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categorical predictor

What It Really Means to Take an Interaction Out of a Model

by Karen Grace-Martin Leave a Comment

When you’re model building, a key decision is which interaction terms to include.

As a general rule, the default in regression is to leave them out. Add interactions only with a solid reason. It would seem like data fishing to simply add in all possible interactions.

And yet, that’s a common practice in most ANOVA models: put in all possible interactions and only take them out if there’s a solid reason. Even many software procedures default to creating interactions among categorical predictors.

[Read more…] about What It Really Means to Take an Interaction Out of a Model

Tagged With: categorical predictor, interaction, Model Building

Related Posts

  • Simplifying a Categorical Predictor in Regression Models
  • Differences in Model Building Between Explanatory and Predictive Models
  • Should I Specify a Model Predictor as Categorical or Continuous?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

Simplifying a Categorical Predictor in Regression Models

by Jeff Meyer Leave a Comment

One of the many decisions you have to make when model building is which form each predictor variable should take. One specific version of this decision is whether to combine categories of a categorical predictor.

The greater the number of parameter estimates in a model the greater the number of observations that are needed to keep power constant. The parameter estimates in a linear [Read more…] about Simplifying a Categorical Predictor in Regression Models

Tagged With: categorical predictor, interpreting regression coefficients, Model Building, pairwise, R-squared

Related Posts

  • What It Really Means to Take an Interaction Out of a Model
  • Differences in Model Building Between Explanatory and Predictive Models
  • A Strategy for Converting a Continuous to a Categorical Predictor
  • Should I Specify a Model Predictor as Categorical or Continuous?

Same Statistical Models, Different (and Confusing) Output Terms

by Jeff Meyer Leave a Comment

Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?

I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.

We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.

[Read more…] about Same Statistical Models, Different (and Confusing) Output Terms

Tagged With: ANOVA, between groups, categorical predictor, linear regression, oneway, residuals, software, SPSS, SPSS output, Stata, Stata output, Statistical Software, within groups

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Recoding a Variable from a Survey Question to Use in a Statistical Model

by Jeff Meyer Leave a Comment

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

Tagged With: categorical predictor, continuous predictor, predictor variable, recode, survey, survey questions

Related Posts

  • A Strategy for Converting a Continuous to a Categorical Predictor
  • A Useful Graph for Interpreting Interactions between Continuous Variables
  • Should I Specify a Model Predictor as Categorical or Continuous?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

Should I Specify a Model Predictor as Categorical or Continuous?

by Karen Grace-Martin Leave a Comment

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?

Tagged With: categorical predictor, continuous predictor, Discrete Counts, Linear Regression Model, Model Building, numeric variable, predictor variable

Related Posts

  • Recoding a Variable from a Survey Question to Use in a Statistical Model
  • What It Really Means to Take an Interaction Out of a Model
  • Simplifying a Categorical Predictor in Regression Models
  • A Strategy for Converting a Continuous to a Categorical Predictor

The Impact of Removing the Constant from a Regression Model: The Categorical Case

by Jeff Meyer 3 Comments

by Jeff Meyer

In a simple linear regression model, how the constant (a.k.a., intercept) is interpreted depends upon the type of predictor (independent) variable.

If the predictor is categorical and dummy-coded, the constant is the mean value of the outcome variable for the reference category only. If the predictor variable is continuous, the constant equals the predicted value of the outcome variable when the predictor variable equals zero.

Removing the Constant When the Predictor Is Categorical

When your predictor variable X is categorical, the results are logical. Let’s look at an example. [Read more…] about The Impact of Removing the Constant from a Regression Model: The Categorical Case

Tagged With: categorical predictor, constant, continuous variable, Dummy Coded, Interpreting intercept, linear regression, predictor variable

Related Posts

  • A Visual Description of Multicollinearity
  • A Strategy for Converting a Continuous to a Categorical Predictor
  • Same Statistical Models, Different (and Confusing) Output Terms
  • Recoding a Variable from a Survey Question to Use in a Statistical Model

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