Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two…

Systematic sampling error due to self-selection is a common topic in methodological research and a key challenge for every empirical study. Since selection bias is often not sufficiently considered as a potential flaw in research on and evaluations in higher education, the aim of this paper is to raise awareness for the topic using the case of…

Background: Educational researchers have long espoused the virtues of writing with regard to student cognitive skills. However, research on the reliability of the grades assigned to written papers reveals a high degree of contradiction, with some researchers concluding that the grades assigned are very reliable whereas others suggesting that they…

A procedure modelling ideal classical and quantum gases is discussed. The proposed approach is mainly based on the idea that modelling and algorithm analysis can provide a deeper understanding of particularly complex physical systems. Appropriate representations and physical models able to mimic possible pseudo-mechanisms of functioning and having…

Courses: Beginning research methods and statistics courses, as well as advanced communication courses that require reading research articles and completing research projects involving statistics. Objective: Students will understand the difference between significant and nonsignificant statistical results based on p-value.

A classroom exercise for undergraduate and beginning graduate students that takes about one class period is proposed and discussed. It is an easy, interesting exercise that demonstrates important aspects of sampling techniques (sample amount, particle size, and the representativeness of the sample in relation to the bulk material). The exercise…

Discusses some key statistical concepts in probabilistic and non-probabilistic sampling to provide an overview for understanding the inference process. Suggests a statistical model constituting the basis of statistical inference and provides a brief review of the finite population descriptive inference and a quota sampling inferential theory.…

Discusses the importance of a knowledge of basic statistical concepts for a librarian's education, and the problem of implementing such an introductory course. Two class exercises dealing with sampling and probability and the Central Limit Theorem are described, and 11 references are included. (BK)

Weighting and variance estimation are two statistical issues involved in survey data analysis for large-scale assessment programs such as the Higher Education Information and Communication Technology (ICT) Literacy Assessment. Because survey data are always acquired by probability sampling, to draw unbiased or almost unbiased inferences for the…

A sample lacking variance was judged less probable than a variable sample and a sample representing the upper half of the population was judged less likely than one representing both. As range and mean approached an ideal, samples appeared more probable. A hierarchical model of sample cues seems appropriate. (CPT)

The explicit, experimental introduction of a series of logical inconsistencies is described and recommended as a means of disrupting the faulty logic and, thereby, enhancing the use of more appropriate probabilistic reasoning by graduate students enrolled in an introductory inferential statistics course. (14 references) (JJK)

A pivotal theorem which is of critical importance to statistical inference in probability and statistics is the Central Limit Theorem (CLT). The theorem concerns the sampling distribution of random samples taken from a population, including population distributions that do not have to be normal distributions. This paper contains a brief history of…

The vocabulary of sampling is examined in order to provide a clear understanding of basic sampling concepts. The basic vocabulary of sampling (population, probability sampling, precision and bias, stratification), the fundamental grammar of sampling (random sample), sample size and response rate, and cluster, multiphase, snowball, and panel…

The general question examined by this study was whether the tendency of subjects to ignore the known score in giving the best guess for a sample mean was due to a descriptive heuristic such as representativeness or to a mechanistic one such as active balancing. Two experiments were conducted. In Experiment 1, subjects estimated: (1) the mean of a…

Until recently, norms of experimental reasoning have lacked systematic theoretical development. Thus, it has been easy for psychologists like Tversky and Kahneman to misclassify certain human reasoning processes as being Pascalian and invalid, rather than as being Baconian and valid. (CP)