Medicine, Faculty of
Non UBC
Emergency Medicine, Department of
DSpace
Zugic, Kaitlyn E.
Teatero, Missy L.
Phillips, Maggie
Oinonen, Kirsten A.
The extent to which estradiol, progesterone, and testosterone influence mating behavior across the menstrual cycle is unclear. The Proactive and Responsive Mating Strategies Scales (PARMSS) were developed to separately examine two specific components of sexuality and were used here to test divergent hormonal associations. Preliminary psychometric data (N = 364 females) suggest that both scales consist of one factor and demonstrate strong psychometric properties (internal consistency, test–retest reliability, and construct and convergent validity). The PARMSS were used in a repeated-measures observational study to examine the relationships between changes in endogenous hormone levels and both proactive and responsive mating intentions with potential new short-term or long-term partners in healthy pre-menopausal participants (N = 38). At two points in their cycle, participants provided salivary hormone samples in the laboratory and reported the likelihood of engaging in proactive and responsive behaviors with men in photos and vignettes. Participants reported greater responsive than proactive intentions. Increases in estradiol and testosterone across the cycle were associated with increases in short-term mating intentions, particularly responsivity to potential short-term relationship partners. No associations were found for intentions that were proactive or that involved potential long-term partners or for progesterone. Changes in the three hormones explained changes in short-term responsive mating intentions (22% of the variance). The results suggest (a) cyclical changes in estradiol and testosterone are differentially associated with changes in responsive vs. proactive mating intentions and (b) context-dependent changes (i.e., short-term vs. long-term mating intentions and possibly relationship status). The findings require replication with larger and diverse samples.
Citation: Zugic, K.E.; Teatero, M.L.;Phillips, M.; Oinonen, K.A.Short-Term Responsive MatingIntentions Increase with Estradiol andTestosterone across the MenstrualCycle: Scale Development and anObservational Study. Sexes 2024, 5,475–497. https://doi.org/10.3390/sexes5040034Academic Editor: Charikleia StefanakiReceived: 28 June 2024Revised: 2 October 2024Accepted: 4 October 2024Published: 8 October 2024Copyright: © 2024 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).ArticleShort-Term Responsive Mating Intentions Increase withEstradiol and Testosterone across the Menstrual Cycle: ScaleDevelopment and an Observational StudyKaitlyn E. Zugic 1,2,†, Missy L. Teatero 1,†, Maggie Phillips 1,† and Kirsten A. Oinonen 1,*,†1 Department of Psychology, Lakehead University, Thunder Bay, ON P7B 5E1, Canada2 Department of Emergency Medicine, University of British Columbia, Vancouver, BC V5Z 1M9, Canada* Correspondence: koinonen@lakeheadu.ca† These authors contributed equally to this work.Abstract: The extent to which estradiol, progesterone, and testosterone influence mating behav-ior across the menstrual cycle is unclear. The Proactive and Responsive Mating Strategies Scales(PARMSS) were developed to separately examine two specific components of sexuality and wereused here to test divergent hormonal associations. Preliminary psychometric data (N = 364 females)suggest that both scales consist of one factor and demonstrate strong psychometric properties (in-ternal consistency, test–retest reliability, and construct and convergent validity). The PARMSS wereused in a repeated-measures observational study to examine the relationships between changes inendogenous hormone levels and both proactive and responsive mating intentions with potential newshort-term or long-term partners in healthy pre-menopausal participants (N = 38). At two pointsin their cycle, participants provided salivary hormone samples in the laboratory and reported thelikelihood of engaging in proactive and responsive behaviors with men in photos and vignettes.Participants reported greater responsive than proactive intentions. Increases in estradiol and testos-terone across the cycle were associated with increases in short-term mating intentions, particularlyresponsivity to potential short-term relationship partners. No associations were found for intentionsthat were proactive or that involved potential long-term partners or for progesterone. Changes in thethree hormones explained changes in short-term responsive mating intentions (22% of the variance).The results suggest (a) cyclical changes in estradiol and testosterone are differentially associatedwith changes in responsive vs. proactive mating intentions and (b) context-dependent changes (i.e.,short-term vs. long-term mating intentions and possibly relationship status). The findings requirereplication with larger and diverse samples.Keywords: hormones; estradiol; testosterone; mating strategies; sexual behavior; sexual intentions;menstrual cycle; scale development; flirting; courtship1. IntroductionResearch in behavioral endocrinology has been slow in understanding how hormonesaffect sexual behavior across the menstrual cycle [1,2], but recent meta-analyses suggesthormonal links to sexual desire and sexual functioning—e.g., [3,4]. Previous studies alsosuggest that mating preferences differ by menstrual cycle phase and context—e.g., [5],but few studies have directly examined whether endogenous estradiol, progesterone, andtestosterone modulate mating tactics across the cycle. Hormonal mechanisms may differdepending on relationship status [6,7] or current intentions (e.g., short-term [ST] versuslong-term [LT] relationship tactics) [5]. These contextual factors may help explain variabilityin mating strategies/patterns. Researchers have recommended that individual componentsof female sexuality be examined instead of the historical tendency to conceptualize it in aunitary or broad simplistic way (e.g., as sexual desire generally) [8–10]. These issues havelikely limited research on female mating behaviors such that findings are often not directlySexes 2024, 5, 475–497. https://doi.org/10.3390/sexes5040034 https://www.mdpi.com/journal/sexesSexes 2024, 5 476comparable between studies, and different types of mating behaviors (e.g., proactive andresponsive mating behavior) are put in the same categories. As a result, previous researchmay have failed to capture nuances in female sexual behavior. There is a need for studiesexamining the relationship between hormones, the menstrual cycle, and sexual behavior toexplicitly define aspects of female mating behavior.In 1976, Beach proposed three primary classifications for female sexual behavior:attractivity, proceptivity, and receptivity [11]. While primarily applied to animal behavior,this model of sexual behavior may have value for informing research on human sexual-ity (e.g., [12]). In animals, proceptive and receptive sexual behaviors are influenced byendogenous hormones [11,13], tend to co-occur during periods of estrus and, thus, peakfertility [11,13–20], and strongly suggest a role for gonadal hormones in nonhuman femalesexual behavior. Animal research on proceptivity indicates stimulatory effects of estra-diol [21–23], progesterone enhancing estrogenic effects [24–27], and stimulatory effects ofandrogens [21,28–30]. Research on receptivity indicates stimulatory effects of estradiol [20]that may be independent of androgens [30,31], stimulatory effects of progesterone that arenot independent of estradiol [14,20,32], and both low-dose physiological range stimulatoryeffects of exogenous testosterone [24] and high-dose inhibitory effects [19,30].Explicit behavioral definitions of human proceptive and receptive behavior rarelyappear in studies of human female sexual behavior and are inconsistent across studies—e.g., [33–35]. Furthermore, no scale exists that measures both constructs in an equivalentcomparable manner. Previous studies have utilized items that get at one of these constructsor that diffusely measure both together. The present study suggests and delineates twocomponents of human female sexual behavior (i.e., proactive and responsive mating behav-iors), and examines hormonal mechanisms underlying these mating tactics. These termsoverlap, yet differ, from definitions of proceptive and receptive mating behaviors examinedin animal research. Our terms relate to measurable human behavior that can be usedwhen examining mating thoughts, intentions, and potentially physical actions. We defineproactivity as behavior involving a potential mate and visible energy expenditure. Relevantbehaviors would typically increase the likelihood of intercourse and range from approach-ing potential mates to initiating any type of sexual interaction. We define responsivity asconsummatory behaviors that involve little to no energy expenditure and the absence ofapproach behaviors. These behaviors typically occur in response to mating efforts putforward by the potential partner. Examples of responsivity include agreeing to go on a datewhen asked or engaging in sexual activity when a potential mate initiated it. Some conceptsand behaviors may not fit into either category because they are vague and may overlapwith both constructs at different times (e.g., sexual desire) or may be both effortful andsuggestive of behavior signaling responsivity (e.g., going out to potentially meet a mate,dressing in a “sexy” way). Our differentiation between responsive and proactive matingtactics is similar to Basson’s (2000) proposal that female sexual responses can be eitherresponsive/triggered or spontaneous [36]. Responsivity is more reactive, and proactivityis more spontaneous or self-initiated. Proactivity and responsivity may differ qualita-tively and