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Published on: November 15, 2013
Plausible values: how to deal with their limitations.
Christian Monseur1, Raymond Adams
1Université de Liège, FAPSE, Department Education, Bld. du Rectorat, 5 (B32) 4000 Liège, Belgium. cmonseur@ulg.ac.be
Journal of Applied Measurement
|August 13, 2009
Summary
This study examines the use of plausible values in the Programme for International Student Achievement (PISA) assessments. It explores potential biases and the adequacy of variance component estimation in hierarchical data structures.
Area of Science:
- Educational Measurement
- Psychometrics
- Large-Scale Assessment
Background:
- The Programme for International Student Achievement (PISA) utilizes Rasch modeling and plausible values for scaling and reporting results.
- Plausible values, a multiple imputation technique, are integral to modern large-scale assessments.
Purpose of the Study:
- To detail the scaling methodology employed in PISA.
- To critically evaluate the implications of using plausible values generated via a flat linear regression model.
- To assess the suitability of PISA procedures for hierarchical data analysis.
Main Methods:
- Rasch modeling for data scaling.
- Multiple imputation using plausible values.
- Flat linear regression with student background variables as regressors.
- Analysis of secondary data analyses and variance component estimation.
Main Results:
- The study identifies conditions under which secondary analyses may be biased when using variables not included in the plausible value generation model.
- It explores the adequacy of PISA's procedures for estimating variance components in hierarchically structured data, as plausible values were not derived from a multi-level model.
Conclusions:
- The approach to generating plausible values in PISA has implications for the validity of secondary analyses and the estimation of variance components.
- Further investigation is needed to ensure robust statistical inferences from PISA data, particularly concerning nested structures.
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