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GLM: Multi Study (Fixed Effects, Separate Study, Separate Subjects, Random Effects)

Introduction

In most analyses, one wants to calculate statistics not only for a specific single dataset, but either combine datasets of different runs or subjects or generalize the results to the population level.

To be able to do this, one needs to use the Multi-Study GLM dialog in BrainVoyager.

In the dialog, one will find different options leading to different analysis results. On the following pages, theses options will be described step by step:

1. Preparation of a Multi-Study, Multi-Subject Analysis

2. Introduction to Multi-Study, Multi-Subject Analyses

3. Fixed Effects Analysis - FFX: one set of predictors across all studies (functional runs) and participants (concatenation of predictors)

4. Separate Study Predictors - SPST: creation of a separate set of predictors for each study

5. Separate Subject Predictors - SPSB, needed for a Random Effects Analysis - RFX: creation of a separate set of predictors for each subject, but concatenated across runs of each subject

Preparation
BrainVoyager version: 22.4.6 Dataset used: sub-02_ses-01_task-Localizer_run-01_b... more
Multi-Subject Design with Missing Condition(s)
The analysis of effects across participants requires the same number and the sam... more
RFX ROI GLM Computation: Replication of BrainVoyager Calculations Externally
To understand the computation of a random effects GLM contrast, we replicated th... more