quantitatively and likely represent different mating tactics that may be adaptivein different contexts. There is a need for psychometrically sound scales that measurethese constructs separately and to examine hormonal mechanisms involved in these twosexual strategies.Previous human menstrual cycle research has not explicitly examined whether proac-tive and responsive mating tactics/behaviors change across the cycle but has largelyfocused on mate preferences and sexual desire/interest [37–57]. For example, some studiessuggest that the frequency of proactive behaviors such as flirtation and female-initiatedsexual activity peaks around ovulation [38–42], when circulating estradiol and testosteronelevels are highest [43,44]. Around ovulation, some studies report a greater frequency oftotal and self-initiated sexual encounters [40,45,46] and opportunistic mating (i.e., “tradingup” from a primary partner in favor of a more attractive mate with better resources) [47].Sexes 2024, 5 477Further, some large-scale studies and meta-analyses [5,42] suggest changes in reproductivehormones may affect mating intentions.Only a few studies appear to have utilized hormonal assay data to examine associ-ations between endogenous hormones and proactive behavior. Bancroft and colleaguesfound that higher free testosterone (FT) was associated with reported increases in thefrequency of sexual initiation and more permissive sexual morality towards extra-pairrelationships, though FT was not associated with their measure of proceptivity/proactivity,and the authors did not control for other hormones [33]. Marcinkowska and colleaguesfound that neither average levels nor within-subject changes in estradiol or progesteroneacross the cycle significantly predicted sexual initiation [40]. However, a change in estradiolwas a positive predictor and a change in progesterone was a negative predictor of sexualdesire. Further, although wearing red does not fit clearly within our definitions of eitherproactivity or responsivity, Blake and colleagues found no robust association between theestradiol-to-progesterone ratio and the likelihood of wearing red [34]. While a numberof studies examined associations between hormones and desire or sex drive across thecycle (e.g., [57]), more research is needed to examine relationships between estradiol andprogesterone and female sexual proactivity as defined above.In terms of responsivity, one study examined the relationship between endogenoushormone levels and female sexual responsivity, although they did not use this terminol-ogy [58]. Estradiol was positively associated with self-reports of extra-pair desire, andprogesterone was positively associated with in-pair sexual desire across the cycle. In con-trast, another study found a negative association between progesterone and general sexualdesire [57]. No studies explicitly looked at testosterone and sexual responsivity in naturallycycling individuals. However, cycle phase was not associated with sexual responsivity tosexual stimuli or with sexual activity in naturally cycling females [59].Overall, the limited research on hormonal associations with female sexual proactivityand responsivity suggests that estradiol and testosterone may be positively associated withgeneral female mating intentions. The influence of progesterone on proactive mating tacticsis less clear, with some hormonal studies suggesting an excitatory role in proactive matingtactics in partnered females (e.g., [58]), whereas others (e.g., [60]) suggest an inhibitoryrole or no role. There is a gap in terms of studies that explicitly examine if changes inestradiol, progesterone, and testosterone differentially affect proactive and responsivemating behavior. This may be related to there being a gap in measurement tools thatreliably differentiate aspects of female sexuality. Such research is important to understandthe effects of hormonal fluctuations within women.There is a need to consider individual differences in hormonal sensitivity, and not justcross-sectional hormone levels, when examining relationships between hormones and behavior—e.g., [61,62]. This need has been highlighted in research on female sexuality [10,61,63]. Examiningthe extent to which hormonal change across the cycle within participants is associated withconcurrent change in sexual behavior using a within-subjects design is one way to considerhormone levels while controlling for some aspects of both hormonal sensitivity and individualdifferences in hormonal change. Recent studies suggest that changes in hormone concentra-tions may be most predictive of fluctuations in psychological variables such as stress or mood,including premenstrual symptoms—e.g., [40,64]. Basic science research suggests mechanismswhereby pulsatile steroid hormone release (i.e., initial hormonal change vs. continuous highor low hormone levels) can activate time- and rate-dependent mechanisms that affect genetranscription and behavior [64]. Thus, the present study examined how changes in hormonesacross the menstrual cycle are associated with changes in mating tactics (proactivity and respon-sivity) with new potential mates in four different contexts (ST vs. LT relationship contexts andwritten scripts versus visual pictures of the men). Examining mating intentions with potentialnew mates, as opposed to intentions in LT relationships, allows measurement in everyoneregardless of relationship status, relationship interest, or attractiveness. Given that estradiol andtestosterone tend to covary together around ovulation, analyses with statistical control for otherhormones were also conducted.Sexes 2024, 5 478The aims of the present paper were (a) to present information and initial psychometricdata on the development of a measure of proactive and receptive mating intentions and(b) to examine whether changes in intentions across the menstrual cycle are associated withchanges in estradiol, progesterone, and testosterone. We hypothesized that female matingintentions with new or unfamiliar potential mates are positively associated with estradioland testosterone and negatively associated with progesterone. No directional hypotheseswere made about relationships between hormones and mating intentions as a function ofthe different contexts.2. Materials and Methods2.1. ParticipantsParticipants were recruited within a larger project examining “hormones and sociosex-uality” [65]. An initial sample of 629 completed a screening questionnaire, 364 participantsmet inclusion criteria for the larger study and completed at least one testing session, and310 completed both sessions online or in the laboratory. Psychometric analyses on themating intentions measures were completed using data from those who completed onetesting session (N = 364). Participant flow is reflected in the EQUATOR STROBE flowchart in Figure 1. Those selected to participate in the two sessions across their menstrualcycle were of reproductive age (i.e., 16 to 44 years); had not had an abortion/miscarriageor used emergency contraception in the past three months; were neither pregnant, in thepost-partum period, nor menopausal; had some attraction to men; and completed questionsto assess these eligibility criteria. Overall, 47 participants living in northwestern Ontario,Canada participated in the laboratory portion of the present two-session cross-sectionalobservational study and met inclusion criterion [i.e., a regular menstrual cycle and free-cycling (not using hormonal contraceptives) and, thus, were eligible to provide salivasamples for hormone assay]. For inclusion in these analyses, participants were furtherscreened with the following five post-hoc exclusion criteria: (1) missing hormone data atone of the two time points (n = 8); (2) no sexual attraction to men (n = 1); (3) pregnant(n = 0); (4) lactating (n = 1); and (5) menopausal (n = 0). As the visual stimuli included pho-tos of men, and this study examined hormonal mating mechanisms from an evolutionaryperspective (i.e., with reproduction as the goal), all participants had menstrual cycles andsome attraction to men.The final sample included 38 female participants (assigned female at birth) recruitedfrom a university campus and the local community who completed the screening portionof the larger study and attended two laboratory sessions between October 2012 and August2014. They ranged in age from 18 to 41 (M = 22.62, SD = 5.36) and were of predomi-nantly Caucasian/European descent (76.3%). Fifteen (39.5%) participants were single, 15(39.5%) were in a relationship, and 8 (21.1%) changed relationship status over the course ofthe study.Sexes 2024, 5 479Sexes 2024, 5, 5 Figure 1. EQUATOR STROBE participant flow diagram. Note: Excluded participants at each stage are reflected by underlined text. 2.2. Measures 2.2.1. Demographics Demographic information was collected within a screening questionnaire (e.g., eth-nicity, age, relationship status, menstrual cycle regularity, sexual orientation, sociosexual-ity). A menstrual cycle item developed in our lab assessed cycle regularity (see Appendix A), with the last two responses reflecting a regular cycle. The Kinsey scale was used to measure sexual orientation ranging from 1 (exclusively heterosexual) to 6 (exclusively ho-mosexual) [66] as well as the option of asexual. It was used when determining inclusion criteria. The Multidimensional Model of Sociosexuality (MDSOI) [67] assesses sociosexu-ality with three subscales (i.e., ST mating orientation (STMO), LT mating orientation (LTMO), and sociosexual behavior), reflecting individual differences in motivations to en-gage in both types of sexual/romantic relationships, separately from actual behavior. In-ternal consistencies have been found to range from 0.83 to 0.95 [67]. Scores were examined as potential covariates and used to examine construct validity. 