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Zdarzają się sytuacje kiedy kredyt tradycyjny jest z jakiegoś powodu niedostępny dla pożyczkobiorcy. Jeśli mamy nagłe potrzeby, czas ma szczególne znaczenie, dlatego szybkość uzyskania pożyczki jest bardzo ważna. Jeżeli nie chcemy mieć do czynienia z biurokracją lub zbędnymi formalnościami albo nie mamy możliwości złożenia niektórych dokumentów, szukamy oferty kredyty bez zaświadczeń. Kredyt gotówkowy bez zaświadczeń jest szczególnie popularny dlatego, że jest dostępny i łatwy w uzyskaniu. Jest idealnym wyjściem dla osób bezrobotnych, zadłużonych lub otrzymujących niestabilny dochód. Kredyty bez zaświadczeń kredyty-pozabankowe24.pl

Professionals was assigned to dependency classification or typical category making use of the the latter meanings

Statistical study

SPSS to have Screen (ver. 21.0; SPSS Inc., Chi town, IL, USA) was applied getting analytical investigation. Market qualities was basically claimed while the frequency and you can payment. Chi-square decide to try was applied examine addiction and regular communities towards attributes out-of sex, socio-monetary condition, relatives construction, depression, stress, ADHD, puffing, and alcohol explore. Pearson relationship studies is actually performed to select the correlation between mobile phone dependency scores or any other parameters of interest. In the end, multivariate binary logistic regression analysis is did to assess this new determine from intercourse, despair, anxiety, ADHD, smoking, and you will alcoholic beverages play with to your portable addiction. The analysis is complete playing with backward strategy, that have addiction class and you can normal classification given that depending parameters and women gender, anxiety classification, stress classification, ADHD category, puffing group, and alcohol communities as the independent variables. A good p property value lower than 0.05 is actually considered to imply statistical relevance.

Results

One of the 5051 youngsters recruited with the data, 539 were omitted on account of partial responses. Therefore, a maximum of 4512 people (forty-five.1% men, n = 2034; 54.9% women, n = 2478) were among them analysis. The newest mean chronilogical age of this new victims are (SD = step one.62). The brand new sociodemographic services of your subjects was summarized in Dining table step 1. To possess source, 4060 children (87.8%) had been mobile customers (84.2% of men, letter mature woman sex = 1718 away from 2041; 90.6% off women, letter = 2342 off 2584) among the many 4625 pupils exactly who taken care of immediately issue out of cellphone control (426 did not work).

Table 2 shows clinical characteristics between smartphone addiction and normal groups. Of the 4512 participants, 338 (7.5%) were categorized to the addiction group, while 4174 belonged to the normal group. The mean age in the addiction group and normal group was ± 1.63 and ± 1.44, respectively, with no statistical difference between the groups (t = 0.744, p = 0.458). Furthermore, socio-economic status and family structure had no statistical difference between the groups (? 2 = 3.912, p = 0.141; ? 2 = 0.685, p = 0.710). Apart from age, socio-economic status, and family structure, all other variables showed statistically significant differences between the addiction group and the normal group. These include: female sex (OR 1.75, 95% CI 1.38–2.21), depression (OR 4.15, 95% CI 3.26–5.28), anxiety (OR 4.41, 95% CI 3.43–5.64), cigarette smoking (OR 2.06, 95% CI 1.44–2.96), and alcohol use (OR 1.62, 95% CI 1.22–2.16). The largest difference among all variables was noted with ADHD symptomspared to 26.0% of addiction group also belonging to the ADHD group, only 3.4% in the normal group were in the ADHD group. The odds ratio for smartphone addiction in ADHD group compared to non-ADHD was (? 2 = , p < 0.001).

Table 3 shows the Pearson correlation coefficients of smartphone addiction with other variables. Total smartphone addiction score showed greatest correlation with total CASS score (r = 0.427, p < 0.001). The total SAS score was also associated with total BDI score, total BAI score, female sex, smoking group, and alcohol use group in a statistically significant manner.

To identify the variables associated with smartphone addiction, multivariate logistic regression analyses were performed. All variables showing statistically significant difference between addiction group and normal group were entered and analyzed using backward method. In the goodness-of-fit test of the regression analysis model, the ? 2 log likelihood was and statistically significant (p < 0.001). In the first model tested, alcohol use had no statistically significant effect on smartphone addiction (B = 0.161, OR = 1.174 p = 0.375, 95% CI 0.823–1.675) and was, thus, removed from the final model. Table 4 shows the final model of the analysis; the odds ratio for smartphone addiction of female sex to males was 2.01 (95% CI 1.54–2.61). Odds ratio of ADHD group compared to non-ADHD group for song all variables (95% CI 4.60–9.00).

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