Frontiers | Tinder Use and Romantic Relationship Formations: A Large-Scale Longitudinal Study
The current paper aims to investigate if Tinder use predicts romantic relationship formation 1 year later and to identify demographic, personality, mental health, and substance use covariates in the relationship between Tinder use and romantic relationship formation. Data were collected by online surveys (two waves) among students in Bergen, Norway. The first survey was administered during fall 2015 (T1). The follow-up took place 1 year later (fall 2016, T2). The sample consisted of the 5253 participants who reported to be single at T1. The surveys included questions about Tinder use, demographics, personality (the Five-Factor Model’s personality traits), mental health (i.e., symptoms of depression and anxiety), alcohol use, and use of illegal substances. Bivariate comparisons were conducted to assess differences in demographics, personality traits, mental health, and substance use between Tinder users and non-Tinder users. Further, crude and adjusted binary logistic regressions were employed to investigate if Tinder use at T1 predicted romantic relationship formation at T2, when controlling for relevant covariates. Tinder users differed from non-users on almost all included variables. Compared to non-users, Tinder users were younger and more likely to be men, born in Norway, childless, and non-religious. Tinder users had higher scores on extroversion and agreeableness and lower scores on openness compared to non-users. Further, compared to non-users, Tinder users reported...
;import{g as i,s as o,r as s,e as a,a as u,h as l,b as c,i as h,t as f,c as d,d as p,f as m,j as g,k as y,l as b,K as v,_ as w,S as E,m as _,o as C,w as M,n as S,p as O,q as B,u as D,v as I,x as T,y as x,z as R,A as N,G as k,J as F,B as L,C as P,X as U,D as j,E as q,F as z,H as $,I as V,L as H,M as W,N as G,O as K,P as Z,Q as Y,R as J,T as X,U as Q,V as AA,W as eA,Y as tA,Z as rA,$ as nA,a0 as iA,a1 as oA,a2 as sA,a3 as aA,a4 as uA,a5 as lA,a6 as cA,a7 as hA,a8 as fA,a9 as dA,aa as pA,ab as mA,ac as gA,ad as yA,ae as bA,af as vA,ag as wA,ah as EA,ai as _A,aj as CA,ak as MA,al as SA,am as OA}fr
Explore this link on the map →saved by
related reading
- Allison P. Davis: My Tinder Decadethecut.com
- Notionnanransohoff.com
- Juicebox (PeopleGPT) - The Leading AI Recruiting Platformjuicebox.ai
- BDSM Test - The Original BDSM & Kink Testbdsmtest.org
- Tinder Newsroom - Newstinderpressroom.com
- GitHub - ansonyuu/matchmaking: Embedding space of names clustered based on their interests using the sentence-transformers all-MiniLM-L6-v2 model · GitHubgithub.com
- Dropboxdropbox.com
- People Are Dating All Wrong, According to Data Science | WIREDwired.com
- People Are Dating All Wrong, According to Data Science | WIREDwired.com
- Bad News - Play the fake news game!getbadnews.com
- Predict students' dropout and academic success | Kagglekaggle.com
- 'Date Me' Google Docs and the Hyper-Optimized Quest for Love | WIREDwired.com