Planning longitudinal studies can be challenging as various design decisions need to be made. Often, researchers are in search for the optimal design that maximizes statistical power to test certain parameters of the employed model. We provide a user-friendly Shiny app OptDynMo available at https://shiny.psychologie.hu-berlin.de/optdynmo that…

Having one's funding cut in the course of conducting a longitudinal study has become an increasingly real challenge faced by developmental researchers. The main purpose of the current work is to propose "post hoc" planned missing (PHPM) data designs as a promising solution in such difficult situations. This study discusses general…

Recruiting participants for studies of early-life longitudinal development is challenging, often resulting in practical upper bounds in sample size and missing data due to attrition. These factors pose risks for the statistical power of such studies depending on the intended analytic model. One mitigation strategy is to increase measurement…

Managing and analysing large qualitative datasets pose a particular challenge for researchers seeking a consistent and rigorous approach to qualitative data analysis. This paper describes and demonstrates the development and adoption of a matrix tool to guide the qualitative data analysis of a large sample (N = 122) of interview data. The paper…

Cluster randomized control trials often incorporate a longitudinal component where, for example, students are followed over time and student outcomes are measured repeatedly. Besides examining how intervention effects induce changes in outcomes, researchers are sometimes also interested in exploring whether intervention effects on outcomes are…

Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test--retest effects or high attrition over time. Planned missingness designs (PMDs),…

SEERNet digital learning platforms (DLPs) are developing new infrastructure to support research in authentic contexts where student learning is happening. In order to contextualize this work within the larger field, we trace historical precedents along four main categories: data repositories, data collection services, research design interfaces,…

Many experimental designs in educational and behavioral research involve at least one level of clustering. Clustering affects the precision of estimators and its impact on statistics in cross-sectional studies is well known. Clustering also occurs in longitudinal designs where students that are initially grouped may be regrouped in the following…

This study introduces a longitudinal diagnostic classification model, called the LTA+HDCM, which is a fusion of latent transition analysis (LTA; Collins & Flaherty, 2002; Collins & Wugalter, 1992) and the hierarchical diagnostic classification model (HDCM; Templin & Bradshaw, 2014). The primary goals in this study are (1) to evaluate…

This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features…

This open access book discusses challenges in school improvement research and different methodological approaches that have the potential to foster school improvement research. Research on school improvement and accountability analysis places high demands on a study's design and method. The potential of combining the depth of case studies with the…

Introduction: Planned missing designs are relatively new to the field of psychological and developmental research. These designs allow researchers to randomly assign participants to missing data conditions; participants are either randomly selected to receive or not receive particular measures or items during data collection (Little &…

Selection of appropriate study design and analytical methods is critical for producing robust research, as the design and analytical approach used can ultimately shape results and their interpretation. The objective of this research was to examine how findings from a large repeat cross-sectional data system compare to those from a sample of…

Capturing developmental and learning-induced brain dynamics is extremely challenging as changes occur interactively across multiple levels and emerging functions. Different levels include the (social) environment, cognitive and behavioral levels, structural and functional brain changes, and genetics, while functions include domains such as math,…

Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, researchers assess only part of the sample on a high-quality expensive measure, while the entire sample is given a more inexpensive, but biased measure. The…