%0 Journal Article
%T Multilevel Statistical Analysis of Job Satisfaction and Its Impact on Organizational Commitment
%A Percy Huata Panca
%A Vladimiro Ibañez Quispe
%A Jose Panfilo Tito Lipa
%A Leonel Coyla Idme
%A Julio César Machaca Mamani
%A Felix Henry Castillo Gutierrez
%A Roenfi Guerra Lima
%J Journal of Organizational Behavior Research
%@ 2528-9705
%D 2026
%V 11
%N 1
%R 10.51847/GCORLZEhtR
%P 151-163
%X Job satisfaction is widely understood as a proximal attitudinal predictor of organizational commitment. However, workplace data are typically hierarchical because employees are embedded in teams, departments, and supervisory units. Single-level analyses cannot distinguish individual satisfaction effects from shared contextual influences. They also cannot test whether the strength of the satisfaction–commitment association differs across work units. This article specifies a multilevel statistical analysis of job satisfaction and organizational commitment. The analysis focuses on individual-level effects, between-unit variance decomposition, and cross-level moderation by team climate and leadership quality. A two-level model is proposed with employees nested within work units. The analytic strategy includes an unconditional model, random intercept model, optional random slope model, intraclass correlation estimation, and maximum likelihood model comparison. Conceptually, the model is expected to show a positive within-unit association between job satisfaction and organizational commitment. It is also expected to reveal nontrivial between-unit variance and possible moderation by unit-level leadership quality. Multilevel modeling provides a more accurate framework for testing whether satisfied employees are more committed to their organizations. It also supports organizational interventions that target both employee attitudes and unit-level conditions.
%U https://odad.org/article/multilevel-statistical-analysis-of-job-satisfaction-and-its-impact-on-organizational-commitment-skudepn8bt1oghx