The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
Survey questions are often structured without regard for ease of use within a statistical model.
Take for example a survey done by the Centers for Disease Control (CDC) regarding child births in the U.S. One of the variables in the data set is “interval since last pregnancy”. Here is a histogram of the results.
[Read more…] about Recoding a Variable from a Survey Question to Use in a Statistical Model
At times it is necessary to convert a continuous predictor into a categorical predictor. For example, income per household is shown below.
This data is censored, all family income above $155,000 is stated as $155,000. A further explanation about censored and truncated data can be found here. It would be incorrect to use this variable as a continuous predictor due to its censoring.
[Read more…] about A Strategy for Converting a Continuous to a Categorical Predictor
What’s a good method for interpreting the results of a model with two continuous predictors and their interaction?
Let’s start by looking at a model without an interaction. In the model below, we regress a subject’s hip size on their weight and height. Height and weight are centered at their means.
[Read more…] about A Useful Graph for Interpreting Interactions between Continuous Variables
Predictor variables in statistical models can be treated as either continuous or categorical.
Usually, this is a very straightforward decision.
Categorical predictors, like treatment group, marital status, or highest educational degree should be specified as categorical.
Likewise, continuous predictors, like age, systolic blood pressure, or percentage of ground cover should be specified as continuous.
But there are numerical predictors that aren’t continuous. And these can sometimes make sense to treat as continuous and sometimes make sense as categorical.
[Read more…] about Should I Specify a Model Predictor as Categorical or Continuous?
by Jeff Meyer
We often have a continuous predictor in a model that we believe has non-constant relationship with the dependent variable along the predictor’s range. But how can we be certain? What is the best way to measure this?
Sometimes including a quadratic term will capture the change in the slope as we move from the bottom of the range to the top of the range. But a quadratic term only works in two situations:
We could also create a categorical variable. Each category within the categorical variable would represent a specific range within the continuous variable. [Read more…] about Segmented Regression for Non-Constant Relationships
Linear regression with a continuous predictor is set up to measure the constant relationship between that predictor and a continuous outcome.
This relationship is measured in the expected change in the outcome for each one-unit change in the predictor.
One big assumption in this kind of model, though, is that this rate of change is the same for every value of the predictor. It’s an assumption we need to question, though, because it’s not a good approach for a lot of relationships.
Segmented regression allows you to generate different slopes and/or intercepts for different segments of values of the continuous predictor. This can provide you with a wealth of information that a non-segmented regression cannot.
In this webinar, we will cover [Read more…] about Member Training: Segmented Regression