This report aims to showcase the value of implementing a Bayesian framework to analyse and report results from international large-scale surveys and provide guidance to users who want to analyse the data using this approach. The motivation for this report stems from the recognition that Bayesian statistical inference is fast becoming a popular…

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

Modeling the distribution of site level effects is an important problem, but it is also an incredibly difficult one. Current methods rely on distributional assumptions in multilevel models for estimation. There it is hoped that the partial pooling of site level estimates with overall estimates, designed to take into account individual variation as…

The number of increases a particular stock makes over a fixed period follows a Poisson distribution. This article discusses using this easily-found data as an opportunity to let students become involved in the data collection and analysis process.

Being able to characterize the size of raindrops is useful in a number of fields including meteorology, hydrology, agriculture and telecommunications. Associated with this article are data sets containing surface (i.e. ground-level) measurements of raindrop size from two different instruments and two different geographical locations. Students may…

In the modern world information has become the new power. An increasing amount of efforts are being made to gather data, resources being allocated, time being invested and tools being developed. Data collection is no longer a myth; however, it remains a great challenge to create value out of the enormous data that is being collected. Data modeling…

This article is concerned with the question of whether the missing data mechanism routinely referred to as missing completely at random (MCAR) is statistically examinable via a test for lack of distributional differences between groups with observed and missing data, and related consequences. A discussion is initially provided, from a formal logic…

Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…

The results of this study determined that community college students who transfer to for-profit institutions are indeed different from students who follow traditional routes defined as public and non-profit institutions. This study compares the demographic characteristics, academic experiences, and socioeconomic factors of California community…

This paper explains how to combine probabilities from discrete distributions, such as probability distributions for nonparametric tests. (Author/BW)

A large-scale analysis was conducted of problem behavior by observing 20 individuals living in residential facilities. Data were converted into scatter plot formats. When the data were transformed into aggregate "control charts," 12 of 15 sets of data revealed 30-minute intervals during which problem behavior was more likely to occur.…