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categorical variable

Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

by Jeff Meyer  24 Comments

We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?

What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? [Read more…] about Understanding Interactions Between Categorical and Continuous Variables in Linear Regression

Tagged With: categorical variable, continuous variable, interaction, Interpreting Interactions, linear regression

Related Posts

  • Using Marginal Means to Explain an Interaction to a Non-Statistical Audience
  • Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction
  • When NOT to Center a Predictor Variable in Regression

What Is Latent Class Analysis?

by Karen Grace-Martin  12 Comments

One of the most common—and one of the trickiest—challenges in data analysis is deciding how to include multiple predictors in a model, especially when they’re related to each other.

Here’s an example. Let’s say you are interested in studying the relationship between work spillover into personal time as a predictor of job burnout.

You have 5 categorical yes/no variables that indicate whether a particular symptom of work spillover is present (see below).

While you could use each individual variable, you’re not really interested if one in particular is related to the outcome. Perhaps it’s not really each symptom that’s important, but the idea that spillover is happening.

[Read more…] about What Is Latent Class Analysis?

Tagged With: categorical variable, conditional probability, Correlated Predictors, inclusion probability, latent class analysis, latent variable

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Two-Way Tables and Count Models: Expected and Predicted Counts

by Jeff Meyer  1 Comment

by Jeff Meyer

In a previous article, we discussed how incidence rate ratios calculated in a Poisson regression can be determined from a two-way table of categorical variables.

Statistical software can also calculate the expected (aka predicted) count for each group. Below is the actual and expected count of the number of boys and girls participating and not participating in organized sports.

cm-twowaytables-1

 

 

 

 

 

 

 

The value in the top of each cell is the actual count (40 boys do not play organized sports) and the bottom value is the expected/predicted count (36 boys are predicted to not play organized sports).

The Poisson model that we ran in the previous article generated the following table: [Read more…] about Two-Way Tables and Count Models: Expected and Predicted Counts

Tagged With: categorical variable, expected count, poisson, predicted count, two-way table

Related Posts

  • Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data
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Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression

by Jeff Meyer  42 Comments

The concept of a statistical interaction is one of those things that seems very abstract. Obtuse definitions, like this one from Wikipedia, don’t help:

In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.

First, we know this is true because we read it on the internet! Second, are you more confused now about interactions than you were before you read that definition? [Read more…] about Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression

Tagged With: categorical variable, Dummy Coded, interaction, linear regression

Related Posts

  • Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
  • Linear Regression for an Outcome Variable with Boundaries
  • Interpreting Interactions Between Two Effect-Coded Categorical Predictors
  • Interpreting Lower Order Coefficients When the Model Contains an Interaction

Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables

by Jeff Meyer  Leave a Comment

A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to which variable type it is.

For example, a categorical variable like marital status could be coded in the data set as a single variable with 5 values: [Read more…] about Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables

Tagged With: categorical variable, dummy variable, indicator, Missing Data, Multiple Imputation

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Member Training: Correspondence Analysis

by guest contributer  1 Comment

Correspondence analysis is a powerful exploratory multivariate technique for categorical variables with many levels. It is a data analysis tool that characterizes associations between levels of two or more categorical variables using graphical representations of the information in a contingency table. It is particularly useful when categorical variables have many levels.

This presentation will give a brief introduction and overview of the use of correspondence analysis, including a review of chi square analysis, and examples interpreting both simple and multiple correspondence plots.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

[Read more…] about Member Training: Correspondence Analysis

Tagged With: categorical variable, Correspondence Analysis, levels, multivariate

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  • Member Training: Multinomial Logistic Regression

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