Statistical analysis
SPSS 26 was used to calculate descriptive statistics for the variables under study, and to obtain Cronbach’s alpha coefficients, corrected item-total correlations, and correlations among the dimensions in the TFI and external criteria. Additionally, an R function was used for alpha coefficients confidence intervals. Given the nature of the study with voluntary participation and interviewers present, there was a very low percentage of missing data. There was only one missing data point (0.2%), from the institutionalized sample in a single indicator. With such very low level of missingness in the datasets, there is no need to handle the missing data, and therefore list-wise selection was employed across the statistical analyses. The factor structure was tested with CFAs estimated with Weight Least Square Mean and Variance (WLSMV) corrected estimation in Mplus 8.6. WLSMV was selected because the variables are binary and lacked multivariate normality. Several fit indices were used for assessing model fit: Chi-square statistic; Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA); and Standardized Root Means-square Residuals (SRMR). Criteria for reasonable fit were [Finally, Spearman’s correlations were used to study the convergent and divergent validity of the physical, psychological, and social domains of the TFI with other frailty assessment tools. Based on Cohen’s criteria, a correlation coefficient of 0.10 ≤ 0.30, 0.30 ≤ 0.50, and ≥ 0.50 indicated weak, moderate, and strong correlations, respectively [