The Prediction of Grit Based on God Attachment, Life hope, and Happiness in retired women teachers in khominishar city

Subject Areas : Woman and family

Elham Rezaei ¹ (Master student in family counseling, Islamic Azad University of Khomeini Shahr, Khomeini, Iran)
Fatemeh Javadzadeh ² (* Assistant Professor, Counseling Department, Khomeini Shahr Branch, Islamic Azad University, Isfahan, Iran (Corresponding Author))

Received: 2020-12-08 Accepted : 2021-02-17 Published : 2021-11-22

Keywords: happiness, Life expectancy, attachment to god, Grit, retired women teachers,

Abstract :

The aim of this study was to predict Grit  based on attachment to God, life expectancy and happiness in retired female educators of Khomeini Shahr city. The research method was correlational type of prediction and the statistical population included all retired female educators in Khomeini Shahr in 2020, whose number was 167 people. Using Morgan table, the sample size was estimated to be 113 people according to the statistical population. Finally, the sample size was reduced to 100 people, including statistical loss. Data collection tools included the Grit Questionnaire's Duckworth (2007), the Attachment to God Beck and McDonald's Questionnaire (2004), the Snyder's Life Hope Questionnaire (1991), and the Argyle, Martin, & Croesland's Oxford Questionnaire (1989) Happiness Questionnaire. Data analysis was performed at two levels of descriptive statistics (mean and standard deviation) and inferential statistics (Pearson correlation coefficient and multiple regression) using spss-22 software. The results showed that attachment to God (β = 0.49), happiness (β = 0.23) and life expectancy (β = 0.42) respectively, were 47/5% able to predict the Grit of retired female educators. The results also showed that Sub-component of attachment to God, relationship with God, trust and attention to God, 34/2% were able to predict Grit And Sub-component of life expectancy, factor thinking, and strategic thinking accounted for 37/9% percent of the ability to predict Grit at the significance level of p≤0.01.

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