Decisions are important and are normally made in the best interest and support of any organizations’ growth; however technology and data type or availability can make the management take chances. Return on investment is the net income that a person or a firm gets from the money they spend and it is computed by dividing the net profit obtained after taxes by the total assets, whereas in return on investment in interaction, the return is on interaction by the customers and the firm does not invest any money (Psacharopoulos, 1994). The ROI is more powerful and valid as technologies involved will attract more customers; moreover, the involved persons will work hard to get more returns.
Decision tree analysis using CHAID offers an alternative statistical method that identifies the elements to be used in the analysis process. Compared to the multiple regression analysis that uses the influential variables that explain the variance in the identified element to be used as the measure to the students’ satisfaction, the tree analysis identifies the important variable of the students’ experience that differentiate them as satisfied and dissatisfied (Thomas & Galambos, 2004). It identifies the independent variable that is related to the target and then assesses it by categorizing into groups.
In comparison to the regression analysis system where data that has large amount of it missing or found to be applicable to small subset areas of survey are eliminated, the tree analysis system does not discard them but instead handle the missing data as a separate category that can be combined with other categories if they are homogenous (Thomas et at., 2004).
Decisions made from the survey data are important and should be implemented as they portray the exact scenario. Tree analysis process gives the correspondents an opportunity to express their views on the issues being discussed, while the regression system provide a defined set of questions stating the level of satisfaction. The CHAID analysis can therefore be applied by manufacturers where they seek there customers’ option on the quality of there products. It is therefore an effective method for decision making.
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References
Psacharopoulos, G. (1994). Returns to investment in education: A global update. World development, 22(9), 1325-1343.
Thomas, E., & Galambos, N. (2004). What satisfies students: Mining Student-Option Data with
Regression and Decision Tree Analysis. Research in Higher Education, 45(3), 1-20.-
