The issue of missing data raises concerns in all statistical and educational research. In this study, we focus on missing data in school-based assessment data generated by progressed high school learners (those who did not meet the promotional requirements for their current grades but were allowed to move to the next grade because of policy…

Using secondary data has many advantages, but there are also many limitations, including the lack of relevant information. This article draws on a previous study that used secondary data to investigate substance use in young, elite athletes. Three types of missing data appeared: missing data, lack of information about the data collection process,…

A growing body of literature has focused on missing data methods that factorize the joint distribution into a part representing the analysis model of interest and a part representing the distributions of the incomplete predictors. Relatively little is known about the utility of this method for multilevel models with interactive effects. This study…

Preprocessing is a critical step in the analysis pipeline of spectroscopic data. However, students are rarely introduced to preprocessing when learning spectral techniques in laboratory courses which in turn may affect and delay their progress in the field. Despite its undoubtable importance, students will be mainly performing spectroscopic…

Education plays a major role in the development of the consciousness of the whole society. Education has been improved by analyzing educational data related to student academic performance. By using data mining techniques and algorithms on data from the educational environment, students' performances can be predicted. In this study, a novel Hybrid…

The accounting profession is undergoing a radical change that supplants many historical accounting duties by requiring technical skills to be added to the accountant's tool chest. Stakeholders are increasingly adopting disruptive technologies, including data analytics, blockchain, artificial intelligence, and cloud computing. We reviewed 185…

Visual analysis is the predominant method of analysis in single-case research (SCR). However, most research suggests that agreement between visual analysts is poor, which may be due to a lack of clear guidelines and criteria for visual analysis, as well as variability in how individuals are trained. We developed a survey containing questions about…

Statistical Analysis Software (SAS) is a widely used tool for data management analysis across a variety of fields. The procedure for item response theory (PROC IRT) is one to perform unidimensional and multidimensional item response theory (IRT) analysis for dichotomous and polytomous data. This review provides a summary of the features of PROC…

Purpose: The analysis of Ecological Momentary Assessment (EMA) data can be difficult to conceptualize due to the complexity of how the data are collected. The goal of this tutorial is to provide an overview of statistical considerations for analyzing observational data arising from EMA studies. Method: EMA data are collected in a variety of ways,…

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery (SD) approach based on MapReduce. The proposed SD approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope…

The nature of teaching and learning has evolved over the years, especially as technology has evolved. Innovative application of educational analytics has gained momentum. Indeed, predictive analytics have become increasingly salient in education. Considering the prevalence of learner-system interaction data and the potential value of such data, it…

This paper showcases how a reflexive practice, that includes arts-based activities, deepened understandings experienced by a doctoral student of psychology while completing the data analysis section of a metasynthesis. The metasynthesis focused on qualitative studies, examining the mental and spiritual care of persons living with diabetic foot…

This article presents a project that uses a business context to introduce students to a multiphase approach to analytics while relying primarily on an introductory Excel course for prerequisite knowledge. A range of analytics techniques--including Excel Pivot tables and charts, regression trend lines, and linear programming--are combined into an…

Educational institutions are creating a considerable amount of data regarding students, faculty and related organs. This data is an essential asset for academic institutions as it has valuable insights, knowledge and intelligence for the policymakers. Students are the fundamental entities and primary source of data creation in any educational…

With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…