LIFESPAN: A tool for the computer-aided design of longitudinal studies

Abstract

Researchers planning a longitudinal study typically search, more or less informally, a multivariate space of possible study designs that include dimensions such as the hypothesized true variance in change, indicator reliability, the number and spacing of measurement occasions, total study time, and sample size. The main search goal is to select a research design that best addresses the guiding questions and hypotheses of the planned study while heeding applicable external conditions and constraints, including time, money, feasibility, and ethical considerations. Because longitudinal study selection ultimately requires optimization under constraints, it is amenable to the general operating principles of optimization in computer-aided design. Based on power equivalence theory (MacCallum et al., 2010; von Oertzen, 2010), we propose a computational framework to promote more systematic searches within the study design space. Starting with an initial design, the proposed framework generates a set of alternative models with equal statistical power to detect hypothesized effects, and delineates trade-off relations among relevant parameters, such as total study time and the number of measurement occasions. We present LIFESPAN (Longitudinal Interactive Front End Study Planner), which implements this framework. LIFESPAN boosts the efficiency, breadth, and precision of the search for optimal longitudinal designs. Its initial version, which is freely available at http://www.brandmaier.de/lifespan, is geared toward the power to detect variance in change as specified in a linear latent growth curve model.

Keywords: effective error; latent growth curve modeling; optimal design; power equivalence theory; statistical power; structural equation modeling.

Figure 1

Main screen of LIFESPAN. This screenshot shows the specification mode of LIFESPAN. Text fields allow researchers to type in study design parameters, for instance, time span or the number of measurement occasions, and best guesses about true variances in intercept and linear change. At the top, the current study design is displayed as a path diagram.

Figure 2

Iso-power plots for bivariate trade-offs between parameters in a LGCM based on the OCTO-Twin Study. Number of occasions and residual variance (top left), number of occasions and time span (top right), variance of slope and residual variance (bottom left), and variance of intercept and time span (bottom right). The original study design is marked with a cross in each panel.