Abstract
Time-window sequential analyses test whether a target behavior occurs within a temporal window (e.g., within 2 seconds) after an antecedent behavior more than is expected by chance. This type of question is common when we need to know how one person or event may immediately affect another event or person in the natural environment. Theoretically, the significance of sequential associations from time-window analysis can be tested on the single subject level (Bakeman & Quera, 1995). The present Monte Carlo study was conducted to test the Type I error rates and the difference in sequential associations derived from four methods of time-window sequential analysis. The four methods vary according to whether they analyze the duration of antecedent and target behaviors. The results indicate that time-window sequential analysis method is generally valid. The results were most accurate when antecedent duration and target onset was analyzed. Although analyzing duration of the antecedent did affect the results, the effect size for the difference in results due to presence or absence of measuring duration of the antecedent was extremely small. Time-window analysis results appear unaffected by the decision to analyze the duration of the target event.
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REFERENCES
Bakeman, R., & Gottman, J. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). New York: Cambridge University Press.
Bakeman, R., McArthur, D., & Quera, V. (1996). Detecting group differences in sequential association using sampled permutations: Log odds, kappa, and phi compared. Behavior Research Methods, Instruments and Computers, 28(3), 446–457.
Bakeman, R., & Quera, V. (1995). Analyzing interaction: Sequential analysis with SDIS & GSEQ.New York: Cambridge University Press.
Bakeman, R., Robinson, B., & Quera, V. (1996). Testing sequential association: Estimating exact pvalues using sampled permutations. Psychological Methods, 1(1), 4–15.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
Reynolds, H. T. (1984). Analysis of nominal data (2nd ed.). Beverly Hills, CA: Sage.
Yoder, P. J., Bruce, P., & Tapp, J. (2001). Comparing sequential associations within a single dyad. Behavior Research Methods, Instruments and Computers, 33(3), 331–338.
Yoder, P. J., & Feurer, I. D. (2000). Quantifying the magnitude of sequential association between events or behaviors. In T. Thompson & D. Felce (Eds.), Behavioral observation: Technology and applications in developmental disabilities (pp. 317–333). Baltimore, MD: Paul H. Brookes.
Yoder, P. J., Short-Meyerson, K., & Tapp, J. (2004). Measurement of behaviors with special emphasis on sequential analysis of behavior. In E. Emerson, C. Hatton, T. Thompson, & T. Parmenter (Eds.), International handbook of research methods in intellectual disability (pp. 179–202). New York: Wiley.
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Yoder, P.J., Tapp, J. Empirical Guidance for Time-Window Sequential Analysis of Single Cases. Journal of Behavioral Education 13, 227–246 (2004). https://doi.org/10.1023/B:JOBE.0000044733.03220.a9
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DOI: https://doi.org/10.1023/B:JOBE.0000044733.03220.a9