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Related Experiment Video

Updated: Jun 7, 2025

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics BM-PROMA

Published on: August 28, 2021

Can One Pool Over Site in a Multi-Site Study With Categorical Item Measuring Instruments? A Multiple Testing

Tenko Raykov¹, Khaled Alkherainej²

¹Michigan State University, East Lansing, USA.

Educational and Psychological Measurement

|November 20, 2024

Summary

This study introduces a new statistical method to check if data from different research sites can be combined. This approach ensures reliable analysis in educational and behavioral research by testing response distribution identity.

Keywords:

Benjamini–Hochberg procedure categorical item collapsibility integrative data analysis multi-site study polytomous item

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Area of Science:

Educational Research
Behavioral Science
Psychometrics

Background:

Multi-site studies are common in educational and behavioral research.
Combining data across sites requires careful validation.
Existing methods may not adequately address site-specific response variations.

Purpose of the Study:

To present a statistical procedure for assessing data collapsibility across multiple research sites.
To enable the pooling of data from diverse study locations.
To provide a method applicable to polytomous items in multi-component instruments.

Main Methods:

The core method involves testing for cross-site identity of response distributions.
It utilizes statistical tests suitable for polytomous item data.
The procedure is designed for practical application in empirical research.

Main Results:

The proposed procedure effectively determines if data can be pooled across sites.
It confirms the generalizability of the method to various item types.
The technique is demonstrated using data from a child development survey.

Conclusions:

The outlined procedure offers a robust way to examine data collapsibility in multi-site research.
It facilitates more efficient and powerful data analysis by allowing data pooling.
The method is accessible through widely available statistical software.