Dataset Information

Embedding multilevel survival analysis of dyadic social interaction in structural equation models: hazard rates as both outcomes and predictors.

ABSTRACT: Demonstrate multivariate multilevel survival analysis within a larger structural equation model. Test the 3 hypotheses that when confronted by a negative parent, child rates of angry, sad/fearful, and positive emotion will increase, decrease, and stay the same, respectively, for antisocial compared with normal children. This same pattern will predict increases in future antisocial behavior.Parent-child dyads were videotaped in the fall of kindergarten in the laboratory and antisocial behavior ratings were obtained in the fall of kindergarten and third grade.Kindergarten antisocial predicted less child sad/fear and child positive but did not predict child anger given parent negative. Less child positive and more child neutral given parent negative predicted increases in third-grade antisocial behavior.The model is a useful analytic tool for studying rates of social behavior. Lack of positive affect or excess neutral affect may be a new risk factor for child antisocial behavior.

SUBMITTER: Stoolmiller M

PROVIDER: S-EPMC3936259 | biostudies-other | 2014 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Embedding multilevel survival analysis of dyadic social interaction in structural equation models: hazard rates as both outcomes and predictors.

Stoolmiller Mike M Snyder James J

Journal of pediatric psychology 20131015 2

<h4>Objective</h4>Demonstrate multivariate multilevel survival analysis within a larger structural equation model. Test the 3 hypotheses that when confronted by a negative parent, child rates of angry, sad/fearful, and positive emotion will increase, decrease, and stay the same, respectively, for antisocial compared with normal children. This same pattern will predict increases in future antisocial behavior.<h4>Methods</h4>Parent-child dyads were videotaped in the fall of kindergarten in the lab ...[more]

PMID: 24133296

Similar Datasets

Project description:Occupational health research has mainly addressed determinants of negative health effects, typically employing individual-level self-report data. The present study investigated individual- and department-level (means of each work unit) effects of psychological/social work factors on mental distress and positive affect. Employees were recruited from 63 Norwegian organizations, representing a wide variety of job types. A total of 4158 employees, in 918 departments, responded at baseline and at follow-up two years later. Multilevel linear regressions estimated individual- and department-level effects simultaneously, and accounted for clustering of data. Baseline exposures and average exposures over time ([T1+T2]/2) were tested. All work factors; decision control, role conflict, positive challenge, support from immediate superior, fair leadership, predictability during the next month, commitment to organization, rumors of change, human resource primacy, and social climate, were related to mental distress and positive affect at the individual and department level. However, analyses of baseline exposures adjusted for baseline outcome, demonstrated significant associations at the individual level only. Baseline "rumors of change" was related to mental distress only and baseline "predictability during the next month" was not a statistical significant predictor of either outcome when adjusted for outcome at baseline. Psychological and social work factors were generally related to mental distress and positive affect in a mirrored way. Impact of exposures seemed most pervasive at the individual level. However, department-level relations were also discovered. Supplementing individual-level measures with aggregated measures may increase understanding of working conditions influence on employees`health and well-being. Organizational improvements focusing on the work factors in the current study should be able to reduce distress and enhance positive affect. Furthermore, both targeting individual employees and redesigning working conditions at the work unit level seems important.

Dataset Information

Embedding multilevel survival analysis of dyadic social interaction in structural equation models: hazard rates as both outcomes and predictors.

Publications

Embedding multilevel survival analysis of dyadic social interaction in structural equation models: hazard rates as both outcomes and predictors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets