Event Title

Comparison of Three Different Methods for Missing Data Estimation in Growth Curve Model: FIML, Multiple Imputation, MIPCA

Start Date

3-2-2021 4:10 PM

End Date

3-2-2021 5:40 PM

Date of Publication

February 2021

Document Type

Presentation

Abstract

The present study aims to evaluate the performance of three different modern missing data estimation methods available for growth curve modeling ¬– full information maximum likelihood and two different multiple imputation strategies. As missing data estimation in growth curve model requires missing at (completely) random assumption, the result of three methods from violated data (i.e., missing not at random) would be investigated as well. The result of a Monte Carlo simulation study will provide methodological guidelines for practice of GCM under diverse missing data environments.

Keywords

FIML, Multiple Imputation, MIPCA

Description

Methodology, Discussant: Grant Morgan

Persistent Identifier

http://hdl.handle.net/10950/2846

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Feb 3rd, 4:10 PM Feb 3rd, 5:40 PM

Comparison of Three Different Methods for Missing Data Estimation in Growth Curve Model: FIML, Multiple Imputation, MIPCA

The present study aims to evaluate the performance of three different modern missing data estimation methods available for growth curve modeling ¬– full information maximum likelihood and two different multiple imputation strategies. As missing data estimation in growth curve model requires missing at (completely) random assumption, the result of three methods from violated data (i.e., missing not at random) would be investigated as well. The result of a Monte Carlo simulation study will provide methodological guidelines for practice of GCM under diverse missing data environments.