In most regression models, there is one response variable and one or more predictors. From the model’s point of view, it doesn’t matter if those predictors are there to predict, to moderate, to explain, or to control. All that matters is that they’re all Xs, on the right side of the equation.
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Exogenous and Endogenous Variables in Structural Equation Modeling
July 22nd, 2022 by Karen Grace-Martin
Correlated Errors in Confirmatory Factor Analysis
July 13th, 2022 by Jeff Meyer
Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly.
But we can represent the latent construct by combining a set of questions on a scale, called indicators. We do this via factor analysis.
Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.
Member Training: Assumptions of Linear Models
June 30th, 2022 by TAF Support
What are the assumptions of linear models? If you compare two lists of assumptions, most of the time they’re not the same.
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When Linear Models Don’t Fit Your Data, Now What?
June 20th, 2022 by Karen Grace-Martin
When your dependent variable is not continuous, unbounded, and measured on 
an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
What Is Specification Error in Statistical Models?
June 8th, 2022 by Karen Grace-Martin
When we think about model assumptions, we tend to focus on assumptions like independence, normality, and constant variance. The other big assumption, which is harder to see or test, is that there is no specification error. The assumption of linearity is part of this, but it’s actually a bigger assumption.
What is this assumption of no specification error? (more…)
The Difference Between an Odds Ratio and a Predicted Odds
May 20th, 2022 by Karen Grace-Martin
When interpreting the results of a regression model, the first step is to look at the regression coefficients. Each term in the model has one. And each one describes the average difference in the value of Y for a one-unit difference in the value of the predictor variable, X, that makes up that term. It’s the effect size statistic for that term in the model. (more…)