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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Predictive Inference Using Latent Variables with Covariates.

Lynne Steuerle Schofield¹, Brian Junker, Lowell J Taylor

¹Department of Mathematics and Statistics, Swarthmore College, 500 College Avenue, Swarthmore, PA, 19081, USA, lschofi1@swarthmore.edu.

|September 19, 2014

Summary

This summary is machine-generated.

Plausible values (PVs) are useful for education survey data analysis. However, standard PV methods can bias results when latent proficiency is an independent variable, necessitating alternative approaches.

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Area of Science:

Educational measurement
Statistics
Econometrics

Background:

Plausible values (PVs) are a standard tool for multiple imputation in large-scale education survey data analysis.
PVs are used to measure latent proficiency variables, which are common in educational research.

Purpose of the Study:

To re-evaluate the standard institutional PV methodology for latent proficiency as a dependent variable.
To identify and address biases in the standard institutional PV methodology when latent proficiency is an independent variable.

Main Methods:

Reconsideration of the standard institutional PV methodology for dependent latent proficiency.
Analysis of inferential biases in institutional PV methodology when latent proficiency is an independent variable.
Application of a mixed-effects structural equations model as an alternative approach.

Main Results:

The standard institutional PV methodology is applicable with greater generality when latent proficiency is the dependent variable.
The standard institutional PV methodology produces biased inference when latent proficiency is an independent variable due to model restrictions.
The proposed alternative approach based on mixed-effects structural equations successfully avoids these biases.

Conclusions:

The standard institutional PV methodology is robust for dependent latent proficiency but requires careful consideration for independent latent proficiency.
Alternative methods, such as mixed-effects structural equations, are necessary to prevent biased inference in specific educational data analysis contexts.
Accurate measurement of latent proficiency is crucial for valid statistical inference in educational research.