Within for every single website, i built-up step 1,000 profiles regarding heterosexual males and 1,000 users off heterosexual ladies. Profiles check for profiles through geographic venue, decades, and you will gender filters.
To make sure a geographical dispersion from profiles, i chose equal amounts of users away from four significant towns and additionally urban, suburban, and you may outlying elements: Los angeles, Denver, Chicago, Atlanta, and you will New york city. We at random chosen zip rules away from all the four parts to search for users. Within for every single area code, for each gender, we after that randomly selected users one of four age groups: very early more youthful adulthood (aged 18–30 decades; letter = 1,000; Yards = , SD = 3.17), later young adulthood/early midlife (old 31–49 ages; letter = 1,000; M = , SD = 5.61), later midlife (old 50–64 many years; n = step one,000; M = , SD = step 3.99), and the elderly (aged over 65 age; n = 1,000; Yards = , SD = 4.29). I used such stratifications in order to guarantee a complete age range out-of matchmaking profiles inside the sampling. Because the the elderly category you are going to incorporate to 3 decades, we handled decades just like the a continuous adjustable in lieu of just like the a good collection varying for the analyses.