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About this document
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- 59 pages
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- $45 one-time, yours to keep
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- Subject
- Statistics
This document explains the general linear model (GLM) and how to use it for statistical analysis. It covers univariate and multivariate models, and includes an example of a one-way repeated measures ANOVA.
What's inside
- Definition and types of GLM
- One-way repeated measures ANOVA example
- Research question and assumptions
- Procedure for one-way repeated measures ANOVA
- Descriptive statistics and output
Who it is for: For students learning about the general linear model in statistics. (Undergraduate)
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Summary
The general linear model (GLM) is a statistical model for normally distributed dependent variables and combinations of categorical and continuous independent variables. Students learn how to use GLM for different types of analysis and how to present the results.
General Linear Model
GLM can accommodate univariate and multivariate models. It can fit repeated measures or within-subjects models. It can also handle combinations of categorical predictors and continuous predictors.
One-Way Repeated Measures ANOVA
This design measures subjects under different conditions or at multiple times. It can compare responses to different questions measured on the same scale. An example shows how to assess the effectiveness of cataract operations.
Multivariate Tests
These tests check for statistically significant effects in multivariate models. Wilks’ Lambda is a commonly reported statistic. Effect size is assessed using Eta squared. Results can show changes over time or differences between groups.
Presenting the Results
Results from multivariate analysis of variance can be presented in a structured way. This includes preliminary assumption testing, significance tests, and inspection of mean scores. Tables of means for each group should also be provided.
Key terms
- Dependent variable
- The outcome that is measured and predicted by the model.
- Independent variable
- The predictor variables that influence the dependent variable.
- Categorical predictors
- Variables that divide data into categories, also called factors.
- Continuous predictors
- Variables that can take any value within a range, also called covariates.
- Multivariate models
- Models that have two or more dependent variables.
- Repeated measures
- Designs where the same subjects are measured multiple times.
Questions this document helps with
- What are the different types of GLM models?
- How do you conduct a one-way repeated measures ANOVA?
- What statistics are used in multivariate tests?
Summary written with AI from the document's text.
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