Most marital research in the last two decades has been conducted with predominately white, middle-class, North American married couples; little is known about marital communication practices of couples who live in different cultures. In an attempt to understand the dynamics of cultural orientations to marriages, two types of couples from Kerala (India) and a sample of couples from Michigan (U.S.A.) were compared on their marital satisfaction and communication practices. The labels "arranged" and "love" are used in India to indicate the ways people begin their marital relationships, and the label "companionate" identifies American romantic marriages. The Primary Communication Inventory was used to measure verbal and nonverbal communication practices, while the Dyadic Adjustment Scale was used to measure marital satisfaction. Results from the analyses revealed that arranged marrieds were significantly higher in marital satisfaction than were the love marrieds or companionate marrieds. No significant difference existed between love marrieds and companionate marrieds on marital satisfaction scores. Analyses of the data also indicated that significantly less verbal and nonverbal communication occurred in the arranged marriages than in the love marriages. The love couples, however, reported using significantly less verbal communication than companionate couples. Despite the cultural differences, the love marrieds in Kerala and companionate marrieds in Michigan were more similar in their verbat and nonverbal communication and marital satisfaction scores than were those of the arranged and love married in Kerala. In love and companionate marriages, effective communication appears to be more influential in producing high marital satisfaction, wheras in arranged marriages, cultural tradition and commitment to a relationship appear to be very influential factors in producing high marital satisfaction.
Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of the following ANN parameters: weights, learning rates, threshold functions, and transformation functions. The spreadsheet ANN model project is given early in the semester, just after ANN is introduced. Students can see effects of ANN parameters as they make changes to spreadsheet model inputs, greatly enhancing discussion of ANN processes. After working with the spreadsheet model, students have expressed an appreciation for decisions based on patterns of historic data, and they like the ability to peek “behind the curtain” at processes of predictive software packages.
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