2.2.2. Proactive and Responsive Mating Strategies Scales (PARMSS) The PARMSS were developed in our lab by the third and fourth authors and include two scales that measure proactive and responsive mating intentions in a theoretical en-counter with a new potential mate using two similar sets of eight items (see Appendix B). Each pair of items differs only in terms of the type of mating intentions: (1) proactive (e.g., asking a person for their phone number) or (2) responsive (e.g., giving a person your Figure 1. EQUATOR STROBE participant flow diagram. Note: Excluded participants at each stageare reflected by underlined text.2.2. Measures2.2.1. DemographicsDemographic information was collected within a screening questionnaire (e.g., ethnic-ity, age, relationship status, menstrual cycle regularity, sexual orientation, sociosexuality).A menstrual cycle item developed in our lab assessed cycle regularity (see Appendix A),with the last two responses reflecting a regular cycle. The Kinsey scale was used to measuresexual orientation ranging from 1 (exclusively heterosexual) to 6 (exclusively homosex-ual) [66] as well as the option of asexual. It was used when determining inclusion criteria.The Multidimensional Model of Sociosexuality (MDSOI) [67] assesses sociosexuality withthree subscales (i.e., ST mating orientation (STMO), LT mating orientation (LTMO), andsociosexual behavior), reflecting individual differences in motivations to engage in bothtypes of sexual/romantic relationships, separately from actual behavior. Internal consis-tencies have been found to range from 0.83 to 0.95 [67]. Scores were examined as potentialcovariates and used to examine construct validity.2.2.2. Proactive and Responsive Mating Strategies Scales (PARMSS)The PARMSS were developed in our lab by the third and fourth authors and includetwo scales that measure proactive and responsive mating intentions in a theoretical en-counter with a new potential mate using two similar sets of eight items (see Appendix B).Each pair of items differs only in terms of the type of mating intentions: (1) proactive (e.g.,asking a person for their phone number) or (2) responsive (e.g., giving a person your phonenumber if asked). The PARMSS utilize a common metric across proactive nd responsiveSexes 2024, 5 480behaviors, posing the same questions in a proactive manner (i.e., How likely would yoube to buy him a drink?) and a responsive manner (i.e., How likely would you be to allowhim to buy you a drink?). Items are rated on a 9-point scale from 1 (not at all likely) to 9(extremely likely). In order to capture current hormonal context, participants were asked tocomplete the ratings based “on how you feel now as well as your feelings and behaviorin the past 48 h (i.e., over the past 2 days)”. The scales can be used with photo or videostimuli or vignettes. Initial psychometric work was completed in two dissertations [65,68].Test development involved first specifying and defining the two domains as discussedabove. Next, item generation involved both deductive and inductive methods to ensurecontent validity. A literature review was performed, and items in existing scales and studieswere inspected. Next, item generation took place along with discussion and review withfour researchers in the area. Independent review and discussion focused on ensuringitems fit with scales and that no salient content was missed. Items were then pre-tested onmembers of the laboratory to again ensure that the items reflected the domains of interest,the response options were meaningful, and salient items had not been missed. Respondentsverbalized their mental process when providing answers. As reported below, cross-sectionaldata were collected for exploratory factor analyses and possible item reduction, factorswere extracted, and eigenvalues and inter-item and item-total correlations were examined.Evidence of one factor per scale was considered as support for content validity. Internalconsistency (Cronbach’s alpha) estimates and test–retest reliability data were calculated.Finally, initial estimates of concurrent and divergent validity were calculated by lookingat correlations with measures of ST and LT mating orientation, previous sexual behaviors,masturbation frequency, and history of infidelity.2.2.3. Mating VignettesThe PARMSS were completed for three contexts (i.e., with a potential ST relationshipvignette, with a potential LT relationship vignette, and with photographs of attractive men;see vignettes in Appendix C). The two vignettes were adapted from previous research [69].The photographs (n = 4) of men were selected based on high ratings of attractiveness in aprevious study [68]. Participants were asked to read each vignette or look at each photoand then respond to the 16 PARMSS items.For each of the three contexts (the two vignettes and the picture-rating task), proac-tivity and responsivity scores were calculated by aggregating the eight relevant items.Thus, there were six main scores: (1) ST proactivity, (2) LT proactivity, (3) picture proac-tivity, (4) ST responsivity, (5) LT responsivity, and (6) picture responsivity. Higher scoresreflect greater intentions to engage in the particular mating behavior. Six higher levelscores were also calculated: total proactivity (ST proactivity + LT proactivity); total re-sponsivity (ST responsivity + LT responsivity); total picture mating intentions (pictureproactivity + picture responsivity); total ST mating intentions (ST proactivity + ST respon-sivity); total LT mating intentions (LT proactivity + LT responsivity); and total PARMSSmating intentions (all six main scale scores aggregated).2.2.4. Hormone AssaysSaliva samples were collected during two laboratory sessions. Participants wereasked to refrain from eating, exercising, smoking, brushing their teeth, wearing chap-stick/lipstick, or drinking anything but water for one hour prior to attending the labsession. Prior to providing the sample, participants rinsed their mouths with chilled wa-ter. Participants were provided with verbal instructions to fill the test tube with roughly2 mL of saliva over the course of completing the laboratory questionnaire. Samples werecollected in 10 mL polypropylene test tubes and stored in a laboratory freezer at no less than−34 ◦C until shipment to a Salimetrics laboratory at Penn State University for immunoassaywith the Salimetrics® 17β-Estradiol, Progesterone, and Testosterone Enzyme ImmunoassayKits. Saliva samples were immunoassayed for estradiol, progesterone, and testosteroneat sensitivities of 0.1, 5, and 1 pg/nL, respectively. Intra-assay coefficients of variationSexes 2024, 5 481(CV) were 3.39% (estradiol), 2.02% (testosterone), and 4.28% (progesterone). Inter-assayCV ranges were 5.12%, 5.69%, and 3.51%, respectively. All participants provided a salivasample in both lab sessions. Testosterone could not be assayed in one sample because thequantity of saliva was not sufficient.2.3. ProcedureThe project was part of a larger study [68] that received approval from the LakeheadUniversity Research Ethics Board, and biosafety approval was obtained for all proceduresinvolving saliva collection. The design best fits with a repeated-measures observationalstudy. Questionnaire data were collected via Survey Monkey, a secure online database,and enhanced SSL security encrypted responses. The EQUATOR STROBE Guidelines forreporting observational studies were followed in the report of this study [70].2.3.1. Recruitment and ScreeningPotential participants were directed to an online screening questionnaire. Participantswere then contacted to schedule two lab sessions.2.3.2. Lab Sessions: Time 1 and Time 2Laboratory sessions were scheduled between 8:30 and 10:30 am to control for diurnalfluctuation of hormones and sexual interest. Measures of menstrual cycle phase wereinitially used to schedule the two laboratory sessions at two specific cycle phases (i.e., theperiovulatory and luteal phases) using backwards counting or reverse counting and in acounterbalanced manner. An attempt was made to schedule sessions to correspond withdays 12 (−17) for the periovulatory phase (i.e., the estimated highest fertility day) andeither 22 (−7) or 26 (−3) for the premenstrual phase of the estimated menstrual cycles tocapture both the early and late premenstrual luteal phase. Further, LH testing was usedto attempt to schedule periovulatory sessions on days 0 or +1 from the LH surge, with anacceptable range being two days before the LH surge and within three days after the LHsurge (i.e., −2 to +3 when 0 represents the day of the positive LH test). Volunteers wereprovided with a kit of at least five LH strips and instructions. If they obtained a positiveresult more than two days before their scheduled periovulatory laboratory session, theirappointment was changed to occur within two days of the positive test. These phases weretargeted because they differ in conception likelihood; patterns of sexual functioning; as wellas relative hormonal concentrations of estradiol, progesterone, and testosterone. However,participant data were included in the present study as long as they had data at two timepoints, regardless of cycle phase, given the goal of examining a full range of hormonalvariability and how hormonal change is associated with within-subject mating intentions.For both lab sessions, participants provided a saliva sample and completed the PARMSSfor the three contexts (i.e., the ST and LT vignettes and the picture-rating task).2.3.3. Statistical AnalysesChange scores were computed between the lab sessions (Time 1 minus Time 2) forthe 3 hormones (estradiol, progesterone, and testosterone) and the 12 PARMSS scores. Therelationships between change in hormones and change in PARMSS scores were examinedusing: (a) Pearson