Effect of Personal and Occupational Characteristics on Attitudes to an Obligatory Retirement Age—A Content Analysis Investigation

Abstract

This study is a pioneer study examining the effects of personal and occupational background variables on the attitude of faculty members to an obligatory retirement age in academia. Previous studies on performance measures of academic faculty in research, teaching, academic administration, and contribution to the community, testified to associations between faculty member achievements and their personal characteristics (gender, age) as well as features related to their academic field of occupation (faculty, academic rank, tenure). Hence, these quality measures of academic faculty have meaning for and influence on research, even after the customary retirement age.
 
 Obligatory retirement age is a well-known issue and it is arousing much interest in general, and in academia in particular. Academic work includes activity focused on research, teaching, advisory work, participation in academic committees and conferences—namely, activities that require human thinking. This leads to the question of whether and to what degree personal and occupational characteristics are associated with the attitude of faculty members to retirement age.
 
 One hundred and eight questionnaires administered to senior faculty were collected in a case study of a single university. Qualitative and statistical research tools were employed, with the aim of creating a model that expresses the association between faculty members’ personal and occupational characteristics and their attitude to retirement age in academia. The research findings show that the background variables affecting the attitude of faculty members to retirement are age and tenure—faculty members’ age and status as tenured faculty determine their objection to the obligatory retirement age.

Economies of Scale Through E-Teaching in the Post-COVID Era – Students’ Improvements Recommendations Using Mixed Methods Design

“…It is a popular statistical technique for multivariate data analysis in social and behavioural sciences, and it use in education has been recently increased (Leguina, 2015) The fit indices used were CFI, TLI, RMSEA, SRMR, and the CMIN/DF ratio. Acceptable fit indices are as follows : CFI, and TLI > .9 (Eckhaus & Davidovitch, 2020), CMIN/DF should be should be < 3 (Eckhaus & Davidovitch, 2019a), however, the more strict approach argues that the ratio should be between 1 and 2 (Rohani, Sadeghi, Eslami, Raei, & Jafari-Koshki, 2018), RMSEA and SRMR < = 0.05 (Franklin, Burns, & Lee, 2014).…”