Within per web site, i amassed step 1,000 pages out of heterosexual guys and step 1,000 users regarding heterosexual females. Profiles check for pages through geographical place, years, and you may gender filters.
To make sure a geographical dispersion from profiles, we selected equal amounts of users out-of five big places in addition to urban, residential district, and you may outlying portion: La, Denver, Chicago, Atlanta, and you can Nyc. We at random chose zip codes regarding each of the five section to look for users. Within this for every postcode, for each gender, i up coming at random chosen pages among four age range: early younger adulthood (old 1829 years; n = 1,000; Meters = , SD = step three.17), later younger adulthood/very early midlife (old 31forty two years; n = step 1,000; Yards = , SD = 5.61), later midlife (aged fifty64 age; letter = step one,000; M = , SD = 3.99), and you may the elderly (old more than 65 years; n = step 1,000; M = , SD = cuatro.29). We utilized such stratifications to assure the full age groups regarding relationship pages when you look at the sampling. As the the elderly category you’ll make use of doing 3 decades, we treated many years given that an ongoing varying unlike since the a collection varying inside the analyses.
Off for every single character, we removed: gender, years, ethnicity, and also the On the Me otherwise Within my Conditions free response area. To assure anonymity off character editors, i didn’t get extra demographic recommendations (e.g., training, religious preferences, income) that’ll act as determining suggestions. Read more
