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Educational and Psychological Measurement
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Algorithm for Autocorrelation Analysis of Secular Trends

David J. Krus

Arizona State University

Hyon O. Ko

Arizona State University

An algorithm for autocorrelation analysis of time series data was presented and compared with two alternative computational methods. The algorithm was based on continuous variance adjustment following withdrawal of an element from the lagged series. The algorithm not only reduces the number of computations during the repeated passes through the data vector but also avoids a dampening effect toward the end of the series.

Educational and Psychological Measurement, Vol. 43, No. 3, 821-828 (1983)
DOI: 10.1177/001316448304300317
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Home page
 Educational and Psychological MeasurementHome page
D. J. Krus and K.-H. T. Liang
Wanted-Central Tendency and Variability of Test Norm Tables: How to Recover them?
Educational and Psychological Measurement, March 1, 1984; 44(1): 123 - 129.
[Abstract]