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Related Experiment Video

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Published on: March 1, 2022

A trifactor model for integrating ratings across multiple informants.

Daniel J Bauer¹, Andrea L Howard¹, Ruth E Baldasaro¹

¹Department of Psychology.

Psychological Methods

|October 2, 2013

Summary

Psychologists can now use the new trifactor model to analyze multiple informant data. This psychometric model separates target, informant, and item variability for more accurate trait assessment.

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Area of Science:

Psychometrics
Psychological Measurement
Quantitative Psychology

Background:

Psychologists frequently collect data from multiple informants (e.g., parents, peers) to assess individuals.
Existing psychometric models may not adequately differentiate between individual traits and informant biases.
Accurate assessment requires distinguishing between target-level, informant-level, and item-level influences on ratings.

Purpose of the Study:

To introduce and detail the trifactor model for analyzing multiple informant data.
To demonstrate how the trifactor model separates variability sources in psychological assessments.
To provide a framework for evaluating item quality and understanding rating biases.

Main Methods:

Development of a novel trifactor model for multiple informant data analysis.
Leveraging item-level data to distinguish between target, informant, and item variability.
Application of the model as a measurement model rather than a multitrait-multimethod model.

Main Results:

The trifactor model effectively separates target, informant, and item variability.
It allows for the examination of a single trait on a single target using item-level data.
The model facilitates the evaluation of item quality and the identification of rater biases.

Conclusions:

The trifactor model offers a robust psychometric approach for multiple informant data.
It enhances the accuracy of psychological assessments by accounting for different sources of variability.
This model aids in developing better scales and generating more reliable, bias-purged scores.