Untangling the Complexities of Female Sexuality: A Mixed Approach

UNTANGLING THE COMPLEXITIES OF FEMALE SEXUALITY: A MIXED APPROACH A THESIS SUBMITTED TO THE DEPARTMENT OF PSYCHOLOGY OF THE STATE UNIVERSITY OF NEW YORK AT NEW PALTZ By Rachael A. Carmen i ii UNTANGLING THE COMPLEXITIES OF FEMALE SEXUALITY: A MIXED APPROACH By Rachael A. Carmen Signatures: (Adviser) Glenn Geher Alice Andrews Justin Garcia Date Approved A Thesis Submitted to the Department of Psychology of The State University of New York at New Paltz in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Psychology ii iii ACKNOWLEDGEMENTS I would like to thank Glenn Geher for being such a supportive, understanding, and kind-hearted advisor. He made an exception for me back in 2007 when I was an undergraduate and desperately needed a psychology class in order to fulfill requirements for my major; his class was full, but he knew how important it was. Since then, Glenn has taught me general psychology, psychological statistics, research methods, personality psychology, social psychology, evolutionary psychology and evolutionary studies. He even went one step further and gave me the honor of leading his research lab on campus for two years. Glenn nurtured my writing skills and encouraged me to publish my work. Since having Glenn as my mentor, I have gotten ten publications and I am currently working on another five. He gave me a chance as an undergraduate, helped me get into the graduate program, and trusted me as the supervisor for his research lab. I am so appreciative for everything he has done for me. Glenn taught me how to manage my time, conduct myself in a professional manner, and gave me the courage to present my research and speak in public. In many ways, Glenn changed my life. Justin Garcia was a reader for my thesis, but in many respects, he was my second advisor. He gave me his time when Glenn was busy, and would offer words of encouragement when I felt lost. Justin gave me money from his own start-up fund at Indiana University to use as an incentive for individuals to come back for the second half of the study. He gave me countless hours of feedback from beginning to end, helped me find scales and articles and was always patient with me. Whenever I felt the weight of my thesis on my shoulders, Justin was always there to pick me up and dust my shoulders off. Justin is a brilliant, caring, hard-working individual, and I know that I was extremely fortunate to have him on my thesis committee. Alice Andrews and I connected when I took her class on Human Sexuality during my last semester as an undergraduate. I had always been interested in human sexuality but never considered applying evolutionary theory to it. I enjoyed the fact that iii iv information I learned from Glenn could be applied to information I learned from Alice (and vice versa). Glenn promoted evolutionary theory, while Alice tied theory in with our understanding of human sexuality; this is the beauty of evolutionary studies. Her classes were fascinating and fun, and her feedback was always so thorough and informative. Alice encouraged my interest in researching human sexuality, and always made me feel like the world was at my fingertips. She gave me the opportunity to be her teaching assistant (twice), and in doing so, promoted my confidence in teaching important class material. In many respects, Alice helped me grow up. My boyfriend of nearly eleven years, T.J., has supported me since I was in high school. He’s worked countless hours so that I could get my degree(s) without having the added stress of holding a job. Without him, I’m not entirely sure I would have been able to make it through the program, let alone, finish this monster of a thesis. T.J. stayed up late with me, was my shoulder to cry on, and always offered me words of love and encouragement when I began to doubt myself. More times than I could count, he would come home from working an eight-hour day and prepare dinner for us so I could finish analyzing data, writing a section, or emailing someone. I am so fortunate to have such a thoughtful, loving, selfless individual in my life. I am so grateful for the relationship I have with my mother (Debbie). From sitting down with me and helping me do my homework when I was eight years old to never giving up on me in my darkest times, she was always there for me. She works to quite literally support my brother (Arthur) and I while we are in school. Books, school clothes, notebooks and my computer were given to me by my mother because she knew those things were necessary for me to succeed. She helped me get a reliable car and then put gas in it weekly so I could get to and from the University all these years. All of this was possible because my mother is a loving, hardworking, strong woman, and her husband (my step-father) supported her so she could support me. I cannot wait to go out into the world outside of academia and make her proud. iv v I have always looked up to my father (Jim) and the wisdom he exudes. He’s funny, charismatic, and extremely hard working. My father would often remind me that I’m the first one in my family (and extended family) to get a Master’s Degree, but warn me on the importance of staying humble. He is always the first to read my articles and give me feedback...in many respects; my father is my biggest critic, and my biggest fan. I have always been amazed at his ability to explain various phenomena at great length, especially the conversations we had of the cosmos. My brother (Arthur) has been by my side since childhood. Every important milestone in our lives has been shared with one another; I went to his football games and wrestling meets, he went to my art shows and presentations. He has always encouraged me and pushed me to do better while teaching me important lessons in patience. Though we often fought as children (which most siblings do), our friendship got stronger as we grew into adults. I am so grateful to have such an amazing person in my life. I also wanted to thank T.J.’s mother, Bruna, for all the support she has given to us throughout the years. She was behind the scenes working to help support T.J. and I while we went to school and tried to find our place in the world. Her kindness is endless and I cannot begin to explain how much it means to me. I am so grateful to have two amazing women in my life that I look up to. In many ways, Mandy is so much more than a friend; she is a colleague, an inspiration, and a mentor. She pushes me to do my best, and is always there to offer words of wisdom in times of doubt. I’m so grateful to have met such an amazingly intelligent woman who always sees the bright side of things. I look forward to publishing many papers with her throughout our careers. Also, thank you to Kat, Kristen, Samantha, Asha, Megan, and Meagan for being there for me since high school. Thank you to Lindsay for helping me find scales for this research and always offering me sweet words of encouragement. Thank you to all the past and present members of my research lab on campus, I enjoyed being a supervisor for v vi two years and watching everyone’s research evolve to what it is today. Thank you to Luke for the always amazing theoretical conversations and fun times we had at conferences—laughing about life gave me an amazing balance of happiness and ambition. Thank you to Crossfit Mid Hudson for teaching me that life is never balanced until you work out your mind and body. The countless hours I spent at the box changed the way I saw the world. I’ve always loved learning, but never thought twice about overcoming obstacles when it came to my fitness. By combining a healthy mind with a healthy body, I feel truly inspired to keep going for the first time in my life. I cannot wait to see what the future holds. Though this may sound slightly absurd, I would like to thank my dog, Chloe. I was just a teenager when T.J. brought home a Great Pyrenees puppy, and she has been with us as we grew from awkward teenagers into (not as awkward) adults. From cuddling with me in bed after a long day of classes, to sitting at my feet while I typed for hours on end, Chloe has been a source of everlasting compassion for me. vi vii TABLE OF CONTENTS I. Acknowledgements………………………………………………. iii II. Abstract…………………………………………………………... v III. Introduction………………………………………………………. 1 Defining the Female Orgasm…..…………………………….. 3 Types of Orgasms………….…..…………………………….. 3 Orgasm Rates in Women…..…..…………………………….. 4 Why do Women Orgasm? (Proximate Motivations)…..…….. 5 Why do Women Orgasm? (Ultimate Motivations)...………....7 Life History Strategy……….…..……………………………..9 Hypotheses……..…..………………………………………… 10 IV. Method…………………………………………………………… 12 Participants/ Measures……………………………………….. 12 V. Results……………………………………………………………. 16 Hypothesis One………………………………………………. 16 Hypothesis Two……..……………………………………….. 23 Hypothesis Three…………………………………………….. 37 VI. Discussion………………………………………………………... 38 The Ovulatory Cycle……...………………………………….. 38 Female Sexuality…………………………………………….. 39 Life History Strategy……………………………...…………..40 Emotional Intelligence……………………………………….. 40 Limitations…………..……………………………………….. 41 Future Directions…………………………………………….. 43 Conclusions………….……………………………………….. 44 vii viii VII. References………………………………………………………... 46 VIII. Tables…………………………………………………………….. 48 IX. Appendices………………………………………………………..69 viii ix ABSTRACT Human sexuality is fascinating. Though it is such an integral part of our everyday lives, our understanding is lacking (to say the least)—especially when it comes to female sexuality. “Human” sexuality has been studied for nearly a hundred years, but the findings were usually in regard to males (as was most psychological research at the time). Because of this unbalance, this research attempts to answer questions solely surrounding female sexuality. In order to truly piece apart female sexuality, one hundred and forty five females at a small college in the Northeast were given three sexuality scales: (The Sexual Self-Efficacy Scale for Female Functioning (SSES) (Bailes et al., 1998), The Female Sexual Function Index (FSFI) (Rosen et al., 2000) and The Sexual Self-Schema Scale (SSSS) (Andersen & Cyranowski, 1994)). Additionally, to ascertain what variables play key roles in female sexuality, they were also given the Trait Emotional Intelligence Questionnaire (TEI-Que) (Schutte et al., 1998), The High-K scale (Giosan, 2006), The Mating Intelligence Scale (slightly revised) (MI) (Geher & Kaufman, 2007), and The Perceived Stress Reactivity Scale (PSRS) (Schlotz, Yim, Zoccola, Jansen & Schulz, 2011). Statistical analyses show that Emotional Intelligence and Life History Strategy are strongly positively correlated with higher levels of sexuality. ix Humans are part of a complex life cycle that has existed on this earth since its conception, 4.54 billion years ago (Sagan, 1985). We, like everything in this universe, are made up atoms that have traveled light-years across the universe (Sagan, 1985). As Neil deGrasse Tyson elegantly puts it, “We are children of the stars." Our presence today has manifested primarily through generations of our ancestors’ sexual choices. Human sexuality has, is, and always will be an integral part of our lives. For thousands of years, cultures have been leaving artifacts that tell stories of their own sexuality: Indians printed the Kama Sutra; Assyrians had law tablet to address penalties for adultery; Ancient Greeks and Romans painted vases depicting various sexual acts, erected statues with accentuated genitalia and even had large scale orgies at social gatherings (Zacks, 1995). Arguably, this link dates back even further, to the evolutionary origins of modern Homo sapiens. Take a minute and look at yourself in the mirror. You are the summation of hundreds of thousands of generations worth of genetics, mutations, and personality quirks—partially thanks to sex. Sexual reproduction is the crux of our existence, yet much of western culture considers openly discussing sex (especially female sexuality) taboo. Though sexuality is an integral part of our lives, a great deal of effort is made to closet our sexual tendencies—and in doing so, many individuals, especially women, are left to fend for themselves in a world of ambiguity. Though some might disagree, it seems that the friction between cultural and political forces and female sexuality has resulted in a spillover in the scientific world, leading to a lack of research (especially on the female orgasm). In our sexual education classes we are taught how to put a condom on a banana and the ramifications of unprotected sex (i.e., pregnancy, sexually transmitted infections, etc.), yet talk of sex past the confines of institutionalized 1 2 awareness is non-existent. Of course, teaching adolescents about sexual health is by no means wrong; the issue here is that even when our youth culture finally learns about sexuality, the focus is on prevention (of STI’s or even sex itself). How can it be that in modern times, something