To derive the most benefit from online dating, it is important to manage your expectations. This includes being aware of incorrect generalizations that are drawn from flawed research. These are often perpetuated by social media and word of mouth. Approaching online dating while expecting to fail will likely result in a negative experience, as the way that we think about things affects our experiences (The Beck Institute, n.d.).
There is a widespread statistic claiming that 80% of the women engaging in online dating are liking the top 20% of attractive men. This results in men who consider themselves unattractive or moderately attractive believing that they do not stand a chance in online dating. Contrary to popular belief, this statistic was not a product of formal research. The claim originated in a 2015 article posted on the Medium platform with the title “Tinder Experiments II: Guys, unless you are really hot you are probably better off not wasting your time on Tinder — a quantitative socio-economic study” (Worst-Online-Dater, 2015).
While the article is by no means actual quantitative research, this piece will examine its methodology using actual research standards. It will also examine the conclusions drawn by the writer of the Medium article, evaluating them against findings of formal studies. The aim is to come to a balanced and factual perspective about men’s odds of finding a partner when dating online.
When conducting psychology research with the goal of reaching conclusions that can be generalised, sample size and representativeness are important (Leung, 2015). Sample size refers to the total number of participants involved in the study. To be representative, the sample must also consist of a diverse set of participants. This purported study was conducted using a sample of 27 women from the writer’s friend group. A sample size of 27 does not constitute quantitative research. Additionally, a sample from the writer’s group of friends is not representative. Owing to this small size and limited population pool, this informal survey is neither generalizable nor representative.
Another pitfall to avoid when conducting psychological research is to reduce the possibility of bias. Bias may be introduced by the participants in the study or by the researchers themselves.
When collecting data from research participants, data can be collected by direct observation or by asking the participant questions (this is called self-reporting). Self-reported data is subject to bias, and the writer specifically mentioned the social desirability bias that is inherent in surveying women he knows. Put simply, the women he surveyed might tell him what he wants to hear or what they think will portray them in a positive light.
To compensate for the biased self-reported data, the writer attempts to support his conclusions by citing other empirical research which looked into the average like rates on dating apps. However, there is a flaw here as well. The average like rate does not support the big conclusion drawn in the article, which is that most women like only a small pool of guys on the apps. To his credit, the writer also acknowledged this as a flaw in his own writing (Worst-Online-Dater, 2015).
Yet another bias is at play with the article—the writer’s own confirmation bias. A confirmation bias is a psychological phenomenon in which people seek information that supports their preconceptions or preexisting beliefs (Althubaiti, 2016). While the writer of this article is anonymous, conclusions can be drawn about his views by his pseudonym “Worst-Online-Dater” and his own statement that he “wasn’t getting any hot Tinder dates”. His research and conclusions are clearly affected by his personal bias toward finding an explanation for his own negative experiences.
What Actual Research Says
What does actual peer-reviewed research have to say? One recent study of attraction in online dating found attractiveness to be highly subjective, and was unable to establish any universal rules about who might be found typically attractive on dating apps (Roshchupkina et al., 2023). Therefore, there is no basis for the belief that online dating is only successful for men who look a particular way.
One common observation is correct though: men get fewer matches on dating apps than women do (Timmermans & Courtois, 2018). And the reason for this is twofold. First, men are not very selective when swiping. A typical approach is to swipe on all profiles that they view as even remotely attractive in hopes of maximizing their chances of a match. Second, women have the opposite strategy: they are very selective upfront, choosing to go for quality over quantity. This difference in strategy is a product of evolutionary development, with men aiming to secure as many chances to mate as possible, and women aiming to increase the quality of their mate and the chances of ongoing partner support (Dinh et al., 2022). What we see in online dating are these existing human behavioural patterns amplified by the perceived abundance of partners created by dating apps (Alexopoulos et al., 2020). In other words, the apps that we ourselves developed make dating feel harder by presenting us with seemingly unlimited choices.
Dating apps are not skewed in favour of attraction, since research shows that perceptions of attractiveness vary and do not coalesce around a universal standard. Dating app experiences are skewed by the differences between mating behaviours: women are selective, men are not. Recognizing that demand and supply favours women (Ponseti et al., 2022) will help you to manage your expectations and be patient. The next article in the series examines specific challenges and risks that men face when using dating apps.
Alexopoulos, C., Timmermans, E., & McNallie, J. (2020). Swiping more, committing less: Unraveling the links among dating app use, dating app success, and intention to commit infidelity. Computers in Human Behavior, 102, 172–180. https://doi.org/10.1016/j.chb.2019.08.009
Althubaiti A. (2016). Information bias in health research: definition, pitfalls, and adjustment methods. Journal of multidisciplinary healthcare, 9, 211–217. https://doi.org/10.2147/JMDH.S104807
Dinh, R., Gildersleve, P., Blex, C., & Yasseri, T. (2022). Computational courtship understanding the evolution of online dating through large-scale data analysis. Journal of Computational Social Science, 5(1), 401-426
Leung L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of family medicine and primary care, 4(3), 324–327. https://doi.org/10.4103/2249-4863.161306
Ponseti, J., Diehl, K., & Stirn, A. V. (2022). Is dating behavior in digital contexts driven by evolutionary programs? A selective review. Frontiers in psychology, 13, 678439 Roshchupkina, O., Kim, O., & Lee, E. J. (2023). Rules of Attraction: Females Perception of
Male Self-Representation in a Dating App. Asia Marketing Journal, 24(4), 169-177. Timmermans, E., & Courtois, C. (2018). From swiping to casual sex and/or committed relationships: Exploring the experiences of Tinder users. The Information Society, 34(2), 59-70.
Worst-Online-Dater. (2015, March 25). Tinder Experiments II: Guys, unless you are really hot you are probably better off not wasting your time on Tinder — a quantitative socio-economic study. Medium. https://web.archive.org/web/20150830181700/https://medium.com/@worstonlinedater/tinder-experiments-ii-guys-unless-you-are-really hot-you-are-probably-better-off-not-wasting-your-2ddf370a6e9a
