Contained in this per website, we collected step 1,000 users off heterosexual guys and step 1,000 profiles of heterosexual people. Profiles seek pages via geographic location, ages, and you can gender filters.
To be certain a geographical dispersion out of users, i selected equivalent numbers of profiles away from four major metropolitan areas together with metropolitan, suburban, and you may outlying areas: Los angeles, Denver, Chicago, Atlanta, and you can New york city. We randomly chose zero requirements regarding all the four portion to look for users. Inside for every single area code, per gender, we following at random chose pages among four a long time: very early more youthful adulthood (aged 1831 ages; n = 1,000; M = , SD = step three.17), later young adulthood/early midlife (old 2944 age; n = step one,000; M = , SD = 5.61), late midlife (aged 5064 age; letter = step one,000; Yards = , SD = step three.99), and you may the elderly (old over 65 decades; letter = step 1,000; Yards = , SD = cuatro.29). We used such stratifications in order to guarantee a complete age range out-of matchmaking profiles during the testing. Since the the elderly category you are going to make use of up to three decades, i treated age because the an ongoing varying in lieu of while the good collection changeable when you look at the analyses.
Regarding per character, we extracted: gender, many years, ethnicity, and also the In the Me or In my Terms and conditions totally free impulse section. In order to guarantee anonymity regarding reputation editors, i did not obtain most demographic information (age.grams., knowledge, religious choice, income) that’ll serve as distinguishing recommendations.