Understanding Assumptions and Pseudo-Replication in Two-Factor GLMs
Explore the assumptions of two-factor GLMs, focusing on independence, and learn about spotting pseudo-replication in the context of treatment factors. These key concepts are crucial for accurate statistical analysis in research settings.
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Presentation Transcript
Assumptions of 2-Factor GLM Briefly review assumptions of GLMs Highlight independence for 2 Factor GLMs 1
Assumptions of General Linear Models (in order of decreasing importance) 1. Random sampling 2. Independence 3. Homogeneity of variances 4. Normality
Spotting pseudo-replication (1 Factor GLM) NOT pseudo-replication NOT pseudo-replication A B C D E F G A B C D E F Treatment 1 Treatment 2 H I J K L M N A B C D E F Treatment 3 O P Q R S T A B C D E F Measurements from an individual subject share a letter
Spotting pseudo-replication (1 Factor GLM) Pseudo-replication NOT pseudo-replication A A B B C C A B C D E F Treatment 1 Treatment 2 D D E E F F A B C D E F Treatment 3 G G H H I I A B C D E F Measurements from an individual subject share a letter
Spotting pseudo-replication (1 Factor GLM) NOT pseudo-replication Treatment 1 A B C D E F Treatment 2 A B C D E F Treatment 3 A B C D E F Measurements from an individual subject share a letter
Spotting pseudo-replication (2 Factor GLM) pseudo-replication Female Male Drug 1 A B C D E F Drug Drug 2 A B C D E F Drug 3 A B C D E F Measurements from an individual subject share a letter
Spotting pseudo-replication (2 Factor GLM) Not pseudo-replication pseudo-replication Female Male Female Male Drug 1 A B C D E F Drug 1 A B C D E F Drug 2 Drug 2 G H I J K L A B C D E F Drug 3 Drug 3 M N O P Q R A B C D E F Measurements from an individual subject share a letter
