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Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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One-Way ANOVA

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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...

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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Differential item functioning analysis by applying multiple comparison procedures.

Paolo Eusebi¹, Svend Kreiner

¹Paolo Eusebi, Regional Health Authority of Umbria, Via M.Angeloni, 61, 06124 Perugia, Italy, paoloeusebi@gmail.com.

Journal of Applied Measurement

|January 7, 2015

Summary

This summary is machine-generated.

This study introduces a new method for detecting differential item functioning (DIF) in test items. The proposed procedure controls the false discovery rate, enhancing the validity and objectivity of test scores.

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Area of Science:

Psychometrics
Educational Measurement
Statistical Analysis

Background:

Rasch measurement framework emphasizes valid and objective test scores.
Differential item functioning (DIF) is a key concern for score validity and objectivity.
Existing DIF assessment methods include Mantel-Haenszel tests and partial gamma coefficients.

Purpose of the Study:

To illustrate Multiple Comparison Procedures (MCP) for DIF analysis with many unordered groups.
To propose a novel single-step procedure for DIF detection controlling the false discovery rate (FDR).
To introduce a stepwise MCP procedure for identifying homogeneous subgroups regarding DIF effects.

Main Methods:

Application of Multiple Comparison Procedures (MCP) for DIF analysis.
Development of a single-step procedure controlling the false discovery rate (FDR).
Extension of the procedure for both dichotomous and polytomous items.

Main Results:

The proposed single-step procedure effectively controls the FDR for DIF detection.
The method provides evidence against the null hypothesis of no DIF.
Identified subsets of groups that are homogeneous concerning DIF effects.

Conclusions:

The novel MCP approach enhances DIF analysis in complex group structures.
The procedure contributes to the development of more valid and objective test scores.
The method offers a robust tool for psychometricians and test developers.