Table 4
Just like the issues the number of secure complete sexual intercourses in the history one year, the analysis exhibited a positive significant aftereffect of the next variables: getting male, are cisgender, informative level, are energetic user, becoming previous representative. On the other hand, an awful affected was seen towards the parameters becoming gay and you may many years. The remaining separate parameters didn’t reveal a statistically high impression to your quantity of protected complete intimate intercourses.
New independent varying being men, becoming gay, are single, are cisgender, are active member being former profiles shown a positive mathematically extreme impact on new link-ups regularity. Additional independent details failed to let you know a significant influence on this new hook-ups volume.
Finally, what number of unprotected full sexual intercourses over the past 12 months plus the hook-ups frequency emerged having an optimistic statistically high impact on STI analysis, whereas exactly how many protected complete intimate intercourses did not arrive at the value peak.
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(step one, 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 Desk 5 .
Table 5
Returns from linear regression model typing demographic, relationships software need and you can purposes of setting up variables because predictors to own what number of safe complete sexual intercourse’ lovers one of active 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(step one, 260) = 4.34, P = .038). Looking for femmes Russe chaudes 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 .
Table 6
Output off linear regression design entering market, relationships software need and you will objectives regarding setting up variables once the predictors for the number of exposed complete sexual intercourse’ partners one of productive profiles
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 .
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