regression models

7 Practical Guidelines for Accurate Statistical Model Building

Model Building–choosing predictors–is one of those skills in statistics that is difficult to teach. It’s hard to lay out the steps, because at each step, you have to evaluate the situation and make decisions on the next step.

If you’re running purely predictive models, and the relationships among the variables aren’t the focus, it’s much easier. Go ahead and run a stepwise regression model. Let the data give you the best prediction.

But if the point is to answer a research question that describes relationships, you’re going to have to get your hands dirty.

It’s easy to say “use theory” or “test your research question” but that ignores a lot of practical issues. Like the fact that you may have 10 different variables that all measure the same theoretical construct, and it’s not clear which one to use. [Read more…] about 7 Practical Guidelines for Accurate Statistical Model Building

When Dependent Variables Are Not Fit for Linear Models, Now What?

by Karen Grace-Martin 28 Comments

When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, your model will not meet the assumptions of linear models.

Today I’m going to go into more detail about 6 common types of dependent variables that are not continuous, unbounded, and measured on an interval or ratio scale and the tests that work instead.

Side note: the usual advice is to use nonparametric tests when normality [Read more…] about When Dependent Variables Are Not Fit for Linear Models, Now What?

The Steps for Running any Statistical Model

by Karen Grace-Martin 27 Comments

No matter what statistical model you’re running, you need to go through the same steps. The order and the specifics of how you do each step will differ depending on the data and the type of model you use.

These steps are in 4 phases. Most people think of only the third as modeling. But the phases before this one are fundamental to making the modeling go well. It will be much, much easier, more accurate, and more efficient if you don’t skip them.

And there is no point in running the model if you skip phase 4.

If you think of them all as part of the analysis, the modeling process will be faster, easier, and make more sense.

Phase 1: Define and Design

In the first 5 steps, the object is clarity. You want to make everything as clear as possible to yourself. The more clear things are at this point, the smoother everything will be. [Read more…] about The Steps for Running any Statistical Model

Likert Scale Items as Predictor Variables in Regression

by Karen Grace-Martin 26 Comments

I was recently asked about whether it’s okay to treat a likert scale as continuous as a predictor in a regression model. Here’s my reply. In the question, the researcher asked about logistic regression, but the same answer applies to all regression models.

1. There is a difference between a likert scale item (a single 1-7 scale, eg.) and a full likert scale , which is composed of multiple items. If it is a full likert scale, with a combination of multiple items, go ahead and treat it as numerical. [Read more…] about Likert Scale Items as Predictor Variables in Regression

The Distribution of Independent Variables in Regression Models

by Karen Grace-Martin 27 Comments

I often hear concern about the non-normal distributions of independent variables in regression models, and I am here to ease your mind.

There are NO assumptions in any linear model about the distribution of the independent variables. Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables. But no, the model makes no assumptions about them. They do not need to be normally distributed or continuous.

It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values. A highly skewed independent variable may be made more symmetric with a transformation.

Is Multicollinearity the Bogeyman?

by Karen Grace-Martin Leave a Comment

Multicollinearity occurs when two or more predictor variables in a regression model are redundant. It is a real problem, and it can do terrible things to your results. However, the dangers of multicollinearity seem to have been so drummed into students’ minds that it created a panic.

True multicolllinearity (the kind that messes things up) is pretty uncommon. High correlations among predictor variables may indicate multicollinearity, but it is NOT a reliable indicator that it exists. It does not necessarily indicate a problem. How high is too high depends on [Read more…] about Is Multicollinearity the Bogeyman?