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Michael Rutter ended the conference with a thorough overview of the topics raised throughout the conference—summarizing, re-posing, and answering many of the key questions about LSs that were raised.

On the topic of case-control versus LSs, Rutter gave an overview of the pros and cons of each, discussing how the different study designs deal with issues of reporting difficulties, time coverage, missing data, within-individual change, causal chain effects, escape from risk, and unexpected outcomes. He also highlighted some of the issues involved in measuring change (in individuals and populations) and how researchers need to take into account random and systematic error, as well as changing concepts, measurement, and standards over time.

Rutter also discussed the issue of pooling measures across data sets and time points in LSs, noting the value of: having some carry-over of scales/items between time points, nested designs, and latent variable methods. He also emphasized the strengths of LS data (such as the ability to deal with latent and missing variables), and gave a recap of the importance of disentangling age, cohort, and period effects in LS designs.

On the topic of inferring causality in LSs, Rutter summarized some of the common impediments to this endeavor, including: social selection processes, person effects on environment, genetic mediation on environmental risk (and other 3rd variables), and the moderating effects of genes and previous experience. In response to these hazards, he offered three key solutions: 1) careful choice of samples and strategies that pull apart variables that go together, 2) longitudinal data (almost always), and 3) pitting alternative hypotheses against one another.

Rutter also discussed the advantages of natural experiments such as: twin and adoptee (and twin-singleton) situations, planned and unplanned interventions, and secular trends, and he emphasized the need to identify mediating mechanisms. On the topic of whether experimental thinking is always needed in LSs, Rutter emphasized that there can be no inference of causation without manipulation, and natural or planned experiments can be used to see what happens when one variable is manipulated. But multiple different methodological strategies allow us to converge on the “truth”, and what is key is to specify the assumptions behind any study design and test alternative hypotheses against each other.

Noting that there is a reciprocal relationship between studies of normality and psychopathology, Rutter pointed out that there are two separate issues involved comparitive/developmental psychopathology research: 1) the value of using one (pathology or normality) to cast light on other (autism and ToM), and 2) the difficulties involved with concepts of dimensionality vs. major discontinuity between abnormality and normality.

Rutter also noted the importance of taking into consideration: biology/medical studies in LS studies in the field of psychology and the centrality of genetics in moderating the effects of experience (acknowledging the dynamic two-way interaction between biology and psychology). He also emphasized the potential of functional neuroimaging, animal models, and neuroendocrinology as tools to help us in the psychological sciences.

Rutter outlined a number of topics he wished were discussed in more detail in the conference, including: epidemiology (representativeness of samples, issues of attrition); influence of experiences on the organism (cognitive processing and sets, neuroendocrine and biological influences); origins of risk/protective factors; sibling comparisons; and a more explicit focus on gene/environment interactions across development.

On the topic of public policy, Rutter noted that LSs have a great deal to offer in terms of informing public policy on such important topics such as the long-term effects of interventions (preventive and therapeutic), the continuities and discontinuities in processes and traits, and factors influencing resilience. However, he also raised a note of skepticism about how science is often used by policy makers—noting that while science is concerned with answering questions about nature, policy makers want scientists to provide facts to support policies that have already been established. Policy is often decided by values not facts, and the same facts can be used to support different policies. He did note that there exist some hopeful examples of scientific studies (e.g., in schools where teachers collaborated in data gathering; epidemiological studies on physical health that influence service delivery, etc.) that have influenced policy—and we can learn from these examples.

And finally, to one of the central questions of the conference: What can non-experimental studies accomplish, Rutter answered that passive LSs can: generate hypotheses (ruling out invalid and ruling in valid ones) and while they cannot prove causation, they can point to key mediators to be tested experimentally. As to whether or not LSs have a future (a question posed early on by Ross Parke), Rutter answered “yes”, and that LSs should include more experimental thinking and be integrated with more sophisticated methodologies, but they don’t always need to be a long-term or the first approach.