Descriptive statistics associated with sexual habits of your total test and you can the three subsamples out of active users, previous users, and you may non-users
Being unmarried reduces the number of exposed complete sexual intercourses
In regard to the number of partners with whom participants had protected full sex during the last year, the ANOVA revealed a significant difference between user groups (F(dos, 1144) = , P 2 = , Cramer’s V = 0.15, P Figure 1 represents the theoretical model and the estimate coefficients. The model fit indices are the following: ? 2 = , df = 11, P 27 the fit indices of our women mexican model are not very satisfactory; however, the estimate coefficients of the model resulted statistically significant for several variables, highlighting interesting results and in line with the reference literature. In Table 4 , estimated regression weights are reported. The SEM output showed that being active or former user, compared to being non-user, has a positive statistically significant effect on the number of unprotected full sexual intercourses in the last 12 months. The same is for the age. All the other independent variables do not have a statistically significant impact.
Production regarding linear regression design entering market, relationship software incorporate and objectives off installations details due to the fact predictors to have just how many safe complete sexual intercourse’ people among effective profiles
Yields away from linear regression model entering market, matchmaking apps use and motives from construction variables since the predictors to possess just how many protected complete sexual intercourse’ couples among productive profiles
Hypothesis 2b A second multiple regression analysis was run to predict the number of unprotected full sex partners for active users. The number of unprotected full sex partners was set as the dependent variable, while the same demographic variables and dating apps usage and their motives for app installation variables used in the first regression analysis were entered as covariates. The final model accounted for a significant proportion of the variance in the number of unprotected full sex partners among active users (R 2 = 0.16, Adjusted R 2 = 0.14, F-change(1, 260) = 4.34, P = .038). In contrast, looking for romantic partners or for friends, and being male were negatively associated with the number of unprotected sexual activity partners. Results are reported in Table 6 .
Wanting sexual couples, several years of software application, being heterosexual had been absolutely for the level of unprotected complete sex partners
Production out of linear regression design typing group, relationships software use and you may intentions out of installations parameters because the predictors to have exactly how many unprotected full sexual intercourse’ partners among productive users
Trying to find sexual people, several years of app usage, being heterosexual was in fact definitely associated with amount of unprotected complete sex couples
Output away from linear regression model entering demographic, relationship programs usage and you can objectives from setting up variables as the predictors having just how many unprotected complete sexual intercourse’ couples certainly active pages
Hypothesis 2c A third multiple regression analysis was run, including demographic variables and apps’ pattern of usage variables together with apps’ installation motives, to predict active users’ hook-up frequency. The hook-up frequency was set as the dependent variable, while the same demographic variables and dating apps usage variables used in the previous regression analyses were entered as predictors. The final model accounted for a significant proportion of the variance in hook-up frequency among active users (R 2 = 0.24, Adjusted R 2 = 0.23, F-change(1, 266) = 5.30, P = .022). App access frequency, looking for sexual partners, having a CNM relationship style were positively associated with the frequency of hook-ups. In contrast, being heterosexual and being of another sexual orientation (different from hetero and homosexual orientation) were negatively associated with the frequency of hook-ups. Results are reported in Table 7 .
