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Latest Blog Posts

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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Member Training: Using Open Data in Research: Opportunities and Challenges

October 1, 2020

Open data, particularly government open data is a rich source of information that can be helpful to researchers in almost every field, but what is open data? How do we find what we’re looking for? What are some of the challenges with using data directly from city, county, state, and federal government agencies?

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Series on Easy-to-Confuse Statistical Concepts

September 29, 2020

There are many concepts in statistics that are easy to confuse. Sometimes the problem is the terminology. We have a whole series of articles on Confusing Statistical Terms. But in these cases, it’s the concepts themselves. Similar, but distinct concepts that are easy to confuse. Some of these are quite high-level, and others are fundamental. […]

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What It Really Means to Take an Interaction Out of a Model

September 17, 2020

When you’re model building, a key decision is which interaction terms to include. As a general rule, the default in regression is to leave them out. Add interactions only with a solid reason. It would seem like data fishing to simply add in all possible interactions. And yet, that’s a common practice in most ANOVA […]

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September Member Training: Inference and p-values and Statistical Significance, Oh My!

September 1, 2020

Statistical inference using hypothesis testing is ubiquitous in science. Several misconceptions and misinterpretations of p-values have arisen over the years, which can lead to challenges communicating the correct interpretation of results.

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How Big of a Sample Size do you need for Factor Analysis?

August 21, 2020

Most of the time when we plan a sample size for a data set, it’s based on obtaining reasonable statistical power for a key analysis of that data set. These power calculations figure out how big a sample you need so that a certain width of a confidence interval or p-value will coincide with a […]

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Effect Size Statistics: How to Calculate the Odds Ratio from a Chi-Square Cross-tabulation Table

August 12, 2020

Lest you believe that odds ratios are merely the domain of logistic regression, I’m here to tell you it’s not true. One of the simplest ways to calculate an odds ratio is from a cross tabulation table. We usually analyze these tables with a categorical statistical test. There are a few options, depending on the […]

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Member Training: Explaining Logistic Regression Results to Non-Researchers

August 1, 2020

Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?

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This Month’s Statistically Speaking Live Training

  • February Member Training: Choosing the Best Statistical Analysis

Upcoming Workshops

  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
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Data Analysis with SPSS
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by Stephen Sweet and
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