In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…

Despite the increasing use of the statistical programming language R in statistics and data analysis (SDA), its implementation in communication science education is limited. Experiences, recommendations, and a critical exchange are therefore scarce. The following contribution addresses this very gap. At the Department of Journalism and…

This article describes five elementary statistics projects involving the COVID-19 data made available to the public in csv files by the Centers for Disease Control and Prevention. The first project examined data available at the beginning of the COVID surge in New York City in spring, 2020, and used the correlation coefficient to estimate the…

In the contemporary society a massive amount of data is generated continuously by various means, and they are called Big-Data sets. Big Data has potential and limits which need to be understood by statisticians and statistics consumers, therefore it is a challenge to develop Big-Data Literacy to support the needs of constructive, concerned, and…

Functional Data Analysis (FDA) has attracted substantial attention for the last two decades. Within FDA, classifying curves into two or more categories is consistently of interest to scientists, but multi-class prediction within FDA is challenged in that most classification tools have been limited to binary response applications. The functional…

Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables,…

In regression analysis courses, there are many settings in which the response variable under study is continuous, strictly positive, and right skew. This type of response variable does not adhere to the normality assumptions underlying the traditional linear regression model, and accordingly may be analyzed using a generalized linear model…

Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and…

This paper presents a novel design for an upper-level undergraduate statistics course structured around data rather than methods. The course is designed around curated datasets to reflect real-world data science practice and engages students in experiential and peer learning using the data science competition platform Kaggle. Peer learning is…

A major challenge in the widespread application of Mokken scale analysis (MSA) to educational performance assessments is the requirement of complete data, where every rater rates every student. In this study, simulated and real data are used to demonstrate a method by which researchers and practitioners can apply MSA to incomplete rating designs.…

Multidimensional Item Response Theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that…

This article presents findings on the consequences of matrix sampling of context questionnaires for the generation of plausible values in large-scale assessments. Three studies are conducted. Study 1 uses data from PISA 2012 to examine several different forms of missing data imputation within the chained equations framework: predictive mean…

In order to study the manifestation of data sense and identify ways of thinking about variability in authentically realistic problems in a group of Chilean fifth-grade students, a lesson plan was designed and implemented, within the framework of statistical literacy and using the "lesson study" modality, in which students were urged to…

"The Times They Are a-Changin'" says the old Bob Dylan song. But it is not just the times that are a-changin'. For statistical literacy, the very earth is moving under our feet (apologies to Carole King). The seismic forces are (i) new forms of communication and discourse and (ii) new forms of data, data display and human interaction…

Researchers and data analysts are sometimes faced with the problem of very small samples, where the number of variables approaches or exceeds the overall sample size; i.e. high dimensional data. In such cases, standard statistical models such as regression or analysis of variance cannot be used, either because the resulting parameter estimates…