Posts Tagged ‘ ANOVA ’

Asymmetric funnel plots without publication bias

January 9, 2016
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Asymmetric funnel plots without publication bias

In my last post about standardized effect sizes, I showed how averaging across trials before computing standardized effect sizes such as partial \(\eta^2\) and Cohen's d can produce arbitrary estimates of those quantities. This has drastic implications...

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Averaging can produce misleading standardized effect sizes

January 7, 2016
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Averaging can produce misleading standardized effect sizes

Recently, there have been many calls for a focus on effect sizes in psychological research. In this post, I discuss how naively using standardized effect sizes with averaged data can be misleading. This is particularly problematic for meta-analysis, where differences in number of trials across studies could lead to very misleading results.There are two main types of effect sizes in typical use: raw effect sizes and standardized effect sizes. Raw…

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The fallacy of placing confidence in confidence intervals (version 2)

April 21, 2015
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I, with my coathors, have submitted a new draft of our paper "The fallacy of placing confidence in confidence intervals". This paper is substantially modified from its previous incarnation. Here is the main argument:"[C]onfidence intervals may not be u...

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Multiple Comparisons with BayesFactor, Part 2 – order restrictions

January 18, 2015
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Multiple Comparisons with BayesFactor, Part 2 – order restrictions

In my previous post, I described how to do multiple comparisons using the BayesFactor package. Part 1 concentrated on testing equality constraints among effects: for instance, that the the effects of two factor levels are equal, while leaving the third free to be different. In this second part, I will describe how to test order restrictions on factor level effects. This post will be a little more involved than the…

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Multiple Comparisons with BayesFactor, Part 1

January 17, 2015
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Multiple Comparisons with BayesFactor, Part 1

One of the most frequently-asked questions about the BayesFactor package is how to do multiple comparisons; that is, given that some effect exists across factor levels or means, how can we test whether two specific effects are unequal. In the next two posts, I'll explain how this can be done in two cases: in Part 1, I'll cover tests for equality, and in Part 2 I'll cover tests for specific…

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One-way ANOVA with fixed and random effects from a Bayesian perspective

December 22, 2014
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One-way ANOVA with fixed and random effects from a Bayesian perspective

This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it … Continue reading One-way ANOVA with fixed and random effects from a Bayesian perspective

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One-way ANOVA with fixed and random effects from a Bayesian perspective

December 22, 2014
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One-way ANOVA with fixed and random effects from a Bayesian perspective

This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it … Continue reading One-way ANOVA with fixed and random effects from a Bayesian perspective

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Applied Statistics Lesson of the Day – Additive Models vs. Interaction Models in 2-Factor Experimental Designs

Applied Statistics Lesson of the Day – Additive Models vs. Interaction Models in 2-Factor Experimental Designs

In a recent “Machine Learning Lesson of the Day“, I discussed the difference between a supervised learning model in machine learning and a regression model in statistics.  In that lesson, I mentioned that a statistical regression model usually consists of a systematic component and a random component.  Today’s lesson strictly concerns the systematic component. An […]

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Example 2014.3: Allow different variances by group

February 27, 2014
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Example 2014.3: Allow different variances by group

One common violation of the assumptions needed for linear regression is heterscedasticity by group membership. Both SAS and R can easily accommodate this setting. Our data today comes from a real example of vitamin D supplementation of milk. Four sup...

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Applied Statistics Lesson of the Day – The Completely Randomized Design with 1 Factor

Applied Statistics Lesson of the Day – The Completely Randomized Design with 1 Factor

The simplest experimental design is the completely randomized design with 1 factor.  In this design, each experimental unit is randomly assigned to each factor level.  This design is most useful for a homogeneous population (one that does not have major differences between any sub-populations).  It is appealing because of its simplicity and flexibility – it can […]

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