While there are a number of distributional assumptions in regression models, one distribution that has no assumptions is that of any predictor (i.e. independent) variables.
It’s because regression models are directional. In a correlation, there is no direction–Y and X are interchangeable. If you switched them, you’d get the same correlation coefficient.
But regression is inherently a model about the outcome variable. What predicts its value and how well? The nature of how predictors relate to it (more…)
Yesterday I gave a little quiz about interpreting regression coefficients. Today I’m giving you the answers.
If you want to try it yourself before you see the answers, go here. (It’s truly little, but if you’re like me, you just cannot resist testing yourself).
True or False?
1. When you add an interaction to a regression model, you can still evaluate the main effects of the terms that make up the interaction, just like in ANOVA. (more…)
Here’s a little quiz:
True or False?
1. When you add an interaction to a regression model, you can still evaluate the main effects of the terms that make up the interaction, just like in ANOVA.
2. The intercept is usually meaningless in a regression model. (more…)
Here’s a little tip.
When you construct Dummy Variables, make it easy on yourself to remember which code is which. Heck, if you want to be really nice, make it easy for anyone else who will analyze the data or read the results.
Make the codes inherent in the Dummy variable name.
So instead of a variable named Gender with values of 1=Female and 0=Male, call the variable Female.
Instead of a set of dummy variables named MaritalStatus1 with values of 1=Married and 0=Single, along with MaritalStatus2 with values 1=Divorced and 0=Single, name the same variables Married and Divorced.
And if you’re new to dummy coding, this has the extra bonus of making the dummy coding intuitive. It’s just a set of yes/no variables about all but one of your categories.
Someone who registered for my upcoming Interpreting (Even Tricky) Regression Models workshop asked if the content applies to logistic regression as well.
The short answer: Yes
The long-winded detailed explanation of why this is true and the one caveat:
One of the greatest things about regression models is that they all have the same set up: (more…)
This one is relatively simple. Very similar names for two totally different concepts.
Hierarchical Models (aka Hierarchical Linear Models or HLM) are a type of linear regression models in which the observations fall into hierarchical, or completely nested levels.
Hierarchical Models are a type of Multilevel Models.
So what is a hierarchical data structure, which requires a hierarchical model?
The classic example is data from children nested within schools. The dependent variable could be something like math scores, and the predictors a whole host of things measured about the child and the school.
Child-level predictors could be things like GPA, grade, and gender. School-level predictors could be things like: total enrollment, private vs. public, mean SES.
Because multiple children are measured from the same school, their measurements are not independent. Hierarchical modeling takes that into account.
Hierarchical regression is a model-building technique in any regression model. It is the practice of building successive linear regression models, each adding more predictors.
For example, one common practice is to start by adding only demographic control variables to the model. In the next model, you can add predictors of interest, to see if they predict the DV above and beyond the effect of the controls.
You’re actually building separate but related models in each step. But SPSS has a nice function where it will compare the models, and actually test if successive models fit better than previous ones.
So hierarchical regression is really a series of regular old OLS regression models–nothing fancy, really.
Confusing Statistical Terms #1: Independent Variable
Confusing Statistical Terms #2: Alpha and Beta
Confusing Statistical Terms #3: Levels
