Quality of life (QoL) is now firmly recognized as a significant outcome measure in public health, clinical and patient care research (1, 2). Despite a growing trend in conducting longitudinal QoL studies, the longitudinal changes in QoL in the general population remain poorly understood due to the limited number of studies. Furthermore, few studies have discussed the use of different statistical methods in analyzing the longitudinal change in QoL. This paper aimed to discuss the application of traditional statistical approach: R-ANOVA and newer statistical approaches: LMM and LGCA in analyzing the longitudinal change in QoL. The underlying assumptions, characteristics and specifications of each of the statistical methods were explained. Different public health studies that examined the longitudinal change of QoL would be elaborated in order to show how the criterions of each statistical method were fulfilled in the research analysis. Additionally, the limitations of applying the traditional statistical approach: R-ANOVA and the newer statistical approaches: LMM and LGCA in analyzing longitudinal QoL data will be discussed with the emphasis on how each analytical method overcome the weaknesses of one another. The understanding of the application of different statistical approaches in analyzing the longitudinal change in QoL can advance the future development of a robust statistical approach for QoL research. / published_or_final_version / Community Medicine / Master / Master of Public Health