READ ME File For 'Dataset supporting PhD thesis: Examining the role of women’s self-pleasure in relationships: Individual and dyadic analyses' Dataset DOI: 10.5258/SOTON/D2469 ReadMe Author: Dilan Kılıç Onar, University of Southampton ORCID ID https://orcid.org/0000-0002-0264-8906 This dataset supports the thesis entitled Examining the role of women’s self-pleasure in relationships: Individual and dyadic analyses AWARDED BY: University of Southampton DATE OF AWARD: 2023 DESCRIPTION OF THE DATA Electronically created and collected data comes from an online survey via the the Qualtrics. Convenience and snowball sampling were used to recruit couples between January 2021 and January 2022 via social media (e.g., Facebook, Twitter) and Prolific. SPSS software is required to view the data. This dataset contains: Total of 6 dataset. 3 of the datasets contain dyadic data to support publication of “(Dis)Similarities in attitudes between partners about women’s solo masturbation: A dyadic approach to solo masturbation and its associations with sexual satisfaction and sexual self-esteem” 3 of the datasets contain individual-level data to support publication of “The Role of Mutual Masturbation within Relationships: Associations with Sexual Satisfaction and Sexual Self-Esteem” Date of data collection: 22/01/2021- 28/01/2022 Information about geographic location of data collection: Data collected online. The survey was set up in the UK. Licence: CC-BY Related projects/Funders: Dilan Kılıç Onar’s PhD studentship funding was provided by the Republic of Turkey-Ministry of National Education. Related publication: Kılıç Onar, D., Armstrong, H., & Graham, C.A. (2023). The Role of Mutual Masturbation within Relationships: Associations with Sexual Satisfaction and Sexual Self-Esteem International Journal of Sexual Health. [ADD IN] Date that the file was created: July, 2023 -------------- Notes: 1. Rename file, giving it an appropriate name and removing the word 'template'. 2. Remove [] adding in information where required. 3. Remove any sections not relevant to your dataset 4. Remove these notes before saving