Factor analysis is a multivariate statistical technique used to reduce a large number of variables into a smaller set of factors that capture the essential information. This study applies factor analysis to identify underlying factors in a questionnaire measuring tourist satisfaction. The data were collected from 200 international tourists who visited Nepal in 2019. The study used Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of Sphericity to assess the suitability of the data for factor analysis. The KMO value was 0.813, indicating adequate sampling, and Bartlett's test was significant (p < 0.001), confirming that the correlation matrix is not an identity matrix. The determinant score was 0.038, indicating no multicollinearity. The study used principal component analysis to extract factors. Kaiser's criterion and Scree test were used to determine the number of factors. The results showed that three factors were extracted, accounting for 60.2% of the total variance. These factors were labeled as 'Hospitality', 'Destination Attraction', and 'Relaxation'. Each factor was identified based on the loadings of the variables. The factor loadings were above 0.4, indicating that the variables are strongly associated with the factors. The internal consistency of the questionnaire was confirmed using Cronbach's alpha, which was above 0.7 for all factors. The average variance extracted (AVE) was above 0.5, confirming convergent validity. Composite reliability was also calculated and was above 0.6 for all factors. The study concludes that factor analysis is a useful tool for reducing the number of variables in a questionnaire and identifying the underlying factors that explain the variability in the data. The three factors identified in this study—Hospitality, Destination Attraction, and Relaxation—were found to be significant in measuring tourist satisfaction. The results suggest that decision-makers and policy-makers should focus on these factors when developing tourism strategies. The study also highlights the importance of using factor analysis in questionnaire development and data reduction. However, the findings cannot be generalized to the entire population, and further research with a larger sample size is recommended.Factor analysis is a multivariate statistical technique used to reduce a large number of variables into a smaller set of factors that capture the essential information. This study applies factor analysis to identify underlying factors in a questionnaire measuring tourist satisfaction. The data were collected from 200 international tourists who visited Nepal in 2019. The study used Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of Sphericity to assess the suitability of the data for factor analysis. The KMO value was 0.813, indicating adequate sampling, and Bartlett's test was significant (p < 0.001), confirming that the correlation matrix is not an identity matrix. The determinant score was 0.038, indicating no multicollinearity. The study used principal component analysis to extract factors. Kaiser's criterion and Scree test were used to determine the number of factors. The results showed that three factors were extracted, accounting for 60.2% of the total variance. These factors were labeled as 'Hospitality', 'Destination Attraction', and 'Relaxation'. Each factor was identified based on the loadings of the variables. The factor loadings were above 0.4, indicating that the variables are strongly associated with the factors. The internal consistency of the questionnaire was confirmed using Cronbach's alpha, which was above 0.7 for all factors. The average variance extracted (AVE) was above 0.5, confirming convergent validity. Composite reliability was also calculated and was above 0.6 for all factors. The study concludes that factor analysis is a useful tool for reducing the number of variables in a questionnaire and identifying the underlying factors that explain the variability in the data. The three factors identified in this study—Hospitality, Destination Attraction, and Relaxation—were found to be significant in measuring tourist satisfaction. The results suggest that decision-makers and policy-makers should focus on these factors when developing tourism strategies. The study also highlights the importance of using factor analysis in questionnaire development and data reduction. However, the findings cannot be generalized to the entire population, and further research with a larger sample size is recommended.