Differences in Sexual Habits Certainly Relationships Apps Profiles, Former Profiles and you can Low-users
Descriptive analytics about sexual practices of your complete try and you can the three subsamples from effective pages, previous pages, and you may non-users
Becoming solitary reduces the quantity of exposed complete 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 https://kissbridesdate.com/german-women/berlin/, 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.
Returns from linear regression model entering demographic, relationship apps utilize and you will motives from construction variables given that predictors having just how many secure complete sexual intercourse’ couples one of productive profiles
Returns off linear regression design entering group, matchmaking apps usage and you may objectives of installation details just like the predictors getting just how many safe complete sexual intercourse’ couples one of active 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(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 lovers, numerous years of application use, and being heterosexual had been surely on the amount of exposed full sex partners
Returns away from linear regression design typing market, dating software utilize and you will motives regarding installations details because predictors having how many unprotected full sexual intercourse’ people among effective pages
Looking sexual partners, years of software application, being heterosexual were positively with the amount of exposed full sex lovers
Production of linear regression design entering demographic, relationships applications utilize and you will objectives regarding setting up details since the predictors to own what amount of exposed full sexual intercourse’ lovers among active 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 .