The Satisfaction with Life Scale adapted for Children: Measurement invariance across gender and over time

Psychol Assess. 2018 Sep;30(9):1261-1266. doi: 10.1037/pas0000598. Epub 2018 Jun 11.

Abstract

This paper examined measurement invariance (MI), both across gender and over time, of the Satisfaction With Life Scale adapted for Children (SWLS-C). Adapted from the adult SWLS, the SWLS-C is a self-report measure for children and adolescents to assess their life satisfaction. The sample comprised elementary school students in British Columbia, Canada (n = 4,026) who responded to the SWLS-C in Grade 4 (M(age) = 9.3, SD = 0.6; 48.9% girls) and approximately 3 years later in Grade 7. We examined MI regarding gender, time, and both gender and time (i.e., interactional invariance) using Clustered Repeated Measures Multi-Group Confirmatory Factor Analysis with a mean- and variance-adjusted weighted least squares (WLSMV) estimation. Residual invariance by gender was supported at Grades 4 and 7; scalar invariance was supported longitudinally for each gender and overall. In the "interactional" model, including gender and time, analyses indicated scalar MI, but not residual MI. Analyses of latent factor means indicated that SWLS-C scores significantly decreased for both girls and boys from Grade 4 to Grade 7. The decrease was more pronounced for girls, but gender differences at either age were not significant. The pattern of observed mean scores differed, as it indicated no significant decrease for boys' SWLS-C scores, but significant gender differences at both time points. However, given the lack of residual invariance, comparisons of observed SWLS-C mean scores across gender and over time may be compromised. The different results for latent and observed mean SWLS-C scores highlight the importance of routinely conducting MI analyses for group comparisons. (PsycINFO Database Record

MeSH terms

  • Canada
  • Child
  • Female
  • Follow-Up Studies
  • Humans
  • Male
  • Personal Satisfaction*
  • Psychometrics / standards*
  • Psychometrics / statistics & numerical data
  • Quality of Life*
  • Sex Factors