The Sexual Communication Scale (SeCS)

ABSTRACT

Many measures of comfort and frequency of sexual communication between partners are limited in gender/sex and sexual orientation inclusivity, how constructs are measured, and for whom. We conducted two studies to investigate a revised and extended version of the Female Partner’s Communication During Sexual Activity Scale: the Sexual Communication Scale (SeCS). We revised the gender/sex language to improve inclusion and added items to assess frequency and comfort with sexual communication. In Study 1, an exploratory factor analysis (n = 578) supported a three-factor structure (Frequency of bidirectional communication, α = .96; Ease of own communication, α = .90; Ease of partner’s communication, α = .83). In Study 2, a confirmatory factor analysis (n = 1479) further supported the three-factor structure. Specifically, the three-factor model provided a reasonably good fit (χ2 (44) = 511.35, p < .001, CFI = .97, GFI = .95, AGFI = .91, SRMR = .00, RMSEA = .08). In both studies, we found small or no differences in men and women’s comfort and frequency of sexual communication. The results provide initial support that the SeCS is an internally consistent, multidimensional gender/sex inclusive tool for future research on sexual communication.

Acknowledgments

We would like to thank members of the INSITE lab who have provided feedback at different points in this project. The first author completed part of this research with time and funding support from the University of Ottawa’s Undergraduate Research Opportunities Program.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Data Availability Statement

The data supporting our findings are available upon request from the corresponding author [KS].

Notes

¹ An EFA is a statistical technique used to explore the number of distinct constructs needed to account for the pattern of correlations between items in a dataset (Sakaluk & Short, 2017)

² Mahalanobis distance (MD) measures distances between points, even correlated points, for multiple variables. MD is useful for detecting multivariate outliers (i.e., unusual combinations of two or more variables; Hill et al., 2006). Using MD, researchers calculate each participant’s distance from the average response pattern, identifying distant response patterns that are statistically unlikely (i.e., p < .001; Tabachnick & Fidell, 2013).

³ The Kaiser-Meyer-Olkin Test determines how well data is suited for Factor Analysis. The test determines the sampling adequacy of each variable in the model as well as the entire model. The statistic is a measure of the proportion of common variance among variables. The lower this value, the better the data for Factor Analysis. Values between .8 and 1 indicate sampling is adequate (Tabachnick & Fidell, 2013).

⁴ Bartlett’s Test of Sphericity compares the identity matrix to the correlation matrix. We are able to see if there is enough redundancy between the variables to summarize them with fewer factors. This test is performed to verify that a data reduction technique can compress the data in a meaningful way (Tabachnick & Fidell, 2013).

⁵ Eigenvalues represent the total amount of variance that can be explained by a given principal component or factor (Sakaluk & Short, 2017). If an eigenvalue is equal or greater than one it shows that a factor explains more variance than a single observed variable.

⁶ For the full two-factor EFA with loadings and commonalities based on a Maximum Likelihood factor analysis please contact the corresponding author, Krystelle Shaughnessy.

⁷ A CFA is a statistical technique in which researchers specify the number of factors a measure should contain as well as which variables load on which factor and then test how well their data fit the specified model. It is used following the results of an EFA to confirm the factor structure of a measure. The interested reader is referred to (Sakaluk & Short, 2017) for a simplified summary of EFA and CFA differences.

Additional information

Funding

This work was supported with funding from the University of Ottawa's Undergraduate Research Opportunities Program.