Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event…

We provide tools for identification and exploration of data with very large variability having power law tails. Such data describe extreme features of processes such as fire losses, flood, drought, financial gain/loss, hurricanes, population of cities, among others. Prediction and quantification of extreme events are at the forefront of the…

This study proposed a three-stage measurement model utilizing the Latent Growth Curve Modeling and Latent Class Growth Analysis. The measurement model was illustrated using repeated data collected through a four-week prospective study tracking the subjective well-being of volunteer college students (n=154). Firstly, several unconditional growth…

There is a longstanding debate on whether the analysis of covariance (ANCOVA) or the change score approach is more appropriate when analyzing non-experimental longitudinal data. In this article, we use a structural modeling perspective to clarify that the ANCOVA approach is based on the assumption that all relevant covariates are measured (i.e.,…

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…

Processes that unfold over individuals' life courses are often associated with inequalities later in life. The literature lacks methodological approaches to analyze inequalities in outcomes between groups, for example, between women and men, in a life-course-sensitive manner. We propose a combination of methods--of sequence analysis, which enables…

Imputation or likelihood-based approaches to handle missing data assume the data are missing completely at random (MCAR) or missing at random (MAR). However, little research has examined the missingness pattern before using these imputation/likelihood methods. Three missingness mechanisms -- MCAR, MAR, and not missing at random (NMAR) -- can be…

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…

Using national data from the US Department of Education, Integrated Postsecondary Education Data System (IPEDS) and the US Census Bureau, trends in graduate school enrollment percentages for Hispanic and White students from 2002 to 2018 were analyzed and compared. Major findings from three regression analyses included: (1) a strong, statistically…

Longitudinal social network studies can easily suffer from insufficient statistical power. Studies that simultaneously investigate change of network ties and change of nodal attributes (selection and influence studies) are particularly at risk because the number of nodal observations is typically much lower than the number of observed tie…

This study proposes transformation functions and matrices between coefficients in the original and reparameterized parameter spaces for an existing linear-linear piecewise model to derive the interpretable coefficients directly related to the underlying change pattern. Additionally, the study extends the existing model to allow individual…

Research designs that include planned missing data are gaining popularity in applied education research. These methods have traditionally relied on introducing missingness into data collections using the missing completely at random (MCAR) mechanism. This study assesses whether planned missingness can also be implemented when data are instead…

Currently the design standards for single case experimental designs (SCEDs) are based on validity considerations as prescribed by the What Works Clearinghouse. However, there is a need for design considerations such as power based on statistical analyses. We compute and derive power using computations for (AB)[superscript k] designs with multiple…

Nonresponse bias is a widely prevalent problem for data on education. We develop a ten-step exemplar to guide nonresponse bias analysis (NRBA) in cross-sectional studies and apply these steps to the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011. A key step is the construction of indices of nonresponse bias based on proxy…

Q methodology is a unique, yet underutilized methodology designed specifically to scientifically study subjectivity. Q, as it is most often referred to, is an appropriate methodology whenever a researcher is interested in uncovering and describing the multiple divergent viewpoints on any topic. Such discovery of viewpoints provides insight into…