event studied after the fact
o Ex.) cholera outbreak in London, studied by Snow…showed that cholera
was waterborn…pipes intertwined and random
o Ex.) Fox News Effect: observed in news channel influenced voted
turnout…claimed new channel converted 200,000 voters and influence the
outcome of Bush v. Gore…only in 20% households, researchers compared
cities who got it and those that did not…RANDOM CLAIM: people didn’t
control if they received the channel BUT didn’t control if actually watched
it…affluence in area linked to monopoly or voter preference- also did not
control if people watched it in their homes
OBSERVATIONAL STUDIES:
o Cross Sectional: looking across a unit…Time is held constant
 Often uses averages to define variables…if not, cross sectional
could focus on an outlier and change the results
o Time Series: time is variable and unit is constant
Measurement:
o Valid= measures what you are claiming to measure
 Face- measure what it’s claiming to?
 Content- does it incorporate all important elements?
 Construct- does it relate to other important measures?
o Reliable= how consistent something is…will it always yield the same
result?
YOU CAN HAVE SOMETHING BE VALID AND NOT RELIABLE & VICE
VERSA (prefer to have both at the same time)
Reliable, not valid
Not reliable, but average would be valid
Not reliable, and not valid
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Reliable and Valid
CONCEPTUALIZATION & MEASUREMENT:
Empirical= based off observations…data
o We must have precise measurements of X&Y to establish a causal
relationship…if not, cannot be confident in validity of theory
Three Issues of Measurement:
o Conceptual clarity- what do me mean by concept (i.e democracy)… what
is the exact nature of the concept we’re trying to measure
o Reliability- does repeating the measurement yield the same result?...
repeatability and consistency. Produces identical results
o Validity- does the measure accurately represent the concept while an
invalid measure measures something other than originally intended
 FACE: does the measure seem to get at the concept
 CONTENT: what are the essential elements of the concepts
Measurement bias is the systematic overreporting or underreporting of “true”
values for the variable
Policy IV Project= measures democracy on scale of -10 (strongly autocratic) 
+10 (strongly democratic)
STATISTICAL INFERENCE:
Think of variables in terms of label (description of variable) and its values
(denominations in which the variable occurs)
THREE TYPES OF VARIABLES:
o Categorical- variables for which cases have values that are different or the
same as values for other cases, but whose values cannot be naturally
ranked- ordered from least to greatest (ex. religious identification)…NO
RANKING. JUST DESCRIPTIVE CATEGORIES
o Ordinal- variables for which cases have values that are different or the
same as values for other cases…do not have equal unit differences (ex.
researchers assigned numerical values to how people responded to a
survey on financial stability now versus a year ago)…there is a ranking,
no “equal unit differences”
o Continuous- variables that do have equal unit differences (ex. age in years,
height, weight etc) is equal unit difference and a ranking
In analysis, we sometimes treat ordinal variables as if they were continuous
Central tendency= typical values for a particular variable
o Measures of central tendency:
 MEAN- average value of the variable (KNOW EQUATION). Use
with continuous variables and when distribution is symmetric.
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MEDIAN- value of the case that sits as the exact center of our
cases when we rank them from the smallest to the largest observed
values. 50% below and above- for continuous variables, use if
outliers are present or if there is a skewed distribution
 MODE- most frequently occurring value…only measure of central
tendency that is appropriate for categorical variables or ordinal.
Variation= (dispersion) tell the distribution of values that it takes across the cases
for which we measure it
Samples are able to tell the researcher something about the population as a whole
Significant correlation in the sample does NOT guarantee similar one in the
population
Statistical Inference= process of making probabilistic statements about a
population characteristic based on our knowledge of sample characteristics
Normal distribution is symmetrical so that the mode, median and mean are all
equal
o Normal curves also never reach zero
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Frequency distribution= distribution of scores or numbers that are NOT normally
shaped
CENTRAL LIMIT THEOREM: no matter what the underlying shape of the
frequency distribution (uniform, normal etc) the hypothetical distribution of the
sample means (called the sampling distribution) will be normal, with mean equal
to the true population mean and standard deviation equal to the standard error of
the mean
STANDARD DEVIATION:
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STANDARD ERROR (of the mean):
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The distribution of the sample approaches normality as the sample size increases
CONFIDENCE INTERVALS:
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Central Limit Theorem only applies to randomly selected samples
o Cannot use with a sample of convenience because they are not random and
results cannot reveal anything about the underlying population
Smaller standard error tighter confidence interval
Larger standard error wider confidence interval
If interested in estimating population values based on our sample, with as much
precision as possible, then it is desirable to have tighter instead of wider
confidence intervals
BIVARIATE HYPOTHESIS TEST:
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P –VALUE:
o “p”= probability
 observes relationship we are finding data due to random chance
 tells probability we would see the observed relationship between
two variables in our sample data of there were truly no relationship
between them in the unobserved population
o values range between 0-1
 lower p-value greater confidence we have that there is a
systematic relationship between two variables for which we
estimated the particular p-value
o RELATIONSHIP MIGHT BE SPURIOUS
(CONFOUNDING VARIABLE)
o relates to what we know about a sample to what we know about a
population as a whole
o it is tempting to assume that when p-value is very clost to zero it indicates
relationship between X&Y is strong…NOT NECESSARILY TRUE
 P-value represents the degree of confidence that there is a
relationship in the underlying population, but provides no
information on strength of relationship
we compare actual relationship between X&Y in sample data to what we would
expect to find if X&Y were not related in the underlying population
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o the more different the empirically observed relationship (in the sample) is
from what we would expect to find if there were a relationship in the
population, the more confident we are that X&Y are related in the
population
lower p-value increases confidence that there is a relationship
o variables are statistically significant if p< .05
o finding that X&Y have statistically significant relationship does NOT
mean the relationship between X&Y is strong and causal
 must examine the 4 causal hurdles