Optimal planned missing data design for linear latent growth curve models

Missing not at random models for latent growth curve analyses.

The past decade has seen a noticeable shift in missing data handling techniques that assume a missing at random (MAR) mechanism, where the propensity for missing data on an outcome is related to other analysis variables. Although MAR is often reasonable, there are situations where this assumption is unlikely to hold, leading to biased parameter estimates. One such example is a longitudinal stud...

Latent Growth Curve Models

Linear Models for Functional Data

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Missing by Design: Planned Missing-Data Designs in Social Science

This article presents research designs that employ modern statistical tools to optimize costs and precision of research along with some additional methodological advantages. In planned missing-data designs some parts of information about respondent are purposely not collected. This gives fl exibility and opportunity to explore a broad range of solutions with considerably lower cost. Modern stat...

A Vernacular for Linear Latent Growth Models

In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals’change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the α construct (amount of X at a chosen temporal reference point) and β construct (growth in X per unit time) are not invariant with respect to choice of r...