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June Member Training: Data Cleaning

Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.

Member Training: Practical Suggestions for Improving Your Scatterplots

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The scatterplot is a simple display of the relationship between two, or sometimes three, variables. You have a wide range of options for displaying a scatterplot. In particular, you can control the location, size, shape, and color of the points in your scatterplot.

Member Training: Confusing Statistical Terms

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Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.

Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics. [Read more…] about Member Training: Confusing Statistical Terms

Member Training: A Gentle Introduction to Multilevel Models

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In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running example will be used to clarify the ideas and the meaning of the MLM results.

Member Training: How to Avoid Common Graphical Mistakes

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Good graphs are extremely powerful tools for communicating quantitative information clearly and accurately.

Unfortunately, many of the graphs we see today confuse, mislead, or deceive the reader.

These poor graphs result from two key limitations. One is a graph designer who isn’t familiar with the principles of effective graphs. The other is software with a poor choice of default settings.

Member Training: Practical Advice for Establishing Reliability and Validity

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How do you know your variables are measuring what you think they are? And how do you know they’re doing it well?

A key part of answering these questions is establishing reliability and validity of the measurements that you use in your research study. But the process of establishing reliability and validity is confusing. There are a dizzying number of choices available to you.