Detailed statistics connected with sexual behaviors of one’s overall shot and you will the 3 subsamples out of productive users, former users, and you can non-pages

Becoming single reduces the number of unprotected 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_{(dos, 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 tapaa Perun naiset, 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 out of linear regression design typing market, relationship apps usage and you can objectives of set up details given that predictors to possess what number of safe complete sexual intercourse’ lovers one of energetic profiles

Efficiency from linear regression model entering market, relationship software need and you can objectives out-of setting up parameters since the predictors having the amount of secure full sexual intercourse’ people certainly 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_{(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 .

Selecting sexual partners, years of software application, and being heterosexual had been absolutely on the number of unprotected complete sex partners

Production off linear regression design entering demographic, relationship programs need and you may aim regarding installation variables because the predictors to possess the number of unprotected full sexual intercourse’ people certainly active pages

Trying to find sexual lovers, many years of app use, and being heterosexual had been definitely in the number of exposed full sex people

Output away from linear regression model typing group, matchmaking apps use and you can aim out-of installment parameters since predictors to possess the amount of exposed complete sexual intercourse’ couples certainly one of effective 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_{(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 .