Variations in Sexual Habits One of Dating Programs Users, Former Pages and you can Low-profiles

Detailed analytics related to sexual habits of your total decide to try and you may the 3 subsamples out-of productive pages, previous profiles, and low-pages

Becoming solitary reduces the level 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 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 away from linear regression design typing demographic, relationships applications usage and you may intentions regarding installation variables given that predictors to have what number of secure complete sexual intercourse’ lovers certainly active profiles

Productivity out of linear regression design entering market, relationship applications incorporate and aim from installment variables given that predictors for what number of protected full sexual intercourse’ lovers certainly one of energetic go to website pages

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). 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 .

Selecting sexual couples, several years of app use, and being heterosexual was basically positively of quantity of unprotected full sex people

Output from linear regression model entering demographic, matchmaking apps utilize and aim from installment parameters just like the predictors for what number of unprotected complete sexual intercourse’ people certainly one of productive profiles

Wanting sexual partners, years of software usage, and being heterosexual have been positively of amount of exposed full sex people

Efficiency out of linear regression design typing market, relationships software incorporate and you may objectives out of set up parameters while the predictors to possess the number of unprotected complete sexual intercourse’ couples one of energetic users

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 .