Longitudinal Dynamic Analyses of Depression and Academic Achievement in the Hawaiian High Schools Health Survey Using Contemporary Latent Variable Change Models

Abstract

The scientific literature consistently supports a negative relationship between adolescent depression and educational achievement, but we are certainly less sure on the causal determinants for this robust association. In this paper we present multivariate data from a longitudinal cohort-sequential study of high school students in Hawai‘i (following McArdle, 2009; McArdle, Johnson, Hishinuma, Miyamoto, & Andrade, 2001). We first describe the full set of data on academic achievements and self-reported depression. We then carry out and present a progression of analyses in an effort to determine the accuracy, size, and direction of the dynamic relationships among depression and academic achievement, including gender and ethnic group differences. We apply three recently available forms of longitudinal data analysis: (1) Dealing with Incomplete Data -- We apply these methods to cohort-sequential data with relatively large blocks of data which are incomplete for a variety of reasons (Little & Rubin, 1987; McArdle & Hamagami, 1992). (2) Ordinal Measurement Models (Muthén & Muthén, 2006) -- We use a variety of statistical and psychometric measurement models, including ordinal measurement models to help clarify the strongest patterns of influence. (3) Dynamic Structural Equation Models (DSEMs; McArdle, 2009). We found the DSEM approach taken here was viable for a large amount of data, the assumption of an invariant metric over time was reasonable for ordinal estimates, and there were very few group differences in dynamic systems. We conclude that our dynamic evidence suggests that depression affects academic achievement, and not the other way around. We further discuss the methodological implications of the study.

Annual review of Asian American Psychology, 2014.

“…P. Kwan et al, 2014), participatory action research (PAR; Francisco, 2014), and critical indigenous pedagogy of place (CIPP; Trinidad, 2014). In addition, one quantitative-correlational analytical method – longitudinal dynamic analysis (McArdle, Hamagami, Chang, & Hishinuma, 2014) – was introduced; this method could be used for determining the (causal) direction of the relation between two variables for large datasets, such as the Hawaiian High Schools Health Survey (HHSHS). As an example of articles focused on measurement or methodology, we introduce one qualitative study.…”

On the Mathematical Relationship Between Latent Change Score and Autoregressive Cross-Lagged Factor Approaches: Cautions for Inferring Causal Relationship Between Variables

“…In the actual use of the LCS model, sometimes residual variances and covariances are fixed to zero, and error covariances ω yx may be added to the model instead of residual covariances (McArdle, 2009;McArdle, Hamagami, Chang, & Hishinuma, 2014;McArdle et al, 2004, see footnote 4). This can also be an effective way to avoid improper solutions.…”

Potential causal relationship between depressive symptoms and academic achievement in the Hawaiian high schools health survey using contemporary longitudinal latent variable change models.

“…Detailed explanation and results of these analyses were beyond the more clinical and applied scope of the present article, but can be found in McArdle, Hamagami, Hishinuma, and Chang (2011).…”