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Summary
This summary is machine-generated.

Linear mixed models (LMMs) are often misapplied to discrete count data in single-case experimental designs (SCEDs). Generalized linear mixed models (GLMMs) are more appropriate, showing better fit and power, though LMMs control Type I error better.

Keywords:
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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Behavioral Research Methods

Background:

  • Single-case experimental designs (SCEDs) are frequently analyzed using hierarchical or multilevel modeling.
  • Linear mixed models (LMMs) are the predominant statistical approach for SCED meta-analysis.
  • LMMs assume normally distributed residuals, which is often violated with discrete SCED outcome data (e.g., counts, rates).

Purpose of the Study:

  • To evaluate the performance of misspecified LMMs versus generalized linear mixed models (GLMMs) for analyzing discrete SCED count data.
  • To compare LMM and GLMM across goodness of fit, parameter recovery, Type I error rate, and statistical power.
  • To provide practical guidance for researchers analyzing SCED count data.

Main Methods:

  • A simulation study was conducted using SCED count data generated from a GLMM.
  • The performance of a misspecified LMM and a correctly specified GLMM was compared.
  • A transformation was used to enable comparison of fixed effect parameter recovery between the two models.

Main Results:

  • The LMM demonstrated poorer goodness of fit and lower statistical power compared to the GLMM.
  • Fixed effect parameter recovery was comparable between the LMM and GLMM.
  • The LMM exhibited a lower Type I error rate than the GLMM.

Conclusions:

  • GLMMs are recommended for analyzing SCED count data due to superior fit and power.
  • Researchers should carefully consider model assumptions when analyzing discrete SCED data.
  • Guidelines are provided for applied researchers regarding LMM use with SCED count data.