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EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE1

Open access
Jones, Richard R., Weinrott, Mark R., Vaught, Russell S.·1978·Journal of Applied Behavior Analysis

Abstract

Comparisons between visual and time‐series inferences from behavorial data show that serial dependency in scores is likely to disrupt agreement between the two methods of analysis. If researchers follow an earlier recommendation that time‐series analysis be used to supplement or confirm visual analysis, this study's findings suggest that the two methods will disagree most often when the data contain high levels of autocorrelation and when reliable behavorial changes are indicated by time‐series analysis.

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Crossref · 10.1901/jaba.1978.11-277

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Metadata

Authors
Jones, R. R., Weinrott, M. R., & Vaught, R. S.
Venue
Journal of Applied Behavior Analysis
Publisher
Wiley
Published
1978-06
Volume
11
Issue
2
Pages
277-283
ISSN
  • 0021-8855 (ISSN-L)
  • 1938-3703
OpenAlex
W2163352683
Cited by
186
References
0
FWCI
6.42
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE1. https://app.cochat.ai/papers/10.1901/jaba.1978.11-277
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Jones, R. R., Weinrott, M. R., & Vaught, R. S. (1978). EFFECTS OF SERIAL DEPENDENCY ON THE AGREEMENT BETWEEN VISUAL AND STATISTICAL INFERENCE1. Journal of Applied Behavior Analysis. https://doi.org/10.1901/jaba.1978.11-277

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.