Human Biology Toolkit Analytical Strategies in Human Growth Research

Human growth research requires knowledge of longitudinal statistical methods that can be analytically challenging. Even the assessment of growth between two ages is not as simple as subtracting the first measurement from the second, for example. This article provides an overview of the key analytical strategies available to human biologists in increasing order of complexity, starting with a review on how to express cross-sectional measurements of size, before covering growth (conditional regression models, regression with conditional growth measures), growth curves (individual growth curves, mixed effects growth curves, latent growth curves), and patterns of growth (growth mixture modeling). The article is not a statistical treatise and has been written by a human biologist for human biologists; as such, it should be accessible to anyone with training in at least basic statistics. A summary table linking each analytical strategy to its applications is provided to help investigators match their hypotheses and measurement schedules to an analysis plan. In addition, worked examples using data on non-Hispanic white participants in the Fels Longitudinal Study are used to illustrate how the analytical strategies might be applied to gain novel insight into human growth and its determinants and consequences. All too often, serial measurements are treated as cross-sectional in analyses that do not harness the power of longitudinal data. The broad goal of this article is to encourage the rigorous application of longitudinal statistical methods to human growth research. Am. J. Hum. Biol. 27:69–83, 2015. VC 2014 The Authors American Journal of Human Biology Published by Wiley Periodicals, Inc. The assessment of human growth requires serial observation (Cameron, 2012), thereby resulting in longitudinal data that present an analytical challenge (Hauspie et al., 2004). This article describes the key analytical strategies available to human biologists. Each strategy has one or more applications; one of the applications of regression with conditional growth measures (Keijzer-Veen et al., 2005), for example, is to identify sensitive developmental periods for some outcome (Adair et al., 2009). By explaining each strategy, and summarizing this information and each strategies applications in Table 1, researchers are provided with a guide to the best methods to test their hypotheses. This resource is not only for human biologists wanting to analyze existing data, but also for those wanting to design studies that correctly match their hypotheses and measurement schedule to an analysis plan. Clearly, the field of growth research is broad. This article does not cover all of the techniques available and is not a statistical treatise on growth analysis methods. Most growth research published by human biologists can be assigned to one of three themes. The first theme comprises questions about how children grow and normal variation and between-population variation in this process (Duran et al., 2013; Johnson et al., 2013b; Kurki, 2013; Staiano et al., 2013); the second about the evolutionary, intergenerational, bio-cultural, genetic, and other factors that affect the growth process (Alwasel et al., 2013; Azcorra et al., 2013; Bingham et al., 2013; Vazquez-Vazquez et al., 2013); and the third about the consequences of certain growth traits and patterns of growth (Moura-Dos-Santos et al., 2013; Redmond et al., 2013; Richards et al., 2013; Ruiz-Castell et al., 2013). After reading this article, the investigator should be well equipped to select the best analytical strategy to test any hypothesis that falls into one of these themes. The strategies are presented in increasing order of complexity, starting with a review on how to express crosssectional measurements of size (internal Z-scores, external Z-scores, indices, conditional size measures), before covering growth (conditional regression models, regression with conditional growth measures), growth curves (individual growth curves, mixed effects growth curves, latent growth curves), and patterns of growth (growth mixture modeling). Each of the main sections ends with a worked example using data on non-Hispanic white participants in the Fels Longitudinal Study (Roche, 1992) to illustrate how the analytical strategies might be applied to gain novel insight into human growth and its determinants and consequences.