How Much Pornography Use Do Americans Think Is “Average” for a Man and Woman? Findings from a National Survey

ABSTRACT

Though research has examined pornography viewing frequency and its correlates in national samples, researchers have yet to assess how much pornography use the general population thinks is “average” for men and women. Drawing on data from a nationally representative sample of American adults (Men: N = 1,127; Women = 1,382; total mean age = 50.0, SD = 17.4), it was hypothesized that Americans’ estimations of how much pornography use is average for men and women would be shaped by perceptual mechanisms as well as the influence of religious subculture. Results show that age, personal pornography use, self-reported addiction to pornography, and religiosity (for men), were associated with Americans’ perceptions of what is average for others. The association with personal pornography use was amplified for same-gender estimations, and Americans estimated the average man views pornography more frequently than the average woman. Americans rarely reported viewing pornography at higher rates than what they estimated for others. This study provides initial steps toward understanding gendered impressions of average pornography use and provides recommendations for how future research could explore differing mechanisms of same-gender and cross-gender perceptions.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

¹ Due to non-response, the use of online non-probability surveys has increased in recent years and certain vendors are better than others (Kennedy et al., 2016). Independent evaluations of several vendors using online panels in comparison with traditional probability panels found YouGov “consistently outperformed the others including the probability-based [sample]” (Kennedy et al., 2016; Rivers, 2016).

² We used indicators for age groups rather than continuous age (and age²) to allow for more cohort specific inferences clearly interpretable by the reader directly from the tables. However, we did substitute age and age² into the model to visualize the curvilinear age effect in Figure 2. Model fit was substantively unchanged in the supplemental model. We use categorical income to avoid a large loss of participants from the “prefer not to say” group as well as for improved interpretability. Supplementary analyses substituting continuous measures show highly similar results; therefore we report the categorical predictors.

Additional information

Funding

No new funding was provided for this analysis.