Introduction to the special section on developmental approaches to prevention science

As outlined by the Society for Prevention Research, prevention science encompasses three major domains that feed into each other (Biglan et al., 2011). First, in the epidemiology domain, research is conducted to identify malleable risk and protection factors and understand predictors of problem and positive developmental outcomes. Second, informed by epidemiological research, the intervention domain aims to develop, test, and disseminate preventive interventions to reduce negative and promote positive developmental outcomes. Finally, the third domain, research methodology, allows the proper examination of the research questions emanating from the two other domains.

State-of-the-art research methods are paramount to answer the complex, theory-driven and developmentally focused questions examined in prevention science. Rigorous methods are a necessity to reach proper conclusions on the predictors of developmental outcomes and the efficacy of preventive interventions. While inadequate findings derived from status-quo methods can lead to policies based on misinformation, the use of justified research methods allows policies to build upon the most accurate results (Little, 2015), thus serving a primary goal of prevention science of improving individual and public health (Biglan et al., 2011). To achieve this goal, the development and dissemination of research methods allowing researchers to find appropriate answers to increasingly complex questions is primordial.

The present special section on Developmental Approaches to Prevention Science aims to contribute to the improvement and implementation of advanced research methods in prevention science, with papers both presenting the development and application of new research methods and providing guidance in the implementation of established cutting-edge methods. These papers cover a wide array of measurement and analytic methods that can be applied to developmental questions in prevention science and beyond.

Technology makes the implementation of some effective measurement methods easier. For example, the visual analog scale, which is more valid than the Likert-type scale, used to be difficult to implement, but can now be easily implemented when using electronic data collection methods (Funke & Reips, 2012; Rioux & Little, 2020). In addition to this increased accessibility to measurement methods that were previously difficult to implement, technology brings new avenues for measurement. This new avenue is showcased in the paper by Jensen and Hussong (2021), which exemplifies the analysis of data gathered from text messages, in this case to examine alcohol-talk in college students.

Modeling change over time and between-person differences in change over time is an important goal in developmental prevention research. When modeling change in an observed score over time with multilevel or structural equation modeling approaches, each observed score counts toward the estimation of model parameter equally (Grimm et al., 2016), but they can differ in terms of their measurement precision. To take this into account, Grimm et al. (2021) propose and illustrate an approach to weight observed scores according to scores’ standard errors or measurement.

When examining bidirectional effects in prevention research, a common approach is the cross-lagged panel model (Selig & Little, 2012). An alternative approach that is increasingly discussed is continuous-time modeling, which aims to identify a continuous process that was measured through discrete measurement occasions (Voelkle et al., 2012). While a cross-lagged panel model with measurements at appropriate time intervals (Rioux & Little, 2020) is ideal to examine a process that occurs at discrete time points, the continuous time model is particularly relevant when theory suggests that the process occurs continuously. Hecht and Voelkle (2021) provide details about this approach and an example of its application to prevention science by examining the bidirectional relationship between physical activity and health. Deboeck et al. (2021) build on this approach to compare models with different underlying continuous processes, providing an example showing how this method could be applied in prevention research to compare intervention and control groups.

Secondary data analyses are relatively common in prevention science and can be particularly useful for developmental questions relying on longitudinal data (Greenhoot & Dowsett, 2012). When data from several studies are available, secondary data analyses can be conducted with integrative data analysis (Bainter & Curran, 2015), in which the data from two or more studies are pooled before conducting analyses to make between- and within-studies inferences. Curran et al. (2021) examined the trifactor model as an effective method to include multiple reporter assessments in integrative data analyses.

Finally, regardless of the analytic technique, prevention researchers usually have to deal with missingness in their data. The implementation of appropriate missing data treatments is important to minimize bias in the results (Lang & Little, 2018). Rioux and Little (2021) review the history and state of missing data reporting and treatments in intervention studies, a cornerstone of prevention science, and provide guidance for future studies.

Together, the articles in this special issue provide guidance to prevention researchers looking to implement the best methods to answer their developmental questions and contribute to the field of developmental methodology through the development and examination of new measurement and analytical methods. This adds to an increasing amount of resources and tools that may be used by developmental prevention scientists to best design their studies and analyze their data. Prevention scientists must carefully choose the methods that are best suited to answer their research questions. This is how we can fulfil prevention science’s goal of improved individual and public health, as this is how we can ensure that the research used to develop evidence-based practice and policy is accurate and reliable.