Emotion Regulation and Sex Addiction among College Students (2017)

International Journal of Mental Health and Addiction

February 2017 , Volume 15, Issue 1, pp 16–27

Craig S. Cashwell, Amanda L. Giordano, Kelly King, Cody Lankford, Robin K. Henson

Abstract

For individuals with sexual addiction, sexual behaviors often are the primary means of regulating distressing or undesirable emotion. In this study, we sought to examine differences in aspects of emotion regulation between students in the clinical range of sexual addiction and those in the nonclinical range. Among a sample of 337 college students, 57 (16.9 %) scored in the clinical range of sexual addiction and students in the clinical range differed significantly from students in the nonclinical range on three aspects of emotion regulation: (a) nonacceptance of emotional responses, (b) limited engagement in goal-directed behaviors in response to negative affect, and (c) minimal emotion regulation strategies. Implications for interventions on college campuses are provided.

Emotion Regulation and Sex Addiction Among College Students

            Researchers indicate that approximately 75% of students enter college with previous sexual experience (Holway, Tillman, & Brewster, 2015) and college students engage in sexual behaviors that could be loosely categorized as healthy, problematic, or compulsive. At one end of the spectrum, the freedom and educational opportunities afforded by the college environment may cultivate healthy individuation from the family of origin and exploration of personal values, beliefs, and norms, including those related to sexuality (Smith, Franklin, Borzumato-Gainey, & Degges-White, 2014). Many college students develop a better understanding of themselves and their personal values and engage in sexual activities congruent with their personal belief systems. Other students, however, may encounter the many risk factors of the college environment and engage in problematic or risky sexual behavior.

For example, one potential risk factor involves sexual norms of college campuses, as students tend to overestimate the number of sexual partners and prevalence of sexual activity of their peers (Scholly, Katz, Gascoigne, & Holck, 2005). These sexual norms may foster pressure to conform to inaccurate sexual expectations and contribute to a variety of negative consequences, such as unwanted pregnancy (James-Hawkins, 2015), sexually transmitted infections (STIs; Wilton, Palmer, & Maramba 2014), sexual assault (Cleere & Lynn, 2013), and shame (Lunceford, 2010). Another factor contributing to risky sexual behavior among college students is alcohol use. Researchers have linked alcohol consumption to number of sexual partners among adolescents and young adults. Specifically, Dogan, Stockdale, Wildaman, and Coger (2010) conducted a longitudinal study over 13 years and found that alcohol use was positively correlated with the number of sexual partners among young adults. Although risky sexual behavior among college students may lead to negative or harmful outcomes, these acts do not necessarily denote sexual addiction. It is only when students experience a loss of control over their sexual behaviors and continue to engage despite negative consequences that sexual addiction may be present (Goodman, 2001).

Sexual Addiction

            Although some controversy exists around sex addiction, particularly given its absence in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), leading experts in many disciplines generally agree that sex addiction is indeed a disease (Carnes, 2001; Goodman 2001; Phillips, Hajela, & Hilton, 2015). Goodman (1993) proposed diagnostic criteria for sexual addiction by inserting the term sexual behavior into the criteria for substance abuse and dependence. From this perspective, sex addiction is not about type or frequency of sexual activity. Instead, sex addition consists of preoccupation and ritualization of sexual activity, an inability to stop or decrease both internal (e.g., preoccupation, fantasy) and external behaviors (e.g., viewing of pornography, paying for sex) despite unwanted consequences, the experience of tolerance (resulting in increased frequency, duration, or riskiness of behaviors), and withdrawal (i.e., dysphoric mood when the behavior is stopped).

Other experts agree that out-of-control sexual behavior is problematic, yet choose to conceptualize the issue as a hypersexual disorder rather than addiction (Kafka, 2010; 2014; Kor, Fogel, Reid, & Potenza, 2013). From this perspective, out-of-control sexual behavior is an impulse control disorder. These researchers posit that more research regarding the etiology of hypersexuality is needed before classifying it as an addiction (Kor et al., 2013).

