Large-scale cross-national studies designed to measure student achievement use different social, cultural, economic and other background variables to explain observed differences in that achievement. Prior to their inclusion into a prediction model, these variables are commonly scaled into latent background indices. To allow cross-national comparisons of the latent indices, measurement invariance is assumed. However, it is unclear whether the assumption of measurement invariance has some influence on the results of the prediction model, thus challenging the reliability and validity of cross-national comparisons of predicted results. To establish the effect size attributed to different degrees of measurement invariance, we rescaled the ‘home resource for learning index’ (HRL) for the 37 countries (n=166,709 students) that participated in the IEA’s combined ‘Progress in International Reading Literacy Study’ (PIRLS) and ‘Trends in International Mathematics and Science Study’ (TIMSS) assessments of 2011. We used (a) two different measurement models [one-parameter model (1PL) and two-parameter model (2PL)] with (b) two different degrees of measurement invariance, resulting in four different models. We introduced the different HRL indices as predictors in a generalized linear mixed model (GLMM) with mathematics achievement as the dependent variable. We then compared three outcomes across countries and by scaling model: (1) the differing fit-values of the measurement models, (2) the estimated discrimination parameters, and (3) the estimated regression coefficients.