This makes it possible to obtain reliable measures of acceptance rates in online dating experiment
Thus, Hirsch et al. are providing an interesting correlation to studying those databases where researchers can not access into the contents of the messages exchanged.
[…As the number of reciprocated responses increased, the percentage of messages in each category declined, so that only 3% of men’s, and 7% of women’s, sent messages resulted in more than five exchanges. (2010a). Obviously, the likelihood of any given message resulting in a reciprocated exchange and eventual date is extremely small..].
In the second study (see POF Experiment. Study 1) I’m going to measure response rates at the second message. It would be too laborious perform a message exchange for each pairwise to determine whether each initial recipient is willing to provide to each sender her phone number and set a date. I assume this estimate is quite interesting, and would give us a response rates data fairly close to real rates of mate acceptance.
The dummy profiles will be introduced on the same mating system, the dating site Plenty of Fish, over the same 1-week period. They were placed into the other geographical place, a big-sized city named B.
These messaged users are not randomly chosen. I chose a sample of 100 female users for each dummy through advanced search. They were to meet the following parameters:
Body type: thin, athletic and average. I discarded profiles self-identified as a few extra pounds, big & tall / bbw and prefer not to say.
Kreager et al (2014) allude to these correlations in their own online dating study :
Within the subset of browsed female profiles, I chose as target those profiles that contained pictures from women that could be included within the upper half of facial attractiveness spectrum. This is, since avarage or medium-attractiveness until highly-attractiveness ( 5 to 10 oints on a 1-10 scale).
This second study is composed of 4 male profiles, 2 of them highly attractive (males Z and Y; r (> 8), 2 moderately attractive: male F (r= 6.93), male C (r=6.34).
Recall that to ease attractiveness comparisons, I’ve sorted the ratings into five equal categories of attractiveness (highly-attractive: 8-10, moderately-attractive:6-8, medium-attractive:4-6, and below medium-attractive ( 4).
I assigned higher occupational and educational levels to less physically attractive profiles. The highly attractive profiles were assigned with blue-collar occupations, while the rest of the profiles were configured with white-collar professions (see study 1).
Figure 11. Screenshot showing Contact History: Contacts initiated. The number of females contacted were 100. Note that the screenshot after the 7 day period shows 99 users, because 1 of them has removed her account.
Mail inbox = 62 ( 9 of them were first responses . So 9 female users responded bi curious websites to the first contact but after the first screenshot). I won’t send a second message to these delayed 9 users, since the possible answers will be out of data collection period of 7 days.
Reply proportion at second message = 43 out of 100. (*) The data is unreliable because 9 users who responded to the first message did too late and I could not record their behavior for a second message .
The last category captured the mean number of exchanges (six) required until the relationship resulted in an offline date by Hitsch et al
Figure 16. Screenshot showing Contact History: Contacts Inicitated. The number of Contacts Initiated was 100 (I made a wrong screen capture).
Figure 17. Screenshot showing Contacts Initiated. The number of females contacted were 100. Note that the screenshot after the 6 day period shows 98 users, because 2 of them have removed their accounts.