that is such a big part of who we are is seen as taboo? Part of the problem, it seems, is that people fail to see the interconnected nature of sexuality and the reason we are here today. Evolutionary psychologists claim that our very basic need for sexual reproduction is in fact linked to our behavior in modern day society (Buss, 2003; Meston & Buss, 2007; Meston & Buss 2009; Miller, 2000; Pillsworth, Haselton & Buss 2004). This phenomenon can better be explained in terms of ultimate and proximate causation. Our everyday behaviors are considered proximate in that they are current manifestations of an ultimate evolutionary drive (Alessi, 1992). For instance, the orgasm is a proximate cause for the ultimate evolutionary reason of passing one’s genes down to subsequent offspring. According to Dawkins (1976), we (and all other living creatures) are vehicles for our genes—transporting them throughout our lives and transmitting them via sexual reproduction. Basically, sexual reproduction shuffles generations upon generations worth of genes (including our parents’) and we are the outcome. Simply put, we wouldn’t be here today if it weren’t for sexual intercourse. Though there are many different ways to have sexual intercourse, it often ends when one, or both partners orgasm. From an evolutionary perspective, orgasm rates in the general population are particularly interesting because (male) orgasms are needed to perpetuate one’s genes. The female orgasm, on the other hand, has remained shrouded in mystery for centuries. Today, the literature is still full of intellectual holes. Only within the last decade or so have pioneers in the field (Wallen & Lloyd, 2011; Meston, Levin, 2 3 Sipski, Hull & Heiman, 2004; Zietsch, Miller, Bailey & Martin, 2011) begun to pave the way for female orgasm research. Defining the Female Orgasm Though research on the female orgasm has received increasing attention within the last decade, the female orgasm has been defined and discussed for thousands of years. In 400 B. C., Hippocrates (the father of Western medicine), explained the female orgasm in detail: “During intercourse, once a woman’s genitals are vigorously rubbed and her womb titillated, a lustfulness overwhelms her down there, and the feeling of pleasure and warmth pools out through the rest of the body” (Zacks, 1995, p. 10). 2,412 years later and the definition is nearly the same as Hippocrates’ own. Meston et al. (2004) define the female orgasm as …A variable, transient peak sensation of intense pleasure, creating an altered state of consciousness, usually with an initiation accompanied by involuntary, rhythmic contractions of the pelvic striated circumvaginal musculature, often with concomitant uterine and anal contractions and myotonia that resolves the sexually induced vasocongestion (sometimes only partially), generally with an induction of well-being and contentment (p.174). Types of Orgasms. King, Belsky, Mah and Binik (2010) explain that there might in fact be several different types of orgasms a woman can have, and that these variations might be indicative of partner characteristics. Though King et al. (2010) were cautious to say so, it seemed that when reanalyzing data from an older study, five distinct types of orgasm emerged: Type I (high pleasure/ high sensations with partner), Type II, (high pleasure/ medium sensations with partner), Type III (medium pleasure/ low sensations 3 4 with partner), Type IV (low pleasure/ low sensation with partner), and masturbatory orgasms. Interestingly enough, type I and II orgasms (achieved with partners) scored higher than orgasms without partners in terms of pleasure and sensation, but when it came to type III and IV orgasms, they scored lower than masturbatory orgasms even though the partner orgasms were sufficient enough to elicit an orgasm (King et al., 2010). Partner characteristics could very well be responsible for the discrepancy between orgasm types—but of course, more testing needs to be done before any conclusions can be made. Orgasm Rates in Women. Once there is a consensus on what the female orgasm is and why it is important, the way in which a female can have an orgasm varies. Interestingly enough, a surprising amount of women cannot orgasm from sexual intercourse (Kinsey et al., 1953). Numerous studies have been conducted (dating back to as early as the 1930s) on a female’s orgasm frequency during sexual intercourse—with the average percentage of women always able to achieve an orgasm this way falling between 18% (Stanley, 1995) and 59% (Kinsey et al., 1953). Yet, when women are asked about orgasm frequency during masturbation, the numbers change drastically: in a sample of 5,940, Kinsey at al. (1953) found that 62% of their female sample masturbated, and of that 62%, only 4% did not have orgasms. Similarly, Hite (1976) found that in a sample of 1,844 women, 82% masturbated and only 4% of those women did not orgasm from selfstimulation. Interestingly, “The most striking things about female masturbation are how likely it is to produce orgasm and how little it resembles, mechanically, the stimulation received from intercourse” (Lloyd, 2005, p. 25). So it seems that, for the most part, 4 5 sexual intercourse is not as conducive to the female orgasm as manual stimulation of the clitoris. Why do Women Orgasm? Proximate Motivations Anatomy Apparently, there are some key anatomical differences that can help facilitate or impede orgasm. The secret lies in the placement of the clitoris. In 1924, Marie Bonaparte (whose great-grand-uncle was Napoleon) was the first to document the differences in distance of the clitoris. She could never manage to have an orgasm during the act of sex, and was convinced that it was because of her clitoris. After studying a sample of women, she found some very interesting results: Women whose length between her vaginal opening and clitoris was longer than two and a half inches reported never being able to orgasm during sex (Wallen & Lloyd, 2011). On the other end of the scale were the women whose length between her vagina and clitoris was less than an inch away—they were almost always guaranteed to have an orgasm during sex (Wallen & Lloyd, 2011). She also found that height seemed to be an indicator of this phenomenon as well: shorter women were more likely to have less space between their clitoris and vaginal opening, and consequentially, were more likely to orgasm during sexual intercourse (Wallen & Lloyd, 2011). Interestingly enough, Bonaparte’s sample was revisited years later by Wallen and Lloyd (2011) who re-analyzed the data and confirmed Bonaparte’s initial finding. In addition to the length between the clitoris and vaginal opening, it seems that the ability to orgasm from sexual intercourse is reliant on the space between a female’s 5 6 urethra and vaginal canal. The urethrovaginal space is the area in-between the urethra and the anterior vaginal wall that is “constituted of fibro-connective tissue and large numbers of blood vessels, glands, muscular fibers and nerve endings” (Gravina et al., 2008, p. 611). According to Gravina et al., (2008) women who have the ability to orgasm from sexual intercourse have an urethrovaginal space thicker than those who cannot. Though the evidence was far from conclusive, the aforementioned research studies point to the idea that our anatomy (among many other things) plays a big role in our individual ability to orgasm. The Brain Additionally, there seems to be a difference in the way female and male brains respond to an orgasm. When looking at the brains response to male and female orgasms, Holstege et al. (2003) found that male ejaculation produces an extreme activation of the VTA (ventral tegmental area)—a very important part of our brains’ reward system— while female orgasms produce a very different response in the brain: neurons fell silent (Holstege et al., 2003). Basically, when a male has an orgasm, it stimulates areas in the brain that affect positive reinforcement—when a female has an orgasm, certain areas of her brain seem to turn off. Specifically, the neurons in the left lateral orbitofrontal cortex (which may govern self-control over basic desires such as sex) reduced firing at the point of orgasm (and shortly thereafter) (Portner, 2008). This finding could have many implications, but most importantly, these data lend themselves to the idea that men and women experience orgasms differently. Physically (and possibly emotionally, and psychologically) a difference exists between how women and men. 6 7 Ultimate Motivations Biological. There are countless explanations as to why human females are able to orgasm: intense pleasure, a “Mr. Right” indicator, to break psychological and emotional boundaries between two people, or (a personal favorite), “to create psychological resuscitation—like an electric shock redistributing the potentials of the brain” (Meston et al., 2004, p. 177). Yet, all of the aforementioned reasons are still only proximate mechanisms in that they only address the current facts at hand. Though research on the female orgasm has received increasing attention within the last decade, there is still much debate surrounding the biological (ultimate) function of female orgasm. There are numerous competing theories on what function the female orgasm exactly serves. The upsuck hypothesis, pair-bond theory (Hrdy, 1981), and a non-adaptive by-product (Lloyd, 2005) are among the most compelling theories in the field today. Upsuck Hypothesis. According to Baker and Bellis (1995), the female orgasm is used to manipulate ejaculate inside the vagina. During orgasm, the cervix repeatedly “dips” into the seminal pool “sucking up” semen and pushing it up into the uterus. The orgasm either facilitates the dipping and/ or mixing of the cervical mucus with the pool and/ or it increases the time that the cervix is in the pool. Though this hypothesis fascinating, it lacks empirical evidence. Pair-Bond Theory. Hrdy (1981) claims that the female orgasm evolved to increase the likelihood that the offspring will receive care from both parents by enforcing long-term sexual relationships (Hrdy, 1981). Because human offspring are altricial, it is often the case that more than one individual will be needed to assist in the rearing of offspring. Most of the time this would be a partner—but of course is not limited to a 7 8 family member, friend, or other sexual partners. This theory runs on the assumption that sexual traits in humans evolved under group-living conditions. According to Hrdy (1981) and Morris (1963) human estrus is concealed (and promotes continuous receptivity) due to the need for human pair-bonds—for partners and in groups. According to Hrdy (1981), (in ancestral group living conditions) it would be most advantageous for a female to attract and copulate with multiple partners (including members of their gender) in order to confuse paternity, reduce harm, and increase the amount of caretakers for their offspring. Non-Adaptive Byproduct. In Elizabeth Lloyd’s (2005) book, The case of the female orgasm: Bias in the science of Evolution, she discusses the idea that contrary to what many individuals think in the field, the female orgasm is nothing more than a byproduct of male anatomy. It is not until nine weeks of development that a fetus begins to develop differentiating sex organs. That is, the nerves that compose a penis and vagina are one in the same (after nine weeks the nerves either form a penis or a clitoris). Because of this, Lloyd (2005) claims that the female orgasm did not evolve; it is simply just a byproduct of the need to orgasm (and subsequently ejaculate) in males. A Combination. Contrary to a popular theory by Elizabeth Lloyd (2005), Bingham and Souza (2009) claim that the female orgasm is in fact an adaptation and that it happens as a way for females to select their ideal mate via cervical tenting (when the entrance of the cervix dips repeatedly into the pool of fluids in the vagina—i.e., semen). By combining ideas from Hrdy (1981) and Baker and Bellis (1995), Bingham and Souza (2009) propose that the combination of cervical tenting and higher mating promiscuity led to the female orgasm being used as a way of choosing the most fit mate to beget 8 9 offspring. By (unconsciously) timing her orgasm, it makes it possible/plausible for the female to take up more sperm from more desirable males. Though there is a plethora of competing theories, many can agree that regardless of why a female can orgasm, the evolutionary point of sexual intercourse is to get genetic material into the future (i.e., have children). Life History Strategy Biological motivations map onto the idea known as life history strategy (or, LHS). Put simply, life history strategy posits that natural selection aims to produce the largest number of reproductively viable surviving offspring (in any species). The central tradeoff in this theory is between the number of offspring an individual can have vs. the timing of reproduction (otherwise