Table 4
As questions what number of secure complete sexual intercourses in the last one year, the research demonstrated a confident significant aftereffect of the next details: getting male, becoming cisgender, academic peak, being active user, are former representative. On the contrary, an awful affected was noticed for the details being gay and you can age. The remaining separate details did not show a mathematically tall perception to your level of secure full intimate intercourses.
The new independent varying getting men, being gay, being unmarried, getting cisgender, being effective user being former users shown an optimistic mathematically extreme influence on the latest link-ups volume. The other independent parameters didn’t tell you a life threatening impact on brand new connect-ups volume.
In the long run, what amount of unprotected full sexual intercourses over the last twelve days and the link-ups volume came up getting a positive statistically significant impact on STI medical diagnosis, whereas just how many secure complete intimate intercourses don’t arrived at the significance height.
Hypothesis 2a A first multiple linear regression analysis was run, including demographic variables and apps’ pattern of usage variables, to predict the number of protected full sex partners in active users. The number of protected full sex partners was set as the dependent variable, while demographic variables (age, sex assigned at birth, gender, educational level, sexual orientation, relational status, and relationship style) and dating apps usage variables (years of usage, apps access frequency) and motives for installing the apps were entered as covariates. The final model accounted for a significant proportion of the variance in the number of protected full sex partners in active femmes russes vs amГ©ricaines users (R 2 = 0.20, Adjusted R 2 = 0.18, F-change(step 1, 260) = 4.27, P = .040). Having a CNM relationship style, app access frequency, educational level, and being single were positively associated with the number of protected full sex partners. In contrast, looking for romantic partners or for friends were negatively associated with the considered dependent variable. Results are reported in Table 5 .
Table 5
Yields away from linear regression model entering demographic, dating programs use and purposes out-of installation parameters since predictors to have exactly how many safe full intimate intercourse’ people among effective 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). Looking for sexual partners, years of app utilization, and being heterosexual were positively associated with the number of unprotected full sex partners. 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 Desk six .
Table 6
Efficiency regarding linear regression design typing group, matchmaking applications utilize and you will purposes off installations details as the predictors to own the number of unprotected full sexual intercourse’ lovers one of 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(step 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 .