categorical variable

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

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.

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

Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression

by Jeff Meyer 36 Comments

by Jeff Meyer, MPA, MBA

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

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

Member Training: Correspondence Analysis

by guest 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.

Member Training: Latent Class Analysis

by Karen Grace-Martin Leave a Comment

Latent Class Analysis is a method for finding and measuring unobserved latent subgroups in a population based on responses to a set of observed categorical variables.

This webinar will present an overview and an example of how latent class analysis works to find subgroups, how to interpret the output, the steps involved in running it. We will discuss extensions and uses of the latent classes in other analyses and similarities and differences with related techniques.

Member Training: Dummy and Effect Coding

by Karen Grace-Martin 2 Comments

Why does ANOVA give main effects in the presence of interactions, but Regression gives marginal effects?

What are the advantages and disadvantages of dummy coding and effect coding? When does it make sense to use one or the other?

How does each one work, really?

In this webinar, we’re going to go step-by-step through a few examples of how dummy and effect coding each tell you different information about the effects of categorical variables, and therefore which one you want in each situation.

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About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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