EASP Seedorn Grant Report by Carla A. Roos and Team
06.09.2021, by Tina Keil in grant report
Laura M. Vowels (University of Southampton), Carla A. Roos (University of Groningen), Jasmina Mehulic (University of Zagreb), Siobhan O’Dean (University of New South Wales), M. Dolores Sanchez Hernandez (University of Granada)
![from top left: Laura M. Vowels, Carla A. Roos, Jasmina Mehuli, Siobhan O’Dean, M. Dolores Sanchez Hernandez](../../getmedia.php/_media/easp/202109/1455v0-orig.jpg?s=full)
The Association between Responsiveness to Sexual Needs and Sexual/Relationship Satisfaction
Our research aimed to examine whether greater responsiveness to sexual needs is associated with higher sexual and relationship satisfaction. To this end, we conducted a series of 3 studies. Together these three studies have a strong potential impact on theory, research, and clinical practice. First, we provide people’s first-hand accounts of what sexual needs responsiveness means for them, distinguish between perceptions of self and partner sexual needs responsiveness, and investigate moderators at multiple levels ranging from individual to societal. We also examined the causal effect of sexual needs responsiveness on sexual and relationship well-being using causal machine learning. Together these studies have helped us realise our goal of developing a comprehensive theoretical model of sexual needs responsiveness and provide a detailed insight into the factors underlying one of the central aspects of romantic relationships: sex. Below we outline each of the studies/activities undertaken, and outline the findings from each study.
Study 1
In study 1, we asked participants about their own understanding and experiences with sexual responsiveness. In a content analysis on the responses to these open-ended questions, the following themes came up.
In general, ongoing communication about sex was mentioned a lot. When specified, this was often described as open and honest communication about each other’s needs and wishes. Communication also often involved explicitly asking for likes and dislikes. Sometimes talk was more directive in that partners simply told each other what they wanted or needed. Communication didn’t only comprise verbal cues but also non-verbal body language, sometimes this was intentional (guiding), sometimes unintentional. Of course, listening to all this communication was also very important, and manifested itself in partners remembering each other’s preferences. Especially couples in a longer relationship, this communication and listening culminated in simply knowing each other’s needs and wishes and being completely in tune.
In case of disagreement between partners or when one of the partners could not respond in the way requested by the other, partners tried to integrate their needs to find a compromise that is acceptable for them both. Partners could focus on their mutual satisfaction, for example by making sure they both come. Other times, partners seemed to focus mostly on responding to each other’s (implicit or explicit) requests. This could be done either happily in that partners derived joy from pleasing each other, or without enjoyment and focused on preventing upsetting or even losing the partner. Relatedly, partners also recognized responsiveness in being flexible and willing to change routines and try out new things, either upon request or self-initiated.
Another very prominent theme in all descriptions was showing respect. This was evident in partners recognizing each other’s boundaries, needs, and interests, and behaving to respect these (i.e., holding back at the right moment). In this way, partners co-created a safe space of mutual consent.
Study 2
Study 2 was conducted using a student sample across 4 countries; Australia (n = 133), Croatia (n = 169), The Netherlands (n = 173) and Spain (n = 271). The purpose of this study was to explore the association between both self and partner sexual needs responsiveness and relationship and sexual well-being, as well as to indentify potential individual (e.g., self-esteem, attachment styles, stress), relational (e.g., trust, commitment, relationship length), and cultural (e.g., sex education, economic situation) level predictors of sexual needs responsiveness, sexual and relationship well-being.
For this purpose we created two similarly worded sexual needs responsiveness scales, one pertaining to the self, and the other to the partner. The two constructs were analyzed using exploratory factor analysis with an oblique rotation. A single factor was extracted for the self scale, with loadings in the .65 - .84 range explaining 58% of the variance with a high H value of .95. A single factor was extracted for the partner scale as well, explaining 68% of the variance with loadings in the .75-.89 range and a high H value of .97 indicating a well-defined latent variable. Both scales were highly reliable with McDonalds' Omega for the self scale at .95 and at .97 for the partner scale.
We used an explainable machine learning algorithm, random forests (Breiman, 2001) with Shapley values (Lundberg et al., 2019; 2020) to understand which factors were the most important predictors of sexual well-being, relationship satisfaction, and sexual needs responsiveness. The results showed that the Sexual Needs Responsiveness (SNR) scale, both for self and partner, were highly predictive of sexual well-being (see Figure 1) and relationship satisfaction. Indeed, only relationship quality and desire for one’s partner were more important predictors of one’s own sexual well-being than perceiving their partner as meeting one’s sexual needs. We also found that sexual well-being, sexual communication, attachment avoidance, and relationship quality were among the most important predictors of perceiving both self and partner as sexually responsive.
Study 3
Study 3 provided a confirmatory study on a UK sample (n = 1,000) aiming to further validate the SNR scale and determine whether sexual needs responsiveness is causally linked to sexual and relationship well-being. We have finished data collection and plan to analyze the results in the upcoming months. We will use a causal machine learning technique, targeted learning (van der Laan & Rubin, 2006), to estimate the size of the causal relationship between sexual needs responsiveness and sexual and relationship well-being.
Note. The left side of the figure shows the average impact that each variable has on the model outcome and the right side of the figure shows the individual data points with red being higher values and blue lower values on each variable.
References
- Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
- Lundberg, S. M., Erion, G, Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2019). Explainable AI for trees: From local explanations to global understanding. arXiv preprint arXiv:1905.04610.
- Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., ... & Lee, S. I. (2020). From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, 2(1), 2522-5839.