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Fitting a Serial Correlation Pattern to Repeated Observations

Lynne K. Edwards

Journal of Educational and Behavioral Statistics, 1991, vol. 16, issue 1, 53-76

Abstract: When repeated observations are taken at equal time intervals, a simple form of a stationary time series structure may be fitted to the observations. Wallenstein and Fleiss (1979) have shown that the degrees-of-freedom correction factor for time effects has a higher lowerbound for data with a serial correlation pattern (or a simplex pattern) than for data without such a structure. The reanalysis of the example data found in Hearne, Clark, and Hatch (1983) indicated that the correction factor from a patterned matrix could be smaller than the counterpart without fitting a simplex pattern. First, an example from education was used to illustrate the computational steps in obtaining these two correction factors. Second, a simulation study was conducted to determine the conditions under which fitting a simplex pattern would be advantageous over not assuming such a pattern. Fitting a serial correlation pattern did not always produce more powerful tests of time effects than not assuming such a pattern. This was particularly true when correlations were high (Ï > .50). Furthermore, it inflated Type I error rates when the simplex shypothesis was not warranted. Indiscriminately fitting a serial correlation pattern should be discouraged.

Keywords: repeated measures; longitudinal studies; serial correlation; Markov simplex (search for similar items in EconPapers)
Date: 1991
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:16:y:1991:i:1:p:53-76

DOI: 10.3102/10769986016001053

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