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Statistical analysis in Small-N Designs: using linear mixed-effects modeling for evaluating intervention

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Summary
This summary is machine-generated.

Linear mixed-effects modeling (LMEM) effectively analyzes small-N designs in treatment studies. This statistical method provides reliable effect sizes and significance measures for both continuous and accuracy data, outperforming other techniques.

Keywords:
Small-N designsdysgraphiamixed-effectstreatment studytutorial

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Area of Science:

  • Statistical analysis
  • Quantitative psychology
  • Clinical research methodology

Background:

  • Small-N Designs (SND) present unique statistical analysis challenges.
  • Linear mixed-effects modeling (LMEM) offers a flexible approach for SND.
  • LMEM provides standardized effect sizes and statistical significance measures.

Purpose of the Study:

  • Conceptually explain LMEM within treatment studies.
  • Provide practical guidance for implementing LMEM in repeated measures SND.
  • Illustrate LMEM application with a longitudinal training study.

Main Methods:

  • Applied LMEM to a longitudinal training study with five individuals with acquired dysgraphia.
  • Analyzed both binomial (accuracy) and continuous (reaction time) repeated measures.
  • Included a simulation study comparing two p-value methods for generalized LMEM.

Main Results:

  • LMEM analysis indicated improvements in spelling accuracy and reaction time.
  • Accuracy improved significantly faster with distributed practice compared to clustered practice.
  • Guidance provided on obtaining and interpreting LMEM effect sizes and significance.

Conclusions:

  • LMEM is a preferable alternative to visual analysis for training studies.
  • LMEM is robust for small-N repeated measures data (continuous and binomial).
  • LMEM yields standardized, statistically rigorous effect size estimates.