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Latest Blog Posts

Linear Regression Analysis – 3 Common Causes of Multicollinearity and What Do to About Them

February 11, 2022

Multicollinearity is simply redundancy in the information contained in predictor variables. If the redundancy is moderate, it usually only affects the interpretation of regression coefficients. But if it is severe-at or near perfect redundancy, it causes the model to "blow up." (And yes, that's a technical term). But the reality is that there are only five situations where it commonly occurs. And three of them have very simple solutions.

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Member Training: Heterogeneity in Meta-analysis

January 31, 2022

Meta-analysis allows us to synthesize the results of separate studies. The goal is to assess the mean effect size and also heterogeneity – how much the effect size varies across studies. 

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The Difference Between R-squared and Adjusted R-squared

January 20, 2022

One of the most useful and intuitive statistics we have in linear regression is the Coefficient of Determination: R² It tells you how well the model predicts the outcome and has some nice properties.

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Specifying Fixed and Random Factors in Mixed Models

January 10, 2022

One of the difficult decisions in mixed modeling is deciding which factors are fixed and which are random. And as difficult as it is, it’s also very important. Correctly specifying the fixed and random factors of the model is vital to obtain accurate analyses. Now, you may be thinking of the fixed and random effects […]

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Member Training: R for Menu Users Software Tutorial

December 30, 2021

In this nearly 6-hour tutorial you will learn menu-based R libraries so you can use R without having to fuss with R code. These libraries don’t cover everything R can do, but they do quite a bit and can set you up to make running R much easier.

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Interpreting Regression Coefficients

December 20, 2021

Updated 12/20/2021 Despite its popularity, interpreting regression coefficients of any but the simplest models is sometimes, well….difficult. So let’s interpret the coefficients in a model with two predictors: a continuous and a categorical variable.  The example here is a linear regression model. But this works the same way for interpreting coefficients from any regression model […]

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Guidelines for writing up three types of odds ratios

December 6, 2021

Odds ratios have a unique part to play in describing the effects of logistic regression models. But that doesn’t mean they’re easy to communicate to an audience who is likely to misinterpret them. So writing up your odds ratios has to be done with care.

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Member Training: Difference in Differences

November 30, 2021

The great majority of all regression modeling explores and tests the association between independent and dependent variables. We are not able to claim the independent variable(s) has a causal relationship with the dependent variable. There are five specific model types that allow us to test for causality. Difference in differences models are one of the […]

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Logistic Regression Analysis: Understanding Odds and Probability

November 22, 2021

People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent. They measure the same thing on different scales. Imagine how confusing it would be if people used degrees Celsius and degrees Fahrenheit interchangeably. "It's going to be 35 degrees today" could really make you dress the wrong way.

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Six terms that mean something different statistically and colloquially

November 8, 2021

by Kim Love and Karen Grace-Martin Statistics terminology is confusing. Sometimes different terms are used to mean the same thing, often in different fields of application. Sometimes the same term is used to mean different things. And sometimes very similar terms are used to describe related but distinct statistical concepts.

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This Month’s Statistically Speaking Live Training

  • Member Training: Introduction to SPSS Software Tutorial

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Poisson and Negative Binomial Regression Models for Count Data

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  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
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