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Titolo:
Evaluating data from behavioral analysis: visual inspection or statisticalmodels?
Autore:
Fisch, GS;
Indirizzi:
Yale Univ, Dept Epidemiol & Publ Hlth, New Haven, CT 06520 USA Yale Univ New Haven CT USA 06520 iol & Publ Hlth, New Haven, CT 06520 USA Yale Univ, Ctr Child Study, Div Biostat, New Haven, CT 06520 USA Yale Univ New Haven CT USA 06520 dy, Div Biostat, New Haven, CT 06520 USA
Titolo Testata:
BEHAVIOURAL PROCESSES
fascicolo: 1-3, volume: 54, anno: 2001,
pagine: 137 - 154
SICI:
0376-6357(200105)54:1-3<137:EDFBAV>2.0.ZU;2-N
Fonte:
ISI
Lingua:
ENG
Soggetto:
RANDOMIZATION TESTS; INFERENCE; DESIGNS; BIAS;
Keywords:
visual inspection; behavior analysis; statistical inference; randomization tests; signal detection analysis;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Citazioni:
29
Recensione:
Indirizzi per estratti:
Indirizzo: Fisch, GS Yale Univ, Dept Epidemiol & Publ Hlth, 60 Coll St, New Haven, CT06520 USA Yale Univ 60 Coll St New Haven CT USA 06520 Haven, CT 06520 USA
Citazione:
G.S. Fisch, "Evaluating data from behavioral analysis: visual inspection or statisticalmodels?", BEHAV PROC, 54(1-3), 2001, pp. 137-154

Abstract

Traditional behavior analysis relies upon single-subject study designs andvisual inspection of graphed data to evaluate the efficacy of experimentalmanipulations. Attempts to apply statistical inferential procedures to analyze data have been successfully opposed for many decades, despite problemswith visual inspection and increasingly cogent arguments to utilize inferential statistics. In a series of experiments, we show that trained behavioranalysts often identify level shifts in responding during intervention phases ('treatment effect') in modestly autocorrelated data. but trends are tither misconstrued as level treatment effects or go completely unnoticed. Errors in trend detection illustrate the liabilities of using visual inspection as the sole means by which to analyze behavioral data. Meanwhile, because of greatly increased computer power and advanced mathematical techniques,previously undeveloped or underutilized statistical methods have become far more sophisticated and have been brought to bear on a variety of problemsassociated with repeated measures data. I present several nonparametric procedures and other statistical techniques to evaluate traditional behavioral data to augment, not replace, visual inspection procedures. (C) 2001 Elsevier Science B.V. All rights reserved.

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Documento generato il 01/10/20 alle ore 15:25:43