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Full-Text Articles in Education
Beyond Constant Comparison Qualitative Data Analysis: Using Nvivo, Nancy L. Leech, Anthony J. Onwuegbuzie
Beyond Constant Comparison Qualitative Data Analysis: Using Nvivo, Nancy L. Leech, Anthony J. Onwuegbuzie
Nancy Leech
The purposes of this paper are to outline seven types of qualitative data analysis techniques, to present step-by-step guidance for conducting these analyses via a computer-assisted qualitative data analysis software program (i.e., NVivo9), and to present screenshots of the data analysis process. Specifically, the following seven analyses are presented: constant comparison analysis, classical content analysis, keyword-in-context, word count, domain analysis, taxonomic analysis, and componential analysis. It is our hope that providing a clear step-by-step process for conducting these analyses with NVivo9 will assist school psychology researchers in increasing the rigor of their qualitative data analysis procedures. (Contains 9 figures and …
A Proposed Fourth Measure Of Significance: The Role Of Economic Significance In Educational Research, Nancy L. Leech, Anthony J. Onwuegbuzie
A Proposed Fourth Measure Of Significance: The Role Of Economic Significance In Educational Research, Nancy L. Leech, Anthony J. Onwuegbuzie
Nancy Leech
The purpose of this paper is to examine economic significance as a fourth measure of significance. In addition to describing and operationalising the concept of economic significance, a typology of economic significance indices is presented, including an example of how to compute these measures, as well as how to utilise them in applied research. We demonstrate how interventions that yield no statistical, practical or clinical effects may be economically significant. Economic significance is not only relevant in the majority of educational research studies, but also is more readily understood by policy makers and stakeholders than are the other three measures …
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Dr Ling Tan
A number of parameters must be specified for a data-mining algorithm. Default values of these parameters are given and generally accepted as ‘good’ estimates for any data set. However, data mining models are known to be data dependent, and so are for their parameters. Default values may be good estimates, but they are often not the best parameter values for a particular data set. A tuned set of parameter values is able to produce a data-mining model of better classification and higher prediction accuracy. However parameter search is known to be expensive. This paper investigates GA-based heuristic techniques in a …