These philosophical differences in the terminology of out-of-control sexual behavior and diagnostic criteria make obtaining accurate prevalence rates challenging, yet Carnes (2005) posited that up to 6% of Americans have a sexual addiction. Studies on particular subsets of the population, however, reveal different frequencies. With particular relevance to this study, researchers have found rates of sexual addiction and hypersexuality among college students to be consistently higher than the general population. For example, Reid (2010) found that 19% of college males met criteria for hypersexuality and Giordano and Cecil (2014) found 11.1% of male and female undergrads met this criteria. Additionally, Cashwell, Giordano, Lewis, Wachtel, and Bartley (2015) reported 21.2% of male and 6.7% of female undergraduates in their sample met criteria for further sexual addiction assessment. Accordingly, the high prevalence of out-of-control sexual behavior among college students indicates a need for better understanding of predictive factors. Because of the emotional nature and impulsivity associated with sexual addiction, one construct related to sexual addiction that may have particular relevance for college students is emotion regulation.    

Emotion Regulation

Emotion regulation (ER) is at the center of a burgeoning literature, with many contending definitions, emphases, and applications (Prosen & Vitulić, 2014). For the purposes of the present study, we broadly defined ER as the process of observing, assessing, and altering emotional reactions in order to meet one’s goals (Berking & Wupperman, 2012). Active dimensions of ER include the ability to (a) be aware of, understand and accept emotions, (b) act in goal-directed, non-impulsive ways during negative emotion states, (c) use adaptive regulation strategies that are context-dependent, and (d) cultivate an awareness that negative emotions are a part of life (Buckholdt et al., 2015). Gratz and Roemer (2004) determined that the process of ER differs from attempts to exert control over emotions, eliminate emotions, or suppress emotions. In fact, researchers have found that controlling, eliminating, or suppressing emotions can create higher levels of emotion dysregulation and physiological distress (Gratz & Roemer, 2004). Instead of suppressing or judging one’s emotional experience, ER is a process in which one identifies and accepts the present emotion in order to diminish its exigency and encourage deliberate behavioral responses (Gratz & Roemer, 2004). This definition implies that an attentiveness towards and comfort with emotions constitutes a healthy response.

The process of ER is continual, making it crucial to the development and maintenance of both positive mental health and mental health disorders (Berking & Wupperman, 2012). Research on the connection between ER and psychological flexibility indicates the importance of possessing an array of regulatory strategies and the ability to modify them to fit the demands of varied contexts (Bonanno & Burton, 2013; Kashdan & Rottenberg, 2010). Individuals who successfully apply flexible ER strategies often are more adaptive and generally enjoy greater mental health outcomes and a protective buffer against mental disorders (Aldao, Sheppes & Gross, 2015). Similarly, some have begun to establish profiles of ER that relate to psychopathology (Dixon-Gordon, Aldao, & De Los Reyes, 2015; Fowler et al., 2014). Researchers, then, should further examine specific clinical populations and their unique experiences with emotion dysregulation (Berking & Wupperman, 2012; Sheppes, Suri & Gross, 2015), including those struggling with sex addiction.

Sexual Addiction and Emotion Regulation

Goodman (1993, 2001) described addictive sexual behavior as serving two functions: producing pleasure and reducing internal affective distress. Thus, behavioral addictions produce reward or euphoric states occasioned by the release of dopamine in the brain (positive reinforcement) as well as provide negative reinforcement or relief from undesirable dysphoric emotional states (e.g., reduce anxiety or alleviate depression). Indeed, Adams and Robinson (2001) purported that sexual addiction is a means by which individuals seek to escape emotional distress and self-soothe, and that sexual addiction treatment must have an ER component.

In support of this proposition, Reid (2010) found that hypersexual men had statistically significantly higher negative emotionality (i.e., disgust, guilt, and anger) and statistically significantly lower positive emotionality (i.e., joy, interest, surprise) than a control sample. Specifically, self-directed hostility was the strongest predictor of hypersexual behavior among the clinical sample. Moreover, in a qualitative study of men with out-of-control sexual behavior, Guigliamo (2006) discovered eight themes in participants’ responses to how they understand their problem. Several of the themes represent the association between sexual behaviors and ER such as: (a) compensation for personal feelings of low self-esteem or self-loathing and, (b) escape from disturbing or deadening feelings. These two themes emerged from 9 of the 14 participant responses (Guigliamo, 2006). Therefore, previous research supports the notion that out-of-control sexual behavior may occur, at least in part, as an effort to reduce distressing emotions.  

