Publication

Statistical Power in Longitudinal Network Studies

Stadtfeld, C., Snijders, T. A. B., Steglich, C. & van Duijn, M., 1-May-2018, In : Sociological Methods & Research. 35 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Stadtfeld, C., Snijders, T. A. B., Steglich, C., & van Duijn, M. (2018). Statistical Power in Longitudinal Network Studies. Sociological Methods & Research. https://doi.org/10.1177/0049124118769113

Author

Stadtfeld, Christoph ; Snijders, Tom A. B. ; Steglich, Christian ; van Duijn, Marijtje. / Statistical Power in Longitudinal Network Studies. In: Sociological Methods & Research. 2018.

Harvard

Stadtfeld, C, Snijders, TAB, Steglich, C & van Duijn, M 2018, 'Statistical Power in Longitudinal Network Studies', Sociological Methods & Research. https://doi.org/10.1177/0049124118769113

Standard

Statistical Power in Longitudinal Network Studies. / Stadtfeld, Christoph; Snijders, Tom A. B.; Steglich, Christian; van Duijn, Marijtje.

In: Sociological Methods & Research, 01.05.2018.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Stadtfeld C, Snijders TAB, Steglich C, van Duijn M. Statistical Power in Longitudinal Network Studies. Sociological Methods & Research. 2018 May 1. https://doi.org/10.1177/0049124118769113


BibTeX

@article{f6ca53c26be042948892de9d4568e853,
title = "Statistical Power in Longitudinal Network Studies",
abstract = "Longitudinal social network studies may easily suffer from a lack of statistical power. This is the case in particular for studies that simultaneously investigate change of network ties and change of nodal attributes. Such selection and influence studies have become increasingly popular due to the introduction of stochastic actor-oriented models (SAOMs). This paper presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which SAOMs are to be applied. It describes how researchers can test different possible research designs decisions (e.g., about network delineation and study time) under uncertainty about the prevalence and strength of various social mechanisms. Two detailed case studies illustrate that network size, number of data collection waves, effect sizes, missing data, and participant turnover can have a serious effect on the statistical power of longitudinal social network studies.",
author = "Christoph Stadtfeld and Snijders, {Tom A. B.} and Christian Steglich and {van Duijn}, Marijtje",
year = "2018",
month = "5",
day = "1",
doi = "10.1177/0049124118769113",
language = "English",
journal = "Sociological Methods & Research",
issn = "0049-1241",
publisher = "SAGE Publications Inc.",

}

RIS

TY - JOUR

T1 - Statistical Power in Longitudinal Network Studies

AU - Stadtfeld, Christoph

AU - Snijders, Tom A. B.

AU - Steglich, Christian

AU - van Duijn, Marijtje

PY - 2018/5/1

Y1 - 2018/5/1

N2 - Longitudinal social network studies may easily suffer from a lack of statistical power. This is the case in particular for studies that simultaneously investigate change of network ties and change of nodal attributes. Such selection and influence studies have become increasingly popular due to the introduction of stochastic actor-oriented models (SAOMs). This paper presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which SAOMs are to be applied. It describes how researchers can test different possible research designs decisions (e.g., about network delineation and study time) under uncertainty about the prevalence and strength of various social mechanisms. Two detailed case studies illustrate that network size, number of data collection waves, effect sizes, missing data, and participant turnover can have a serious effect on the statistical power of longitudinal social network studies.

AB - Longitudinal social network studies may easily suffer from a lack of statistical power. This is the case in particular for studies that simultaneously investigate change of network ties and change of nodal attributes. Such selection and influence studies have become increasingly popular due to the introduction of stochastic actor-oriented models (SAOMs). This paper presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which SAOMs are to be applied. It describes how researchers can test different possible research designs decisions (e.g., about network delineation and study time) under uncertainty about the prevalence and strength of various social mechanisms. Two detailed case studies illustrate that network size, number of data collection waves, effect sizes, missing data, and participant turnover can have a serious effect on the statistical power of longitudinal social network studies.

U2 - 10.1177/0049124118769113

DO - 10.1177/0049124118769113

M3 - Article

JO - Sociological Methods & Research

JF - Sociological Methods & Research

SN - 0049-1241

ER -

ID: 54551993