Educational research outcomes frequently rely on an assumption that measurement metrics have interval-level properties. While most investigators know enough to be suspicious of interval-level claims, and in some cases even question their findings given such doubts, there is a lack of understanding regarding the measurement conditions that create…

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…

This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed-effects models when performing intersectional studies. The research questions are as follows: (1) What are typical strata representation rates and outcomes on physics research-based assessments? (2) To what…

This paper presents a model for developing research questions that are aligned to research aims for research design in e-learning. The model is proposed as a solution to the on-going problem of heterogeneity of research problems. The model is based on Burrell and Morgan's four social paradigms, and integrates four research aims, namely, explore,…

Learner engagement correlates with important educational outcomes, including academic achievement and satisfaction. Although research is already exploring learner engagement in blended contexts, no theoretical framework guides inquiry or practice, and little consistency or specificity exists in engagement definitions and operationalizations.…

This report aims to showcase the value of implementing a Bayesian framework to analyse and report results from international large-scale surveys and provide guidance to users who want to analyse the data using this approach. The motivation for this report stems from the recognition that Bayesian statistical inference is fast becoming a popular…

Parallel to the increasing level of maturity of the field of research on higher education, an increasing number of scholarly works aims at synthesising and presenting overviews of the field. We identify three important pitfalls these previous studies struggle with, i.e. a limited scope, a lack of a content-related analysis, and/or a lack of an…

In this article, the authors provide a response to the special issue on "The Science of Teacher Professional Development: Iterative Design Studies Across Content Areas." In doing so, they present the framework of professional development (PD) enactment developed by Mary Kennedy and apply it to the three studies highlighted in this…

Data analysis is a significant methodological component when conducting quantitative education studies. Guidelines for conducting data analyses in quantitative education studies are common but often underemphasize four important methodological components impacting the validity of inferences: quality of constructed measures, proper handling of…

This paper addresses the wicked problem of measuring the productivity of learning and teaching in higher education. We show how fundamental validity issues and difficulties identified in educational productivity research point to the need for a qualitatively different framework when considering the entire question. We describe the work that needs…

Qualitative research and quantitative research have their own advantages, so mixed use both to improve scientific research. Too many Chinese Ethnic Minority Educational Research in qualitative research, and few use quantitative research, leading the results with many subjective factors. The reason is that selecting ethnic minority educational…

In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…

In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…

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…

Missing data are a common occurrence in survey-based research studies in education, and the way missing values are handled can significantly affect the results of analyses based on such data. Despite known problems with performance of some missing data handling methods, such as mean imputation, many researchers in education continue to use those…