RESUMO
The purpose of the study was to construct and validate a Belief in Divine Retribution Scale for the Pakistani Muslim population. The process of construction and validation was completed by following standardized guidelines for scale construction (Boateng et al., 2018). The present study was carried out in four phases. In phase I, the task of item generation was completed through literature review and interviews (inductive and deductive approaches). Phase II aimed at exploration of factor structure. Exploratory factor analysis was carried out on a sample of seven hundred Muslim participants. Data for EFA were collected through a purposive sampling technique, which comprised both men (n = 339) and women (n = 361) with an age range of 18 to 69 years. Results of EFA revealed a two-factor structure with a cumulative variance of 42.59 and with a Cronbach alpha reliability of .83. To confirm the obtained factor structure, Phase III was carried out on a sample of three hundred Muslim participants. The results of CFA confirmed the two-dimensional factor structure with a good model fit to the data. Phase IV provided evidence of convergent and discriminant validity of the scale. Moreover, data for validation were collected from an independent sample (N = 204). Finally, the results of validation revealed that there exists a significant positive correlation of Belief in Divine Retribution Scale with Belief in Just World Scale, which provided evidence of convergent validity. However, there exists a non-significant correlation of Belief in Divine Retribution Scale with Religious Practice Subscale of Short Muslim Practice and Belief Scale, and it provided evidence of discriminant validity. Implications along with limitations and suggestions for future research have also been mentioned.
RESUMO
Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positions using a camera. This work provides a strategy for automated identification of USL numbers based on a bag-of-words (BoW) paradigm. For classification purposes, support vector machine (SVM), Random Forest, and K-nearest neighbor (K-NN) are used with the BoW histogram bin frequencies as characteristics. The proposed technique outperforms others in number classification, attaining the accuracies of 88%, 90%, and 84% for the random forest, SVM, and K-NN respectively.
