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Related Concept Videos

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
One-Way ANOVA01:18

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One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Factorial Design02:01

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
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Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Related Experiment Video

Updated: May 30, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Discriminant analysis for repeated measures data: a review.

Lisa M Lix1, Tolulope T Sajobi

  • 1School of Public Health, University of Saskatchewan , Saskatoon, SK, Canada.

Frontiers in Psychology
|August 12, 2011
PubMed
Summary
This summary is machine-generated.

Discriminant analysis (DA) methods are evolving for repeated measures data, handling missing values, unbalanced designs, and high-dimensional variables. This review covers advancements using covariance pattern and linear mixed-effects models.

Keywords:
classificationlongitudinalmissing datamultivariaterepeated measures

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Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Discriminant analysis (DA) is used for classification and variable importance assessment.
  • Recent advancements focus on complex data structures in repeated measures designs.

Purpose of the Study:

  • To review developments in discriminant analysis for repeated measures data.
  • To highlight methods for handling missing observations, unbalanced designs, and high-dimensional data.
  • To focus on covariance pattern and linear mixed-effects models.

Main Methods:

  • Literature review of discriminant analysis procedures.
  • Focus on covariance pattern models.
  • Focus on linear mixed-effects models.
  • Illustrative numeric example using SAS software.

Main Results:

  • DA procedures have been developed for repeated measures data with missing observations.
  • DA methods now accommodate unbalanced measurement occasions.
  • DA is applicable to high-dimensional repeated measures data involving multiple variables.

Conclusions:

  • Covariance pattern and linear mixed-effects models are key for modern discriminant analysis in repeated measures.
  • These advanced DA techniques enhance the analysis of complex longitudinal and multivariate data.
  • Implementation is demonstrated with practical software examples.