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Latest Blog Posts

What are Sums of Squares?

January 9, 2021

A key part of the output in any linear model is the ANOVA table. It has many names in different software procedures, but every regression or ANOVA model has a table with Sums of Squares, degrees of freedom, mean squares, and F tests. Many of us were trained to skip over this table, but

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January Member Training: A Gentle Introduction To Random Slopes In Multilevel Models

December 31, 2020

A Gentle Introduction to Random Slopes in Multilevel Modeling …aka, how to look at cool interaction effects for nested data. Do the words “random slopes model” or “random coefficients model” send shivers down your spine? These words don’t have to be so ominous. Journal editors are increasingly asking researchers to analyze their data using this […]

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The Difference Between Model Assumptions, Inference Assumptions, and Data Issues

December 23, 2020

Have you ever compared the list of assumptions for linear regression across two sources? Whether they’re textbooks, lecture notes, or web pages, chances are the assumptions don’t quite line up. Why? Sometimes the authors use different terminology. So it just looks different. And sometimes they’re including not only model assumptions, but inference assumptions and data […]

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When Unequal Sample Sizes Are and Are NOT a Problem in ANOVA

December 18, 2020

Few data sets are completely balanced, with equal sample sizes in every condition. But are they really the scary problem your stats professor made them out to be? Only sometimes.

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December Member Training: Missing Data

December 1, 2020

Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.

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The Importance of Including an Exposure Variable in Count Models

November 19, 2020

When our outcome variable is the frequency of occurrence of an event, we will typically use a count model to analyze the results. There are numerous count models. Regardless of the model we use, there is a very important prerequisite that they all share. We must identify the period of time or area of space in which the counts were generated. We must model exposure.

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Count Models: Understanding the Log Link Function

November 12, 2020

In generalized linear models, there is a link function, which is the link between the mean of Y on the left and the fixed component on the right. In order to make the model fit in a linear form for these other distributions, we often need to take some function of the mean.

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November Member Training: Preparing to Use (and Interpret) a Linear Regression Model

November 1, 2020

You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

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Measurement Invariance and Multiple Group Analysis

October 23, 2020

Creating a quality scale for a latent construct (a variable that cannot be directly measured with one variable) takes many steps. Structural Equation Modeling is set up well for this task. One important step in creating scales is making sure the scale measures the latent construct equally well and the same way for different groups […]

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Missing Data: Two Big Problems with Mean Imputation

October 15, 2020

Mean imputation: So simple. And yet, so dangerous. Perhaps that’s a bit dramatic, but mean imputation (also called mean substitution) really ought to be a last resort. It’s a popular solution to missing data, despite its drawbacks. Mainly because it’s easy. It can be really painful to lose a large part of the sample you […]

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This Month’s Statistically Speaking Live Training

  • January Member Training: A Gentle Introduction To Random Slopes In Multilevel Models

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

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Data Analysis with SPSS
(4th Edition)

by Stephen Sweet and
Karen Grace-Martin

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