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Proximity coefficients as a measure of interrelationships in sequences of behavior.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Proximity coefficients as a measure of interrelationships in sequences of behavior. / Taylor, Paul J.
In: Behavior Research Methods, Vol. 38, No. 1, 02.2006, p. 42-50.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Taylor, PJ 2006, 'Proximity coefficients as a measure of interrelationships in sequences of behavior.', Behavior Research Methods, vol. 38, no. 1, pp. 42-50. <http://www.ingentaconnect.com/content/psocpubs/berm/2006/00000038/00000001/art00005>

APA

Taylor, P. J. (2006). Proximity coefficients as a measure of interrelationships in sequences of behavior. Behavior Research Methods, 38(1), 42-50. http://www.ingentaconnect.com/content/psocpubs/berm/2006/00000038/00000001/art00005

Vancouver

Taylor PJ. Proximity coefficients as a measure of interrelationships in sequences of behavior. Behavior Research Methods. 2006 Feb;38(1):42-50.

Author

Taylor, Paul J. / Proximity coefficients as a measure of interrelationships in sequences of behavior. In: Behavior Research Methods. 2006 ; Vol. 38, No. 1. pp. 42-50.

Bibtex

@article{7963043ce45f4e5bb67c7a1a8b9ee4f3,
title = "Proximity coefficients as a measure of interrelationships in sequences of behavior.",
abstract = "Although a range of methods allow investigators to measure the local dependencies among behaviors in a sequence, only indirect methods are available for measuring the interrelationships among behaviors across an entire sequence. This article introduces a new “proximity” coefficient that measures interrelationships among behaviors as a direct function of their intrinsic organization within a sequence. The coefficient does not depend on a user-defined “window” of analysis and provides an efficient use of data that facilitates comparisons across actors, over time periods, and between single cases. An analysis of artificial data shows further properties of the coefficient, including a diagonal value that reflects the degree to which a behavior is reciprocated, and an asymmetry in values that depicts the relative precedence among behaviors. Extensions of the coefficient to the multivariate case, and its relation to existing methods of analyzing sequences, are discussed.",
author = "Taylor, {Paul J.}",
year = "2006",
month = feb,
language = "English",
volume = "38",
pages = "42--50",
journal = "Behavior Research Methods",
issn = "1554-351X",
publisher = "Springer New York LLC",
number = "1",

}

RIS

TY - JOUR

T1 - Proximity coefficients as a measure of interrelationships in sequences of behavior.

AU - Taylor, Paul J.

PY - 2006/2

Y1 - 2006/2

N2 - Although a range of methods allow investigators to measure the local dependencies among behaviors in a sequence, only indirect methods are available for measuring the interrelationships among behaviors across an entire sequence. This article introduces a new “proximity” coefficient that measures interrelationships among behaviors as a direct function of their intrinsic organization within a sequence. The coefficient does not depend on a user-defined “window” of analysis and provides an efficient use of data that facilitates comparisons across actors, over time periods, and between single cases. An analysis of artificial data shows further properties of the coefficient, including a diagonal value that reflects the degree to which a behavior is reciprocated, and an asymmetry in values that depicts the relative precedence among behaviors. Extensions of the coefficient to the multivariate case, and its relation to existing methods of analyzing sequences, are discussed.

AB - Although a range of methods allow investigators to measure the local dependencies among behaviors in a sequence, only indirect methods are available for measuring the interrelationships among behaviors across an entire sequence. This article introduces a new “proximity” coefficient that measures interrelationships among behaviors as a direct function of their intrinsic organization within a sequence. The coefficient does not depend on a user-defined “window” of analysis and provides an efficient use of data that facilitates comparisons across actors, over time periods, and between single cases. An analysis of artificial data shows further properties of the coefficient, including a diagonal value that reflects the degree to which a behavior is reciprocated, and an asymmetry in values that depicts the relative precedence among behaviors. Extensions of the coefficient to the multivariate case, and its relation to existing methods of analyzing sequences, are discussed.

M3 - Journal article

VL - 38

SP - 42

EP - 50

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-351X

IS - 1

ER -