Longitudinal Study of Indigenous Children (LSIC) technical report: education

Longitudinal Study of Indigenous Children (LSIC) technical report: education
Author/editor: Biddle, N, Edwards, B, Lovett, R, Radoll, P, Sollis, K & Thurber, K.
Year published: 2019
Issue no.: 5


Globally, Australia’s Longitudinal Study of Indigenous Children (LSIC) is the only longitudinal child cohort study on the developmental outcomes of Indigenous children. The study surveys Aboriginal and Torres Strait Islander Australian children aged either 6–18 months
(B cohort) or 3.5–5 years (K cohort) when the study began in 2008. This report evaluates the education measures in the LSIC and contains recommendations for data collectors and analysts, researchers, and policy makers.

Education measures in the LSIC are internally valid and perform as expected. The LSIC is a robust dataset that, if used carefully, can improve our understanding of the development of Indigenous children, and help design good public policy. We highly commend the  contributions of the Indigenous children, their families, the interviewers and field staff, the Indigenousresearchers, and other stakeholders to theongoing success of LSIC.

Some measures perform better than others. In particular, the school climate measures and student-rated teacher relationship showed high correlation. However, measures of academic self-concept were not related to outcomes. Most academic measures showed only small correlations with student outcomes, except the Student–Teacher Relationship Scale, which showed the strongest and most consistent relationships with outcomes. The Student– Teacher Relationship Scale is one of the few education measures consistently collected over the waves, and may be particularly important in understanding Indigenous children’s learning.

The education measures had similar patterns ofassociations for children living in remote areas,but this was not evident for school affective disengagement, as measured with the School Liking and Avoidance Questionnaire (SLAQ), for children in remote areas. For children in remote areas, associations between SLAQ and other education measures were lower and few were statistically significant, including those for child outcomes. The pattern of correlations was similar for the B and K cohorts, and suggests that affective disengagement reported by study children has a qualitatively different meaning for children in remote areas.

Data users should exercise caution if using SLAQ for research that includes children from remote areas. One limitation of the LSIC identified in this paper is that few variables appear to explain the variation in the education measures. When undertaking multivariate analysis of the LSIC, researchers may find constructing models with high explanatory power difficult. We recommend including data items (directly or through data linkage) that can be used to understand variation in education measures. For data collectors, we
recommend asking fewer questions but asking them consistently, and being careful and explicit when attempting to balance specificity and generalisability.

For analysts, we recommend using the data with confidence, while remaining aware that some variables perform better than others and that models using the education measures (especially those specific to the LSIC) tend to have low explanatory power. We also recommend taking advantage of the longitudinal data rather than the the cross-sectional data. We showed a few variables that were significantly associated with change over time in the National Assessment Program – Literary and Numeracy (NAPLAN)
(in particular, housing circumstances), and a consistent association between parent-reported health and school attendance. Worse health at a given time was associated with a lower probability of attending school every day in the previous week.

For reviewers of papers based on LSIC data, we recommend taking into account the unique circumstances of the survey and that models will be estimated with low precision and with variables that differ from those collected in other datasets. Finally, for policy makers, we recommend making decisions using longitudinal research and considering funding a top-up sample. We also suggest that a dedicated analytical hub is established in a research institution to increase the visibility and use of the data.

Updated:  7 February 2020/Responsible Officer:  Centre Director/Page Contact:  CASS Marketing & Communications