Differences in Sexual Behaviors Certainly Dating Apps Profiles, Former Users and you can Non-pages

Descriptive analytics related to sexual behaviors of the total attempt and you can the three subsamples of active pages, former pages, and you will non-profiles

Getting solitary reduces the number of unprotected full 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_{(2, 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 model are not very satisfactory; however, the estimate coefficients of the model resulted statistically significant for kissbridesdate.com read more 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.

Output out of linear regression model typing market, relationship software use and you will intentions of installations details as the predictors to own how many secure complete sexual intercourse’ couples one of energetic users

Returns away from linear regression design entering demographic, matchmaking applications use and you can purposes out-of installation parameters because predictors to own what amount of protected full sexual intercourse’ people certainly 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_{(step 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 .

Shopping for sexual partners, several years of app usage, being heterosexual was basically definitely of the number of exposed full sex couples

Yields off linear regression model typing group, dating software incorporate and you may motives out-of installation variables because predictors for exactly how many unprotected full sexual intercourse’ people certainly active pages

In search of sexual couples, numerous years of software utilization, being heterosexual was indeed seriously regarding the level of unprotected complete sex people

Productivity out of linear regression design typing market, dating programs use and intentions out-of installment variables because predictors to have just how many unprotected complete sexual intercourse’ partners certainly effective 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_{(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 .