Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to…

Obtaining reliable and timely estimates of migration flows is critical for advancing the migration theory and guiding policy decisions, but it remains a challenge. Digital data provide granular information on time and space, but do not draw from representative samples of the population, leading to biased estimates. We propose a method for…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

Multidimensional Item Response Theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that…

This publication describes the methods and procedures used for the 2008/18 Baccalaureate and Beyond Longitudinal Study (B&B:08/18). The B&B graduates, who completed the requirements for a bachelor's degree during the 2007-08 academic year, were first surveyed as part of the 2008 National Postsecondary Student Aid Study (NPSAS:08), and then…

This paper describes a method for pooling grouped, ordered-categorical data across multiple waves to improve small-sample heteroskedastic ordered probit (HETOP) estimates of latent distributional parameters. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in each of a small…

In Huggins-Manley & Han (2017), it was shown that WLSMV global model fit indices used in structural equating modeling practice are sensitive to person parameter estimate RMSE and item difficulty parameter estimate RMSE that results from local dependence in 2-PL IRT models, particularly when conditioning on number of test items and sample size.…

Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful…

The recent change in the general social survey (GSS) to a rotating panel design is a landmark development for social scientists. Sociological methodologists have argued that fixed-effects (FE) models are generally the best starting point for analyzing panel data because they allow analysts to control for unobserved time-constant heterogeneity. We…

This article describes (1) the survey methodological and statistical characteristics of the nonrandomized method for surveying sensitive questions for both cross-sectional and panel survey data and (2) the way to use the incompletely observed variable obtained from this survey method in logistic regression and in loglinear and log-multiplicative…

Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…

The last 20 years has seen an uptick in research on missing data problems, and most software applications now implement one or more sophisticated missing data handling routines (e.g., multiple imputation or maximum likelihood estimation). Despite their superior statistical properties (e.g., less stringent assumptions, greater accuracy and power),…

Researchers in the field of early adolescence interested in quantifying the environmental influences on a response variable of interest over time would use cluster sampling (i.e., obtaining repeated measures from students nested within classrooms and/or schools) to obtain the needed sample size. The resulting longitudinal data would be nested at…

Students learning the skills of science benefit from opportunities to move between the scientific problems and questions they confront and the mathematical tools available to answer the questions and solve the problems. Indeed, students learn science best when they are actively engaged in pursuing answers to authentic and relevant questions. We…

Researchers and data analysts are sometimes faced with the problem of very small samples, where the number of variables approaches or exceeds the overall sample size; i.e. high dimensional data. In such cases, standard statistical models such as regression or analysis of variance cannot be used, either because the resulting parameter estimates…