The connection between sexual addiction and ER may be particularly relevant for collegiate samples. College students undergo several important transitions and face many stressors during the college years. For example, Hurst, Baranik, and Daniel (2013) examined 40 qualitative articles on collegiate stressors and identified the following prominent sources of college student stress: relationship stressors, lack of resources (money, sleep, time), expectations, academics, transitions, environmental stressors, and diversity, among others.

In addition to context-specific stressors, the prevalence of mental health issues among college students is well documented. In a study of over 14,000 college students on 26 different campuses, researchers found that 32% had at least one mental health concern (including depression, anxiety, suicide, or self-injury). In light of these stressors and mental health concerns, researchers have investigated the relationship between compulsive sexual behavior and collegiate emotionality. In a study of 235 female college students, Carvalho, Guerro, Neves, and Nobre (2015) found that trait negative affect (chronic states of negative emotions) and difficulty identifying emotions significantly predicted sexual compulsivity among college females. These findings support the notion that awareness and understanding of emotions, an important dimension of ER (Gratz & Roemer, 2008), may be particularly problematic for students with sex addiction.  

The stressors and mental health concerns of college students may make them more susceptible to the development of sexual addiction as a means to regulate distressing or undesirable emotions. Indeed, compulsive sexual behavior may reflect a student’s predominant ER strategy, providing limited flexibility and temporary relief. To date, however, there is limited empirical attention to ER as it pertains to college students’ sexually addictive behaviors. Accordingly, the purpose of this study was to examine whether differences in ER difficulties exist between a group of students in the clinical range for sexual addiction and a group of students in the nonclinical range. Specifically, we hypothesized that statistically significant differences in ER difficulties would exist between the two groups, with students in the clinical range of sexual addiction exhibiting more difficulty than those in the nonclinical range.

Methods

Participants and Procedures

            Recruitment for this study occurred at a large, public university in the southwest. After attaining Institutional Review Board approval, we utilized convenience sampling to contact undergraduate professors seeking permission to administer our survey during class meeting times. We attained permission to visit 12 undergraduate classes from a variety of disciplines (i.e., art, accounting, biology, theater, education, sociology) and invited all undergraduate students 18-years of age or older to participate in the study. Students who chose to participate had the opportunity to enter into a drawing for a gift card to a local retail store. Data collection yielded 360 participants. Inclusion criteria consisted of current enrollment at the university and at least 18 years of age. Seventeen participants did not report their age and were removed. Additionally, six survey packets were incomplete and thus excluded from further analysis. Thus, The final sample consisted of 337 participants.

Participants reported an average age of 23.19 (SD = 5.04). The majority of the participants identified as female (n = 200, 59.35%), with 135 participants (40.06%) identifying as male, one participant (.3%) identifying as transgender, and one participant (.3%) not responding to this item. In terms of race/ethnicity, our sample was fairly diverse: 11.57% identified as Asian (n = 39), 13.06% identified as African American/Black (n = 44), 17.21% identified as Latino/Hispanic (n = 58), 5.64% identified as Multi-racial (n = 19), 0.3% identified as Native American (n = 1), 50.74% identified as White (n = 171), and 1.48% identified as other (n = 5). Participants also represented multiple sexual orientations: 2.1% identified as gay (n = 7), 0.9% identified as lesbian (n = 3), 4.7% identified as bisexual (n = 16), 0.6% identified as other, and 91.4% identified as heterosexual (n = 308). The vast majority of participants were upperclassman at their university as 0.9% classified themselves as freshman (n = 3), 6.5% as sophomores (n = 22), 30.9% as juniors (n = 104), and 56.7% as seniors (n = 191), with one participant (.3%) not responding to this item. Thirty-five participants (10.39%) indicated that they had a mental health diagnosis, with the largest group of these participants reporting some type of mood disorder (n = 27).

