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Modeling multiple dependent variables in meta-analysis of single-case experimental design using multilevel modeling.

Eunkyeng Baek1, Wen Luo2

  • 1Educational Psychology, Texas A&M University, 718E Harrington Tower, 4225 TAMU, College Station, TX, 77843-4225, USA. baek@tamu.edu.

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Summary

Modeling multiple dependent variables (DVs) in meta-analyses of single-case experimental designs (SCED) improves accuracy. This approach provides precise effects and unbiased average effects, unlike analyses that ignore multiple DVs.

Keywords:
Heterogeneous error structureMeta-analysisModerator analysisMultilevel modelingMultiple dependent variablesSingle-case experimental design

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Area of Science:

  • Psychology
  • Educational Research
  • Statistics

Background:

  • Meta-analyses of single-case experimental designs (SCED) often incorporate multiple dependent variables (DVs).
  • However, these DVs are seldom integrated into analytical models, potentially leading to statistical issues.
  • Previous research highlighted these issues, but the precise impact on meta-analytic results remains underexplored.

Approach:

  • This simulation study systematically investigated the consequences of not modeling multiple DVs in SCED meta-analyses.
  • Multilevel modeling techniques were employed to analyze simulated SCED data.
  • The study examined how factors like the number of DVs, heterogeneity, autocorrelation, and moderator effects influence parameter estimation.

Key Points:

  • Modeling multiple DVs in SCED meta-analyses offers significant advantages over non-modeling approaches.
  • This method allows for the precise estimation of effects for individual DVs.
  • It yields unbiased average effects and accurate estimates of error variances at both study and observation levels.

Conclusions:

  • The findings underscore the importance of explicitly modeling multiple DVs in SCED meta-analyses for enhanced accuracy and precision.
  • Researchers should adopt methods that account for multiple DVs to obtain more reliable results.
  • Understanding the influence of heterogeneity and other factors is crucial for robust meta-analytic findings in SCED research.