known as the r/K selection theory) (Figueredo et al., 2005). Though life-history strategies exist in all animals, the characteristics of human’s life histories are much more detailed than most other animals, and because of that, humans are considered in terms of a continuum on the “K” scale (high-K vs. low-K) (Figueredo et al., 2005). Individuals that are considered high-K strategists generally have better health, developmental stability, overall consistency in their mental and physical functioning, and high degrees of investment in genetically related individuals (Figueredo et al., 2005). Individuals with a low-K life history strategy on the other hand, tend to stress the more sexual side of reproduction—they prefer mating and reproductive effort to parental and somatic effort. Figueredo et al. (2005) gives details about the “Life-History Theory” (MacArthur & Wilson, 1967) which explains: An individual’s total bioenergetic and material resources are allocated between Somatic Effort, resources devoted to the continued survival of the individual 9 10 organism and Reproductive Effort, resources devoted to the production and support of offspring. Reproductive Effort consists of Mating Effort, resources devoted to obtaining and retaining sexual partners, and Parental Effort, resources devoted to enhancing the survival of offspring. Perhaps then, an individuals’ LHS could be predictive of orgasm frequency/intensity and/or overall heightened sexuality. Additionally, if an aspect of an individuals’ LHS were comprised of Reproductive effort, it would behoove one to question individual variation in ovulatory cycle length. Could it be that women with a fast LHS have more chances to conceive? That is, women that have a faster LHS could possibly have a shorter ovulatory cycle, leading to the possibility of ovulating twice a month—ultimately increasing the likelihood of conception probability. On the other hand, women that have a slower LHS could possibly have a longer ovulatory cycle, leading to the possibility of not even ovulating within a month (i.e., has a 35 day cycle). Emotional Intelligence Lastly, Emotional Intelligence seems like it could play a large role in an individual’s sexuality due to the fact that emotions are essentially at the core of all human interactions. Casey, Garrett, Brackett and Rivers (2008) explain that EI is the ability to perceive/ understand and manage emotion, and the ability to use emotion to facilitate thought. Interestingly, research has found that Emotional Intelligence is important for partner selection and relationship satisfaction (Brackett, Warner, & Bosco, 2005). Because of EI’s link to both partner selection and relationship satisfaction, we wonder if it is also linked with certain aspects of female sexuality. 10 11 These ideas are interesting—and backed with various research findings—but the majority of this research only attempts to answer why and not who, what, when and where. In order to better address the aforementioned questions, this research attempts to study female sexuality from multiple directions. Hypotheses: 1) Women who are closer to ovulation will be more likely to have an increased libido which will be marked by an increase in sexual interest and, for some, increased frequency in orgasm. The likelihood of increased sexuality will be affected by: A) Stress (negatively). B) Relationship status/ quality (positively). C) Religious/ cultural beliefs (negatively). 2) Women with high Emotional and Mating intelligence will see a slight increase during peak fertility days (the window during which one can get pregnant). High Emotional and Mating Intelligence should positively predict increased sexuality across the cycle, particularly during ovulation. 3) Individuals who have a “high-k life history strategy” will be more likely to have a longer length between ovulatory cycles, while individuals that have “low-k life history strategy” will be more likely to have shorter length between ovulatory cycles. Additionally, it is predicted that women with slower life history strategies will experience higher amounts of sexuality due to the idea that high-K strategists generally have better health, developmental stability, and overall consistency in their mental and physical functioning (Figueredo et al., 2005). 11 12 As was mentioned numerous times in the paper, female sexuality is just beginning to get much needed research attention. Just like our genetics, each person’s ability to orgasm is unique to himself or herself. This research is by no means attempting to box women into categories of “orgasmic” vs. “non-orgasmic,” it is simply attempting to add more information to the much-lacking database of female sexuality. It is our job as researchers to try to piece together the evidence to provide women (and men) with answers that many generations before have inconclusively searched for. Perhaps we will never find the absolute truth, but this research attempts to get a close as possible. METHOD Participants The participant population was recruited through adverts sent out to campus emails, through the Psychology Department’s subject pool, and through social media forums (e.g., Facebook). Participants were led to a link that connected to qualtrics.com, where the survey was hosted. Procedure and Measures Individuals were asked general demographic questions, questions in regard to relationship/ marital status, religious beliefs, socioeconomic status, propensity for monogamy, and general questions about their menstrual cycle (i.e., average length, date of their last menses). The following scales were used: The Sexual Self-Efficacy Scale for Female Functioning (SSES) (Bailes et al., 1998). This scale is comprised of thirty-seven questions that participants are asked to rate their degree of confidence for (on a scale of 10-100). These questions are broken into eight subscales—Interpersonal Orgasm (e.g., “Have an orgasm while partner stimulates you by means other than intercourse”), Interpersonal Interest/ Desire (e.g., “Be interested 12 13 in sex”), Sensuality (e.g., “Enjoy intercourse”), Individual Arousal (e.g., “Feel sexually aroused in response to erotica”), Affection (e.g., “Enjoy an exchange in of affection without having intercourse”), Communication (e.g., “Ask the partner to provide the type and amount of sexual stimulation needed”), Body Acceptance (e.g., “Feel comfortable being nude with the partner”), and Refusal (e.g., refuse a sexual advance by the partner). Trait Emotional Intelligence Questionnaire (TEI-Que) (Schutte et al., 1998). This scale is comprised of thirty questions in which the participants are given a seven point likert scale and asked to rate how much they agree (or disagree) with questions like “I’m ususally able to influence the way other people feel.” The Female Sexual Function Index (FSFI) (Rosen et al., 2000). Because the original intention of this research was to see if there was any difference in how a woman rates various questions depending on where she is in her ovulatory cycle, this scale was slightly altered. All the questions in the original scale stated, “In the past four weeks…” but for the purpose of this study, this was altered to read, “In the last week.” Other than that, there were no other alterations made. This scale is comprised of nineteen questions that participants are asked to rate on a scale of zero to five. These questions are broken into six subscales—Desire (e.g., “Over the last week, how often did you feel sexual desire or interest?”), Arousal (e.g., “Over the last week, how would you rate your level of sexual arousal during sexual activity or intercourse?”), Lubrication (e.g., “Over the last week, how often did you become lubricated (“wet”) during sexual activity or intercourse?”), Orgasm (e.g., “Over the last week, when you had sexual stimulation or intercourse, how often did you reach orgasm (climax)?”), Satisfaction (e.g., “Over the last week, how satisfied have you been with your sexual relationship with your partner?”), and Pain (e.g., “Over the last week, how would you rate your level (degree) of discomfort or pain during or following vaginal penetration?”). The Sexual Self-Schema Scale (SSSS) (Andersen & Cyranowski, 1994). Participants are given a likert scale of zero to six, the question, “To what extent does the 13 14 term ____ describe me?” and 50 words. These questions are broken into three factors (i.e., subscales): Factor 1 (e.g., loving, arousable, romantic, passionate), Factor 2 (e.g., uninhibited, experienced, direct, straightforward), and Factor 3 (the negative factor) (e.g., cautious, timid, self-conscious, prudent). The High-K scale (Giosan, 2006). Participants are given twenty-three questions and a likert scale from one to five and asked to rate their level of agreement (e.g., “The activity I engage in, both at work and elsewhere, are safe (not life threatening)”). The Mating Intelligence Scale (slightly revised) (MI) (Geher & Kaufman, 2007). This scale was slightly revised so we could include participants that did not consider themselves (strictly) heterosexual. Participants were given twenty-four questions and asked to answer “true” or “false” (e.g., “I can tell when a partner is being genuine and sincere in his/her affections toward me”). The Perceived Stress Reactivity Scale (PSRS) (Schlotz, Yim, Zoccola, Jansen & Schulz, 2011). Participants are given twenty-three questions; each on a likert scale of one to three with various answers each ranging in levels of reactivity. These questions are broken into six types of reactivity (i.e., subscales): Prolonged Reactivity (e.g., “When tasks and duties accumulate to the extent that they are hard to cope with… a) My sleep is unaffected, b) My sleep is slightly disturbed c) My sleep is very disturbed”), Reactivity to Work Overload (e.g., “When tasks and duties build up to the extent that they are hard to manage…a) I am generally untroubled, b) I usually feel a little uneasy, c) I normally get quite nervous”), Reactivity to Social Conflict (e.g., “When I have conflicts with others that may not be immediately resolved…a) I generally shrug it off, b) It usually affects me a little, c) It usually affects me a lot”), Reactivity to Failure (e.g., “When I make a mistake…a) I generally get over it easily, b) I am normally annoyed for a while c) I am normally annoyed for a long time”), Reactivity to Social Evaluation (e.g., When I am unsure what to do or say in a social situation…a) I generally stay cool, b) I often feel 14 15 warm, c) I often begin to sweat”), and Perceived Stress Reactivity total score (all five are aggregated to get the total score). 15 16 RESULTS One hundred and forty-five females participated in this study. The age range was from 18 to 66 with a mean age of 25.84 and a SD of 7.97. Hypothesis One: Does Ovulatory Cycle Relate to Sexuality? Two variables for ovulatory cycle were computed: “categorical ovulation” was computed by using a cut off of –2 to 6—if participants fell within these proximity days, they were considered “ovulating.” The second variable (“continuous ovulation”) was calculated by using the numbers given on a continuous scale—numbers that fell below –4 were not included—numbers above –4 were transformed into absolute values. To address whether women who are closer to ovulation will be more likely to experience increased sexuality (depending on stress and relationship status/quality), zeroorder correlations among these variables were computed. Ovulatory status was not significantly related to any of the sexuality scales or subscales. Interestingly, relationship status/ quality was significantly positively related to the participant’s FSFI (Female Sexual Functioning Index) (r(123)= .398, p <.001), SSES (Sexual Self Efficacy Scale) (r(120)= .253, p <.01), and SSSS (Sexual Self Schema Scale) (r(119)= .207, p <.05) totals, while religiosity was negatively related to participants’ relationship quality score (r(124)= -.158, p <.05) (religiosity was measured by simply asking the participant how important religion was in their life; 1 meant very important, while 5 meant not important at all). That is, higher quality relationships correspond to higher scores on all three indices of sexuality. Further, identifying religion as being important in one’s life corresponds to higher relationship quality. 