Statistical Issues in the Study of Temporal Data: Daily Experiences

ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subje...

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Bibliographic Details
Published inJournal of personality Vol. 59; no. 3; pp. 609 - 662
Main Authors West, Stephen G., Hepworth, Joseph T.
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.09.1991
Blackwell
Duke University Press
Subjects
Adaptation, Psychological
Biological and medical sciences
Data processing
Fundamental and applied biological sciences. Psychology
Humans
Life Change Events
Models, Statistical
Personality Tests - statistics & numerical data
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics
Psychometrics. Statistics. Methodology
Social life & customs
Social research
Statistics
Statistics. Mathematics
Human
Personal experience
Statistical analysis
Life events
Daily living
Review
Time analysis
Online AccessGet full text
ISSN0022-3506
1467-6494
DOI10.1111/j.1467-6494.1991.tb00261.x

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Abstract ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time‐series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross‐sectional and time‐series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
AbstractList This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time‐series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross‐sectional and time‐series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
A review of statistical issues that arise in temporal data, particularly with respect to daily experience data, is presented. The conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time‐series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross‐sectional and time‐series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.
Author Hepworth, Joseph T.
West, Stephen G.
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  fullname: Hepworth, Joseph T.
  organization: Arizona State University
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https://www.ncbi.nlm.nih.gov/pubmed/1960644$$D View this record in MEDLINE/PubMed
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Issue 3
Keywords Human
Personal experience
Statistical analysis
Life events
Daily living
Review
Time analysis
Language English
License http://onlinelibrary.wiley.com/termsAndConditions#vor
CC BY 4.0
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Notes ArticleID:JOPY609
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We thank Leona Aiken, David A. Kenny, Ronald Kessler, David P. MacKinnon, William R. Shadish, Jr., Patrick Shrout, the editors, particularly Glenn Affleck, and an anonymous reviewer for their helpful comments on an earlier version of the manuscript. Stephen West was partially supported by NIMH Grant P50MH39246 and Joseph Hepworth was partially supported by NIMH Grant T32MH18287 during the writing of this article; the data described herein were collected under NIMH Grant R03MH39235.
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PublicationTitle Journal of personality
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Publisher Blackwell Publishing Ltd
Blackwell
Duke University Press
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Snippet ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to...
ABSTRACT This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to...
This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of...
A review of statistical issues that arise in temporal data, particularly with respect to daily experience data, is presented. The conclusion highlights some of...
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StartPage 609
SubjectTerms Adaptation, Psychological
Biological and medical sciences
Data processing
Fundamental and applied biological sciences. Psychology
Humans
Life Change Events
Models, Statistical
Personality Tests - statistics & numerical data
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics
Psychometrics. Statistics. Methodology
Social life & customs
Social research
Statistics
Statistics. Mathematics
Title Statistical Issues in the Study of Temporal Data: Daily Experiences
URI https://api.istex.fr/ark:/67375/WNG-PMTPPFPX-B/fulltext.pdf
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fj.1467-6494.1991.tb00261.x
https://www.ncbi.nlm.nih.gov/pubmed/1960644
https://www.proquest.com/docview/1296681840
https://www.proquest.com/docview/235947220
https://www.proquest.com/docview/37219539
https://www.proquest.com/docview/72509492
Volume 59
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