Longitudinal Methods

Siek-Toon Khoo, Stephen G. West, Wei Wu, and Oi-Man Kwok

The previous chapters in this volume have focused on the measurement of participants using multiple methods, multiple measures, and in multiple situations. In this chapter the focus shifts to the measurement of the same set of participants on multiple occasions, ideally using the same (or equivalent) measurement instruments. This focus on multiple occasions does not fundamentally alter the application of basic concepts and approaches presented in previous chapters (see Eid, chap. 16, this volume; Eid & Diener, chap. 1, this volume). What is new in this chapter is that longitudinal designs explicitly determine the temporal ordering of the observations. This temporal ordering of observations provides an enhanced ability to elucidate stability and change in individuals over time, to study time-related processes, and to establish the direction of hypothesized causal relationships (Dwyer, 1983; Singer & Willett, 2002).

Longitudinal studies are becoming increasingly prominent in several areas of psychology including clinical, community, developmental, personality, and health. For example, Biesanz, West, and Kwok (2003) found that 24% of the studies published in the 2000 and 2001 volumes of the Journal of Personality: Personality Process and Individual Differences section and the Journal of Personality included two or more waves of data collection. In the area of psychology most focused on issues of stability and change, we found that 32% of the articles in Developmental Psychology in 2002 met these minimum criteria for a longitudinal study of two waves of data collection. This compares to only 15% of the articles published in 1990.

A more in-depth review focused on the longitudinal studies in the 2002 volume of Developmental Psychology provides a glimpse of current practice (see also Morris, Robinson, & Eisenberg, chap. 25, this volume). The duration of studies ranged from 12 weeks to 28 years. Approximately 25% of the studies collected only two waves of data, whereas approximately 25% of the studies reported 6 or more waves of data collection, with one study collecting more than 50 waves of data. Measures included standardized measures of ability and intelligence; self-, peer, parent, and teacher reports; ratings and counts of behaviors by trained observers; peer nominations; and physical measures such as weight and heart rate. Although most of the studies included a substantial core set of measures that were administered at each wave, some studies used different measures at each measurement wave, precluding the examination of change over time. The majority of articles reported traditional correlation/regression analyses or analysis of variance. Collins and Sayer (2001), McArdle and Nes-selroade (2003), and Singer and Willett (2002) have highlighted the potential advantages of newer approaches to the analysis of longitudinal data, yet approaches such as structural equation modeling (approximately 10%) and growth modeling and

We thank Jeremy Biesanz, Patrick Curran, coeditor Michael Eid, Paras Mehta, Roger Millsap, Steven Reise, and an anonymous reviewer for their comments on an earlier version of this chapter.

examination of growth trajectories (approximately 15%) continue to represent a distinct minority of longitudinal studies.

This chapter considers a number of unique issues that arise when measurements are taken on multiple occasions. We begin with a consideration of some desiderata of measurement from cross-sectional research and consider how they may apply in longitudinal research. We then consider three different longitudinal models: (a) autoregressive models that focus on the stability of participants' relative standing on a construct over time; (b) latent trait-state models that partition the variance in measured constructs into relatively stable (trait) and measurement occasion specific (state) components; and (c) growth curve models that estimate individual growth trajectories. Finally, we consider these longitudinal models in light of measurement concerns and indicate some methods through which these concerns can be addressed.

SOME DESIDERATA FOR GOOD MEASUREMENT: LESSONS FROM CROSS-SECTIONAL RESEARCH

Sources on traditional and modern approaches to measurement (Crocker & Algina, 1986; Embretson & Reise, 2000; Lord & Novick, 1968; McDonald, 1999; West & Finch, 1997) have emphasized issues that arise in narrow windows of time that characterize cross-sectional and short-term (test-retest) studies. These approaches have developed several desiderata for good measurement; three are presented following. We also begin to consider how these desiderata may need to be extended for longitudinal studies. In this section we will use the framework of classical test theory and assume that measures have been collected on a numerical scale.

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