16 17 On the other hand, stress was negatively related to relationship quality/status (r(124)= .193, p< .05), and the participant’s FSFI (r(147)= -.218, p <.01), SSES (r(144)= -.194, p <.05), and SSSS (r(142)= -.397, p <.001) totals. The higher the scores on the Perceived Stress Reactivity Scale (PSRS), the lower the scores on relationship quality and all three sexuality scales. These correlations are summarized in Table 1. Because there were multiple indices (and facets) of a participant’s sexuality, numerous multiple regressions were run. Ovulatory status was not significantly related to any of the sexuality subscales or any of the sexuality total scores. Ovulatory status aside, there was some interesting trends regarding sexuality, stress, and religiosity. Sexuality. To address the overall amount of variability in sexuality explained by the ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences and to examine the unique amount of variability explained by all the above on sexuality, numerous multiple regressions were conducted. We used three scales to measure sexuality (SSES, SSSS, and FSFI) and each of those scales was further broken into multiple sub-scales. Sexual Self-Efficacy Scale. The overall amount of variability in sexuality (measured with the SSES) explained by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences was minimal (not significant). Female Sexual Function Index. The overall amount of variability in sexuality (measured with the FSFI) explained by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences was significant in multiple ways. Total. First, a significant amount of variability in the participant’s total FSFI score was accounted for by the set of ovulatory cycle, stress, religiosity, relationship quality, 17 18 and sexual preferences (R2= .33, F(5, 42) = 4.11, p < .01). Thus, approximately 33% of variability in a participant’s total FSFI score can be accounted for by information regarding participants’ ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. This information is summarized in Table 2. As can be seen in the table, ovulatory cycle uniquely accounts for a significant amount of variability in sexuality (sr2= .18, p < .01) in addition to relationship quality (sr2= .08, p < .05)—stress (sr2= .00, ns), religiosity (sr2= .03, ns), and sexual preferences (sr2= .09, ns) do not. These results suggest that stress, religiosity, and sexual preferences have such a strong zero-order correlation with sexuality—perhaps because of its overlap with ovulatory cycle and relationship quality. After controlling for the overlapping variance between stress, religiosity, sexual preferences, the ovulatory cycle and relationship quality, it seems that where a woman is in her ovulatory cycle and the quality of her relationship is significantly predictive of sexuality while stress, religiosity, and sexual preferences, may not be. Interestingly, the ovulatory cycle predicts sexuality in the opposite direction that was predicted. That is, the further women were from ovulating, the higher their FSFI total score. When multiple regressions were conducted for each of the FSFI subscales, four were significant: Arousal, Lubrication, Orgasm, and Satisfaction. Arousal. A significant amount of variability in the participant’s arousal FSFI score was accounted for by the set of ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences (R2= .34, F(5, 43) = 4.48, p < .01). Thus, approximately 34% of 18 19 variability in a participant’s arousal FSFI score can be accounted for by information regarding participants’ ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. This information is summarized in Table 3. As can be seen in the table, ovulatory cycle uniquely accounts for a significant amount of variability in sexuality (sr2= .24, p < .001)—relationship quality (sr2= .03, ns), stress (sr2= .00, ns), religiosity (sr2= .03, ns), and sexual preferences (sr2= .00, ns) do not. These results suggest that relationship quality, stress, religiosity, and sexual preferences have such a strong zero-order correlation with sexuality because of its overlap with ovulatory cycle and relationship quality. After controlling for the overlapping variance between stress, religiosity, sexual preferences, the ovulatory cycle and relationship quality, it seems that where a woman is in her ovulatory cycle is significantly predictive of sexuality while stress, religiosity, and sexual preferences, are not. Again, the ovulatory cycle predicts sexuality in the opposite direction that was predicted. That is, the further women were from ovulating, the higher their FSFI arousal score. Lubrication. A significant amount of variability in the participant’s lubrication FSFI score was accounted for by the set of ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences (R2= .26, F(5, 42) = 4.48, p < .05). Thus, approximately 26% of variability in a participant’s lubrication FSFI score can be accounted for by information regarding participants’ ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by 19 20 ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. This information is summarized in Table 4. As can be seen in the table, ovulatory cycle uniquely accounts for a significant amount of variability in sexuality (sr2= .22, p < .01)— relationship quality (sr2= .01, ns), stress (sr2= .01, ns), religiosity (sr2= .01, ns), and sexual preferences (sr2= .01, ns) do not. These results suggest that relationship quality, stress, religiosity, and sexual preferences have such a strong zero-order correlation with sexuality because of its overlap with ovulatory cycle and relationship quality. After controlling for the overlapping variance between stress, religiosity, sexual preferences, the ovulatory cycle and relationship quality, it seems that where a woman is in her ovulatory cycle is significantly predictive of sexuality while stress, religiosity, and sexual preferences, are not. Again, these findings go along with the same trends seen above: the ovulatory cycle predicts sexuality in the opposite direction. That is, the further women were from ovulating, the higher their FSFI lubrication score. Orgasm. A significant amount of variability in the participant’s orgasm FSFI score was accounted for by the set of ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences (R2= .29, F(5, 43) = 3.55, p < .05). Thus, approximately 26% of variability in a participant’s orgasm FSFI score can be accounted for by information regarding participants’ ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. This information is summarized in Table 5. As can be seen in the table, ovulatory cycle uniquely accounts for a significant amount of variability in sexuality (sr2= .12, p < .05) in addition to relationship quality 20 21 (sr2= .08, p < .05)—stress (sr2= .01, ns), religiosity (sr2= .06, ns), and sexual preferences (sr2= .04, ns) do not. These results suggest that stress, religiosity, and sexual preferences have such a strong zero-order correlation with sexuality because of its overlap with ovulatory cycle and relationship quality. After controlling for the overlapping variance between stress, religiosity, sexual preferences, the ovulatory cycle and relationship quality, it seems that where a woman is in her ovulatory cycle and the quality of her relationship is significantly predictive of sexuality while stress, religiosity, and sexual preferences, are not. The findings regarding the ovulatory cycle are in fact, in the opposite direction, again. That is, the further women were from ovulating, the higher their FSFI orgasm score. Satisfaction. A significant amount of variability in the participant’s satisfaction FSFI score was accounted for by the set of ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences (R2= .25, F(5, 43) = 2.91, p < .05). Thus, approximately 25% of variability in a participant’s orgasm FSFI score can be accounted for by information regarding participants’ ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. This information is summarized in Table 6. As can be seen in the table, ovulatory cycle uniquely accounts for a significant amount of variability in sexuality (sr2= .09, p < .05) in addition to relationship quality (sr2= .11, p < .05)—stress (sr2= .02, ns), religiosity (sr2= .02, ns), and sexual preferences (sr2= .01, ns) do not. These results suggest that stress, religiosity, and sexual preferences have such a strong zero-order correlation with 21 22 sexuality because of its overlap with ovulatory cycle and relationship quality. After controlling for the overlapping variance between stress, religiosity, sexual preferences, the ovulatory cycle and relationship quality, we find that where a woman is in her ovulatory cycle and the quality of her relationship are (again) significantly predictive of sexuality while stress, religiosity, and sexual preferences, are not. The findings regarding the ovulatory cycle are in the opposite direction, again. That is, the further women were from ovulating, the higher their FSFI satisfaction score. Sexual Self-Schema Scale. The overall amount of variability in sexuality (measured with the SSSS) explained by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences was minimal (not significant). The SSSS total scores, and scores from Factor 1 and Factor 2 were not significant, but there was a significant finding regarding the negative facet of this scale, Factor 3. A significant amount of variability in the participant’s SSSS Factor 3 score was accounted for by the set of ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences (R2= .53, F(5, 43) = 9.66, p < .001). Thus, approximately 53% of variability in a participant’s SSSS Factor 3 score can be accounted for by information regarding participants’ ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by ovulatory cycle, stress, religiosity, relationship quality, and sexual preferences. This information is summarized in Table 7. As can be seen in the table, stress uniquely accounts for a significant amount of variability in a individuals negative self-schemas (sr2= .46, p < .001) —ovulatory cycle (sr2= .00, ns), religiosity (sr2= .04, ns), relationship quality (sr2= .01, ns) and sexual preferences (sr2= .00, ns) do 22 23 not. These results suggest that ovulatory cycle, religiosity, relationship quality and sexual preferences have such a strong zero-order correlation with SSSS Factor 3 because of its overlap with stress. After controlling for the overlapping variance between stress, religiosity, sexual preferences, the ovulatory cycle and relationship quality, we find that stress contributes to women’s negative self-perception. Hypothesis Two: Predicting Sexuality from Emotional and Mating Intelligence To address whether higher sexuality is correlated with high Emotional and Mating intelligence exhibit a slight increase during peak fertility days (the window during which one can get pregnant), and to see if High Emotional and Mating Intelligence should positively predict higher levels of sexuality across the cycle, zero-order correlations among these variables were computed. Ovulatory status was not significantly related to Emotional and/or Mating Intelligence. Interestingly, there were some strong effects in regard to Emotional Intelligence and the sexuality scales and subscales. Mating Intelligence was just barely significantly positively related to the participant’s FSFI Desire score (r(150)= 161, p =.05), but other than that, it was not correlated with any other indices of sexuality. Basically, higher scores on the Mating Intelligence scale were correlated with higher scores measured by the FSFI Desire subscale. On the other hand, Emotional Intelligence was significantly positively related to multiple indices of sexuality (i.e., all three sexuality scales). The participants FSFI Desire (r(146)= .199, p< .05), FSFI Arousal (r(146)= .352, p< .001), FSFI Lubrication (r(145)= .310, p< .001), FSFI Orgasm (r(145)= .333, p< .001), FSFI Satisfaction (r(146)= .304, p< .001, and FSFI total (r(144)= .341, p< .001) scores were strongly significantly positively correlated with high scores on the EI 23 24 scale. Additionally, the participants SSSS Factor one (r(144)= .484, p<.001), SSSS Factor two (r(142)= .435, p< .001, and SSSS total (r(139)= .579, p <.001) scores were strongly significantly positively correlated with high scores on the EI scale, while SSSS Factor three (the negative factor) (r(145)= -.371, p<.001) was strongly significantly negatively correlated with high scores on the EI scale. Lastly, the participants SSES Desire (r(145)= .397, p< .001), SSES Orgasm (r(146)= .372, p< .001), SSES Arousal (r(145)= .194, p< .05), SSES Sensuality (r(146)= .353, p< .001), SSES Affection (r(146)= .419, p< .001), SSES Communication (r(143)= .375, p< .001), SSES Body (r(146)= .351, p< .001), SSES Refusal (r(146)= .404, p< .001), and SSES total (r(141)= .454, p< .001) scores were strongly significantly positively correlated with high scores on the EI scale. This information is summarized in Table 8. Basically, this means that higher scores in emotional intelligence were correlated with higher indices of sexuality. To examine the unique amount of variability explained by mating and emotional intelligence on sexuality, numerous multiple regressions were conducted. Again, we used three scales to measure sexuality (SSES, SSSS, and FSFI) and each of those scales were further broken down into multiple sub-scales. Female Sexual Function Index. The overall amount of variability in sexuality (measured with the FSFI) explained by Mating and Emotional Intelligence was positively significant in multiple ways. Total. First, a significant amount of variability in the participant’s total FSFI score was accounted for by MI and EI (R2= .12, F(2, 141) = 9.87, p < .001). Thus, approximately 12% of variability in a participant’s total FSFI score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi- 24 25 squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by EI and MI. This information is summarized in Table 9. