Purpose of the Study: Missing data are a common problem encountered while implementing measurement instruments. Yet the extent to which reliability, validity, average discrimination and difficulty of the test results are affected by the missing data has not been studied much. Since it is inevitable that missing data have an impact on the…

Staff members at Sesame Workshop asked us to conduct a meta-analysis of their proprietary evaluation research on the effects of viewing international versions of "Sesame Street." Given global crises in early childhood education, and given the wide reach of "Sesame Street," it was of considerable interest to us whether there…

This methodological brief introduces basic procedures and issues for conducting a high-quality meta-analysis in gifted education. Specifically, we discuss issues such as how to select a topic and formulate research problems, search for and identify qualified studies, code studies and extract data, choose and calculate effect sizes, analyze data,…

Researching writing-related dispositions is of critical concern for understanding writing transfer and writing development. However, as a field we need better tools and methods for identifying, tracking, and analyzing dispositions. This article describes a failed attempt to code for five key dispositions (attribution, self-efficacy, persistence,…

Considering scoring validity as encompassing both reliable rating scale use and valid descriptor interpretation, this study reports on the validation of a CEFR-based scale that was co-constructed and used by novice raters. The research questions this paper wishes to answer are (a) whether it is possible to construct a CEFR-based rating scale with…

This commentary reflects on the contributions to learning analytics and theory by a paper that describes how multiple theoretical frameworks were woven together to inform the creation of a new, automated discourse analysis tool. The commentary highlights the contributions of the original paper, provides some alternative approaches, and touches on…

Presents general principles for cleaning data sets. Suggests techniques: check for column shifts, data completeness, marginal frequencies, and selected cross-tabulations; examines a sample of cases. Gives strategies for preventing data processing errors. (SK)