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

Andreas M Brandmaier,Ulman Lindenberger,Timo Von Oertzen,Paolo Ghisletta,Christopher Hertzog

doi:10.3389/fpsyg.2015.00272

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.

Highlights

Describing, explaining, and modifying between-person differences in change are central goals in research on lifespan development (Baltes and Nesselroade, 1979; Hertzog, 1996; Baltes et al, 2006; Ferrer and McArdle, 2010; Lindenberger et al, 2011)
Researchers have begun to examine the relative importance of factors that contribute to the statistical power to detect between-person differences in change, such as the true variance in change, the number and precision of indicators, the number and distribution of measurement occasions, and the total time elapsing from the beginning to the end of the study
Researchers using LIFESPAN can: (a) generate a graphical rendition of the model implied by an initial study design; (b) freely and systematically explore the space of alternative power-equivalent study designs; (c) compute and display relevant design indices, such as effective error, growth curve reliability (GCR), growth rate reliability (GRR), or effective curve reliability (ECR); (d) run a Monte Carlo simulation engine to estimate statistical power for a given sample size; and (e) convert the final model from a planning into a data analysis tool

Summary

Introduction

Describing, explaining, and modifying between-person differences in change are central goals in research on lifespan development (Baltes and Nesselroade, 1979; Hertzog, 1996; Baltes et al, 2006; Ferrer and McArdle, 2010; Lindenberger et al, 2011). Researchers using LIFESPAN can: (a) generate a graphical rendition of the model implied by an initial study design; (b) freely and systematically explore the space of alternative power-equivalent study designs; (c) compute and display relevant design indices, such as effective error, GCR, GRR, or ECR; (d) run a Monte Carlo simulation engine to estimate statistical power for a given sample size; and (e) convert the final model from a planning into a data analysis tool To facilitate this transition, LIFESPAN is based on nyx (von Oertzen et al, 2015), a SEM software environment that is freely available (http:// onyx.brandmaier.de), but distributed as a stand-alone program. Further capabilities of nyx include the generation of publication-ready figures, further simulation, and export of the syntax of the final model to three freely available R packages, OpenMx (Boker et al, 2011), lavaan (Rosseel, 2012), and sem (Fox, 2006), and to the commercially available software package Mplus (Muthén and Muthén, 2007)

A Sample Application of Lifespan

Discussion