The benefits of latent variable modeling to develop norms for a translated version of a standardized scale

This article demonstrates the use of structural equation modeling to develop norms for a translated version of a standardized scale, the Supports Intensity Scale – Children’s Version (SIS-C). The latent variable norming method proposed is useful when the standardization sample for a translated version is relatively small to derive norms independently but the original standardization sample is larger and more robust. Specifically, we leveraged a large, representative US standardization sample (n = 4,015) to add power and stability to a smaller Spanish (n = 405) standardization sample. Using a series of multiple-group mean and covariance structures confirmatory factor analyses using effects-coded scaling constraints, measurement invariance was tested across (a) Spanish only and (b) both US and Spanish age bands (5–6, 7–8, 9–10, 11–12, 13–14, and 15–16). After establishing measurement invariance across the US and Spain, tests for latent means and variance differences within age-bands were only performed for Spanish data; the latent means and variances in the US sample were freely estimated. The study findings suggest that the information in the US data stabilized the overall model parameters, and the inclusion of the US sample did not influence on the norms of the SIS-C Spanish Translation.