correlations, (b) partial correlations where changes in the other two hor-mones were statistically controlled, and (c) linear regression analyses with three predictorvariables (changes in estrogen, progesterone, and testosterone) where appropriate. Thelatter two sets of analyses were run with the understanding that hormones change acrossthe cycle in a correlated manner such that it may be difficult to capture the effects of onehormone independently from the others.To explore potential covariates, initial correlations and t-tests examined relationshipsbetween the change scores and age, sociosexuality, sexual orientation, and relationshipstatus. Sociosexuality, sexual orientation, and age were not associated with changes inany of the PARMSS scores or hormone levels (all p < 0.05). Thus, these variables wereSexes 2024, 5 482not used as covariates. However, changes in 10 of 12 PARMSS full and subscale scoresdiffered as a function of relationship status, with singles showing less change or variabilityin their mating intentions between sessions compared to partnered participants (e.g.,total responsivity change: single M = 0.56, SD = 0.98; partnered M = 0.82, SD = 1.71;t(28) = 2.67, p = 0.013). Thus, while subsamples were small, significant effects were followedup with exploratory analyses on single versus partnered participants to examine whetherthe relationship between hormones and mating intentions differs in these two contexts.Statistical analyses were performed using the Statistical Package for the Social Sciences(SPSS), versions 26 and 29.020. The alpha level was set at p < 0.05. Given the large effect sizesin the current project, it was deemed appropriate to designate probability levels between0.05 and 0.07 as non-significant trends. Pearson correlation effect sizes were interpreted asper Cohen’s (1988) recommendations (0.10 = a weak or small effect; 0.30 = a moderate effect;0.50 or larger = a strong or large effect) [71]. Prior to each of the main analyses, the datawere screened for errors and missing values, and distributions were examined for normalityand univariate outliers (±z ≥ 3.29) [72]. Normality was assessed visually and statistically(skewness divided by the standard error of skewness <3; kurtosis divided by the standarderror of kurtosis <3). Missing values were missing completely at random and occurred forless than 5% of values. They could not be replaced, as the scales had less than 10 items,and replacement would violate the 10% rule [72]. Thus, pairwise deletion was used, andsample sizes for the primary correlations ranged from 27 to 38. Statistical test assumptionswere examined, and violations were noted. Data screening revealed two outliers (one fortotal PARMSS change score and one for progesterone change score). Relevant analyseswere run both with and without outliers. Taking into consideration that these reflectreal within-subject change scores, analyses based on the full sample are reported becausethere were no substantive differences in results when the outliers were removed. Asthe main analyses in this study identified significant medium-large correlations rangingfrom 0.41 to 0.48, we conducted a post-hoc power analysis with these effect sizes, alpha(two-tailed) = 0.05, and power = 0.80, using an online sample size calculator [73]. Therequired sample size to identify these effect sizes ranged from 31 to 44. Our sample sizes of27 to 38 overlap the required sample range to identify these effect sizes, but our study isunderpowered to identify small or medium effect sizes (i.e., correlations below 0.44).3. Results3.1. Factor Analyses, Reliability, and Validity of the PARMSSData for factor analyses and reliability and validity tests were collected within twodissertations [65,68]. Given the hypothesis of one factor per scale and the reasonably largenumber of items per factor (eight), the sample size (N = 364) was more than adequatefor factor analysis. Exploratory factor analyses were completed here for data collectedin five contexts (ST and LT vignettes, three photos) separately for both the proactive andresponsive scales (see Tables 1 and 2). Both scree plots and the eigenvalue >1 criterion wereused to determine the number of factors to extract [74]. All factor analyses indicated thatthe proactive scale consisted of only one factor, as did the responsive scale, contributingcontent validity evidence. The Kaiser–Meyer–Olkin (KMO) measure verified the samplingadequacy for all 10 factor analyses, and KMO ranged from 0.875 to 0.912, which is con-sidered great to superb [74]. For the proactive scale (see Table 1), across the five contexts,the eigenvalues for factor 1 ranged from 5.16 to 5.98, all items loaded highly on factor 1(factor loadings ranged from 0.671 to 0.924), and the factor accounted for 64.47% to 74.80%of the variance. Similarly, across the five contexts, the responsive scale (see Table 2) hadeigenvalues for factor 1 that ranged from 5.41 to 5.85, all items loaded highly on factor 1(factor loadings ranged from 0.716 to 0.929), and the factor accounted for 67.56% to 73.10%of the variance.Sexes 2024, 5 483Table 1. Proactive mating intentions scale: item factor loadings, eigenvalues, and internal consistencyacross five contexts.Proactive Scale Items Item Loadings on Factor 1 for Each ContextSTVignetteLTVignette Picture 1 Picture 2 Picture 3Ask this person for their number? 0.758 0.884 0.899 0.904 0.908Smile at, or make eye contact with, this person? 0.671 0.727 0.664 0.717 0.668Ask this person to dance with you? 0.817 0.886 0.878 0.902 0.881Buy this person a drink or ask this person to buy youa drink? 0.809 0.869 0.872 0.874 0.894Initiate kissing with this person? 0.901 0.915 0.920 0.924 0.915Offer this person a ride home or ask this person for aride home? 0.736 0.82 0.818 0.798 0.786Initiate any sexual activity with this person? 0.882 0.887 0.901 0.903 0.881Initiate sex with this person? 0.822 0.846 0.861 0.876 0.873Eigenvalue for first factor 5.158 5.86 5.85 5.984 5.842Percentage of variance accounted for 64.471 73.256 73.122 74.801 73.03Kaiser–Meyer–Olkin Measure of Sampling Adequacy 0.884 0.912 0.907 0.896 0.886Bartlett’s Test of Sphericity—Approx. Chi-Square 2179.997 2899.891 2943.577 3625.036 3365.811df 28 28 28 28 28p <0.001 <0.001 <0.001 <0.001 <0.001N 353 360 359 364 358Cronbach’s Alpha 0.93 0.946 0.938 0.946 0.926Test–Retest Reliability (Pearson Correlation) 0.82 0.796Table 2. Responsive mating intentions scale: item factor loadings, eigenvalues, and internal consis-tency in five contexts.Responsive Scale Items Item Loadings on Factor 1 for Each ContextSTVignetteLTVignette Picture 1 Picture 2 Picture 3Give this person your phone number if asked? 0.826 0.876 0.873 0.9 0.855Return a smile or eye contact from this person? 0.716 0.781 0.729 0.765 0.723Dance with this person if asked? 0.832 0.891 0.872 0.875 0.863Allow this person to buy you a drink? 0.821 0.875 0.842 0.859 0.822Allow this person to kiss you? 0.906 0.925 0.909 0.929 0.903Accept a ride home from this person if offered? 0.752 0.816 0.775 0.795 0.77Allow this person to initiate any sexual activity? 0.874 0.847 0.863 0.868 0.848Allow this person to initiate sex? 0.833 0.802 0.817 0.837 0.813Eigenvalue for first factor 5.405 5.817 5.602 5.848 5.462Percentage of variance accounted for 67.56 72.714 70.024 73.098 68.272Kaiser–Meyer–Olkin Measure of Sampling Adequacy. 0.893 0.896 0.891 0.881 0.875Bartlett’s Test of Sphericity—Approx. Chi-Square 2596.065 3182.129 2859.574 3594.088 3009.983df 28 28 28 28 28p <0.001 <0.001 <0.001 <0.001 <0.001N 353 360 359 364 358Cronbach’s Alpha 0.93 0.946 0.938 0.946 0.926Test–Retest Reliability (Pearson Correlation) 0.82 0.796Internal consistency has been examined in two projects using Cronbach’s alpha. Therewas evidence of good internal consistency: alpha = 0.92 for both the proactive (N = 64)and responsive (N = 63) scales across vignettes and photos [68]. In the present study, theproactive and responsive scales demonstrated good internal consistency across contextsfor both proactive (0.91 to 0.95) and responsive (0.93 to 0.95) scales (N = 364) (see Tables 1and 2). In the first study, proactive and responsive scales had good test–retest reliabilitywith vignettes [r(45) = 0.76, p < 0.01 and r(45) = 0.78, p < 0.01, respectively] and photosSexes 2024, 5 484[both r(45) = 0.88, p < 0.01] over a mean of 23.15 (SD = 13.17) days [68]. In the present study,test–retest reliability ranged from r = 0.76 to 0.77 for the proactive scale and r = 0.80 to0.82 for the responsive scale over a mean of 22.30 days (SD = 41.49) days (N = 295) (seeTables 1 and 2). It should be noted that these test–retest reliability coefficients are likelyunderestimates since participants were purposely assessed at different menstrual cyclephases in both studies [65,68].The mean responsive scale scores were significantly higher than the proactive scalescores in both ST [M = 31.36, SD = 17.03 vs. M = 21.88, SD = 13.90; t(342) = 19.628, p < 0.001]and LT [M = 39.74, SD = 20.44 vs. M =31.17, SD = 19.47; t(349) = 18.60, p < 0.001] vignettecontexts. Proactive and responsive scales were also highly correlated [r(343) = 0.85, p < 0.01].Thus, females who report more proactive mating intentions also generally report being moreresponsive to the sexual/romantic advances of new men.The PARMSS show preliminary evidence of convergent and divergent validity [68].The proactive and responsive scales were positively correlated with ST mating orienta-tion [r(60) = 0.42, p < 0.01; r(60) = 0.49, p < 0.01, respectively] but not LT mating ori-entation [r(60) = 0.15, p = 0.27; r(60) = 0.12, p = 0.36, respectively] or previous sexualbehavior [r(52) = 0.21, p = 0.142; r(52) = 0.18, p = 0.198, respectively] scales from the MD-SOI [67]. As the PARMSS were designed to measure mating intentions in new or potentialrelationships, it is