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Predictive Inference Using Latent Variables with Covariates

Published online by Cambridge University Press:  01 January 2025

Lynne Steuerle Schofield ,
Brian Junker ,
Lowell J. Taylor  and
Dan A. Black
Lynne Steuerle Schofield*
Affiliation:
Swarthmore College
Brian Junker
Affiliation:
Carnegie Mellon University
Lowell J. Taylor
Affiliation:
Carnegie Mellon University
Dan A. Black
Affiliation:
University of Chicago
*
Correspondence should be made to Lynne Steuerle Schofield, Department of Mathematics and Statistics, Swarthmore College, 500 College Avenue, Swarthmore, PA 19081, USA. E-mail: lschofi1@swarthmore.edu
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Abstract

Plausible values (PVs) are a standard multiple imputation tool for analysis of large education survey data, which measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations model of Schofield (Modeling measurement error when using cognitive test scores in social science research. Doctoral dissertation. Department of Statistics and Heinz College of Public Policy. Pittsburgh, PA: Carnegie Mellon University, 2008).

Keywords

latent variable analysisNAEPplausible value methodologymarginal estimation procedures

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 3 , September 2015 , pp. 727 - 747
Copyright
Copyright © 2014 The Psychometric Society

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