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in sexuality (sr2= .12, p < .001), but MI (sr2= .03, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with sexuality because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of sexuality while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of overall sexuality (for the FSFI scale). When multiple regressions were conducted for each of the FSFI subscales, five were significant: Desire, Arousal, Lubrication, Orgasm, and Satisfaction. Desire. A significant amount of variability in the participant’s desire FSFI score was accounted for by MI and EI (R2= .06, F(2, 143) = 4.82, p < .05). Thus, approximately 6% of variability in a participant’s total FSFI score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by EI and MI. This information is summarized in Table 10. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in sexuality (sr2= .04, p < .05), but MI (sr2= 02., ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with desire because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is 25 26 significantly positively predictive of desire while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of desire. Arousal. A significant amount of variability in the participant’s arousal FSFI score was accounted for by MI and EI (R2= .13, F(2, 143) = 10.55, p < .001). Thus, approximately 13% of variability in a participant’s arousal FSFI score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by EI and MI. This information is summarized in Table 10. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in sexuality (sr2= .12, p < .001), but MI (sr2= 00., ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with arousal because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of arousal while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of arousal. Lubrication. A significant amount of variability in the participant’s lubrication FSFI score was accounted for by MI and EI (R2= .10, F(2, 142) = 8.16, p < .001). Thus, approximately 10% of variability in a participant’s lubrication FSFI score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in lubrication accounted for, separately, by EI and MI. This information is 26 27 summarized in Table 11. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in lubrication (sr2= .09, p < .001), but MI (sr2= 01., ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with lubrication because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of lubrication while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of lubrication. Orgasm. A significant amount of variability in the participant’s orgasm FSFI score was accounted for by MI and EI (R2= .12, F(2, 142) = 9.30, p < .001). Thus, approximately 12% of variability in a participant’s orgasm FSFI score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in orgasm accounted for, separately, by EI and MI. This information is summarized in Table 12. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in orgasm (sr2= .11, p < .001), but MI (sr2= 00., ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with orgasm because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of orgasm while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of orgasm. Satisfaction. A significant amount of variability in the participant’s orgasm FSFI 27 28 score was accounted for by MI and EI (R2= .09, F(2, 143) = 7.27, p < .01). Thus, approximately 9% of variability in a participant’s satisfaction FSFI score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in orgasm accounted for, separately, by EI and MI. This information is summarized in Table 13. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in satisfaction (sr2= .09, p < .001), but MI (sr2= 00., ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with orgasm because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of satisfaction while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of (sexual) satisfaction. Sexual Self-Schema Scale. The overall amount of variability in sexuality (measured with the SSSS) explained by Mating and Emotional Intelligence was significant in multiple ways. Total. A significant amount of variability in the participant’s total SSSS score was accounted for by MI and EI (R2= .33, F(2, 136) = 34.43, p < .001). Thus, approximately 33% of variability in a participant’s SSSS total score can be accounted for by information regarding participants’ Mating Intelligence and Emotional Intelligence. Next, semisquared partial correlations were computed to address the unique amount of variability in sexual self schemas accounted for, separately, by MI and EI. This information is summarized in Table 14. As can be seen in the table, EI uniquely accounts for a 28 29 significant amount of variability in an individual’s SSSS total score (sr2= .34, p < .001), but MI (sr2= .00, ns) does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with the SSSS total score because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of the SSSS total score while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of sexual self-schemas. Factor one. A significant amount of variability in the participant’s factor one SSSS score was accounted for by MI and EI (R2= .25, F(2, 141) = 22.91, p < .001). Thus, approximately 25% of variability in a participant’s SSSS factor one score can be accounted for by information regarding participants’ Mating Intelligence and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexual self-schemas accounted for, separately, by MI and EI. This information is summarized in Table 15. As can be seen in the table, EI uniquely accounts for a significant amount of variability in an individual’s SSSS factor one score (sr2= .23, p < .001), but MI (sr2= .01, ns) does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with the SSSS factor one score because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of the SSSS factor one score while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of sexual self-schemas. Factor two. A significant amount of variability in the participant’s factor two SSSS 29 30 score was accounted for by MI and EI (R2= .19, F(2, 139) = 16.30, p < .001). Thus, approximately 19% of variability in a participant’s SSSS factor one score can be accounted for by information regarding participants’ Mating Intelligence and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexual self-schemas accounted for, separately, by MI and EI. This information is summarized in Table 15. As can be seen in the table, EI uniquely accounts for a significant amount of variability in an individual’s SSSS factor one score (sr2= .18, p < .001), but MI (sr2= .00, ns) does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with the SSSS factor two score because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of the SSSS factor two score while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of sexual self-schemas. Factor three. A significant amount of variability in the participant’s SSSS Factor three score was accounted for by Mating and Emotional Intelligence (R2= .16, F(2, 142) = 13.02, p < .001). Thus, MI and EI can account for approximately 16% of variability in a participant’s SSSS Factor three score. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexual self-schemas accounted for, separately, by Mating and Emotional Intelligence. This information is summarized in Table 16. As can be seen in the table, EI uniquely accounts for a significant amount of variability in an individuals negative self-schemas (sr2= .14, p < .001), MI (sr2= .02, ns) does not. This result suggests that Mating Intelligence has such a strong zero-order 30 31 correlation with the SSSS factor three score because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly negatively predictive of the SSSS factor three score while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored lower in measurements of negative sexual self-schemas. Sexual Self-Efficacy Scale. The overall amount of variability in sexuality (measured with the SSES) explained by Mating and Emotional Intelligence was positively significant. Total. First, a significant amount of variability in the participant’s total SSES score was accounted for by MI and EI (R2= .20, F(2, 138) = 17.92, p < .001). Thus, approximately 20% of variability in a participant’s total SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semisquared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by EI and MI. This information is summarized in Table 17. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in sexuality (sr2= .20, p < .001), but MI (sr2= .00, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with sexuality because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of sexuality while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of overall sexuality (for the SSES). When multiple regressions were conducted for each of the SSES subscales, all eight 31 32 were significant (Sensuality, Interpersonal Interest/Desire, Individual Arousal, Interpersonal Orgasm, Affection, Communication, Body Acceptance and Refusal). Sensuality. A significant amount of variability in the participant’s total SSES score was accounted for by MI and EI (R2= .12, F(2, 143) = 10.60, p < .001). Thus, approximately 12% of variability in a participant’s sensuality SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by EI and MI. This information is summarized in Table 18. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in sexuality (sr2= .12, p < .001), but MI (sr2= .00, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with sensuality because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of sensuality while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of sensuality. Interpersonal Interest/ Desire. A significant amount of variability in the participant’s interest/desire SSES score was accounted for by MI and EI (R2= .16, F(2, 142) = 13.35, p < .001). Thus, approximately 16% of variability in a participant’s sensuality SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in sexuality accounted for, separately, by EI and MI. This information is summarized in Table 19. As can be seen in the table, emotional 32 33 intelligence uniquely accounts for a significant amount of variability in sexuality (sr2= .16, p < .001), but MI (sr2= .00, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with sensuality because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence is significantly positively predictive of Interpersonal Interest/ Desire while Mating Intelligence is not. On average, participants that scored higher on the EI scale also scored higher in measurements of Interpersonal Interest/ Desire. Individual Arousal. A significant amount of variability in the participant’s arousal SSES score was accounted for by MI and EI (R2= .05, F(2, 142) = 3.70, p < .05). Thus, approximately 5% of variability in a participant’s arousal SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in Individual Arousal accounted for, separately, by EI and MI. This information is summarized in Table 20. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in Individual Arousal (sr2= .04, p < .05), but MI (sr2= .01, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with Individual Arousal because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence was significantly positively predictive of Individual Arousal while Mating Intelligence was not. On average, participants that scored higher on the EI scale also scored higher in measurements of Individual Arousal. 33 34 Interpersonal Orgasm. A significant amount of variability in the participant’s Interpersonal Orgasm SSES score was accounted for by MI and EI (R2= .15, F(2, 143) = 12.09, p < .001). Thus, approximately 15% of variability in a participant’s Interpersonal Orgasm SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in Interpersonal Orgasm accounted for, separately, by EI and MI. This information is summarized in Table 21. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in Interpersonal Orgasm (sr2= .14, p < .001), but MI (sr2= .01, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with Interpersonal Orgasm because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence was significantly positively predictive of Interpersonal Orgasm while Mating Intelligence was not. On average, participants that scored higher on the EI scale also scored higher in measurements of Interpersonal Orgasm. Affection. A significant amount of variability in the participant’s Affection SSES score was accounted for by MI and EI (R2= .18, F(2, 143) = 15.77, p < .001). Thus, approximately 18% of variability in a participant’s Affection SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in Affection accounted for, separately, by EI and MI. This information is summarized in Table 22. