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When Linear Models Don’t Fit Your Data, Now What?

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?

by Karen Grace-Martin Leave a Comment

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? [Read more…] about What Is Specification Error in Statistical Models?

The Difference Between an Odds Ratio and a Predicted Odds

by Karen Grace-Martin Leave a Comment

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. [Read more…] about The Difference Between an Odds Ratio and a Predicted Odds

Measures of Model Fit for Linear Regression Models

by Karen Grace-Martin 37 Comments

A well-fitting regression model results in predicted values close to the observed data values.

The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed regression model should therefore be better than the fit of the mean model. [Read more…] about Measures of Model Fit for Linear Regression Models

Three Principles of Experimental Designs

by Kim Love Leave a Comment

If you analyze non-experimental data, is it helpful to understand experimental design principles?

Yes, absolutely! Understanding experimental design can help you recognize the questions you can and can’t answer with the data. It will also help you identify possible sources of bias that can lead to undesirable results. Finally, it will help you provide recommendations to make future studies more efficient. [Read more…] about Three Principles of Experimental Designs

SPSS Syntax 101

by Karen Grace-Martin Leave a Comment

You may have heard that using SPSS Syntax is more efficient, gives you more control, and ultimately saves you time and frustration. They’re all true.

I realize that you probably use SPSS because you don’t want to code. You like the menus.

I get it. I like the menus, too, and I use them all the time. But I use syntax just as often.

At some point, if you want to do serious data analysis, you have to start using syntax. [Read more…] about SPSS Syntax 101