Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…

A critical aspect of spoken language development is learning to categorize the sounds of the child's language(s). This process was thought to develop early during infancy to set the stage for the later development of higher-level aspects of language (e.g., vocabulary, syntax). However, many recent studies have shown that speech categorization…

This publication presents information on the vocational education and training (VET) undertaken by school students as part of their senior secondary certificate of education (SSCE). Students can undertake VET through a school-based apprenticeship or traineeship pathway, or other pathway. Highlights include: (1) In 2022, in Australia there were 242…

Although many textbooks on multivariate statistics discuss the common factor analysis model, few of these books mention the problem of factor score indeterminacy (FSI). Thus, many students and contemporary researchers are unaware of an important fact. Namely, for any common factor model with known (or estimated) model parameters, infinite sets of…

In classification learning of artificial stimuli, participants learn the perfectly diagnostic dimension better than the partially diagnostic dimensions. Also, there is a strong preference for a unidimensional categorization based on the perfectly diagnostic dimension. In a different experimental procedure, called array-based classification task,…

Are there some differences so small that we cannot detect them? Are some quantities so similar (e.g., the number of spots on two speckled hens) that they simply look the same to us? Although modern psychophysical theories such as Signal Detection Theory would predict that, with enough trials, even minute differences would be perceptible at an…

This study investigates the correlates of statistics anxiety. Considering that statistics anxiety and spatial anxiety have been separately correlated with related constructs (e.g., mathematics anxiety, academic performance, etc.), the possibility that spatial anxiety plays a role in statistics anxiety is explored. When facing statistics or…

Students' knowledge of data analysis determines the quality of the research they report but they tend to perceive courses on statistics or data analysis as challenging and unappealing. The present study aimed to investigate the correlation between biology education students' attitudes toward statistics and their proficiency in determining various…

Scientific reasoning competencies (SRC) are one part of science teachers' professional competencies. This study examines the contribution of three factors to the development of pre-service science teachers' SRC: the "amount of science education classes," the "amount of science classes" and the pre-service science teachers'…

This article explains the basic ideas and practical challenges in clinical trials of new medicines to show the practical application of statistics in the real-world. The article explores the key considerations for the objectives and design of clinical trials and how these relate to the statistical investigation process. The article also includes…

Matching-adjusted indirect comparison (MAIC) enables indirect comparisons of interventions across separate studies when individual patient-level data (IPD) are available for only one study. Due to its similarity with propensity score weighting, it has been speculated that MAIC can be combined with outcome regression models in the spirit of…

Being a teacher or a student in a class with a large enrollment can be intimidating. Often, teachers view comforts that are common to small classes as unattainable in a larger class, including knowing students' names, using active learning, employing group work, and creating group discussion. Students in large classes may find that the class size…

Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a…

The decomposition of the overall effect of a treatment into direct and indirect effects is here investigated with reference to a recursive system of binary random variables. We show how, for the single mediator context, the marginal effect measured on the log odds scale can be written as the sum of the indirect and direct effects plus a residual…

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different…