Instrumentation

The survey packet contained a demographic questionnaire and two standardized assessment tools. Participants completed the Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004). The 36 items of the DERS yield six factors of ER: (a) Nonacceptance of Emotional Responses, or the tendency to have negative secondary emotional reactions to undesirable emotions, (b) Difficulties Engaging in Goal Directed Behavior, defined as difficulty concentrating and accomplishing desired tasks when having negative emotions, (c) Impulse Control Difficulties, or the struggle to maintain control of behavioral responses when experiencing negative emotions, (d) Lack of Emotional Awareness, defined as not attending to negative emotions, (e) Limited Access to Emotion Regulation Strategies, defined as a belief that, once distressed, there is little that can be done to effectively deal with the distress, and (f) Lack of Emotional Clarity, or the extent to which an individual knows and is clear about the emotions that he or she is experiencing (Gratz & Roemer, 2004).  Participants viewed items related to ER (e.g. “I have difficulty making sense out of my feelings,”) and indicated frequency on a 5-point Likert-type scale ranging from “Almost Never, 0-10% of the time” to “Almost Always, 91-100% of the time.” Higher subscale scores indicate greater difficulty in ER. Researchers have successfully utilized the DERS with samples of individuals dealing with both substance and process addictions (Fox, Hong & Sinha, 2008; Hormes, Kearns & Timko, 2014; Williams et al., 2012) with scores demonstrating high internal consistency and construct validity (Gratz & Roemer, 2004; Schreiber, Grant & Odlaug, 2012). Scores from the DERS subscales had acceptable Cronbach’s alpha levels (Henson, 2001) within the current sample: Nonaccept (.91), Goals (.90), Impulse (.88), Aware (.81), Strategies (.90), and Clarity (.82).  

Finally, we included 20-item Core Subscale of the Sexual Addiction Screening Test-Revised (SAST-R; Carnes, Green & Carnes, 2010) in order to distinguish between clinical and non-clinical sub-groups within our sample. The SAST-R is widely used to screen for sex addiction in a variety of settings and its scores have demonstrated high internal consistency and discriminant validity (Carnes et al., 2010). The Core Subscale has a Yes/No dichotomous response format to examine characteristics of sex addiction common across various populations including preoccupation, loss of control, affective disturbance, and relationship disturbance (Carnes et al., 2010).  A sample item of the SAST-R Core Scale is, “Have you made efforts to quit a type of sexual activity and failed?” The acceptable clinical cutoff score for the SAST-R core subscale is six and indicates a need for further assessment and possible treatment for sexual addiction. Scores in the current sample demonstrated acceptable internal reliability with a Cronbach’s alpha of .81.  

Results

Prior to investigating the primary research questions, we analyzed the means and standard deviations of each of the DERS subscales among students in the clinical range for sexual addiction and those in the nonclinical range (Table 1). To assess for homogeneity of variance, we utilized Box’s M test. This test was statistically significant, suggesting possible violation of the assumption for our current sample. As the Box’s M test is sensitive to nonnormality, however, our unequal sample sizes compounded with the large number of dependent variables likely contributed to this result (Huberty & Lowman, 2000). Therefore, we visually inspected the variance/covariance matrices and confirmed that most fell within reasonable proximity with more similarities than differences.

            To address the primary research question, we utilized a descriptive discriminant analysis (DDA), a multivariate test used in this instance to determine what facets of ER contribute to the separation of the two groups, in this case clinical versus non-clinical (Sherry, 2006). DDA is superior to a one-way MANOVA in that it provides information regarding the relative contribution of each variable in explaining group differences within a multivariate context, as opposed to univariate ANOVAs to follow multivariate results (Enders, 2003). In this way, variables in DDA are combined into a synthetic, composite variable used to discriminate between groups. In our study, the analysis sought to determine if there were multivariate differences between the students in the clinical range of sexual addiction and those in the nonclinical range on the six subscales of the DERS.

We utilized the SAST-R cutoff score to categorize students as clinical or nonclinical for sexual addiction. We classified students who scored six or more on the SAST-R Core Scale as clinical (n = 57, 16.9%) and those who scored less than six as non-clinical (n = 280, 83.1%). Breaking this down by gender, 17.8% of males and 15.5% of females in the sample surpassed the clinical cutoff.

The primary analysis utilizing DDA was statistically significant, indicating group membership differences in the composite dependent variable created from the six subscales (Table 2). Specifically, the squared canonical correlation indicated that group membership accounted for 8.82% of the variance in the composite dependent variable. We interpreted this effect size (1- Wilks’ lambda =.088) as existing in the medium range given the nature of the sample and variables studied (cf. Cohen, 1988). Thus, meaningful differences in ER difficulties existed between participants in the clinical range of sexual addiction and those in the nonclinical range.

