Modeling
Hossein Zaidy; Vahid Chenari; Seyyedrasool Aghadavood; SeyyedAliakbar Ahmady
Abstract
This research was done with the aim of explaining the components and providing a model of organizational happiness among the employees of the Ministry of Energy. According to the practical purpose, the research is combined or mixed in terms of the research method. The statistical community of the qualitative ...
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This research was done with the aim of explaining the components and providing a model of organizational happiness among the employees of the Ministry of Energy. According to the practical purpose, the research is combined or mixed in terms of the research method. The statistical community of the qualitative part was knowledgeable experts. The random sampling method was cluster and stratified and the criterion for determining the sample size was reaching theoretical saturation, which was achieved after 13 interviews. The statistical population of the quantitative stage includes all the executive, administrative and expert departments, including managers and senior experts and employees of the Ministry of Energy. To calculate the sample size, Cochran's formula for unlimited communities was used, and the number of samples is 384 people. Qualitative data analysis was done using the foundational data theory and quantitative data analysis was done using the partial least square (PLS) technique. Based on the results of qualitative analysis, six categories of factors were identified, including causal factors, contextual factors, intervening conditions, strategies, consequences, and central phenomena. After identifying the components involved in organizational happiness, the relationship between the components of the paradigm model of the data base theory showed There is a relationship.
Modeling
Saied Ali Akbar Ahmadi; Toraj Khairatikazerooni
Abstract
The challenges facing educational institutions, especially nonprofit higher education institutions, are to earn money to meet their goals. Student withdrawal is in the opposite direction. By identifying students as opt-outs, preventive and supportive policies can be anticipated to prevent a reduction ...
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The challenges facing educational institutions, especially nonprofit higher education institutions, are to earn money to meet their goals. Student withdrawal is in the opposite direction. By identifying students as opt-outs, preventive and supportive policies can be anticipated to prevent a reduction in the image and hopes to attract revenues. This research is aimed at identifying students at risk by using the Data Mining Data of the Student attention Center of Payame Noor University of Tehran during the years 91-94. Data were extracted from the education system. Of the 20 potentially effective attributes, a 92% accuracy model was identified. In the model, six independent characteristics (age, group, grade, probation, and gender) and an associated attribute (term) were identified and subsequently the degree of importance of the attributes involved in the withdrawal and their relationship with each other was determined. Risk of withdrawal (risk of attention) and risk ranking table for different terms were presented. Findings indicate that age is the most important factor. From a Sunni point of view, the bachelor degree is between the ages of 21-18 in the senior age group of 26-22 and the PhDs 31 to 29 in the most risky groups. In terms of academic and postgraduate degrees, they are the most risky period in the formal and continuing education programs of the research course. attention rates for students aged 19 and 20 are about 50% in the third semester.
