Challenges in longitudinal designs in occupational health psychology

Abstract

Many researchers in occupational health psychology assume that longitudinal research designs permit stronger conclusions concerning the causal relations among the study concepts than cross-sectional designs do. As the temporal order of the variables can be determined unambiguously, longitudinal designs are presumed to offer good opportunities to add further to our understanding of the causal processes generating the phenomena of interest. Consequently, the last three decades have witnessed an increasing popularity for longitudinal designs in both psychology and medicine (1, 2). Not less than 17 (35%) of the 40 original articles published in the 2001 volume of the Scandinavian Journal of Work, Environment and Health utilized a longitudinal design. It is good to see that so many researchers acknowledge the disadvantages of cross-sectional research designs in examining causal processes: correlation is no causation. However, we should bear in mind that longitudinal study designs per se are no guarantee for drawing valid causal inferences and that, in addition to strong points, longitudinal study designs have drawbacks as well. Some of these are widely recognized, such as the issues of selective attrition and testing effects. Selective attrition may result in a restriction of the range of the variables of interest (eg, only healthy workers remain), meaning that the strength of associations among these is underestimated. Testing effects occur when respondents lose interest in participating in a study when they have to answer the same questions time and again or when respondents interpret questions differently because they are becoming more sensitive as regards their work circumstances and health (2). On a more general level we believe that there are three common, important and less widely recognized misunderstandings with respect to longitudinal study designs in occupational health psychology. The first of these is that we can prove causality by using longitudinal study designs . The extent to which causal inferences can be made depends on the following four conditions: temporal ordering of the focal variables, the strength of the statistical association between them, theoretical plausibility of the presumed causal relationship, and exclusion of plausible rival hypotheses for this relationship (2, unpublished manuscript by de Lange et al). While the first three conditions are relatively easy to satisfy with a longitudinal design, it is impossible to exclude the possibility that particular associations are due to variables that were not measured in the study design. Thus we can never prove causal relationships; the best we can do is argue that it is plausible that certain statistical associations can be understood in causal terms. The second common misunderstanding in longitudinal research is that two observations are adequate for studying intraindividual processes . Clearly, two observations provide information about change over time, but this information is usually insufficient for a thorough understanding of the process responsible for these changes. Here, we face the following two questions: (i) how do we choose the right time interval, and (ii) how do we know what happens within the time interval. First, it has often been noted that the measurement interval between the phases of a study should correspond with the time that a causal variable X needs to affect an outcome variable Y (the underlying causal interval). Although this issue is not new, it is our impression that, in occupational health psychology, the interval between the phases of a study is often chosen on pragmatic grounds, rather than on the basis of careful consideration of the possible underlying causal lags (unpublished manuscript by de Lange et al). The length of this causal lag is often unknown (2), but this lack of knowledge should not keep researchers from considering and addressing this issue. Researchers should indicate why they feel that the lags employed in their study are appropriate for investigating the phenomenon under study, on the basis of theoretical arguments and empirical evidence. Such empirical evidence stems from a recent multiphase panel study by Dormann & Zapf (3) on the effects of conflict with supervisors and colleagues on depression. They showed that the strongest effects were found for a 2-year lag, rather than for a shorter or longer interval. Although this does not imply that a 2-year time lag is always optimal (that depends on the variables under study), Dormann & Zapf`s research clearly demonstrates that multiphase studies are more useful for examining causal processes in occupational health psychology than the standard two-phase panel design. Regarding our second question (what happens within the time interval), two observations per subject may permit an estimation of the amount of across-time change, but not of the individual growth curve. If individual scores are available for time 1 and time 2 of the study, we