            Next we examined the standardized discriminant function coefficients and structure coefficients to determine the contribution of each DERS subscale to the differences between the two groups. Our findings revealed that the Nonaccept, Strategies, and Goals subscales were most responsible for the differences between the two groups (Table 3). Specifically, scores on the Nonaccept subscale accounted for 89.3% of the total variance explained, scores on the Strategies subscale accounted for 59.4%, and scores on the Goals subscale accounted for 49.7%. The Clarity and Impulse subscales played secondary roles in defining the group difference, although the variance that Clarity was able to explain in the effect was almost entirely subsumed and explained by other predictor variables, as indicated by its near-zero beta weight and larger structure coefficient. The Aware subscale did not play a substantial role in contributing to the group difference. Examination of group centroids confirmed that the clinical group had higher DERS scores (reflecting more emotion regulation difficulties) than the nonclinical group. All structure coefficients were positive, indicating that those in the clinical group tended to have higher ER difficulties on all of the subscales, even those that did not contribute as much to the multivariate group difference.   

Further, group means and standard deviations specified that Nonaccept, Strategies, and Goals subscale scores were higher among the clinical group compared to the nonclinical group (see Table 1). Therefore, students in the clinical range for sexual addiction reported less acceptance of emotions, more difficulty engaging in goal-oriented behavior, and less access to emotion regulation strategies compared to students in the nonclinical range.

Discussion

            The finding that 57 participants (16.9%) scored over the clinical cutoff on the SAST-R is consistent with previous findings (Cashwell et al., 2015; Giordano & Cecil, 2014; Reid, 2010), indicating that college students may have a higher prevalence of addictive sexual behavior than the general population. These findings are likely due, at least in part, to a stressful environment, large amounts of unstructured time, ubiquitous online access, and an environment that supports the hook-up culture (Bogle, 2008). This finding is not unexpected, then, and also is consistent with the argument that sexual addiction often emerges during late adolescence and early adulthood (Goodman, 2005). What seems unique about this sample is the lack of disparity in prevalence between men and women (17.8% and 15.5%, respectively), whereas previous researchers (Cashwell et al., 2015) found men to have far higher prevalence rates of sex addiction than women. Future researchers should look closely at the various measurement instruments used by researchers and continue to examine and refine what is known about sexual addiction prevalence rates among college men and women.

Our findings supported our hypothesis that students scoring at or above the clinical cutoff on the SAST-R Core Scale would experience more difficulty regulating emotions. Specifically, three of the DERS subscales were largely responsible for the statistically significant differences between the groups, resulting in an overall medium effect size. Our findings revealed that students scoring in the clinical range of the SAST-R experience more difficulty accepting their emotional responses, engaging in goal-directed behavior, and accessing emotion regulation strategies. The fact that students in the clinical range of sexual addiction experience more ER difficulty supports the Goodman’s (1993, 2001) proposition that one of the primary functions of sexual addiction is to regulate negative affect. Therefore, those who experience difficulty regulating their emotional experiences may be at higher risk of engaging in sexual behaviors as a way to relieve affective distress. Over time, this may lead to compulsive and out-of-control sexual behavior.

Polyvagal theory (Porges, 2001, 2003) provides an important conceptual framework for the neurobiological basis of addiction and may, at least in part, explain these findings. According to Porges, behavioral responses (such as addictive sexual behavior) emerge from adaptive strategies informed by the nervous system, and these behavioral responses are linked to ER. For example, stress impacts the ability to regulate physiology and social-behavioral states, often leading to a restricted range of emotional expression. In times of particularly high stress, individuals tend to use more primitive adaptive responses, such as fight, flight, or freeze (Porges, 2001). Often, addictive sexual behavior has a flight or avoidance function, to help the individual suppress or avoid emotions that they experience as distressing. Unfortunately, however, the very behaviors that occasion temporary relief from emotional distress induce long-term increased emotional dysregulation and physiological distress (Gratz & Roemer, 2004), which contributes to the addiction cycle.

