UCSB Political Science 15 Final

selection effect/bias

selection of sample that is not randomized, so is it biased

treatment groups

those who get some treatment of interest in an experiment

control group

those who do not get the treatment of interest

observational study

research where you don't get to randomize who gets the treatment. just observing some relationship in the world

experimental study/randomized control

research designs in which you can randomize who gets the treatment

quasi-experimental research

research in which you have observational data, but you find ways to ensure that the treatment was effectively randomly distributed

internal validity

is the experiment well designed? free from confounders or bias?

external validity

is the finding applicable to other populations, situations, or cases? does it apply outside the context of the research?

Yi

dependent variable, the thing we want to predict

Xi

independent variable, the thing that predicts the DV

Ei

error term, the part the DV or IV doesn't explain, everything NOT in our model. not directly observable

endogeneity

the IV is correlated with the error term

confounder

another unmeasured variable that affects the IV and then also affects the DV

randomness

noise in the data, could go away with larger sample sizes

randomization

randomizing to create treatment and control groups, creates exogeneity

distribution of outcome

the idea that you could observe different outcomes with different probabilities, even when you only observe one outcome

population

overall collection of individuals, beyond just the sample

sample

collection of individuals on which statistical analyses are performed, and from which general trends for the population are inferred

individual

object or unit, single data point contributing to the sample

random variable

the thing being measured in any random experiment

expectation

the bets guess about what number will be drawn from the distribution, loosely thought of as "average" of distribution

variance

how far the numbers you draw tend to be from that best guess

central limit theorem (CLT)

: the distribution of the mean

tends toward a normal distribution.

regression model

Yi = Bo + B1Xi + Ei

B1

slope coefficient, relationship between X and Y

B0

constant, value of Y when X is zero (intercept)

covariance

measures how much two random variables vary together

positive correlation

when X is higher, expect Y is higher

negative correlation

when X is higher, we expect Y is lower

not associated

when X is higher, doesn't tell us anything about Y

correlation

measures the extent to which two variables are linearly related to each other,

bivariate regression

technique to estimate a model with two variables (DV and IV), allows us to quantify the degree to which X and Y move together

omitted variable bias

specific form of endogeneity, often why estimates change if the model changes, X is correlated with something that influences Y (error term is correlated with Y)

unbiased estimate

on average, our estimate is equal to the true parameter

biased estimate

our coefficient is systematically wrong, either too high or too low than the true parameter

robust

whether the model changes or not when the specification changes

outlier

observation that is extremely different from the rest of the observations in the sample. this drags the estimate of the mean/slope towards it.

null distribution

describing how weird our result is if there really is no difference

p-value

probability of observing a difference in mean or a coefficient as big as what we observed, if the null hypothesis were true

critical value

how different two distributions need to be before you conclude they aren't from the same distribution

significantly different

based on critical value

null hypothesis

no effect, no difference in means, no relationship between X and Y, B1 = 0

alternative hypothesis

likely an effect, there is likely a difference in means and relationship between X and Y, B1 DOES NOT = 0

type 1 error

false positive, when we reject a null hypothesis that is actually true. saying there is a relationship when there isn't.

type 2 error

false negative, when we fail to accept the alternative hypothesis, saying there isn't a relationship when there is. (small sample size, study has low power)

substantive significance

the relationship needs to be large enough to matter

statistically significant

reject the null and accept the alternative, based on critical value cutoff, likely a difference in means and relationship between X and Y, B1 = 0

discrete data

comes in 'bins' or groups. ex. on a scale of 1 to 5, how much do you like this class?

continuous data

can take any value in a sequence. ex. annual income, votes for each candidate

categorical data

descriptive, describes how the world is, comes from qualitiative research, can be ordinal (ordered: low, medium, high) or nominal (cannot be ordered: majors)

case study

intensive study of a single spatial and temproal phenomenon

elite interviews

asking people who were involved in the political event or issue about what happened when and why, usually semi-structured and open-ended questions

focus groups

asking a group of people what they think about a given issue

natural experiments

when a researcher identifies a situation in which values of the independent variable have been determined by a random process

goodness of fit

how much of Y does X explain, related to r-squared

residual

part of Y that X doesn't explain, tells us what isn't there

qualitative research

what mechanisms/processes lead to this outcome vs. another, outliers

quantitative research

effects, population-level relationship, what is the relationship on average between two variables? (ex. does poverty predict

difference of means test

comparing the mean of Y for one group against the mean of Y for another

categorical variable

has two or more categories but no ordering