Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…

Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…

Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth-related processes during estimation. This confounding of change…

The present study was motivated by the theory-method mismatch between heterotypic continuity (aspects of development that manifest differently across the lifespan thus cannot be measured the same way over time) and longitudinal measurement equivalence (the statistical assumption that the developmental phenomenon studied is measured on the same…

Growth mixture models (GMMs) are prevalent for modeling unknown population heterogeneity via distinct latent classes. However, GMMs are riddled with convergence issues, often requiring researchers to atheoretically alter the model with cross-class constraints to obtain convergence. We discuss how within-class random effects in GMMs exacerbate…

Many studies have shown that learning to read in a second language (L2) is similar, in many ways, to learning to read in a first language (L1). Nevertheless, reading development also relies upon oral language proficiency and is greatly influenced by orthographic consistency. This longitudinal study aimed to analyze the role of linguistic…

The present longitudinal study aimed to investigate the development of word decoding skills during incremental phonics instruction in Dutch as a transparent orthography. A representative sample of 973 Dutch children in the first grade (M[subscript age] = 6;1, SD = 0;5) was exposed to incremental subsets of Dutch grapheme-phoneme correspondences…

We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a "gold standard" that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based)…

In this longitudinal study, we investigated the role of word reading and mathematical difficulties measured in 9th grade as factors for receiving educational support for learning in upper secondary education in Grades 10 to 12 (from ages 16 to 19) and furthermore as predictors of dropout from upper secondary education within 5 years after…