Interrupted time-series analysis with brief single-subject data.
Assessing change with short time-series data is difficult because visual inference is unreliable with such data, and current statistical procedures cannot control Type I error because they underestimate positive autocorrelation. This article describes these problems and shows how they can be solved with a new interrupted time-series analysis procedure (ITSACORR) that uses a more accurate estimate of autocorrelation. Monte Carlo analyses show that, with short series, ITSACORR provides better control of Type I error than all previous procedures and has acceptable power. Clinical examples also show that ITSACORR is easy to use and functions well with real data.
Crosbie
J
J
Department of Psychology, West Virginia University, Morgantown 26506-6040.
D000293:Adolescent; D000328:Adult; D001521:Behavior Therapy / Q000706:statistics & numerical data; D002648:Child; D002653:Child Behavior Disorders / Q000523:psychology / Q000628:therapy; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D010552:Personality Assessment / Q000706:statistics & numerical data*; D010553:Personality Development; D012984:Software; D016896:Treatment Outcome