not surprising that they provide information on mating motivationsspecific to potential ST relationships. The proactive and responsive scales were not relatedto masturbation frequency [r(50) = 0.13, p = 0.372; r(50) = 0.02, p = 0.867, respectively],suggesting that they measure something other than sex drive (divergent validity). While theproactive scale was not related to a history of infidelity [r(51) = 0.11, p = 0.46], the responsivescale was positively associated [r(51) = 0.34, p = 0.016], suggesting that responsivity is morerelevant to extra-pair sex than proactivity.3.2. Descriptive DataThe absolute values of hormonal change between the two testing sessions wereas follows: estradiol (M = 0.73 pg/mL, SD = 0.58), progesterone (M = 94.44 pg/mL,SD = 109.04), and testosterone (M = 16.00 pg/mL, SD = 11.69). Absolute values of themating intention change scores were as follows: ST proactivity (M = 0.83, SD = 0.80), LTproactivity (M = 1.64, SD = 1.65), picture proactivity (M = 0.74, SD = 0.81), ST respon-sivity (M = 1.20, SD = 1.25), LT responsivity (M = 1.34, SD = 1.54), picture responsivity(M = 0.69, SD = 0.75), total proactivity (M = 1.00, SD = 1.07), total responsivity (M = 1.00,SD = 1.12), total picture (M = 1.43, SD = 1.52), total ST (M = 1.94, SD = 1.91), total LT(M = 2.91, SD = 3.17), and total PARMSS (M = 1.01, SD = 1.16).3.3. Correlations and Partial Correlations3.3.1. EstradiolCorrelations and partial correlations between change scores for estradiol and changescores for the twelve PARMSS scores for the full sample are found in the left panel of Table 3(see Table 3). For the full sample, a change in estradiol level was moderately positivelycorrelated with a change in ratings of ST responsivity (r = 0.48, p = 0.006, n = 32) and achange in total ST mating intentions (r = 0.44, p = 0.013, n = 32) (see Figure 2). Partialcorrelations did not reveal any unique effects of estradiol that were independent of otherhormonal changes.Sexes 2024, 5 485Table 3. Correlations (and partial correlations controlling for change in the other two hormones)between changes in each hormone (estradiol, progesterone, and testosterone) and changes in matingintention scores on the Proactive and Responsive Mating Strategies Scales (PARMSS) across contextsfor all participants across the menstrual cycle a.PARMSS Across Contexts Estradiol(n = 38)Progesterone(n = 38)Testosterone(n = 37)ST proactivity 0.25 (0.12) −0.04 (−0.26) 0.24 (0.14)LT proactivity 0.17 (0.20) −0.04 (−0.29) 0.04 (0.07)Picture proactivity −0.14 (−0.33) −0.01 (−0.08) 0.00 (0.22)ST responsivity 0.48 ** (0.34) 0.01 (−0.24) 0.44 * (0.13)LT responsivity 0.24 (0.25) −0.03 (−0.34) 0.07 (0.10)Picture responsivity −0.01 (−0.27) 0.08 (−0.02) 0.15 (0.30)Total proactivity 0.09 (0.04) −0.03 (−0.26) 0.03 (0.15)Total responsivity 0.26 (0.17) 0.02 (−0.27) 0.15 (0.19)Total picture −0.13 (−0.31) 0.03 (−0.05) 0.10 (0.27)Total ST 0.44 * (0.27) 0.01 (−0.27) 0.41 ** (0.15)Total LT 0.21 (0.23) −0.03 (−0.33) 0.04 (0.09)Total PARMSS 0.20 (0.18) −0.02 (−0.27) 0.10 (0.17)Note: Full sample ns range from 27–38 due to missing data. ST = short-term; LT = long-term. a Time 1 minus (−)Time 2 change scores. Positive correlations indicate that a hormonal increase was associated with an increase inthe mating intention rating, and negative correlations indicate that a hormonal increase was associated with adecrease in the mating intention rating. * p < 0.05. ** p < 0.01.Sexes 2024, 5, 11 (M = 1.43, SD = 1.52), total ST (M = 1.94, SD = 1.91), total LT (M = 2.91, SD = 3.17), and total PARMSS (M = 1.01, SD = 1.16). 3.3. Correlations and Partial Correlations 3.3.1. Estradiol Correlations and partial correlations between change scores for estradiol and change scores for the twelve PARMSS scores for the full sample are found in the left panel of Table 3 (see Table 3). For the full sample, a change in estradiol level was moderately positively correlated with a change in ratings of ST responsivity (r = 0.48, p = 0.006, n = 32) and a change in total ST mating intentions (r = 0.44, p = 0.013, n = 32) (see Figure 2). Partial cor-relations did ot eveal any unique effects of estradi l that were independ nt of other hormonal changes. (A) (B) (C) (D) Figure 2. Change in estradiol and female short-term (ST) mating intentions. Scatterplots reflect cor-relations between changes in estradiol and changes in ST mating intentions across the menstrual cycle. The top row reflects these correlations for responsive mating intentions when evaluating men for a potential ST relationship in (A) the full female sample (r = 0.48, p = 0.006, n = 32), and (B) partnered participants (r = 0.81, p = 0.002, n = 12). The bottom row reflects bivariate correlations for all ST mating intentions when evaluating men for a potential ST relationship for (C) the full female sample (r = 0.44, p = 0.013, n = 32), and (D) partnered participants (r = 0.79, p = 0.002, n = 12). Each graph includes the line of best fit with the 95% CIs. Table 3. Correlations (and partial correlations controlling for change in the other two hormones) between changes in each hormone (estradiol, progesterone, and testosterone) and changes in mating intention scores on the Proactive and Responsive Mating Strategies Scales (PARMSS) across contexts for all participants across the menstrual cycle a. PARMSS Across Con-texts Estradiol (n = 38) Progesterone (n = 38) Testosterone (n = 37) ST proactivity 0.25 (0.12) −0.04 (−0.26) 0.24 (0.14) LT proactivity 0.17 (0.20) −0.04 (−0.29) 0.04 (0.07) Picture proactivity −0.14 (−0.33) −0.01 (−0.08) 0.00 (0.22) ST responsivity 0.48 ** (0.34) 0.01 (−0.24) 0.44 * (0.13) LT responsivity 0.24 (0.25) −0.03 (−0.34) 0.07 (0.10) Figure 2. Change in estradiol and female short-ter (ST) ating intentions. Scatterplots reflectcorrelations between changes in estradiol and changes in ST mating intentions across the menstrualcycle. The top row reflects these correlations for responsive mating intentions hen evaluatingmen for a potential ST relationship in (A) the full female sample (r = 0.48, p = 0.006, n = 32), and(B) partnered participants (r = 0.81, p = 0.002, n = 12). The bottom row reflects bivariate correlationsfor all ST mating intentions when evaluating men for a potential ST relationship for (C) the full femalesample (r = 0.44, p = 0.013, n = 32), and (D) partnered participants (r = 0.79, p = 0.002, n = 12). Eachgraph includes the line of best fit with the 95% CIs.Sexes 2024, 5 4863.3.2. ProgesteroneAs indicated in the middle panel of Table 3 (see Table 3), no significant correlations orpartial correlations were observed between changes in progesterone and changes in anyPARMSS scores.3.3.3. TestosteroneCorrelations and partial correlations between the change scores for testosterone andchange scores for the twelve PARMSS scores are found in the right column of Table 3(see Table 3). For the full sample, changes in testosterone were positively correlated withchanges in ST responsivity (r = 0.44, p = 0.013, n = 31) and changes in total ST matingintentions (r = 0.41, p = 0.021, n = 31) (see the left side of Figure 3). Partial correlations onthe full sample did not reveal any unique effects of testosterone.Sexes 2024, 5, 12 Picture responsivity −0.01 (−0.27) 0.08 (−0.02) 0.15 (0.30) Total proactivity 0.09 (0.04) −0.03 (−0.26) 0.03 (0.15) Total responsivity 0.26 (0.17) 0.02 (−0.27) 0.15 (0.19) Total picture −0.13 (−0.31) 0.03 (−0.05) 0.10 (0.27) Total ST 0.44 * (0.27) 0.01 (−0.27) 0.41 ** (0.15) Total LT 0.21 (0.23) −0.03 (−0.33) 0.04 (0.09) Total PARMSS 0.20 (0.18) −0.02 (−0.27) 0.10 (0.17) Note: Full sample ns range from 27–38 due to missing data. ST = short-term; LT = long-term. a Time 1 minus (−) Time 2 change scores. Positive correlations indicate that a hormonal increase was asso-ciated with an increase in the mating intention rating, and negative correlations indicate that a hor-monal increase was associated with a decrease in the mating intention rating. * p < 0.05. ** p < 0.01. 3.3.2. Progesterone As indicated in the middle panel of Table 3 (see Table 3), no significant correlations or partial correlatio s were observ d betw en chang s in progesterone and changes in any PARMSS scores. 3.3.3. Testosterone Correlations and partial correlations between the change scores for testosterone and change scores for the twelve PARMS scores are foun i t ri l f able 3 (see Tabl 3). For the full sample, changes in t rone were positively correlated with changes in ST responsivity (r = 0.44, p = 0.013, n = 31) a c a es in total ST mating in-tentions (r = 0.41, p = 0.021, n = 31) (see the left side of Figure 3). Partial correlations on the full sample did not reveal any unique effects of testosterone. (A) (B) (C) (D) Figure 3. Change in testosterone and female short-term (ST) mating intentions. Scatterplots reflect correlations between changes in testosterone and changes in ST mating intentions across the men-strual cycle. The top row reflects these correlations for responsive mating intentions when evaluat-ing men for a potential ST relationship in (A) the full female sample (r = 0.44, p = 0.013, n = 31), and (B) partnered participants (r = 0.73, p = 0.007, n = 12). The bottom row reflects these bivariate corre-lations for all ST mating intentions when evaluating men for a potential ST relationship for (C) the full female sample (r = 0.41, p = 0.021, n = 31), and (D) partnered participants (r = 0.69, p = 0.013, n = 12). Each graph includes the line of best fit with the 95% CIs. Figure 3. Change in testosterone and female short-term (ST) mating intentions. Scatterplots reflectcorrelations between changes in testosterone and changes in ST mating intentions across the menstrualcycle. The top row reflects these correlations for responsive mating intentions when evaluatingmen for a potential ST relationship in (A) the full female