Endogeniety simply means that a parameter or variable is correlated with the error term. There are many reasons why this would happen:
- Measurement error
- This happens when we do not have an accurate measure of the independent variables.
- Omitted variables
- This happens when the model does not include all the variables it should, and thus we have an uncontrolled variable.
- This happens when two variables are each affecting the other.
The best way to think about the difference between random and fixed effects is with this picture.
Fixed effects can be thought of as the relationship between predictor and outcome within an entity. In addition:
When you think that there are unobserved or latent variables, a potential technique is Structural Equation Modeling (SEM).
Among other advantages, SEM:
- Can control for random errors
- Can model measurement error so the model is more precise
- Can test elaborate models
Survival analysis is used when we need to choose a point in time to measure survival, success, failure, death, etc..
Two important concepts relate to the time that we observe the data. This is referred to as censoring and it comes in two varieties: left and right.
When the DVs are categorical variables, different analyses should be used.
The most common (and the only one discussed in class) is the case of a binary outcome variable. If this is the case either Logit or Probit should be used. Logistic regression estimates the probability of the outcome variable having a certain value (as opposed tot he value itself).
When there are more than two outcome variables, we need multiple logistic regressions solved simultaneously.
A moderator is a qualitative or quantitative variable that affects the direction and/or strength of the relation between an IV (or predictor) and a dependent or criterion variable.
Here I will write very briefly about some analysis.
This test compares the means to two groups. The goal is to determine if they are statistically different from one another.
Analysis of Variance
These include the MANOVA, ANOVA and MANCOVA and ANCOVA.