         Examination of the most salient subscales contributing to the group differences in our current study (i.e., Nonaccept, Strategies, and Goals), offers insight into the ER process of those in the clinical range for sexual addiction. Although it is not possible to draw firm conclusions on sequencing, it seems at least logical that engaging in goal-directed behavior and accessing ER strategies are predicated upon one’s acceptance of her or his emotional responses. That is, the ability to regulate emotions (Strategies subscale) and engage in goal-directed behavior (Goals subscale) is compromised when one consistently suppresses or avoids emotional distress (Nonaccept subscale). Thus, the nonacceptance aspect of ER seems particularly important conceptually, and also contributed to the majority of variance explained. Items in the Nonaccept subscale indicate that people who reject their negative affect tend to experience strong secondary emotional reactions to their emotional distress, including guilt, shame, embarrassment, anger at self, irritation at self, or feeling weak. It is possible, then, that one of the leverage issues in working with clients with addictive sexual behavior is to facilitate a more self-compassionate response to emotional distress. Results from this study indicate that those with addictive sexual behavior tend to be self-critical when they experience emotional distress and, accordingly, likely are inclined to work to reject or minimize the initial emotional distress to avoid the secondary emotional reaction, hampering their ability to choose healthy emotion-regulation strategies and engage in goal-directed behavior.

         Porges (2001) suggested that therapeutic interventions be used to create calm states and activate the neural regulation of the brainstem, which may help to prompt the regulation of the social engagement system. It is beyond the scope of this paper to fully explore methods and techniques for doing this, but a starting place for clinicians would be mindfulness-based practices (Gordon, & Griffiths, 2014; Roemer, Williston, & Rollins, 2015; Vallejo & Amaro, 2009). For example, Roemer et al. (2015) found that mindfulness practice corresponds with reductions in distress intensity and negative self-referential processing, and increases one’s ability to engage in goal-directed behaviors. Similarly, Menezes and Bizarro (2015) found that focused meditation positively impacted acceptance of negative emotions. Additional intervention strategies may focus on self-compassion (Neff, 2015), and approaches drawn from Acceptance and Commitment Therapy (ACT) to promote acceptance, cognitive defusion, and present moment awareness (Hayes, Luoma, Bond, Masuda, & Lillis, 2006), all of which may support emotion regulation.

         The goal, therefore, of utilizing mindfulness-based strategies is to offer students health alternatives to regulate emotions. In light of the stress and mental illness experienced by many college students, difficulty in emotion regulation is not surprising. Appropriate and effective interventions for addressing these difficulties may consist of providing healthy ways to regulate negative affect (such as mindfulness techniques), thereby minimizing students’ reliance on sexual acts for ER purposes. Because the design of the current study was cross-sectional, additional intervention and longitudinal research is warranted to continue to tease out the possible effect of ER on addictive sexual behavior and the efficacy of specific intervention strategies.

Limitations

         The current findings must be examined within the context of study limitations. All data were collected from intact classrooms at one public university. Although participants were drawn from diverse academic disciplines, it is unknown how these results generalize to other geographical areas or types of universities. Additionally, participation was voluntary and it is unknown how participants who chose to participate may have differed systematically from those who declined. Further, all data were collected via self-report, which may have led some participants to under-report sexual behaviors on the SAST-R or to minimize emotional distress on the DERS. Finally, although group membership provided important insight regarding difficulties in emotion regulation, much variance remains unexplained.

Conclusion

         Results of this study highlight the importance of assessing and treating ER among college students struggling with addictive sexual behavior. While further research is needed to more clearly make this connection, mental health professionals working with addictive sexual behavior would be well served to assess ER processes and strategies among clients struggling with addictive sexual behavior, and to tailor interventions to help students regulate emotional distress in healthier ways and develop goal-directed strategies to cope with the stress of college life.

 

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Table 1

 

DERS Subscale Means and Standard Deviations

 

DERS Subscale

Clinical SA Group

Non Clinical SA Group

 

M

SD

M

SD

Nonaccept

17.05

6.21

12.57

5.63

Clarity

12.32

3.23

10.40

3.96

Goals

16.15

4.48

13.26

5.05

Aware

15.35

4.54

14.36

4.54

Impulse

13.24

5.07

10.75

4.72

Strategies

18.98

6.65

14.84

6.45

Note. Clinical SA Group: n = 57; Non Clinical SA Group: n = 280

 

 

Table 2

 

Wilks’ Lambda and Canonical Correlation for Two Groups

 

Wilks’ Lambda

χ2

df

p

Rc

Rc2

.912

30.67

6

<.001

.297

8.82%

 

 

Table 3

Standardized Discriminant Function Coefficients and Structure Coefficients

 

DERS Variable

Coefficient

rs

rs2

Nonaccept

 .782

.945

89.30%

Clarity

   -.046

.603

36.36%

Goals

    .309

.70549.70%
Aware

    .142

.2657.02%
Impulse

  -.193

.63039.69%
Strategies

  .201

.77159.44%