Standard

Analyzing longitudinal social relations model data using the social relations structural equation model. / Nestler, Steffen; Lüdtke, Oliver; Robitzsch, Alexander.

in: Journal of Educational and Behavioral Statistics, Jahrgang 47, Nr. 2, 04.2022, S. 231-260.

Publikationen: Beitrag in Fachzeitschrift › Artikel in Fachzeitschrift › Forschung › Begutachtung

Harvard

Nestler, S, Lüdtke, O & Robitzsch, A 2022, 'Analyzing longitudinal social relations model data using the social relations structural equation model', Journal of Educational and Behavioral Statistics, Jg. 47, Nr. 2, S. 231-260. https://doi.org/10.3102/10769986211056541

APA

Nestler, S., Lüdtke, O., & Robitzsch, A. (2022). Analyzing longitudinal social relations model data using the social relations structural equation model. Journal of Educational and Behavioral Statistics, 47(2), 231-260. https://doi.org/10.3102/10769986211056541

Vancouver

Nestler S, Lüdtke O, Robitzsch A. Analyzing longitudinal social relations model data using the social relations structural equation model. Journal of Educational and Behavioral Statistics. 2022 Apr;47(2):231-260. https://doi.org/10.3102/10769986211056541

Author

Nestler, Steffen ; Lüdtke, Oliver ; Robitzsch, Alexander. / Analyzing longitudinal social relations model data using the social relations structural equation model. in: Journal of Educational and Behavioral Statistics. 2022 ; Jahrgang 47, Nr. 2. S. 231-260.

BibTeX

@article{91ff4c088dfa4f81b9013392b8bf2803,
title = "Analyzing longitudinal social relations model data using the social relations structural equation model",
abstract = "The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.",
keywords = "Methodological research and method development, social relations model, latent growth model, autoregressive model, longitudinal data, structural equation model",
author = "Steffen Nestler and Oliver L{\"u}dtke and Alexander Robitzsch",
year = "2022",
month = apr,
doi = "10.3102/10769986211056541",
language = "English",
volume = "47",
pages = "231--260",
journal = "Journal of Educational and Behavioral Statistics",
issn = "1076-9986",
publisher = "Sage",
number = "2",

}

RIS

TY - JOUR

T1 - Analyzing longitudinal social relations model data using the social relations structural equation model

AU - Nestler, Steffen

AU - Lüdtke, Oliver

AU - Robitzsch, Alexander

PY - 2022/4

Y1 - 2022/4

N2 - The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.

AB - The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.

KW - Methodological research and method development

KW - social relations model

KW - latent growth model

KW - autoregressive model

KW - longitudinal data

KW - structural equation model

U2 - 10.3102/10769986211056541

DO - 10.3102/10769986211056541

M3 - Journal article

VL - 47

SP - 231

EP - 260

JO - Journal of Educational and Behavioral Statistics

JF - Journal of Educational and Behavioral Statistics

SN - 1076-9986

IS - 2

ER -

ID: 1709849