Utilization of Sample Weights in Single-Level Structural Equation Modeling

• Published in: The Journal of experimental education Vol. 74; no. 2; pp. 163 - 190
• Main Authors: Hahs-Vaughn, Debbie L., Lomax, Richard G.
• Format: Journal Article
• Language: English
• Published: Washington, DC Heldref Publications 01-01-2006; Heldref; Taylor & Francis Inc
• Subjects: Computation; Confidence interval; Data sampling; Design; Design analysis; Education and psychology; Educational Research; Educational sciences; Evaluation Research; Learning; Longitudinal Studies; Measurement, Statistics, and Research Design; Modeling; Multilevel models; National Surveys; Parametric models; Psychology and learning; Research Methodology; Sample size; Sampling; Sampling techniques; Scientific Concepts; Standard error; Statistical Surveys; Structural Equation Models; Survey design; Surveying errors; Surveys; Term weighting; Use Studies; Weights & measures; USA; Post-Secondary Education; design effect adjusted weights; complex samples; sample weights; structural equation modeling; Model; latent variable modeling; Mathematics; Research; design-based approach; Cognitive psychology
• ISSN: 0022-0973; 1940-0683

Complex survey designs often employ multistage cluster sampling designs and oversample particular units to ensure more accurate population parameter estimates. These issues must be accommodated in the analysis to ensure accurate parameter estimation. Incorporation of sample weights in some statistical procedures has been studied. However, research on the behavior of sample weights on estimates, standard errors, and fit measures in latent variable models is negligible, and studies examining methodology on latent variable modeling applications using extant data are rare. Using the Beginning Postsecondary Students Longitudinal Study 1990/92/94, the authors found, with mixed results, that a statistically significant difference exists in estimates and fit indices when weights and designs are applied versus when they are ignored.