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Latest Blog Posts

Member Training: Classic Experimental Designs

October 29, 2021

Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions?

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Interpreting Regression Coefficients: Changing the scale of predictor variables

October 11, 2021

One issue that affects how to interpret regression coefficients is the scale of the variables. In linear regression, the scaling of both the response variable Y, and the relevant predictor X, are both important. In regression models like logistic regression, where the response variable is categorical, and therefore doesn’t have a numerical scale, this only […]

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Best Practices for Data Preparation

October 4, 2021

If you’ve been doing data analysis for long, you’ve probably had the ‘AHA’ moment where you realized statistical practice is a craft and not just a science. As with any craft, there are best practices that will save you a lot of pain and suffering and elevate the quality of your work. And yet, it’s […]

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Member Training: ANOVA Post-hoc Tests: Practical Considerations

October 1, 2021

Post-hoc tests, pairwise or other linear contrasts, are typical in an analysis of variance (ANOVA) setting to understand which group means differ. They incorporate p-value adjustments to avoid concluding that group means differ when they actually do not. There are several adjustments that can be considered for conducting multiple post-hoc tests, including single-step and stepwise […]

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Multiple Imputation in a Nutshell

September 20, 2021

Updated 9/20/2021 Imputation as an approach to missing data has been around for decades. You probably learned about mean imputation in methods classes, only to be told to never do it for a variety of very good reasons. Mean imputation, in which each missing value is replaced, or imputed, with the mean of observed values […]

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Odds Ratio: Standardized or Unstandardized Effect Size?

September 7, 2021

Effect size statistics are extremely important for interpreting statistical results. The emphasis on reporting them has been a great development over the past decade.

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Member Training: Matrix Algebra for Data Analysts: A Primer

August 31, 2021

Data analysts can get away without ever understanding matrix algebra, certainly. But there are times when having even a basic understanding of how matrix algebra works and what it has to do with data can really make your analyses make a little more sense.

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Why report estimated marginal means?

August 18, 2021

The Estimated Marginal Means in SPSS GLM are the means of each factor or interaction you specify, adjusted for any other variables in the model.

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Overfitting in Regression Models

August 9, 2021

The practice of choosing predictors for a regression model, called model building, is an area of real craft. There are many possible strategies and approaches and they all work well in some situations. Every one of them requires making a lot of decisions along the way. As you make decisions, one danger to look out […]

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Member Training: Moving to a World Beyond p<0.05

August 2, 2021

For nearly a hundred years the concept of “statistical significance” has been fundamental to statistics and to science. And for nearly that long, it has been controversial and misused as well.

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This Month’s Statistically Speaking Live Training

  • Member Training: Introduction to SPSS Software Tutorial

Upcoming Free Webinars

Poisson and Negative Binomial Regression Models for Count Data

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

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