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IES Grant

Title: Multilevel Modeling of Single-subject Experimental Data: Handling Data and Design Complexities
Center: NCER Year: 2015
Principal Investigator: Van den Noortgate, Wim Awardee: KU Leuven
Program: Statistical and Research Methodology in Education      [Program Details]
Award Period: 3 years (8/1/15–7/31/18) Award Amount: $899,524
Type: Methodological Innovation Award Number: R305D150007

Co-Principal Investigators: Tasha Beretvas (University of Texas at Austin), John Ferron (University of South Florida), and Mariola Maeyart (Katholieke Universiteit Leuven)

The purpose of the study is to develop and communicate guidance to applied SSED researchers in setting up studies, conducting the analyses, and interpreting and reporting the results. Single-subject experimental designs (SSEDs) are being used to tackle critical research problems where group comparison designs are not feasible, due to logistical and resource constraints or to the nature of the study population. Whereas group comparison designs focus on the average treatment effect at one point of time, single-subject experimental designs (SSEDs) allow researchers to investigate at the individual level the size and evolution of intervention effects. In addition, studying a low incidence or highly fragmented population lends itself to SSEDs. Despite the increased interest in the use of SSEDs and ongoing attention to the methodological implications of this type of design, including a previous IES grant to this research team (Multilevel Synthesis of Single-Case Experimental Data: Further Developments and Empirical Validation), the use of SSEDs is still hampered by many remaining questions concerning the statistical analysis and meta-analysis of data from these designs.

In this project, the research team will conduct various Monte Carlo simulations to investigate the effects of and the estimation of parameters given different types and extents of complexity when using multilevel models to analyze single-case data. The types of complexity will include, among others, the estimation of variance components at multiple levels, outcome measures that yield ordinal or count data, and nonlinear functional forms. Researchers will then identify the best approaches through simulation and use these approaches to analyze data from previously published applied studies. The research team plans to produce peer-reviewed conference presentations and journal articles, software for conducting the estimation procedures investigated, and guidance for single-case researchers about analytical approaches, given various design complexities.

Related IES Funded Project: Multilevel Synthesis of Single-Case Experimental Data: Further Developments and Empirical Validation (R305D110024).

Products and Publications

Journal article, monograph, or newsletter

Jamshidi, L., Heyvaert, M., Declercq, L., Fernández-Castilla, B., Ferron, J.M., Moeyaert, M., ... and Van den Noortgate, W. (2017). Methodological Quality of Meta-Analyses of Single-Case Experimental Studies. Research in Developmental Disabilities.

Joo, S.H., Ferron, J.M., Beretvas, S.N., Moeyaert, M., and Van den Noortgate, W. (2017). The Impact of Response-Guided Baseline Phase Extensions on Treatment Effect Estimates. Research In Developmental Disabilities.

Joo, S.H., Ferron, J.M., Moeyaert, M., Beretvas, S.N., and Van den Noortgate, W. (2017). Approaches for Specifying the Level-1 Error Structure When Synthesizing Single-Case Data. The Journal of Experimental Education, 1–20.

Klingbeil, D.A., Moeyaert, M., Archer, C.T., Chimboza, T.M., and Zwolski Jr, S.A. (2017). Efficacy of Peer-Mediated Incremental Rehearsal for English Language Learners. School Psychology Review, 46(1), 122–140.

Moeyaert, M., Maggin, D., and Verkuilen, J. (2016). Reliability, Validity, and Usability of Data Extraction Programs for Single-Case Research Designs. Behavior Modification, 40(6), 874–900.

Moeyaert, M., Rindskopf, D., Onghena, P., and Van den Noortgate, W. (2017). Multilevel Modeling of Single-Case Data: A Comparison of Maximum Likelihood and Bayesian Estimation. Psychological Methods, 22(4), 760–778.

Moeyaert, M., Ugille, M., Natasha Beretvas, S., Ferron, J., Bunuan, R., & Van den Noortgate, W. (2017). Methods for Dealing With Multiple Outcomes in Meta-Analysis: A Comparison Between Averaging Effect Sizes, Robust Variance Estimation Aand Multilevel Meta-Analysis. International Journal of Social Research Methodology, 20(6), 559–572.

Onghena, P., Michiels, B., Jamshidi, L., Moeyaert, M., and Van den Noortgate, W. (2018). One by One: Accumulating Evidence by Using Meta-Analytical Procedures for Single-Case Experiments. Brain Impairment, 19(1), 33–58.