Spss win 22

AMOS 22.0 was used in the analysis of e-health literacy and technology-use anxiety based on the gender of older adults living in Korea. To analyze the latent mean, the morphological identity, measurement identity, and intercept identity were verified through a confirmatory factor analysis of each measure, and then the factor variance identity was verified. The effect size was calculated while performing the latent mean analysis. To confirm the general characteristics of the respondents, descriptive statistics were performed using SPSS/WIN 22.0. Exploratory factor and reliability analyses using Cronbach’s alpha were performed to establish the validity and reliability of the survey tool (Table 3). Moreover, the suitability of the data for the structural equation model was verified through descriptive statistics and a correlation analysis of the items.

The collected data were analyzed using SPSS Win 22.0 program (SPSS, IBM Corp., Armonk, NY, USA). A descriptive statistics analysis was performed on the measured variables, and the difference in unmet medical needs according to the measured variables was analyzed with X2 tests and t-tests. A logistic regression analysis was performed to identify the predictive factors of unmet medical needs according to gender. The level of statistical significance was set at p < 0.05.

The collected data were analyzed using the SPSS Win 22.0 Program as follows: general characteristics of the participants were examined using frequency analysis, and were analyzed using means, standard deviations, real numbers, and percentages. Homogeneity tests according to general characteristics were conducted using the χ²-test and t-test, while pre-homogeneity testing for dependent variables was done with the t-test. Normality testing for dependent variables was confirmed with the Kolmogorov–Smirnov test, and variables satisfying normal distribution were analyzed using the t-test. The analysis of post hoc mean differences for dependent variables was conducted using the t-test. Additionally, the changes before and after the experiment in both the experimental and control groups were assessed with the paired t-test.

The data was analyzed using SPSS WIN 22.0. Descriptive statistics, such as frequency, mean, and standard deviations, were obtained. Among subjects with risk factors, the Chi-square test and Fisher’s exact test were used to verify the difference in pre- and post-change changes between the experimental and control groups. A hypothetical test of the effectiveness of the risk factor-tailored education program was performed using the Chi-square test and independent t-test. Cronbach’s α coefficient was used to verify the reliability of the tool. All statistical significance levels were set at 0.05.

The data were analyzed using SPSS WIN 22.0. Descriptive statistics were utilized to analyze the general characteristics of the study subjects. Pearson's correlation coefficient was employed to examine the relationships between the general characteristics of visually impaired individuals, job satisfaction, daily life satisfaction, and social participation. Lastly, a multiple linear regression analysis was conducted to investigate the impact of each variable on social participation. Statistical significance was determined at alpha = .05.

The data were analyzed by t-tests, χ²-test, correlation tests and univariate and multivariate logistic regression analyses using SPSS/WIN 22.0 (SPSS Inc., an IBM Company, Seoul, Korea) statistical software for Windows. We determined 95% confidence intervals (95% CIs) for each variable from the means and standard deviations. A p-value of <0.05 was deemed to indicate statistical significance. Weight estimates were also considered.
For the multivariate logistic analysis, we selected variables according to the significance of their odds ratio (OR) in the univariate logistic regression model and putative factors deemed to influence health problems. There was no multicollinearity, and the variance inflation factor was 1.042–1.295, not exceeding the criterion of 10.0. Moreover, to verify the model fit of the regression analysis, a Hosmer-Lemeshow test was conducted to confirm that the differences between the predicted and observed values were not significant (p = 0.357). In addition, the Akaike’s information criteria value was estimated to identify the fit of the final model. The results showed that the study’s final regression equation satisfied all of the assumptions for the regression equations, and thus the regression analysis results were deemed reliable.