Adaptive survey design refers to using targeted procedures to recruit different sampled cases. This technique strives to reduce bias and variance of survey estimates by trying to recruit a larger and more balanced set of respondents. However, it is not well understood how adaptive design can improve data and survey estimates beyond the…

Mixed-mode surveys are known to be susceptible to mode-dependent selection and measurement effects, collectively referred to as mode effects. The use of different data collection modes within the same survey may reduce selectivity of the overall response but is characterized by measurement errors differing across modes. Inference in sample surveys…

Business analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection ("type I errors") is…

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

This study uses Monte Carlo simulations to demonstrate that regression-discontinuity designs arrive at biased estimates when attributes related to outcomes predict heaping in the running variable. After showing that our usual diagnostics are poorly suited to identifying this type of problem, we provide alternatives. We also demonstrate how the…

In studies of relatives, conventional multiple regression may not be appropriate because observations are not independent. Obtaining estimates of regression coefficients and correct standard errors from these populations through a structural equation modeling framework is discussed and illustrated with data from twins. (SLD)