Member Training: Practical Advice for Establishing Reliability and Validity

by guest contributer 

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.

Here’s some practical advice. Reliability and validity are very important. In many research situations, checking and reporting them is central. But you can skip the effort entirely in some situations.

Furthermore, some approaches to reliability and validity only make sense for a composite measurement and can be safely skipped for an individual or univariate measurement.

This talk will outline the ways of establishing different types of reliability and validity. It will take you through the steps to follow in establishing reliability and validity with special emphasis on when you can safely skip some of these steps.

In this Stat’s Amore training, Statistically Speaking mentor Steve Simon will outline the ways of establishing different types of reliability and validity.

You learn the steps to follow in establishing reliability and validity with special emphasis on when you can safely skip some of these steps.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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About the Instructor

Steve Simon works as an independent statistical consultant and as a part-time faculty member in the Department of Biomedical and Health Informatics at the University of Missouri-Kansas City. He has previously worked at Children’s Mercy Hospital, the National Institute for Occupational Safety and Health, and Bowling Green State University.

Steve has over 90 peer-reviewed publications, four of which have won major awards. He has written one book, Statistical Evidence in Medical Trials, and is the author of a major website about Statistics, Research Design, and Evidence Based Medicine, www.pmean.com. One of his current areas of interest is using Bayesian models to forecast patient accrual in clinical trials. Steve received a Ph.D. in Statistics from the University of Iowa in 1982.

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