Speaker
Description
The growing role of social network sites in building and maintaining social relationships, generating social support, and providing information implies the need to develop a tool for measuring online social networks, intended for use in population studies related to health and quality of life. Models based on Item Response Theory are widely used in test development and has proven advantages over classical test theory methods.
The aim of the study was to create a simplified, easy implementable multidimensional instrument to assess all relevant elements of the structure and function of personal online social network.
Data were collected from a nationally representative cross-sectional survey of Polish adults aged 50 and older (N = 1,802) as part of the COURAGE-CAD study. Face-to-face CAPI interviews were conducted. Items to measuring online social networks covered network characteristics, including quantitative dimensions (e.g. network size, structure, contacts frequency), qualitative dimensions (e.g. emotional bond, social support, reciprocity) and alter members (e.g. family, friends). Type of social media used (e.g. social media sites, messaging application, e-mails) was also distinguished.
Dimensionality was explored using exploratory factor analysis with polychoric correlations and robust weighted least squares estimation (WLSMV) to identify latent structures of online social connectivity, and the resulting factor structure was subsequently confirmed in a 30% holdout sample using confirmatory factor analysis, evaluating model fit with standard indices including CFI, TLI, RMSEA, and SRMR. Additionally, Mokken analysis was employed to assess the robustness of the latent structure. To account for the ordinal nature of the items and capture multidimensional latent traits, Item Response Theory models, specifically graded response and generalized partial credit models, were employed to estimate item parameters such as difficulty and discrimination, while generating individual factor scores. Differential item functioning analyses were conducted to detect potential biases across age and gender. The final OSCI was constructed as a factor scores scaled from 0 to 100. Validation analyses included correlations with measures of loneliness, quality of life and Social Network Index (SNI). Analyses were conducted using MPlus and R.
The resulting index provides a validated, multidimensional measure of an individual’s online social network, capable of distinguishing between maintaining offline social networks and building new ones. The tool is suitable for population research, public health monitoring, and automated implementation in online surveys.
This research is funded in whole by National Science Centre, Poland, OPUS23 grant UMO-2022/45/B/NZ7/04030.
53573510746