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The paper describes the application to longitudinal research of a method of data collection which combines quantitative and qualitative material. It is claimed that Group Feedback Analysis (GFA) helps to validate data collected by various methods and produces additional ethnographic material and insight into the process under investigation. When the people who supply the data are experienced in the subject matter, as is the case with managers and other employees in organizational research, GFA makes it possible for them to help with the interpretation of the data instead of leaving it entirely to the researcher. The method, which has previously been used in cross-sectional research, is shown to have utility in a longitudinal design where it can facilitate organizational learning. It can also contribute to the refinement of a theoretical model.

The main purpose of this paper is to advocate a rule-based forecasting technique for anticipating stock index volatility. This paper intends to set up a stock index indicators projection prototype by using a multiple criteria decision making model consisting of the cluster analysis (CA) technique and Rough Set Theory (RST) to select the important attributes and forecast TSEC Capitalization Weighted Stock Index. The projection prototype was then released to forecast the stock index in the first half of 2009 with an accuracy of 66.67%. The results point out that the decision rules were authenticated to employ in forecasting the stock index volatility appropriately.

AbstractStudies concerning innovative behavior have applied many predictor variables, to the point where a purge to reduce redundancy now appears in order. Factor analysis with orthogonal varimax rotation was applied to product perception and demographic, socioeconomic, and predispositional variables in order to reduce their numbers without a significant loss of information. The derived rotated factors were then related by way of factor scores obtained from respondent data to a surrogate measure of innovative behavior using multiple regression equations. The resulting coefficients of determination, while smaller than those originally derived using all of the perceptual and predispositional variables individually, were not reduced by any massive amounts. Thus, the gain from reducing the number of predictor variables through the use of factor analysis seems to more than off‐set the modest reduction in explained variance. Moreover, the resulting factors and their item loadings suggest possible alternative predictor variables to those that have commonly been used in past studies concerning the diffusion of innovations.

The Americans with Disabilities Act (ADA) was designed to promote equal employment opportunities for handicapped individuals in the workplace. The law mandates that employers reasonably accommodate individuals with disabilities who are qualified to perform the job. However, employers are exempted from this requirement if the reasonable accommodation results in an undue hardship for the organization. The legal definitions of reasonable accommodation and undue hardship have evolved over time due to case law resulting from judicial decisions. This paper will review recent decisions by the U.S. Supreme Court and their effect on employer responsibilities under the ADA. Suggestions will be provided as to policies organizations could follow to respond to these requirements.

The key to using an analytic method is to understand its underlying logic and figure out how to incorporate it into the research process. In the case of Qualitative Comparative Analysis (QCA), so far these issues have been addressed only partly. While general introductions and user's guides for QCA software packages are available, prospective users find little guidance as to how the method works in applied data analysis. How can QCA be used to produce comprehensive, ingenious explanations of social phenomena? In this article, the author provides such a hands-on introduction to QCA. In the first two parts, he offers a concise overview of 1. the method's main principles and advantages as well as 2. its vital concepts. In the subsequent part, he offers suggestions for 3. how to employ QCA's analytic tools in the research process and how to interpret their output. Lastly, the author shows 4. how QCA results can inform the data analysis. As the main contribution, he provides a template for how to reassess cases, causal recipes, and single conditions based on QCA results in order to produce better explanations of what is happening in the data. With these contributions, the article helps prospective QCA users to utilize the full potential the method offers for social science research.