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Area of Science:

  • Speech and Language Pathology
  • Applied Statistics
  • Neurorehabilitation

Background:

  • Single-case experimental designs are crucial for evaluating treatment efficacy in aphasia.
  • Autocorrelation in time-series data can distort findings in clinical research.
  • Accurate effect size calculation is vital for interpreting treatment outcomes in anomia studies.

Purpose of the Study:

  • To investigate the influence of autocorrelation on four common effect size measures in anomia treatment research.
  • To compare the performance of Busk and Serlin's d, Young's C, nonoverlap of all pairs, and Tau-U when applied to autocorrelated data.

Main Methods:

  • Extracted 173 highly autocorrelated data series from published anomia treatment studies.
  • Removed autocorrelation using an autoregressive integrated moving average (ARIMA) process.
  • Calculated effect sizes for both original and ARIMA-tized data using the four selected methods.

Main Results:

  • Statistically significant differences were observed between effect sizes calculated from original and ARIMA-tized data across all four methods.
  • This indicates that autocorrelation influences all tested effect size calculations, contrary to some hypotheses.
  • The study found that even methods assumed to be robust were affected by data autocorrelation.

Conclusions:

  • Autocorrelation poses a significant challenge to the accurate calculation of effect sizes in single-case aphasia research.
  • All four examined effect size metrics (Busk and Serlin's d, Young's C, nonoverlap of all pairs, Tau-U) are susceptible to autocorrelation.
  • Development of new effect size measures resilient to autocorrelation is needed to enhance the quality of single-case study analyses.