Dealing with missing data by EM in single-case studies

Behavior Research Methods
Li-Ting ChenChao-Ying Joanne Peng

Abstract

Single-case experimental design (SCED) research plays an important role in establishing and confirming evidence-based practices. Due to multiple measures of a target behavior in such studies, missing information is common in their data. The expectation-maximization (EM) algorithm has been successfully applied to deal with missing data in between-subjects designs, but only in a handful of SCED studies. The present study extends the findings from Smith, Borckardt, and Nash (2012) and Velicer and Colby (2005b, Study 2) by systematically examining the performance of EM in a baseline-intervention (or AB) design under various missing rates, autocorrelations, intervention phase lengths, and magnitudes of effects, as well as two fitted models. Three indicators of an intervention effect (baseline slope, level shift, and slope change) were estimated. The estimates' relative bias, root-mean squared error, and relative bias of the estimated standard error were used to assess EM's performance. The findings revealed that autocorrelation impacted the estimates' qualities most profoundly. Autocorrelation interacted with missing rate in impacting the relative bias of the estimates, impacted the root-mean squared error nonlinearly, and interacte...Continue Reading

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Nov 27, 2015·Multivariate Behavioral Research·Magadalena Harrington, Wayne F Velicer

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Citations

Dec 4, 2019·American Journal of Health-system Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists·Sujita W NarayanAsad E Patanwala
Jan 4, 2020·Behavior Research Methods·Tamal Kumar DePatrick Onghena
Jul 1, 2021·Revista brasileira de psiquiatria : orgão oficial da Associação Brasileira de Psiquiatria, Asociación Psiquiátrica de la América Latina·Jerf W K Yeung

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