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in Affection (sr2= .18, p < .001), but MI 34 35 (sr2= .00, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with Affection because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence was significantly positively predictive of Affection while Mating Intelligence was not. On average, participants that scored higher on the EI scale also scored higher in measurements of Affection. Communication. A significant amount of variability in the participant’s Communication SSES score was accounted for by MI and EI (R2= .14, F(2, 140) = 11.52, p < .001). Thus, approximately 14% of variability in a participant’s Communication SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in Communication accounted for, separately, by EI and MI. This information is summarized in Table 23. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in Communication (sr2= .14, p < .001), but MI (sr2= .00, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with Communication because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence was significantly positively predictive of Communication while Mating Intelligence was not. On average, participants that scored higher on the EI scale also scored higher in measurements of (sexual) Communication. Body Acceptance. A significant amount of variability in the participant’s Body Acceptance SSES score was accounted for by MI and EI (R2= .14, F(2, 143) = 11.18, p < 35 36 .001). Thus, approximately 14% of variability in a participant’s Body Acceptance SSES score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semi-squared partial correlations were computed to address the unique amount of variability in Body Acceptance accounted for, separately, by EI and MI. This information is summarized in Table 24. As can be seen in the table, emotional intelligence uniquely accounts for a significant amount of variability in Body Acceptance (sr2= .13, p < .001), but MI (sr2= .01, ns), does not. This result suggests that Mating Intelligence has such a strong zero-order correlation with Body Acceptance because of its overlap with Emotional Intelligence. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence was significantly positively predictive of Body Acceptance while Mating Intelligence was not. On average, participants that scored higher on the EI scale also scored higher in measurements of Body Acceptance. Refusal. A significant amount of variability in the participant’s Affection SSES score was accounted for by MI and EI (R2= .19, F(2, 143) = 16.75, p < .001). Thus, approximately 19% of variability in a participant’s Refusal score can be accounted for by information regarding participants’ Mating and Emotional Intelligence. Next, semisquared partial correlations were computed to address the unique amount of variability in Refusal accounted for, separately, by EI and MI. This information is summarized in Table 25. As can be seen in the table, emotional intelligence (sr2= .17, p < .001) and mating intelligence (sr2= .03, p < .05) uniquely accounts for a significant amount of variability in Interpersonal Orgasm. After controlling for the overlapping variance between EI and MI, it seems that Emotional Intelligence was significantly positively 36 37 predictive of Refusal while Mating Intelligence was significantly negatively predictive of Refusal. On average, participants that scored higher on the EI and MI scale also scored higher in measurements of Refusal. Hypothesis Three: Life History Strategy, The Ovulatory Cycle, and Sexuality To address a) whether individuals who have a “high-k life history strategy” will be more likely to have a longer length between ovulatory cycles, while individuals that have a “low-k life history strategy” will be more likely to have a shorter length between ovulatory cycles and b) if women with slower life history strategies will experience higher amounts of sexuality due to the idea that high-K strategists generally have better health, developmental stability, and overall consistency in their mental and physical functioning (Figueredo et al., 2005), zero-order correlations among these variables were computed. Life History Strategy was not related to cycle length. On the other hand, Life History Strategy was significantly positively related to multiple indices of sexuality (i.e., all three sexuality scales). The participants FSFI Arousal (r(143)= .386, p< .001), FSFI Lubrication (r(142)= .276, p< .01), FSFI Orgasm (r(142)= .329, p< .001), FSFI Satisfaction (r(143)= .435, p< .001), FSFI Pain (r(143)= .252, p< .01), and FSFI total (r(144)= .350, p< .001) scores were strongly significantly positively correlated with high scores on the LHS scale. Additionally, the participants SSSS Factor one (r(141)= .438, p<.001), SSSS Factor two (r(139)= .244, p< .01, and SSSS total (r(136)= .443, p <.001) scores were strongly significantly positively correlated with high scores on the EI scale, while SSSS Factor three (the negative factor) (r(142)= -.323, p<.001) was significantly negatively correlated with high scores on the LHS scale. Lastly, the participants SSES 37 38 Desire (r(145)= .218, p< .05), SSES Orgasm (r(143)= .187, p< .05), SSES Affection (r(143)= .222, p< .05), SSES Communication (r(140)= .171, p< .05), SSES Body (r(143)= .212, p< .05), SSES Refusal (r(143)= .263, p< .01), and SSES total (r(138)= .219, p< .05) scores were significantly positively correlated with high scores on the LHS scale. Though all scales were significantly related to the LHS scale, it seems that the FSFI and the SSSS were most strongly related. Basically, this means that higher scores in Life History Strategy were correlated with higher indices of sexuality. Interestingly enough, LHS was also strongly negatively related to stress (r(142)= -.345, p< .001). This makes sense in lieu of the differences between a slow and fast Life History Strategy—fast life history strategists will be more likely to experience increased stress. DISCUSSION Overall, not much can be said in regard to how the ovulatory cycle affects female sexuality—but to end here would be an injustice to the story that the research actually tells. Preliminary results for time two proved unfruitful—most likely due to the small sample that returned (about 40). Because of this, we only examined data from time one. Though minimal significance was found in regard to the ovulatory cycle (discussed below), a plethora of extremely interesting trends were found in relation to emotional intelligence, stress, life history strategy, and female sexuality. The Ovulatory Cycle The only trends that were found in regard to the ovulatory cycle were in the opposite direction that was predicted. Though there were no ovulatory trends found in regard to the Sexual Self-Efficacy Scale, or the Sexual Self-Schema Scale, the Female Sexual Function Index (Rosen et al., 2000) did reflect some interesting trends. Ovulatory 38 39 cycle and relationship quality were significantly predictive of high FSFI total scores. This means that individuals that scored high on this scale rated their relationship quality as high and were further away from ovulating. After breaking the scale into subscales, the trend of higher sexuality being indicative of women being further away from ovulating was continuously supported. This finding is interesting, yet we advise one to consider with caution. The way in which we coded the ovulatory cycle data was fundamentally flawed and very possibly could have been detrimental to the initial hypothesis (please see the limitation section for more on this). Female Sexuality Because the aim of the study was to help elucidate female sexuality, three sexuality scales were used for different reasons. The Sexual Self-Schema Scale (Andersen & Cyranowski, 1994) was of interest because it helped answer questions as to how a woman’s self perception is affected by various variables (namely, emotional intelligence, stress and life history strategy). The Sexual Self-Efficacy Scale (Bailes et al., 1998) was used to ascertain women’s confidence levels about various aspects of their sexual selves, and the Female Sexual Function Index (Rosen et al., 2000) supplied us with information on the individual’s immediate sexual feelings (i.e., within the last week). Though all three scales were considered measures of sexuality, all three were created with very different goals—and because of that, we thought it best to include (and analyze) all three. Life History Strategy The findings in regard to Life History Strategy tell us that individuals with a slow life history strategy benefit by experiencing higher levels of sexuality (on average). 39 40 Though the data tell us that LHS is not related to ovulatory cycle length, it is significantly positively related to nearly all the sexuality scales and subscales. Specifically, we found the strongest correlations to be with the Female Sexual Function Index and the Sexual Self-Schema Scale: The higher an individual’s LHS score, the higher they scored on the FSFI and SSSS. Another interesting side finding was that LHS was strongly negatively correlated with stress—this makes sense in lieu of the environments that high k (slow life history) strategists are in (i.e., less stress). Simply put, individuals with slow Life History Strategies score higher on measures of sexuality. Emotional Intelligence Though Mating Intelligence was only significantly related to the FSFI subscale Desire, Emotional Intelligence was strongly significantly positively related to all the sexuality scales and nearly all of subsequently broken down subscales. Though all the sexuality scales were significantly positively related to Emotional Intelligence, the highest correlations we found were in regard to the Sexual Self-Schema Scale: The way in which one perceives her/his sexual self is strongly positively correlated to high Emotional Intelligence. Basically, the higher an individual’s emotional intelligence, the higher they scored on all measures of sexuality. This deserves to be repeated. Individuals with high Emotional Intelligence experience higher levels of sexuality. In sum, individuals with high EI experience higher interpersonal interest/ desire, sensuality, individual arousal, affection, communication, body acceptance, interpersonal orgasm, lubrication, (individual) orgasm, and satisfaction. Though the ovulatory cycle findings were disappointing (to say the least), the fact that Emotional Intelligence strongly predicts heightened sexuality is fascinating. 40 41 Limitations Sample. We had a small, somewhat homogenous sample—and an even smaller sample for time two. Replicating these results with a much larger sample size might prove to be far more fruitful—especially when it comes to collecting data from time two. Ovulatory Coding. The lack of ovulatory findings could be due to a number of things. First and foremost, hormonal assays should be used in any research that is claiming any type of ovulatory cycle effect—especially since ovulatory cycles are so variable. Due to various restrictions, this was not an option for us. Next, the lack of findings could be due to the nature of ovulatory coding we used. Though two different methods were used to try to control for the large amount of variability in individual cycles and the fact that not many women pay close attention to cycle dates, it seems that our coding system was inherently flawed. The “ovulatory continuous” coding was used in order to leave ovulatory status as a continuous variable for the purpose of analysis (and, because our ovulatory cycles are cyclical in nature). At face value, this made sense, but ultimately did not prove to be fruitful. Part of this could be due to the fact that we converted all scores within -4 days after ovulation to absolute values. Knowing that the hormones present in women’s bodies differ depending on where she is in her cycle, it does not make much sense to assume that a women who had ovulated four days prior to taking the survey is the same (hormonally) as a women who is set to ovulate four days after taking the survey. Similarly, the “ovulatory categorical” coding was used in order to put individuals into one of two groups: “ovulating” and “not ovulating.” This way of coding is also inherently flawed because this does not offer an increase/decrease model; it only offers a 41 42 yes/no model. For instance, if a woman is a week away from ovulating and rates various indices of sexuality high, this effect will not be seen because she is in the “not ovulating” group. Additionally, assuming that a woman is ovulating due to the fact that she is nearly halfway done with her cycle is a bad assumption to make, especially since certain phases of the cycle can become shortened or lengthened due to environmental cues or increased stress. In a recent paper by Harris and