sample (r = 0.44, p = 0.013, n = 31), and(B) partnered participants (r = 0.73, p = 0.007, n = 12). The bottom row reflects these bivariatecorrelations for all ST mating intentions when evaluating men for a potential ST relationship for(C) the full female sample (r = 0.41, p = 0.021, n = 31), and (D) partnered participants (r = 0.69,p = 0.013, n = 12). Each graph includes the line of best fit with the 95% CIs.3.3.4. Exploratory Examination of Relationship StatusGiven that the PARMSS scores differed as a function of relationship status, singlesshowed less change in mating intentions between sessions compared to partnered partici-pants, and past studies suggested that hormonal mechanisms may differ with relationshipstatus [6,7], we explored whether relationship status influenced the strength and directionof the significant associations between hormones and ST mating intention scores foundabove. While the sample sizes were small, some large effect sizes were noted.The simple correlations between estradiol and ST mating intentions were only sig-nificant in partnered participants (i.e., ST responsivity: r = 0.81, p = 0.002, n = 12; totalSexes 2024, 5 487ST: r = 0.79, p = 0.002, n = 12; see right side of Figure 2) and not in single people (i.e., STresponsivity: r = 0.14, p = 0.636, n = 14; total ST: r = 0.05, p = 0.875, n = 14). The partialcorrelations were not significant for single (negative correlations: ST responsivity: r =−0.07,p = 0.821, n = 10; total ST: r = −0.23, p = 0.474, n = 10) or partnered participants (positivecorrelations: ST responsivity: r = 0.53, p = 0.116, n = 8; total ST: r = 0.53, p = 0.113, n = 8).However, the latter large effect sizes suggested possible unique effects of estradiol on STmating intention scores for partnered participants.For testosterone, simple correlations with ST mating intentions were also only sig-nificant in partnered participants: ST responsivity (r = 0.73, p = 0.007, n = 12) and totalST mating intentions (r = 0.69, p = 0.013, n = 12; see the right side of Figure 3). No signif-icant simple correlational effects were found for singles, although the correlations werepositive and of medium effect size (ST responsivity: r = 0.33, p = 0.254, n = 14; total ST:r = 0.37, p = 0.195, n = 14). Partial correlations were not significant, suggesting no uniqueeffect of testosterone on ST mating intention scores in single participants (ST responsivity:r = 0.26, p = 0.407, n = 10; total ST: r = 0.38, p = 0.219, n = 10) or partnered participants (STresponsivity: r = 0.25, p = 0.492, n = 8; total ST: r = 0.14, p = 0.693, n = 8).3.4. RegressionsTo follow up on the above findings, we ran regressions to examine the cumulativeproportion of variance that changes in estradiol, progesterone, and testosterone couldaccount for in (a) total ST and (b) ST responsive mating intention changes across the cycle.There were no issues with multicollinearity. Changes in the three hormones explained asignificant amount of variance in ST responsivity change scores [F (3, 27) = 3.82, p = 0.02,R2 = 0.22], and there was a nonsignificant trend for total ST change scores [F (3, 27) = 2.96,p = 0.05, R2 = 0.16]. The changes in hormones across the menstrual cycle best explainedchanges in ST responsive mating intentions. (Exploratory regressions on all 12 PARMSSchange scores can be found in the Supplementary Materials).While acknowledging the small samples and low power, we explored whether rela-tionship status was relevant to the significant effect for ST responsive mating intentions. Afollow-up regression was run on single participants and another on those in a relationship.Changes in the three hormones significantly explained the change in ST responsivity scoresfor partnered participants [F (3, 8) = 5.72, p = 0.022, R2 = 0.56] but not in those who weresingle [F (3, 10) = 0.45, p = 0.721, R2 = 0.15].4. Discussion4.1. Preliminary Psychometric Support for the PARMSSInitial development work on the PARMSS as a measure of proactive and responsivemating intentions with new potential mates was supported here. The results from theexploratory factor analyses with women indicated that each scale was composed of oneinternally consistent reliable factor when used in all three contexts and that both scalescores have reasonable test–retest reliability over a mean 23-day period even thoughfluctuation was expected due to cyclical hormone change. There was a higher endorsementof responsive than proactive mating intentions, which makes sense given the social riskand energy expenditure involved in proactive behavior. Evidence of convergent validitycame from both scales having strong positive correlations with the ST mating orientationscale from the MDSOI [67]. Evidence of divergent validity came from low nonsignificantcorrelations with the MDSOI LT mating orientation and previous sexual behavior scalesand a measure of masturbation frequency. This was expected, as the PARMSS measuremating motivations with potential new mates as opposed to actual behavior. The latter areaffected by attractiveness, opportunity, and relationship status.Sexes 2024, 5 4884.2. A Stimulatory Effect of Estradiol on ST Mating Intentions across the CycleEstradiol was positively associated with overall ST mating intentions and responsiveST Mating Intentions with a medium-large effect size. This is somewhat consistent withhypothesis one. However, exploratory analyses revealed that these effects were driven byparticipants in a relationship. While partial correlations did not reveal unique effects ofestradiol, this may be related to low power or the possibility that estradiol works interac-tively with other hormones to stimulate responsive mating behaviors in ST contexts. Ourfindings fit with some previous studies—e.g., [58,75], suggesting that relationship status ortype of partner (in-pair vs. extra-pair) is an important contextual factor in understandinghormonal mechanisms or menstrual cycle shifts in mating preferences. These findingsalso suggest the possibility that studies with a higher proportion of participants in rela-tionships may be more likely to find significant associations between estradiol and sexualbehavior—e.g., [40].Estradiol, a hormone that peaks with fertility across the menstrual cycle, may increasefemale ST mating intentions. The results are consistent with animal research indicatingstimulatory effects of estradiol on female receptivity—e.g., [30,31]. Our human findingswere strongest for those with a partner. From an evolutionary perspective, this effect likelyreflects an adaptation that increases reproductive success. This finding is consistent withprevious studies suggesting that females display increases in opportunistic mating duringperiods of higher conception likelihood—e.g., [47], show increases in unrestricted sociosexu-ality, unrestricted desire (e.g., for extra-pair partners) with increases in estradiol [58,76], andreport greater responsivity to both in-pair and extra-pair partners at peak fertility [41,42,77].The findings also fit with aspects of the dual mating hypothesis and the good genes sexualselection theories [78] where females pursue a mating strategy in which they secure paternalinvestment through an LT partner and pursue ST extra-pair relationships to gain access togenetic fitness—e.g., [79]. The fact that we found a positive relationship between increasesin estradiol and increases in ST mating intentions for partnered but not single participantsmay fit with a finding that partnered (vs. single) females showed larger cyclic shifts intheir ratings of men’s attractiveness in the context of potential ST relationships [80]. It isnoteworthy that we also found that partnered individuals showed larger changes in matingintentions between sessions than single people. Given that the current study focused onintentions with potential new mates, increases in estradiol may stimulate extra-pair matingmotivation, which is consistent with previous research underscoring estradiol’s role inprocesses central to mate acquisition [81].It is noteworthy that when each type and combination of mating intention was ex-amined (e.g., proactive, responsive, ST, LT), changes in the three hormones (estradiol,testosterone, progesterone) across the cycle best predicted a change in ST responsivity,and particularly so for partnered individuals. While we did not examine in-pair matingintentions, our findings suggest that the hormonal modulation of mating intentions appearsstrongest for ST mating intentions when one already has a partner. Similarly, the findingthat responsive rather than proactive behaviors are more strongly affected by change inestradiol is consistent with previous research—e.g., [82] suggesting that shifts in ST fertile-phase proactivity and responsivity may function to enhance access to extra-pair resourceswhen conception likelihood is highest. The observed increases in ST responsive matingintentions in partnered females at this time suggest that these mating strategies may affordaccess to a larger gene pool without the higher associated risk of losing a relationship dueto any obvious proactive pursuit of an extra-pair partner. The passive and low-risk natureof responsive (versus proactive) behaviors are less likely to compromise the stability of aLT relationship and may function to enhance access to a larger pool of genetic resourceswith potential ST extra-pair partners.Sexes 2024, 5 4894.3. A Stimulatory Effect of Testosterone on Female ST Mating IntentionsIncreases in testosterone were associated with increased responsive ST, and overall ST,mating intentions in the full sample (medium-large effect size), which partially supports hy-pothesis two. These effects were strongest for those in a relationship. However, correlationswere no longer significant upon statistically controlling for estradiol and