An investigation of segmentation analysis modeling as a knowledge discovery technique for the planning and the design of instructional technology research

This study introduces the concept of computer-assisted segmentation analysis modeling (SAM) as a knowledge discovery technique for planning of research in the social sciences, particularly in the field of instructional technology. This investigative study of SAM develops each step in the process, considers major methodological issues and problems, and evaluates computer programs which can be used to facilitate the analysis.;Purposively selected examples from seven doctoral studies of instructional technology at a major Midwestern university are analyzed using an ex-post facto, multiple case contrast design. The SAM technique uses the chi square automatic interaction detection software ;Findings of the seven doctoral studies using linear quantitative statistical methods are revisited and then contrasted to the results of SAM on the same datasets. The exhaustive search methodology of SAM confirms the research findings and discovers additional significant relationships between characteristic segments of the data not revealed in the original studies. These relationships are potential for design and planning of new confirmatory research studies. The user-interactive SAM showed a significant reduction in time and pages of output required to identify the relationships in the data sets compared with the original study methods.;Contribution of this study is the identification and validation of a powerful knowledge discovery technique which can be applied to the planning for research studies in the social sciences, particularly, instructional technology. Researchers and graduate students in the social sciences can have access to a potent exploratory data analysis technique for rapid initial planning and design of proposed confirmatory research studies.