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Developing a Jordanian Measure of Reverence in Muslim Praying ('Khushoo'): Content Validity, Factor Structure and Reliability.

Basim Aldahadha
Published in: Journal of religion and health (2023)
Many Muslims complain of straying while praying because of life's problems and psychological pressures (i.e., reflecting on topics other than prayer). To assess this problem, it was necessary to develop a measure of reverence in Muslim praying (MRMP). The study aimed to collect items from the theoretical literature and investigate these to determine the validity of the content by consultation with a group of experts (n = 17), after which the number of items was reduced to 39. The study focused on two random and completely different samples. The first sample (n = 396) was used to verify the validity of exploratory factor analysis (EFA). The results showed that the following four factors explain 67.27% of the variance in the total scale: groveling, focused attention, contemplation, and praying behavior control. The second sample (n = 362) was used to verify the confirmatory factor analysis (CFA) as well as the convergent validity and reliability of the data. Additionally, the four factors were confirmed using bifactor confirmatory factor analysis (B-CFA) and met the criteria for fitness. Likewise, all correlation values between the MRMP and mental health, mindfulness, happiness, and well-being were significant. In addition, the Cronbach's alpha coefficients of the four factors ranged between 0.85 and 0.77 and the total score was 0.92. Finally, the correlations between the MRMP and the four factors were significant. The study concluded that the MRMP is appropriate for assessing reverence in Muslim praying, can be used to promote psychological health in the context of prayers, and can serve as a foundation for future research.
Keyphrases
  • mental health
  • healthcare
  • systematic review
  • physical activity
  • machine learning
  • palliative care
  • current status
  • electronic health record
  • working memory
  • deep learning
  • big data
  • sleep quality
  • mental illness