progesterone,possibly suggesting that the testosterone effect may be due to confounding by a stimula-tory effect of estradiol or that estradiol and testosterone act together in an interdependentmanner (e.g., aromatization).A stimulatory effect of testosterone on female mating intentions should be viewedalong with past research suggesting a stimulatory role of androgens in female sexualbehavior. First, animal research indicates a testosterone stimulatory effect on femaleproceptivity—e.g., [28,30]. Second, higher androgen sensitivity (but not higher androgenlevels) has been associated with a greater tendency for visual proximal sexual cues toactivate sexual desire in human females [10]. Third, Costa and colleagues reported a stimu-latory effect of testosterone on the sexual desire of single but not partnered females (whilewe found stronger effects for partnered participants and for ST mating intentions) [83].Fourth, Shirazi and colleagues found a positive relationship between testosterone and bothunrestricted sociosexuality and sexual desire [84]. Fifth, Tzalazidis and Oinonen foundpositive associations between androgenic symptoms [i.e., hirsutism and other symptomsof polycystic ovary syndrome (PCOS)] and unrestricted sociosexuality, masturbation fre-quency, and attraction to women [63]. Finally, testosterone has been associated with femalemasturbation, orgasm, and sexual self-esteem [54,55]; flirting with extra-pair men duringthe luteal phase [58]; and general sexual desire and functioning [4]. These studies suggesta role of endogenous androgens in female sexuality, particularly in terms of both sexualinterest/appetite and ST mating intentions.Bancroft and Graham [1] hypothesized the existence of a subgroup of females who,similar to males, have some dependence on testosterone for their sexual behavior. Thepresent findings suggest the possibility that there may be both testosterone-dependentsubgroups of females and testosterone-dependent types of mating intentions. Testosteronemay play a role in activating female mating behavior (i.e., responsive mating intentions) inresponse to the proactive actions of potential ST partners. While subsamples were small,the findings also suggest that relationship status may be a moderating factor worthy offuture research.There are two reasons why it might make sense for testosterone (in addition to estra-diol) to stimulate mating intentions. First, there are high levels of estradiol at both thehigh-fertile (late follicular) and low-fertile (mid-luteal) phases of the cycle, while testos-terone peaks only during the former phase. Thus, a testosterone stimulatory effect couldmaximally direct mating behavior to the more fertile portion of the cycle. Second, fe-male testosterone levels increase when they see an attractive man [85] or have sexualthoughts [86], providing an adaptive mechanism and ideal opportunity for this increasein testosterone to stimulate mating intentions. A stimulatory effect of testosterone wouldbe the most efficient and focused mechanism to orient female mating intentions towardsattractive men (i.e., good genes) both when conception is most likely and when the op-portunity (i.e., an attractive man) presents itself. While their results require replication, afinding by Shirazi and colleagues is worth considering in this context [76]. They found thatthe testosterone increase during the viewing of sexual stimuli occurred only during thefollicular and not the luteal phase. Thus, females may have testosterone surges in responseto male/sexual stimuli only during more fertile times of the cycle.These findings add to the literature on female sexuality, which suggests associationsbetween testosterone and relationship status [87], relationship commitment [88], and en-gagement in new relationships. Ellison suggested that testosterone plays an important rolein the initiation and establishment of sexual relationships [89], and the present findingsare consistent.Sexes 2024, 5 4904.4. No Association between Progesterone and Mating IntentionsThe most obvious explanation for our progesterone findings is that there are indeed norelationships between progesterone and either proactive or responsive mating intentions.This fits with past research. It fits with animal studies suggesting that progesterone on itsown does not stimulate sexual behavior but requires estrogen priming [14,32]. Our findingis also consistent with human studies indicating that progesterone is not associated withgeneral sexual interest [90], extra-pair sexual interest [56], or vaginal–penile intercoursefrequency in sexually active menstruating individuals [49]. However, the current finding isinconsistent with a few studies suggesting that progesterone negatively predicts femalesexual desire and behavior [40,41,76]. It may be noteworthy that we found nonsignifi-cant small–medium negative correlations between changes in progesterone and matingintentions with the vignettes when changes in estradiol and testosterone were statisticallycontrolled. These nonsignificant negative partial correlations raise the possibility thatincreases in progesterone may inhibit mating intentions, but the co-occurring changes inestradiol and testosterone counteract these effects across the menstrual cycle. While wehave attempted to statistically control for the other hormones using partial correlations,hormone interactions are complicated by the fact that hormones can have both agonisticand antagonistic effects on each other (e.g., progesterone decreases the number of estrogenreceptors [91]). Thus, we acknowledge that this statistical control has limitations.4.5. Strengths, Limitations, and Future DirectionsThis study is the first to examine how cyclical changes in endogenous hormones arerelated to female sexual behavior as defined by proactive and responsive mating intentionsin both ST and LT mating contexts. The use of hormonal assay rather than cycle phase esti-mation afforded a more precise understanding of the relationship between these variablesas several concerns have arisen about the consistency, precision, and misclassification ofcycle-phase estimation data [92]. The use of the PARMSS to examine intentions as opposedto actual behavior provided a clearer picture of female sexual intentions by circumventingthe potential confounding variables of mate availability and female attractivity. It alsoallowed for a direct comparison of proactive and responsive mating intentions to findthat responsive mating intentions are much stronger. The focus on state-related sexualintentions as opposed to longer-term trait-like sexual experiences is also a strength, assome studies on sexuality examine associations between one hormone sample and sexualtraits (e.g., one’s experiences over the past 30 days [55]). Further, the exclusion of hor-monal contraception users eliminated the potential confounding variable of exogenoushormone use, e.g., [4]. The use of hormonal change scores (a) is more powerful/sensitivethan between-subject analyses of hormone levels [64] and (b) takes into account hormonalsensitivity and change as opposed to just hormone level.A few limitations are worth noting and suggest areas for future research. First, weprovided preliminary psychometric data on the PARMSS, but further research will increaseconfidence in any findings using the measure. The next steps include confirmatory fac-tor analysis, additional research on validity (e.g., predictive), and determining minimalimportant change and minimal clinically important difference values. It would also beuseful to develop a similar measure to assess proactive and responsive mating intentionsin the context of established relationships. Second, as participants were of young repro-ductive age (i.e., early 20s) and all reported some attraction to men, the hormonal resultsmay not be generalized for participants in their 30s and 40s or individuals without sexualattraction to men. Third, the range of estradiol change scores was restricted, suggestingthat stronger effects may be found if a larger range of estradiol change is captured. Fourth,salivary immunoassays may be limited by cross-reactivities to other substances and mayoverestimate hormone levels [93,94], although the latter issue may be less problematicwhen evaluating change. Fifth, we did not examine or include recent/prior sexual behavioras a covariate. Sixth, while medium to large effect sizes were observed, the study includedsmall samples for relationship status subgroups and our study is underpowered to identifySexes 2024, 5 491small or medium effect sizes (i.e., correlations below 0.44). We consider the relationshipstatus group findings to be worthy of inclusion, yet it is important to emphasize that thesefindings are exploratory and preliminary. Finally, the findings reflect associations, andcausality cannot be implied due to the study design. All findings require replication withlarger samples and exploration with samples that include other sexual orientations.Research on proactive and responsive mating intentions may have implications forimproving the treatment of sexual desire disorders, for understanding how hormonalcontraceptives may affect sexual behavior, and for understanding female mating strate-gies. Differentiating between different aspects of female sexual behavior and desire (e.g.,proactivity and responsivity components) allows for a more sensitive and powerful mea-surement of specific unique aspects of female sexuality. This better allows researchersto detect the effects of hormones when they exist. When different aspects of sexual de-sire/interest/intentions are measured together within larger constructs or are not measuredat all, it reduces the sensitivity of the measures and reduces the likelihood of finding aneffect when it exists for one component. The present findings