Book recommendation: Longitudinal data analysis using structural equation models

In the wake of our recent posts about longitudinal studies we’d like to recommend a recently published book by By John J. McArdle and John R. Nesselroade.


Longitudinal studies are on the rise, no doubt. Properly conducting longitudinal studies and then analyzing the data can be a complex undertaking. John McArdle and John Nesselroade focus on five basic questions that can be tackled with structural equation models, when analyzing longitudinal data:

  • Direct identification of intraindividual changes.
  • Direct identification of interindividual differences in intraindividual changes.
  • Examining interrelationships in intraindividual changes.
  • Analyses of causes (determinants) of intraindividual changes.
  • Analyses of causes (determinants) of interindividual differences in intraindividual changes.

I find it especially noteworthy, that the authors put an emphasis on factorial invariance over time and latent change scores. In my view, this makes this book a must read to become a longitudinal data wizard.

Need another argument? Afraid of cumbersome mathematical language? Here is what the authors say about it: „We focus on the big picture approach rather than the algebraic details.“


Cause and effect: Optimizing the designs of longitudinal studies

A rising number of longitudinal studies have been conducted and published in industrial and organizational psychology recently. Although this is a pleasing development, it needs to be considered that most of the published studies are still cross-sectional in nature and thus are far less suited for establishing causal relationships. A longitudinal study can potentially provide insights into the direction of effects and the size of effects over time.

Despite their advantages, designing longitudinal studies needs careful considerations and poses tricky theoretical and methodological questions. As Taris and Kompier put it in their editorial to volume 28 of the journal Work & Stress: “…they are no panacea and could yield disappointing and even misleading findings…“. The authors focus on two crucial challenges in longitudinal designs that have a strong impact on detecting the true effects among a set of constructs.

Choosing the right time lags in longitudinal designs

Failing to choose the right time lag between two consecutive study waves lead to biased estimates of effects (see also Cole & Maxwell, 2003). If the study interval is much shorter than the true interval, the cause has not sufficient time to affect the outcome. In contrary, if the study interval is too long the true effects may already have been vanished. Thus, the estimated size of an effect is strongly linked to the length between two consecutive measurement waves.


The chosen interval should correspond as closely as possible to the true underlying interval. This needs thorough a priori knowledge or reasoning about the possible underlying causal mechanism and time lags before conducting a study. What to do when deducting or estimating an appropriate time lag is not possible? Taris and Kompier (2014) suggest “that researchers include multiple waves in their design, with relatively short time intervals between these waves. Exactly how short will depend on the nature of the variables under study. This way they would maximize the chances of including the right interval between the study waves“. To improve longitudinal research further, the authors propose that researchers report their reasoning for choosing a particular time lag. This would explicitly make temporal considerations what they are a central part of the theoretical foundation of longitudinal study.

Considering reciprocal effects in longitudinal designs

Building on one of their former articles Taris and Kompier(2014) opt for full panel designs meaning that the presumed independent variable as well as the presumed outcome are measured at all waves. Such a design allows testing for reciprocal effects. Not considering existing reciprocal effects in longitudinal analyses may again lead to biased estimates of effects.