This study explores the application of factor analysis to reduce a large number of variables in a questionnaire measuring tourist satisfaction. The primary goal is to identify the underlying factors that contribute to tourist satisfaction. The research employs exploratory factor analysis (EFA) to cluster related variables into fewer, more manageable factors. Key steps include assessing data suitability, extracting factors, and rotating and interpreting factors. The study uses the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of Sphericity to assess data suitability, and principal component analysis (PCA) and Varimax rotation to extract and interpret factors. The results show that hospitality, destination attraction, and relaxation are the major factors explaining tourist satisfaction. The study confirms the reliability and validity of the factors through Cronbach’s alpha and AVE values. The findings suggest that factor analysis is a valuable tool for decision-makers and policymakers to focus on a few key factors rather than a large number of parameters. However, the study's findings are specific to the sample and may require further research with larger samples to generalize the results.This study explores the application of factor analysis to reduce a large number of variables in a questionnaire measuring tourist satisfaction. The primary goal is to identify the underlying factors that contribute to tourist satisfaction. The research employs exploratory factor analysis (EFA) to cluster related variables into fewer, more manageable factors. Key steps include assessing data suitability, extracting factors, and rotating and interpreting factors. The study uses the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of Sphericity to assess data suitability, and principal component analysis (PCA) and Varimax rotation to extract and interpret factors. The results show that hospitality, destination attraction, and relaxation are the major factors explaining tourist satisfaction. The study confirms the reliability and validity of the factors through Cronbach’s alpha and AVE values. The findings suggest that factor analysis is a valuable tool for decision-makers and policymakers to focus on a few key factors rather than a large number of parameters. However, the study's findings are specific to the sample and may require further research with larger samples to generalize the results.