Using Fixed Thresholds with Grouped Data in Structural Equation Modeling

Valuable methods have been developed for incorporating ordinal variables into structural equation models using a latent response variable formulation. However, some model parameters, such as the means and variances of latent factors, can be quite difficult to interpret because the latent response variables have an arbitrary metric. This limitation can be particularly problematic in growth models, where the means and variances of the latent growth parameters typically have important substantive meaning when continuous measures are used. However, these methods are often applied to grouped data, where the ordered categories actually represent an interval-level variable that has been measured on an ordinal scale for convenience. The method illustrated in this article shows how category threshold values can be incorporated into the model so that interpretation is more meaningful, with particular emphasis given to the application of this technique with latent growth models.     

Associations between school absence and academic achievement: Do socioeconomics matter?

This study examined the extent to which the association between increased student absence and lower achievement outcomes varied by student and school‐level socioeconomic characteristics. Analyses were based on the enrolment, absence and achievement records of 89,365 Year 5, 7 and 9 students attending government schools in Western Australian between 2008 and 2012. Multivariate multi‐level modelling methods were used to estimate numeracy, writing and reading outcomes based on school absence, and interactions between levels of absence and school socioeconomic index (SEI), prior achievement, gender, ethnicity, language background, parent education and occupation status. While the effects of absence on achievement were greater for previously high‐achieving students, there were few significant interactions between absence and any of the socioeconomic measures on achievement outcomes. The results of first‐difference regression models indicated that the negative effect of an increase in absence was marginally larger for students attending more advantaged schools, though most effects were very small. While students from disadvantaged schools have, on average, more absences than their advantaged peers, there is very little evidence to suggest that the effects of absence are greater for those attending lower‐SEI schools. School attendance should therefore be a priority for all schools, and not just those with high rates of absence or low average achievement.