Optimal planned missing data design for linear latent growth curve models

Estimating statistical power for structural equation models in developmental cognitive science: A tutorial in R : Power simulation for SEMs

Determining the compositional structure and dimensionality of psychological constructs lies at the heart of many research questions in developmental science. Structural equation modeling (SEM) provides a versatile framework for formalizing and estimating the relationships among multiple latent constructs. While the flexibility of SEM can accommodate many complex assumptions on the underlying structure of psychological constructs, it makes a priori estimation of statistical power and required sample size challenging. This difficulty is magnified when comparing non-nested SEMs, which prevents the use of traditional likelihood-ratio tests. Sample size estimates for SEM model fit comparisons typically rely on generic rules of thumb. Such heuristics can be misleading because statistical power in SEM depends on a variety of model properties. Here, we demonstrate a Monte Carlo simulation approach for estimating a priori statistical power for model selection when comparing non-nested models in an SEM framework. We provide a step-by-step guide to this approach based on an example from our memory development research in children.

Optimal two-time point longitudinal models for estimating individual-level change: Asymptotic insights and practical implications

Based on findings from a simulation study, Parsons and McCormick (2024) argued that growth models with exactly two time points are poorly-suited to model individual differences in linear slopes in developmental studies. Their argument is based on an empirical investigation of the increase in precision to measure individual differences in linear slopes if studies are progressively extended by adding an extra measurement occasion after one unit of time (e.g., year) has passed. They concluded that two-time point models are inadequate to reliably model change at the individual level and that these models should focus on group-level effects. Here, we show that these limitations can be addressed by deconfounding the influence of study duration and the influence of adding an extra measurement occasion on precision to estimate individual differences in linear slopes. We use asymptotic results to gauge and compare precision of linear change models representing different study designs, and show that it is primarily the longer time span that increases precision, not the extra waves. Further, we show how the asymptotic results can be used to also consider irregularly spaced intervals as well as planned and unplanned missing data. In conclusion, we like to stress that true linear change can indeed be captured well with only two time points if careful study design planning is applied before running a study.

Pay Attention to the Ignorable Missing Data Mechanisms! An Exploration of Their Impact on the Efficiency of Regression Coefficients

The use of modern missing data techniques has become more prevalent with their increasing accessibility in statistical software. These techniques focus on handling data that are missing at random (MAR). Although all MAR mechanisms are routinely treated as the same, they are not equal. The impact of missing data on the efficiency of parameter estimates can differ for different MAR variations, even when the amount of missing data is held constant; yet, in current practice, only the rate of missing data is reported. The impact of MAR on the loss of efficiency can instead be more directly measured by the fraction of missing information (FMI). In this article, we explore this impact using FMIs in regression models with one and two predictors. With the help of a Shiny application, we demonstrate that efficiency loss due to missing data can be highly complex and is not always intuitive. We recommend substantive researchers who work with missing data report estimates of FMIs in addition to the rate of missingness. We also encourage methodologists to examine FMIs when designing simulation studies with missing data, and to explore the behavior of efficiency loss under MAR using FMIs in more complex models.

The 3D-Transition Study: Objectives, Methods, and Implementation of an Innovative Planned Missing-Data Design

The prevalence of mental health problems represents a significant burden on school and community health resources as early as preschool. Reducing this burden requires a better understanding of the developmental mechanisms linking children's early vulnerabilities with mental health after the transition to formal schooling. The 3D-Transition Study (2017-2021) follows 939 participants from a pregnancy cohort in the province of Québec, Canada, as they transition to kindergarten and first grade to examine these mechanisms. Biannual assessments include completed questionnaires from 2 parents as well as teachers, parent-child observations, anthropometric measurements, and age-sensitive cognitive assessments. Saliva is also collected on 11 days over a 16-month period in a subsample of 384 participants to examine possible changes in child salivary cortisol levels across the school transition and their role in difficulties observed during the transition. A combination of planned missing-data designs is being implemented to reduce participant burden, where incomplete data are collected without introducing bias after the use of multiple imputation. The 3D-Transition Study will contribute to an evidence-based developmental framework of child mental health from pregnancy to school age. In turn, this framework can help inform prevention programs delivered in health-care settings during pregnancy and in child-care centers, preschools, and schools.

Applying planned missingness designs to longitudinal panel studies in developmental science: An overview

Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test-retest effects or high attrition over time. Planned missingness designs (PMDs), in which partial data are intentionally collected from all or some of the participants, are viable solutions to these challenges. This article provides an overview of several PMDs with potential utilities in longitudinal studies, including the multi-form designs, multi-method designs, varying lag designs, accelerated longitudinal designs, and efficient designs for analysis of change. For each of the designs, the basic rationale, design considerations, data analysis, advantages, and limitations are discussed. The article is concluded with some general recommendations to developmental researchers and promising directions for future research.