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post hoc test

Member Training: ANOVA Post-hoc Tests: Practical Considerations

by TAF Support Leave a Comment

Stage 2Post-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 adjustments. [Read more…] about Member Training: ANOVA Post-hoc Tests: Practical Considerations

Tagged With: adjustments, ANOVA, post hoc test, Statistical analysis, Stepwise

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Member Training: Multiple Comparisons

by Karen Grace-Martin Leave a Comment

Whenever you run multiple statistical tests on the same set of data, you run into the problem of the Familywise Error Rate. What this means is that the true probabilityStage 2

of a type 1 error somewhere in the family of tests you’re running is actually higher than the alpha=.05 you’re using for any given test.

This is a complicated and controversial issue in statistics — even statisticians argue about whether it’s a problem, when it’s a problem, and what to do about it.

In this webinar, we’ll talk about the meaning and consequences of these issues so you can make informed decisions in your data analysis.

We’ll also go through possible solutions, including post-hoc tests and the false discovery rate.


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.

Not a Member? Join!

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking. You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: false discovery rate, family wise error rate, post hoc test

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Five Advantages of Running Repeated Measures ANOVA as a Mixed Model

by Karen Grace-Martin 22 Comments

There are two ways to run a repeated measures analysis.The traditional way is to treat it as a multivariate test–each response is considered a separate variable.The other way is to it as a mixed model.While the multivariate approach is easy to run and quite intuitive, there are a number of advantages to running a repeated measures analysis as a mixed model.

First I will explain the difference between the approaches, then briefly describe some of the advantages of using the mixed models approach. [Read more…] about Five Advantages of Running Repeated Measures ANOVA as a Mixed Model

Tagged With: Missing Data, mixed model, post hoc test, repeated measures anova

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SPSS GLM: Choosing Fixed Factors and Covariates

by Karen Grace-Martin 87 Comments

The beauty of the Univariate GLM procedure in SPSS is that it is so flexible.  You can use it to analyze regressions, ANOVAs, ANCOVAs with all sorts of interactions, dummy coding, etc.

The down side of this flexibility is it is often confusing what to put where and what it all means.

So here’s a quick breakdown.

The dependent variable I hope is pretty straightforward.  Put in your continuous dependent variable.

Fixed Factors are categorical independent variables.  It does not matter if the variable is [Read more…] about SPSS GLM: Choosing Fixed Factors and Covariates

Tagged With: analysis of covariance, ancova, ANOVA, Covariate, dummy coding, Fixed Factor, linear regression, post hoc test, SPSS GLM

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