Vitzthum (2013), they discuss the inherent flaws in ovulatory cycle research and offer a better alternative to individuals who still wish to collect data on the ovulatory cycle, but who do not have the means to take biological samples: daily probabilities. This method takes into account (variability in) individual cycle length, and uses probabilities to estimate where an individual is in her ovulatory cycle by using a standard algorithm. If hormonal assays are still not an accessible for future research, Harris and Vitzthum’s (2013) method should be used instead. Alternatively, it could just be that the ovulatory cycle does not influence women’s behaviors as much as some individuals claim (though this is hardly not written off due to the findings from this study). The inconclusive/null findings beg for a reorganized scientific method to assess the ovulatory cycle. Future Directions Though all three hypotheses could not be fully supported due to the lack of ovulatory findings, all three were partially supported. Hypothesis one (women who are closer to ovulation will be more likely to have an increased libido which will be mediated by stress, relationship status/ quality, and religious/ cultural beliefs ) was partially supported in that the likelihood of increased sexuality was affected by: stress (negatively) 42 43 and relationship status/ quality (positively). Hypothesis two (women with high Emotional and Mating intelligence will see a slight increase during peak fertility days and high Emotional and Mating Intelligence should positively predict increased sexuality across the cycle) was partially supported in that high Emotional (but not so much Mating) Intelligence positively predicted increased sexuality across the cycle. Lastly, hypothesis three (individuals who have a “high-k life history strategy” will be more likely to have a longer length between ovulatory cycles, while individuals that have “low-k life history strategy” will be more likely to have shorter length between ovulatory cycles AND women with slower life history strategies will experience higher amounts of sexuality) was partially supported due to the finding that women with slower life history strategies experienced higher amounts of sexuality. This finding was most likely due to the idea that high-K strategists have better health, developmental stability, and overall consistency in their mental and physical functioning (Figueredo et al., 2005). Though we were not able to answer whether the results lent support to any of the theories about the female orgasm, we found some extremely fascinating trends regarding female sexuality and how it is affected by various factors. It is our hope that this research can be used to base future research questions on. One could narrow these findings down and do a paper solely on the issues with ovulatory coding and the lack of evidence surrounding ovulatory trends, or, focus solely on the female orgasm. Or, one could broaden her/his research scope and attempt to replicate these findings with a larger, more diverse sample…the possibilities are endless. 43 44 Conclusions Though research on female sexuality has increased in the last twenty or so years, our understanding of human sexuality is far from complete. This research attempts to aid in building a solid foundation in which future research can hopefully be built upon. Humans are (somewhat) unique within the animal kingdom in that sex and sexuality is not simply a part of our lives, it is an integral part of everyday life. For some individuals, sexuality is more than a behavior you exhibit behind closed doors—it is a way of thinking, acting, and perceiving. We do not exist in a vacuum—just as the environment affects us, we in turn, effect the environment—it is a circular feedback system. In sum, these findings suggest that immediate markers of sexual success in women, such as lubrication and orgasm frequency, are strongly connected with one’s emotional capacities and one’s general approach to life. Specifically, people who are relatively high in emotional intelligence seem to encounter much in the way of proximate sexual success. This finding is consistent with the work of Brackett, Warner and Bosco (2005) who found that emotional intelligence is a significant factor in relationship success in general. Further, these findings show that a relatively slow life history strategy (see Figueredo et al., 2005) also corresponds to immediate markers of sexual success – interestingly, then, women who pursue a relatively sexual, fast life history strategy, experience less enjoyment in the moments leading up to, during, and after sex. Finally, the data strongly demonstrate the adverse effects of stress on female sexuality – simply: More stress corresponds to less sexuality all around—less arousal, less orgasms, less satisfaction…overall, less enjoyment. 44 45 In combination, these findings paint a picture of female sexuality as being importantly affected by emotional and social factors in numerous ways. In many ways, our sexuality is a result of a complex number of things—including evolutionary forces, genetic heritage, environmental input, and social/ cultural scripts. Take another moment and look at yourself in the mirror. Our ultimate, most ancestral drive to reproduce plays out in (proximate) modern environments just as it did thousands of years ago. We must acknowledge the fact that we cannot begin to fully understand ourselves unless we sit back and consider our place in the cosmos. 45 46 REFERENCES Alessi, G. (1992). Models of proximate and ultimate causation in psychology. 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Sex surveyed 1949-1994: From mass observations ‘Little Kinsey’ to the national survey to the Hite reports. London: Taylor & Francis. Wallen, K., Lloyd, E. A. (2011). Female sexual arousal: Genital anatomy and orgasm in intercourse. Hormones and Behavior, 59(5), 780-792. Zacks, R. (1995). History Laid Bare: Love sex, and perversity from the ancient Etruscans to Warren G. Harding. New York: HarperPerennial. Zietsch, B. P., Miller, G. F, Bailey, J. M., Martin, N. G. (2011). Female Orgasm Rates are Largely Independent of Other Traits: Implications of “Female Orgasmic Disorder” and Evolutionary Theories of Orgasm. The Journal of Sexual Medicine, 8(8), 23052316. 49 50 TABLES Table 1 Zero-Order Correlations among Categorical and Continuous Ovulation, Relationship Quality, Stress Total, and FSFI, SSES, SSSS Totals. Cat. Ov. Cont. Ov RQ Cat. Ov. -- Cont. Ov. -.59** RQ -.11 -.02 -- PSRS -.07 -.12 -.19* FSFI -.06 SSES .08 SSSS RG PSRS FSFI SSES SSSS RG -- .38** -- .40** -.22** -- -.02 .25** -.19** .38** -- -.01 .08 .21* -.40** .40** .53** -.01 .04 -.16* .06 -.12 .00 * p < . 05, **p < . 01 Key: Cat. Ov.= Categorical Ovulation Cont. Ov.= Continuous Ovulation RQ= Relationship Quality PSRS= Perceived Stress Reactivity Scale total FSFI= Female Sexual Function Index total SSES= Sexual-Self Efficacy Scale total SSSS= Sexual-Self Schema Scale total RG= Religiosity 50 --.08 -- 51 Table 2 Multiple Regression Predicting FSFI Total from Continuous Ovulation, Stress, Religiosity, Relationship Quality, Sexual Preference Criterion Variable: FSFI Total b Β sr2 1.28 .43 .18 .09 .07 .00 Religiosity -1.57 -.18 .03 Relationship Quality 2.47 .31 .08** .56 .23 .08 Predictor Variables Continuous Ovulation Stress Sexual Preference R2= .33** * p < .05, **p < .01 51 52 Table 3 Multiple Regression Predicting FSFI Arousal from Continuous Ovulation, Stress, Religiosity, Relationship Quality, Sexual Preference Criterion Variable: FSFI Arousal b Β Continuous Ovulation .34 .51 .24*** Stress .02 .07 .00 Religiosity -.36 -.18 .03 Relationship Quality .34 .19 .03 Sexual Preference -.37 -.09 .00 sr2 Predictor Variables R2= .34** * p < .05, **p < .01, p < .001*** 52 53 Table 4 Multiple Regression Predicting FSFI Lubrication from Continuous Ovulation, Stress, Religiosity, Relationship Quality, Sexual Preference Criterion Variable: FSFI Lubrication b Β sr2 Continuous Ovulation .36 .49 .22** Stress .03 .09 .01 Religiosity -.30 -.14 .01 Relationship Quality .21 .11 .01 Sexual Preference -.18 -.04 .01 Predictor Variables R2= .26* * p < .05, **p < .01 53 54 Table 5 Multiple Regression Predicting FSFI Orgasm from Continuous Ovulation, Stress, Religiosity, Relationship Quality, Sexual Preference Criterion Variable: FSFI Orgasm b Β sr2 Continuous Ovulation .26 .34 .12* Stress .03 .09 .01 Religiosity -.62 -.28 .06 Relationship Quality .60 .30 .08* Sexual Preference .95 .20 .04 Predictor Variables R2= .29* * p < .05 54 55 Table 6 Multiple Regression Predicting FSFI Satisfaction from Continuous Ovulation, Stress, Religiosity, Relationship Quality, Sexual Preference Criterion Variable: FSFI Satisfation b Β sr2 Continuous Ovulation .21 .31 .09* Stress .05 .15 .02 Religiosity -.32 -.16 .02 Relationship Quality .66 .36 .11* Sexual Preference .54 .13 .01 Predictor Variables R2= .25* * p < .05 55 56 Table 7 Multiple Regression Predicting SSSS Factor 3 from Continuous Ovulation, Stress, Religiosity, Relationship Quality, Sexual Preference Criterion Variable: SSSS Factor 3 b Β sr2 Continuous Ovulation .10 .04 .00 Stress .03 .09 .46*** Religiosity -.62 -.28 .04 Relationship Quality .60 .30 .01 Sexual Preference .95 -.20 .00 Predictor Variables R2= .53*** * p< .05, **p< .01, p< .001* 56 Table 8 Zero-Order Correlations among Mating Intelligence, Emotional Intelligence, Ovulatory Cycle, and Sexuality Variables EI MI OV PS S1 S2 S3 ST EO ED ES EF EA EC EB ER ET FD FA FL FO FS FP FT EI -.03 -.10 -.51** .45** .44** -.37** .58** .37** .40** .35** .42** .19* .38** .35** .40** .45** .20* .35** .31** .33** .30** .16 .34** MI OV PS S1 S2 -.04 -.08 .16 -.13 .01 -.09 --.02 -.13 -.35** .32** -.13 .03 .47** -.13 -.48** -.04 -.07 -.40** .70** .79** .09 -.02 -.18* .41** .37** -.02 -.07 -.16* .42** .42** .07 -.10 -.15 .35** .33** * -.06 -.12 -.17 .39** .35** .12 .05 .02 .27** .19* -.01 -.13 -.12 .40** .39** -.10 -.18 -.26** .21** .36** -.14 -.25** -.23** .19* .25** .03 -.11 -.19* .45** .43** .16* -.03 -.04 .28** .15 .08 -.03 -.26 .32** .35** .09 .02 -.22** .24** .32** .09 .02 -.27** .27** .33** .01 -.06 -.17* .26** .26** .02 .07 -.07 .21** .16 .08 -.02 -.22** .30** .31** S3 --.71** -.26** -.29** -.22** -.20* -.08 -.23** -.21** -.10 -.27** -.14 -.19* -.17 -.22** -.12 -.10 -.18* ST EO ED ES EF EA EC EB -.49** .52** .40** .43** .28** .48** .35** .23** .53** .30** .42** .35** .40** .31** .26** .39** -.76** .73** .45** .57** .69** .41** .30** .88** .35** .34** .33** .39** .22** .19* .35** -.79** .62** .47** .78** .54** .36** .90** .40** .39** .32** .31** .32** .29** .38** -.62** .58** .70** .66** .35** .89** .36** .28** .22** .24** .19* .10 .28** -.33** .55** .44** .49** .71** .14 .25** .21* .22** .28** .11 .25** -.45** .19* .13 .66** .37** .31** .32** .33** .19* .22** .34** -.44** .44** .85** .33** .32** .27** .27** .29** .19* .33** ER -.46** -.58** .50** .04 -.13 .21* .04 .19* .01 .24** .05 .22** .07 .14 .01 .22** .03 ET FD FA FL FO FS FP -.36** .37** .33** .36** .29** .21** .38** -.44** .33** .39** .28** .28** .48** -.91** .86** .81** .80** .96** -.85** -.78** .72** -.77** .69** .77** -.94** .91** .87** .84** FT -- *p < .05, **p < .01. Key on next page. 57 Key for Table 8 EI= Emotional Intelligence MI= Mating Intelligence OV= Ovulatory Proximity PS= Perceived Stress Reactivity Total S1= Sexual Self Schema Scale Factor 1 S2= Sexual Self Schema Scale Factor 2 S3= Sexual Self Schema Scale Factor 3 ST= Sexual Self Schema Scale Factor Total EO= Sexual-Self Efficacy Scale Orgasm Score ED= Sexual-Self Efficacy Scale Desire Score ES= Sexual-Self Efficacy Scale Sensuality Score EF= Sexual-Self Efficacy Scale Affection Score EA= Sexual-Self Efficacy Scale Arousal Score EC= Sexual-Self Efficacy Scale Communication Score EB= Sexual-Self Efficacy Scale Body Score ER= Sexual-Self Efficacy Scale Refusal Score ET= Sexual-Self Efficacy Scale Total Score FD= Female Sexual Function Index Desire Score FA= Female Sexual Function Index Arousal Score FL= Female Sexual Function Index Lubrication Score FO= Female Sexual Function Index Orgasm Score FS= Female Sexual Function Index Satisfaction Score FP= Female Sexual Function Index Pain Score FT= Female Sexual Function Index Total Score 58 59 Table 9 Multiple Regression Predicting FSFI Total from Mating and Emotional Intelligence Criterion Variable: FSFI Total b Β Emotional Intelligence .14 .34 .12*** Mating Intelligence .36 .08 .03 sr2 Predictor Variables R2= .12*** * p <. 05, **p < .01, ***p< .001 Table 10 Multiple Regression Predicting FSFI Desire from Mating and Emotional Intelligence Criterion Variable: FSFI Desire b Β sr2 Emotional Intelligence .01 .19 .04* Mating Intelligence .08 .15 .02 Predictor Variables R2= .06* * p <. 05, **p < .01, ***p< .001 59 60 Table 11 Multiple Regression Predicting FSFI Arousal from Mating and Emotional Intelligence Criterion Variable: FSFI Arousal b Β Emotional Intelligence .03 .35 .12*** Mating Intelligence .07 .07 .00 sr2 Predictor Variables R2= .13*** * p <. 