Predicting the Individuals’ job satisfaction and determining the factors affecting it using the CHAID Decision Tree Data Mining Algorithm Case Study: the National Opinion Research Center of the United States

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  •   Farhad Sheybani

Abstract

As the general attitude of the individual about what he does, job satisfaction is the result of individual perceptions from the workplace and the factors and conditions in it; it is also influenced by his personality traits. Meanwhile, investigating job satisfaction is of great importance in advanced societies. The present study aimed to assess job satisfaction in the United States and evaluate the hypothesis of the existence of job dissatisfaction and the factors affecting it in the studied sample. The various social data, related to job satisfaction and collected by the National Opinion Research Center of the United States, are used in this study. The sample consists of different people including male and female samples from nine different states in the United States. For the purpose of this study, the patterns of data were discovered, and factors affecting job satisfaction were identified using the CHAID decision tree data mining method. Finally, it was found that a small percentage of people are dissatisfied with their job.


Keywords: Job Satisfaction, factors affecting job satisfaction, National Opinion Research Center, data mining, CHAID decision tree.

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Http://www3.norc.org/gss+website/

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How to Cite
[1]
Sheybani, F. 2019. Predicting the Individuals’ job satisfaction and determining the factors affecting it using the CHAID Decision Tree Data Mining Algorithm Case Study: the National Opinion Research Center of the United States. European Journal of Engineering Research and Science. 4, 3 (Mar. 2019), 6-9. DOI:https://doi.org/10.24018/ejers.2019.4.3.1169.