One question that seems to come up pretty often is:
What is the difference between logistic and probit regression?
Well, let’s start with how they’re the same:
Both are types of generalized linear models. This means they have this form:
Karen Grace-Martin
The Difference Between Logistic and Probit Regression
May 12th, 2017 by Karen Grace-Martin
Member Training: Crossed and Nested Factors
May 1st, 2017 by Karen Grace-Martin
We often talk about nested factors in mixed models — students nested in classes, observations nested within subject.
But in all but the simplest designs, it’s not that straightforward. (more…)
Sample Size Estimation Without Past Reliable Pilot Data or Evidence
March 20th, 2017 by Karen Grace-Martin
Here’s a common situation.
Your grant application or committee requires sample size estimates. It’s not the calculations that are hard (though they can be), it’s getting the information to fill into the calculations.
Every article you read on it says you need to either use pilot data or another similar study as a basis for the values to enter into the software.
You have neither.
No similar studies have ever used the scale you’re using for the dependent variable.
And while you’d love to run a pilot study, it’s just not possible. There are too many practical constraints — time, money, distance, ethics.
What do you do?
In Factor Analysis, How Do We Decide Whether to Have Rotated or Unrotated Factors?
January 20th, 2017 by Karen Grace-Martin
I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn’t get through all the questions. This is part of a series of answers to those questions.
If you missed it, you can get the webinar recording here.
Question: How do we decide whether to have rotated or unrotated factors?
Answer:
Great question. Of course, the answer depends on your situation.
When you retain only one factor in a solution, then rotation is irrelevant. In fact, most software won’t even print out rotated coefficients and they’re pretty meaningless in that situation.
But if you retain two or more factors, you need to rotate.
Unrotated factors are pretty difficult to interpret in that situation. (more…)
Can You Use Principal Component Analysis with a Training Set Test Set Model?
January 20th, 2017 by Karen Grace-Martin
I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn’t get through all the questions. This is part of a series of answers to those questions.
If you missed it, you can get the webinar recording here.
Question: Can you use Principal Component Analysis with a Training Set Test Set Model?
Answer: Yes and no.
Principal Component Analysis specifically could be used with a training and test data set, but it doesn’t make as much sense as doing so for Factor Analysis.
That’s because PCA is really just about creating an index variable from a set of correlated predictors.
Factor Analysis is an actual model that is measuring a latent variable. Any time you’re creating some sort of scale to measure an underlying construct, you want to use Factor Analysis.
Factor Analysis is definitely best done with a training and test data set.
In fact, ideally, you’d run multiple rounds of training and test data sets, in which the variables included on your scale are updated after each test. (more…)
Can We Use PCA for Reducing Both Predictors and Response Variables?
January 20th, 2017 by Karen Grace-Martin
I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn’t get through all the questions. This is part of a series of answers to those questions.
If you missed it, you can get the webinar recording here.
Question: Can we use PCA for reducing both predictors and response variables?
In fact, there were a few related but separate questions about using and interpreting the resulting component scores, so I’ll answer them together here.
How could you use the component scores?
A lot of times PCAs are used for further analysis — say, regression. How can we interpret the results of regression?
Let’s say I would like to interpret my regression results in terms of original data, but they are hiding under PCAs. What is the best interpretation that we can do in this case?
Answer:
So yes, the point of PCA is to reduce variables — create an index score variable that is an optimally weighted combination of a group of correlated variables.
And yes, you can use this index variable as either a predictor or response variable.
It is often used as a solution for multicollinearity among predictor variables in a regression model. Rather than include multiple correlated predictors, none of which is significant, if you can combine them using PCA, then use that.
It’s also used as a solution to avoid inflated familywise Type I error caused by running the same analysis on multiple correlated outcome variables. Combine the correlated outcomes using PCA, then use that as the single outcome variable. (This is, incidentally, what MANOVA does).
In both cases, you can no longer interpret the individual variables.
You may want to, but you can’t. (more…)