If students are to understand inferential statistics successfully, they must have a profound understanding of the nature of the sampling distribution. Specifically, they must comprehend the determination of the expected value and standard error of a sampling distribution as well as the meaning of the central limit theorem. Many students in a high…

Gibbs sampling was applied to obtain Bayes inferences in the case of unbalanced multilevel data when the homogeneity of variance assumption fails and when interest focuses on inferences for some or all of the groups' variances. This approach is compared to a more standard analysis based on restricted maximum-likelihood statistics. (SLD)

This study explored social presence and interactivity in an online undergraduate program designed for adult students. Although social presence and interactivity have been shown to be important contributors to student satisfaction, and therefore essential to student recruitment and retention in online programs, the ultimate goal for the examination…

Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate…

In tests with time limits, items at the end are often not reached. Usually, the pattern of missing responses depends on the ability level of the respondents; therefore, missing data are not ignorable in statistical inference. This study models data using a combination of two item response theory (IRT) models: one for the observed response data and…

Reports the results of a study that shows that the factors explaining experts' risk perception are similar to those of a comparable group of non-topical experts with a similar level of technological literacy and to the general public, and that the level of explained variance is quite comparable for experts and the general public. (Contains 32…

Considers objections to comparisonwise position, which holds that, when conducting simultaneous significance procedures, per-test Type I error rate should be controlled and that it is unnecessary to introduce adjustments designed to control familywise rate. Objections collected by Saville in an attempt to refute them are discussed along with…

There is a trend toward abandoning traditional parametric approaches to data analysis, with all their restrictive assumptions, in favor of computer-intensive nonparametric inferential statistical procedures, such as the jackknife and the bootstrap that are based on resampling of the sample data. These techniques are compared with the parametric…

The randomized response technique ensures that individual item responses, denoted as true item responses, are randomized before observing them and so-called randomized item responses are observed. A relationship is specified between randomized item response data and true item response data. True item response data are modeled with a (non)linear…

Understanding the concept of the sampling distribution of a statistic is essential for the understanding of inferential procedures. Unfortunately, this topic proves to be a stumbling block for students in introductory statistics classes. In efforts to aid students in their understanding of this concept, alternatives to a lecture-based mode of…

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem…

People's reactions to coincidences are often cited as an illustration of the irrationality of human reasoning about chance. We argue that coincidences may be better understood in terms of rational statistical inference, based on their functional role in processes of causal discovery and theory revision. We present a formal definition of…

Developed a closed form expression for the asymptotic bias of the explained common variance, or the unexplained common variance under assumptions of multivariate normality in minimum rank factor analysis. Findings from existing data sets show that the presented asymptotic statistical inference is based on a recently developed perturbation theory…

Introduces a new inferential test for acyclic structural equation models (SEM) without latent variables or correlated errors. The test is based on the independence relations predicted by the directed acyclic graph of the SEMs, as given by the concept of d-separation. A wide range of distributional assumptions and structural functions can be…

Developed a sample-based nonanalytical degrees-of-freedom correction factor for situations sampling both subjects and conditions with measurement data departing from essentially parallel form. Assessed the application of this correction factor through a simulation study involving data sets with a range of design characteristics and manifesting…