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Cited by 841

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Sequential Analysis and Observational Methods for the Behavioral Sciences

Bakeman, Roger, Quera, Vicenç·2011·Cambridge University Press eBooks

Abstract

Behavioral scientists – including those in psychology, infant and child development, education, animal behavior, marketing and usability studies – use many methods to measure behavior. Systematic observation is used to study relatively natural, spontaneous behavior as it unfolds sequentially in time. This book emphasizes digital means to record and code such behavior; while observational methods do not require them, they work better with them. Key topics include devising coding schemes, training observers and assessing reliability, as well as recording, representing and analyzing observational data. In clear and straightforward language, this book provides a thorough grounding in observational methods along with considerable practical advice. It describes standard conventions for sequential data and details how to perform sequential analysis with a computer program developed by the authors. The book is rich with examples of coding schemes and different approaches to sequential analysis, including both statistical and graphical means.

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Metadata

Authors
Bakeman, R., & Quera, V.
Venue
Cambridge University Press eBooks
Published
2011-10-10
OpenAlex
W1538186073
Cited by
841
References
15
FWCI
22.21
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Sequential Analysis and Observational Methods for the Behavioral Sciences. https://app.cochat.ai/papers/10.1017/cbo9781139017343
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Bakeman, R., & Quera, V. (2011). Sequential Analysis and Observational Methods for the Behavioral Sciences. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9781139017343

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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