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Identifying Autocorrelation Generated by Various Error Processes in Interrupted Time-Series Regression Designs

A Comparison of AR1 and Portmanteau Tests

Western Michigan University

Joseph W. McKean

Western Michigan University

Regression models used in the analysis of interrupted time-series designs assume statistically independent errors. Four methods of evaluating this assumption are the Durbin-Watson (D-W), Huitema-McKean (H-M), Box-Pierce (B-P), and Ljung-Box (L-B) tests. These tests were compared with respect to Type I error and power under a wide variety of error models and sample sizes. Although the B-P and L-B tests are portmanteau methods that incorporate information from a large portion of the autocorrelation function, the more focused D-W and H-M first-order autoregressive tests are shown to be considerably more powerful. The popular L-B test has unacceptable Type I error and should not be used in the context of the intervention model applied in this study.

Key Words: autocorrelation tests • interrupted time-series designs • intervention models • time-series regression with autoregressive errors

Educational and Psychological Measurement, Vol. 67, No. 3, 447-459 (2007)
DOI: 10.1177/0013164406294774


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