Because the questions the amount of protected complete intimate intercourses regarding the past 12 months, the analysis shown an optimistic tall aftereffect of the second parameters: getting men, becoming cisgender, informative peak, being energetic affiliate, getting former member. Quite the opposite, a negative affected try noticed to the parameters being gay and you may ages. The remainder independent details didn’t inform you a statistically significant feeling to your level of protected complete intimate intercourses.
New separate varying becoming male, are homosexual, are single, getting cisgender, getting productive representative and being former users demonstrated a positive mathematically significant impact on new connect-ups frequency. The other independent parameters didn’t let you know a serious influence on the brand new hook-ups volume.
In the long run, the amount of exposed complete intimate intercourses over the past twelve days in addition to hook up-ups frequency emerged to have a confident statistically extreme affect STI diagnosis, while the number of safe complete sexual intercourses didn’t reach the value level.
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 users (R 2 = 0.20, Adjusted R 2 = 0.18, F-change (1, 260) = 4.27, P poursuivre ce lien ici maintenant = .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 Desk 5 .
Productivity out-of linear regression design entering market, relationships programs need and aim regarding setting up parameters just like the predictors to own how many protected full sexual intercourse’ people among energetic users
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 (step one, 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 Dining table 6 .
Yields out of linear regression model typing demographic, relationship apps usage and objectives regarding installations variables as the predictors getting how many unprotected complete intimate intercourse’ lovers among 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 .