05, **p < .01, ***p< .001 Table 12 Multiple Regression Predicting FSFI Lubrication from Mating and Emotional Intelligence Criterion Variable: FSFI Lubrication b Β Emotional Intelligence .03 .31 .09*** Mating Intelligence .09 .08 .01 sr2 Predictor Variables R2= .10*** * p <. 05, **p < .01, ***p< .001 60 61 Table 13 Multiple Regression Predicting FSFI Orgasm from Mating and Emotional Intelligence Criterion Variable: FSFI Orgasm b Β Emotional Intelligence .03 .33 .11*** Mating Intelligence .07 .07 .00 sr2 Predictor Variables R2= .12*** * p <. 05, **p < .01, ***p< .001 Table 14 Multiple Regression Predicting FSFI Satisfaction from Mating and Emotional Intelligence Criterion Variable: FSFI Satisfaction b Β Emotional Intelligence .03 .30 .09*** Mating Intelligence .01 .01 .00 sr2 Predictor Variables R2= .09** * p <. 05, **p < .01, ***p< .001 61 62 Table 15 Multiple Regression Predicting SSSS Total from Mating and Emotional Intelligence Criterion Variable: SSSS Total b Β sr2 Emotional Intelligence .37 .58 .34*** Mating Intelligence -.22 -.03 .00 Predictor Variables R2= .34*** * p <. 05, **p < .01, ***p< .001 Table 16 Multiple Regression Predicting SSSS Factor 1 from Mating and Emotional Intelligence Criterion Variable: SSSS Factor 1 b Β Emotional Intelligence .16 .48 .23*** Mating Intelligence .38 .11 .01 sr2 Predictor Variables R2= .25*** * p <. 05, **p < .01, ***p< .001 62 63 Table 17 Multiple Regression Predicting SSSS Factor 2 from Mating and Emotional Intelligence Criterion Variable: SSSS Factor 2 b Β sr2 Emotional Intelligence .12 .44 .18*** Mating Intelligence -.05 -.02 .00 Predictor Variables R2= .19*** * p <. 05, **p < .01, ***p< .001 Table 18 Multiple Regression Predicting SSSS Factor 3 from Mating and Emotional Intelligence Criterion Variable: SSSS Factor 3 b Β Emotional Intelligence -.10 -.37 .14*** Mating Intelligence .09 .08 .02 sr2 Predictor Variables R2= .16*** * p <. 05, **p < .01, ***p< .001 63 64 Table 19 Multiple Regression Predicting SSES Total from Mating and Emotional Intelligence Criterion Variable: SSES Total b Β Emotional Intelligence 1.27 .45 .20*** Mating Intelligence .54 .02 .00 sr2 Predictor Variables R2= .21*** * p <. 05, **p < .01, ***p< .001 Table 20 Multiple Regression Predicting SSES Sensuality from Mating and Emotional Intelligence Criterion Variable: SSES Sensuality b Β Emotional Intelligence .20 .35 .12*** Mating Intelligence .39 .07 .00 sr2 Predictor Variables R2= .13*** * p <. 05, **p < .01, ***p< .001 64 65 Table 21 Multiple Regression Predicting SSES Interpersonal Interest/ Desire from Mating and Emotional Intelligence Criterion Variable: SSES Interpersonal Interest/ Desire b Β sr2 Emotional Intelligence .21 .40 .16*** Mating Intelligence -.15 -.03 .00 Predictor Variables R2= .16*** * p <. 05, **p < .01, ***p< .001 Table 22 Multiple Regression Predicting SSES Individual Arousal from Mating and Emotional Intelligence Criterion Variable: SSES Individual Arousal b Β sr2 Emotional Intelligence .08 .19 .04* Mating Intelligence .52 .11 .01 Predictor Variables R2= .05* * p <. 05, **p < .01, ***p< .001 65 66 Table 23 Multiple Regression Predicting SSES Interpersonal Orgasm from Mating and Emotional Intelligence Criterion Variable: SSES Interpersonal Orgasm b Β Emotional Intelligence .29 .37 .14*** Mating Intelligence .66 .08 .01 sr2 Predictor Variables R2= .15*** * p <. 05, **p < .01, ***p< .001 Table 24 Multiple Regression Predicting SSES Affection from Mating and Emotional Intelligence Criterion Variable: SSES Affection b Β sr2 Emotional Intelligence .12 .42 .18*** Mating Intelligence -.23 -.07 .00 Predictor Variables R2= .18*** * p <. 05, **p < .01, ***p< .001 66 67 Table 25 Multiple Regression Predicting SSES Communication from Mating and Emotional Intelligence Criterion Variable: SSES Communication b Β sr2 Emotional Intelligence .17 .38 .14*** Mating Intelligence -.09 -.02 .01 Predictor Variables R2= .14*** * p <. 05, **p < .01, ***p< .001 Table 26 Multiple Regression Predicting SSES Body Acceptance from Mating and Emotional Intelligence Criterion Variable: SSES Body Acceptance b Β sr2 Emotional Intelligence .08 .36 .13*** Mating Intelligence -.27 -.11 .01 Predictor Variables R2= .14*** * p <. 05, **p < .01, ***p< .001 67 68 Table 27 Multiple Regression Predicting SSES Refusal from Mating and Emotional Intelligence Criterion Variable: SSES Refusal b Β sr2 Emotional Intelligence .10 .41 .17*** Mating Intelligence -.42 -.17 .03* Predictor Variables R2= .19*** * p <. 05, **p < .01, ***p< .001 68 Table 28 Zero-Order Correlations among Life History Strategy and Sexuality Variables LHS PS S1 S2 S3 ST EO ED ES EF EA EC EB ER ET FD FA FL FO FS FP FT LHS --.35** .44** .24** -.32** .44** .19** .22** .16 .22* .02 .17* .21* .26** .22* .02 .39** .28** .33** .44** .25** .35** PS S1 --.09 --.32** .32** .47** -.13 -.40** .70** -.18* .41** -.16* .42** -.15 .35** -.18* .39** .02 .27** -.12 .40** -.26** .21** -.23** .19* -.19* .45** -.04 .28** -.26** .32** -.22** .24** -.27** .27** -.17* .26** -.07 .21* -.22** .30** S2 S3 --.48** .79** .37** .42** .33** .35** .19* .39** .36** .24** .43** .15 .35** .32** .33** .26** .16 .31** --.70** -.26** -.29** -.22** -.20** -.09 -.23** -.21* -.10* -.27** -.14 -.19* -.15 -.22** -.12 -.10 -.18* ST EO -.49** .52** .40** .43** .28** .47** .35** .23** .53** .30** .42** .35** .40** .31** .26** .39* -.76** .73** .45** .57** .69** .41** .30** .88** .35** .34** .33** .39** .22** .19* .35** ED ES -.79** .62** .47** .78** .54** .36** .90** .40** .39** .32** .31** .32** .29** .38** -.62** .58** .70** .46** .35** .89** .36** .28** .22** .24** .19* .10 .27** EF EA -.33** -.55** .45** .44** .19* .49** .13 .71** .66** .14 .37** .25** .31** .21* .32** .22** .33** .28** .19* .11 .22** .25** .34** EC EB ER ET FD FA FL FO FS FP FT -.43** -.43** .50** -.85** .58** .50** -.33** .04 -.13 .36** -.32** .21* .04 .39** .37** -.27** .19* .01 .33** .33** .91** -.27** .24** .05 .36** .39** .86** .85** -.29** .22** .07 .29** .27** .81** .78** .72** -.19* .14 -.01 .21** .27** .80** .77** .69** .77** -.33** .22** .03 .38** .48** .96** .94** .91** .87** .84** -- *p < .05, **p < .01. Key on next page. 69 Key for Table 28 LHS= Life History Strategy PS= Perceived Stress Reactivity Total S1= Sexual Self Schema Scale Factor 1 S2= Sexual Self Schema Scale Factor 2 S3= Sexual Self Schema Scale Factor 3 ST= Sexual Self Schema Scale Factor Total EO= Sexual-Self Efficacy Scale Orgasm Score ED= Sexual-Self Efficacy Scale Desire Score ES= Sexual-Self Efficacy Scale Sensuality Score EF= Sexual-Self Efficacy Scale Affection Score EA= Sexual-Self Efficacy Scale Arousal Score EC= Sexual-Self Efficacy Scale Communication Score EB= Sexual-Self Efficacy Scale Body Score ER= Sexual-Self Efficacy Scale Refusal Score ET= Sexual-Self Efficacy Scale Total Score FD= Female Sexual Function Index Desire Score FA= Female Sexual Function Index Arousal Score FL= Female Sexual Function Index Lubrication Score FO= Female Sexual Function Index Orgasm Score FS= Female Sexual Function Index Satisfaction Score FP= Female Sexual Function Index Pain Score FT= Female Sexual Function Index Total Score 70 71 Appendix A: Sexual Self-Efficacy Scale for Female Functioning 71 72 72 73 Appendix B: Emotional Intelligence Scale 73 74 Appendix C: Female Sexual Function Index 74 75 75 76 76 77 Appendix D: Sexual Self-Schema Scale 77 78 Appendix E: Life History Strategy Scale 78 79 79 80 Appendix F: Mating Intelligence Scale Male Version 1. I think most potential partners just like me as a friend. 2. I have slept with many beautiful people. 3. I'm pretty good at knowing if a potential partner is attracted to me. 4. I'm definitely not the best at taking care of kids. 5. I'm good at saying the right things to people I flirt with. 6. I haven't had as many sexual partners compared with other guys I know (who are my age). 7. I have a difficult time expressing complex ideas to others. 8. I am good at picking up signals of interest from potential partners. 9. I'm definitely near the top of the status totem pole in my social circles. 10. I doubt that I'll ever be a huge financial success. 11. If I wanted to, I could convince a potential partner that I'm really royalty from some little-known country. 12. Honestly, I don't understand the minds of potential partners at all! 13. People tend to flirt with me pretty regularly. 14. If a person doesn't seem interested in me, I figure s/he doesn't know what s/he's missing! 15. Potential partners definitely find me attractive. 16. I've dated many intelligent people. 17. People tell me that I have a great sense of humor. 18. When I lie to potential partners, I always get caught! 19. I am usually wrong about who is interested in me romantically. 80 81 20. It's hard for me to get potential partners to see my virtues. 21. At parties, I tend to tell stories that catch the attention of potential partners. 22. I'm not very talented in the arts. 23. I can attract potential partners, but they rarely end up interested in me sexually. 24. When a potential partner smiles at me, I assume s/he's just being friendly. Female Version 1. I can tell when a partner is being genuine and sincere in his/her affections toward me. 2. I doubt I could ever pull off cheating on my partner. 3. I look younger than most women my age. 4. When a potential partner doesn't seem interested in me, I take it personally and assume something is wrong with me. 5. Good looking people never seem into me. 6. I have a sense of style and wear clothes that make me look sexy. 7. I attract many wealthy, successful partners. 8. Honestly, I don't understand the minds of potential partners at all! 9. With me, a partner gets what s/he sees—no pretenses here. 10. If I wanted to make my current partner jealous, I could easily get the attention of other people. 11. Potential partners don't tend to be interested in my mind. 12. I'm definitely more creative than most people. 13. I hardly ever know when a potential partner likes me romantically. 14. I laugh a lot at potential partners’ jokes. 15. If a potential partner doesn't want to date me, I figure s/he doesn't know what s/he's missing! 16. I am not very artistic. 17. My current partner spends a lot of money on material items for me (such as jewelry). 81 82 18. I am usually right on the money about a potential partner’s intentions toward me. 19. I really don't have a great body compared with other women I know. 20. Intelligent people never seem interested in dating me. 21. I believe that most potential partners are actually more interested in long-term relationships than they're given credit for. 22. Most potential partners who are nice to me are just trying to get into my pants. 23. When it comes down to it, I think most potential partners want to get married and have children. 24. If I have sex with a partner too soon, I know s/he will leave me 82 83 Appendix F: Perceived Stress Reactivity Scale 83 84 84 85 Appendix G: Ovulatory Questions Please answer the following to your best knowledge: 1. Are you currently on any type of birth control that might influence your menstrual cycle – for example, the “pill” or the NuvaRing? Yes No Prefer not to answer 1b. How long have you been on this birth control? 1c. Have you changed your type of birth control during the last 3 months – for example, by switching brands or dosage levels? Yes No Prefer not to answer 2. Is your menstrual cycle typically regular? That is, does it follow a normal schedule, or is it instead unpredictable? No, No, it is unpredictable Yes, Yes, it is usually irregular most of the time 1 2 3 it is highly regular 4 3. Please give your best estimate of the length of your menstrual cycle (in days). Circle one of the following: 25 26 27 28 29 30 31 32 33 34 35 * If the option is not listed below, please write it in the space provided: _________. 4. Using the calendar provided, please try to identify the first day (the start) of your last menstrual cycle: 5. Using the calendar provided, please try estimate when you expect your next menstrual cycle to start: If you had any trouble answering any of these questions, please comment here on what was confusing. 85 86 1) Please indicate your age in years _____________. 2) Please indicate your biological sex. 3) Please indicate your gender. 4) Are you currently pregnant? 5) Are you currently breast-feeding? 6) Have you gone through menopause, or are pre or peri menopausal? 7) Have you had a hysterectomy, tubal ligation, or any other forms of surgery that would prevent ovulation/ pregnancy? 8) How old were you when you first began to menstruate? 9) How old were you when you had sexual intercourse for the first time? 10) Are you on any of the following medications or have you used any of the following medications in the last 6 months? Please choose all that are applicable to you: 1) Antibiotics 2) Anti-depressants 3) Anti-epileptics 4) Mood stabilizers 5) Steroids 6) Thyroid medication 7) Anti-hypertensives 8) Anti-psychotics 9) Cholesterol lowering medications 10) Other (each spot will have a fill in area if they choose one of the medication categories). 86