suggest that small increasesin endogenous estradiol and testosterone may enhance female receptivity or responsivityto new potential partners. This is relevant to the role of hormones in behaviors involvedin initiating relationships. While it is unclear if these findings generalize proactive andresponsive behaviors with existing partners or exogenous estradiol and testosterone, futureresearch is warranted. Given that oral contraceptives inhibit endogenous hormonal cyclicityacross the menstrual cycle, it is important to understand how oral contraceptives affectthese aspects of sexuality. Future repeated-measures studies could compare free-cyclingand triphasic oral contraceptive groups on changes in proactive and responsive sexualintentions across the cycle in studies where triphasic oral contraceptive users are examinedin two or more phases with differing estradiol doses. Finally, the findings suggest that peo-ple searching for a sexual/relationship partner should be proactive about it. Participantsreported being much more likely to say “yes” to an advance from a potential mate than tomake that advance themselves.5. ConclusionsOur results provide initial pilot data suggesting (a) psychometric support for thePARMSS, and (b) that endogenous hormones (i.e., estradiol and testosterone) may func-tion to elicit ST responsive mating behaviors, as compared to mating behaviors that areproactive or involve potential new LT partners. This is the first study to separate proactiveand responsive mating intentions, quantify differences in these types of intentions, andexamine how changes in these intentions across the menstrual cycle covary with changesin estradiol, progesterone, and testosterone. The findings underscore the potential valueof the following methodological suggestions for future research examining hormones andfemale sexual behavior: (a) delineating proactive and responsive behaviors, (b) exam-ining the ST and LT mating contexts separately, and (c) using within-subject designs tocontrol for between-subject variability in hormonal sensitivity. Replication studies withlarger sample sizes should also examine relationship status, and possibly sociosexuality, asmodulating variables. These findings contribute to the effort to develop psychometricallysound measures of individual components of female sexuality instead of using unitary orbroad simplistic measures. Such measures can be used to identify hormonal mechanismsin specific aspects of female sexuality to help understand how natural cyclical hormonalfluctuations are associated with normal and abnormal sexual experiences. Understandinghormonal mechanisms in female sexual behavior also has implications for individual-izing treatment for sexual desire disorders and understanding the effects of hormonalcontraceptives on sexual behavior.Sexes 2024, 5 492Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/sexes5040034/s1, Table S1: Multiple Regressions Examining the Ex-tent to Which Changes in Estradiol, Progesterone, and Testosterone Together Predict Change in Proac-tive and Responsive Mating Strategies Scale (PARMSS) Scores in the Full Sample ofFemale Participants.Author Contributions: Conceptualization, K.A.O., M.L.T., M.P. and K.E.Z.; methodology, M.L.T.,K.A.O. and M.P.; formal analysis, K.E.Z., M.L.T., M.P. and K.A.O.; investigation, M.L.T. and M.P.;resources, K.A.O.; data curation, M.L.T., K.E.Z., M.P. and K.A.O.; writing—original draft preparation,K.E.Z., K.A.O. and M.L.T.; writing—review and editing, K.E.Z., K.A.O., M.L.T. and M.P.; visualization,K.E.Z., K.A.O. and M.L.T.; supervision, K.A.O. and M.L.T.; project administration, M.L.T. and K.A.O.;funding acquisition, K.A.O. and M.L.T. All authors contributed equally to this work. All authorshave read and agreed to the published version of the manuscript.Funding: This work was supported by a doctoral award from the Canadian Institutes of HealthResearch (CIHR; Gender and Health Institute) in 2011 to the second author (#237398; #249718);and an internal university Social Sciences and Humanities Research Council of Canada (SSHRC)Research Development Fund Award (R#1463178) to the last author. Funding sources did not haveany involvement in the study design; the collection, analysis, or interpretation of data; the writing ofthe report; or in the decision to submit the article for publication.Institutional Review Board Statement: The study was conducted in accordance with the Declarationof Helsinki and approved by the Lakehead University Research Ethics Board [REB Project # 046 12-13(Romeo File Number 1462780); 27 September 2012].Informed Consent Statement: Written informed consent was obtained from all participants involvedin the study.Data Availability Statement: Data that support the findings of this study have been depositedand are available upon approved request in Borealis, the Canadian Dataverse Repository (https://borealisdata.ca/; accessed on 7 October 2024) [95]. Due to the sensitive nature of some questions(e.g., sexual behavior), participants were not asked to provide consent to share the data publicly.However, access to data that support the findings is available from the corresponding author uponreasonable request.Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; orin the decision to publish the results.Appendix ACycle Regularity ItemCheck the statement that best describes your menstrual cycle:[ ] I have gone through menopause and do not get a period[ ] I am not currently menstruating because I am currently lactating or breast feeding[ ] I never have my period[ ] I have not had my period in the last three months[ ] Some months I get my period and some months I don’t[ ] I usually get my period every month, but it is irregular and I cannot predict when itwill start[ ] I usually get my period within two to three days of when I expect it[ ] My period is like clockwork; the same number of days elapse between periodsAppendix BProactive and Responsive Mating Strategies Scale (PARMSS)Instructions:Please use the following scale ranging from 1 (not at all likely) to 9 (extremely likely)to rate how likely you would be to do each of the following:Sexes 2024, 5 4931. Give this person your phone number if asked2. Ask this person for their number3. Return a smile or eye contact from this person4. Smile, or initiate eye contact with, this person5. Dance with this person if asked6. Ask this person to dance with you7. Allow this person to buy you a drink8. Buy this person a drink or ask this person to buy you a drink9. Allow this person to kiss you10. Initiate kissing with this person11. Accept a ride home from this person if offered12. Offer this person a ride home or ask this person for a ride home13. Allow this person to initiate any sexual activity14. Initiate any sexual activity with this person15. Allow this person to initiate sex16. Initiate sex with this personScoring: Odd-numbered items make up the responsive subscale. Even numbereditems make up the proactive subscale. Items are aggregated for three possible scores:proactive subscale, responsive subscale, and the total PARMSS.Appendix CMating VignettesShort-term Vignette:Consider the following hypothetical situation. Your life is exactly as it is right now.Imagine that you just met someone new that you think would be ideal or attractive for ashort-term relationship. A short-term relationship is primarily sexual in nature and tendsnot to last very long. Examples of this type of relationship include someone you may havea sexual affair with and someone you may have a one-night stand with. Based on how youfeel now as well as your feelings and behavior in the past 48 h (i.e., over the past 2 days),how likely are you to. . .Long-term Vignette:Consider the following hypothetical situation. Your life is as it is now. Imagine thatyou just met someone new that you think would be ideal or attractive for a long-termrelationship. A long-term relationship involves commitment as well as an emotionalconnection. Examples of this type of relationship would include someone you may wantto move in with, someone you may consider leaving a current partner to be with, andsomeone you may wish to marry at some point. Based on how you feel now as well as yourfeelings and behavior in the past 48 h (i.e., over the past 2 days), how likely are you to. . .Pictures:For each of the following four images, please imagine that the person pictured is nowaround your age and complete the ratings that follow based on how you feel now as wellas your feelings and behaviors in the past 48 h (i.e., over the past 2 days).References1. Bancroft, J.; Graham, C.A. The varied nature of women’s sexuality: Unresolved issues and a theoretical approach. Horm. Behav.2011, 59, 717–729. [CrossRef] [PubMed]2. Regan, P.C. Rhythms of desire: The association between menstrual cycle phases and female sexual desire. Can. J. Hum. Sex. 1996,5, 145–156.3. Islam, R.M.; Bell, R.J.; Green, S.; Page, M.J.; Davis, S.R. 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The Proactive and Responsive Sexual Strategies Scales (PARMSS) and Changes in Estradiol, Progesterone,and Testosterone Across the Menstrual Cycle: Two Data Sets. 2024. Available online: https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/VOJUDS (accessed on 7 June 2024).Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individualauthor(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.
Article
eng
Reviewed
Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
CC BY 4.0
Faculty
hormones
estradiol
testosterone
mating strategies
sexual behavior
sexual intentions
menstrual cycle
scale development
flirting
courtship
Short-Term Responsive Mating Intentions Increase with Estradiol and Testosterone across the Menstrual Cycle: Scale Development and an Observational Study
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