Comparing the Heterogeneity in Maslow's Self-actualisation among Students' of Professional Courses and their Demographics.
Published In: Indian Journal of Positive Psychology, 2024, v. 15, n. 2. P. 183 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Siddiqui, Hureen Wasifa; Sunanda, G. Mary 3 of 3
Abstract
Ample research has been done on self-actualisation but seldom research has been carried out on the construct concerning professional courses students. The present work strives to contribute to the dearth of literature in this area hence, self-actualisation is scrutinised among professional course students focusing on Maslow's conception of higher order need. Students of Education, Engineering, Law, management, and Pharmacy participated online and offline in the survey by responding to the Characteristics of Self-actualization Scale (CSAS) authored by Kaufman (2018). Mean, standard deviation, standard error, One-way Analysis of Variance (ANOVA), Tukey test of significance, and Independent sample t-test were computed to know the significant differences between the demographics and self-actualisation scores of the participants. The study proved to be significant for professional courses and age variables and non-significant for gender variables. The findings of the study lays down necessary educational implications for the educational community, therefore, bridging the gaps prevailing in the existing literature on self-actualisation and professional courses for students. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Indian Journal of Positive Psychology. 2024/06, Vol. 15, Issue 2, p183
- Document Type:Article
- Subject Area:Psychology
- Publication Date:2024
- ISSN:2229-4937
- Accession Number:180056024
- Copyright Statement:Copyright of Indian Journal of Positive Psychology is the property of Indian Association of Health, Research & Welfare and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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