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…

The acceptance of Educational Data Mining (EDM) technology is on the rise due to, its ability to extract new knowledge from large amounts of students' data. This knowledge is important for educational stakeholders, such as policy makers, educators, and students themselves to enhance efficiency and achievements. However, previous studies on EDM…

Although knowledge sharing (KS) has been acknowledged as important, universities face issues that may hinder active sharing among its faculty members such as the absence of trust among its members or insufficient incentives rewarded to those who deserved it. The aim of this research is to focus on the impact of knowledge management (KM) factors in…

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…

Purpose: The purpose of this paper is to combine seemingly unrelated factors to explain global competitiveness. The study argues that school discipline and education investment affect competitiveness with the association being mediated by educational performance. Crucially, diachronic effects of discipline on performance are tested to demonstrate…

Budgetary constraints on the public purse have led Australian Federal and State governments to focus increasingly on the efficiency of public institutions, including Technical and Further Education (TAFE) institutes. In this study, we define efficiency as the relationship between financial and administrative inputs and educational outputs. We…

School violence research is often concerned with infrequently occurring events such as counts of the number of bullying incidents or fights a student may experience. Analyzing count data using ordinary least squares regression may produce improbable predicted values, and as a result of regression assumption violations, result in higher Type I…

A well-known approach to linear least squares regression is that which involves minimizing the sum of squared orthogonal projections of data points onto the best fit line. This form of regression is known as orthogonal regression, and the linear model that it yields is known as the major axis. A similar method, reduced major axis regression, is…

The authors propose a general modeling framework called the general monotone model (GeMM), which allows one to model psychological phenomena that manifest as nonlinear relations in behavior data without the need for making (overly) precise assumptions about functional form. Using both simulated and real data, the authors illustrate that GeMM…

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Multilevel modeling provides several advantages over traditional ordinary least squares regression analysis; however, reporting results to stakeholders can be challenging. This article suggests some strategies for presenting complex, multilevel data and statistical results to institutional and higher education decision makers. The article is…

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

Discusses problems in analyzing change in nonexperimental data. Tests three ordinary least-squares regression models to illustrate similarities/differences. Notes that model based on raw difference change scores applies best to studying change processes; model based on outcome scores applies best to assessing consequences of change; and model…

Several data-analysis problems could be addressed in different ways, ranging from a series of related "local" fitting problems to a single comprehensive "global analysis". The approach has become a powerful one for fitting data to moderately complex models by using library functions and the methods are illustrated for the analysis of HCI-IR…

The specification and estimation of a partial least squares model is discussed in terms of assessing the magnitude of the effects of a variety of differences among Swedish schools on the cognitive and affective outcomes of lower secondary students in science. (Author/SB)