Purpose: This article provides a tutorial introduction to ordinal pattern analysis, a statistical analysis method designed to quantify the extent to which hypotheses of relative change across experimental conditions match observed data at the level of individuals. This method may be a useful addition to familiar parametric statistical methods…

Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever-growing field of business analytics, including implementing real-life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the…

We study flexible Bayesian methods that are amenable to a wide range of learning problems involving complex high dimensional data structures, with minimal tuning. We consider parametric and semiparametric Bayesian models, that are applicable to both static and dynamic data, arising from a multitude of areas such as economics, finance and…

The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least-squares (CLS), principal component…

This study aims to reveal the main trends and findings of the studies examining the use of learning analytics in distance education. For this purpose, journal articles indexed in the SSCI index in the Web of Science database were reviewed, and a total of 400 journal articles were analysed within the scope of this study. The systematic review…

Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the…

Extension program evaluations often present opportunities to analyze data in multiple ways. This article suggests that program evaluations can involve more sophisticated data analysis approaches than are often used. On the basis of a hypothetical program scenario and corresponding data set, two approaches to testing for evidence of program impact…

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

In examining national data on Federal Pell Grant eligibility in the National Postsecondary Student Aid Study (NPSAS), we were puzzled to discover that many students who appear to have eligible Expected Family Contributions (EFCs) do not receive the award. We use institutional data from a large public university to understand and enumerate changes…

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

Student login data is a key resource for gaining insight into their learning experience. However, the scale and the complexity of this data necessitate a thorough exploration to identify potential actionable insights, thus rendering it less valuable compared to student achievement data. To compensate for the underestimation of login data…

Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their…

The widespread concern about mathematics achievement has drawn extensive research attention to what skills predict later academic achievement. There is clear and consistent evidence that math achievement at school entry is the strongest predictor of later school success and educational attainment. Early childhood math achievement can thus have…

Although children with autism spectrum disorder are frequently included in mainstream classrooms, it is not known how their social networks change compared to typically developing children and whether the factors predictive of this change may be unique. This study identified and compared predictors of social connectivity of children with and…

Curricula for Computer Science (CS) degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separate CS core studies, the learning skills for these and other required courses may vary a lot, which is shown in students' overall performance. To analyze this situation, we apply…