ERIC Number: EJ1039718
Record Type: Journal
Publication Date: 2014-Sep
Abstractor: As Provided
Reference Count: 24
The Supermatrix Technique: A Simple Framework for Hypothesis Testing with Missing Data
Lang, Kyle M.; Little, Todd D.
International Journal of Behavioral Development, v38 n5 p461-470 Sep 2014
We present a new paradigm that allows simplified testing of multiparameter hypotheses in the presence of incomplete data. The proposed technique is a straight-forward procedure that combines the benefits of two powerful data analytic tools: multiple imputation and nested-model ?2 difference testing. A Monte Carlo simulation study was conducted to assess the performance of the proposed technique. Full information maximum likelihood (FIML) and single regression imputation were included as comparison conditions against which the performance of the suggested technique was judged. The imputation-based conditions demonstrated much higher convergence rates than the FIML conditions. ??2 statistics derived from the proposed technique were more accurate than such statistics derived from both the FIML conditions and the regression imputation conditions. Limitations of the current work and suggestions for future directions are also addressed.
Descriptors: Hypothesis Testing, Data Analysis, Error of Measurement, Computation, Multivariate Analysis, Research Problems, Monte Carlo Methods, Measurement Techniques, Maximum Likelihood Statistics, Statistical Bias, Models, Accuracy
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Sponsor: National Science Foundation
Authoring Institution: N/A
Grant or Contract Numbers: NSF 1053160