Issues in Comprehensive Pediatric Nursing, 34:189-204, 2011 Copyright C Informa Healthcare USA, Inc. ISSN: 0146-0862 print / 1521-043X online DOI: 10.3109/01460862.2011.619861 EARLY AND RISKY SEXUAL BEHAVIOR IN A SAMPLE OF RURAL ADOLESCENTS Lynn Rew, EdD, RN, AHN-BC, FAAN School of Nursing, The University of Texas at Austin, Austin, Texas, USA Tracy Carver, Doctoral candidate Department of Educational Psychology, The University of Texas at Austin, Austin, Texas, USA Chia-Chun Li, BSN, RN, Doctoral candidate School of Nursing, The University of Texas at Austin, Austin, Texas, USA Introduction: Early and risky sexual behavior has been studied primarily in urban adolescents. Method: The purpose of this analysis was to identify psychosocial variables associated with sexual-risk behaviors in a sample of mostly rural adolescents. Six hypotheses were tested, using a resilience framework and data from an ongoing longitudinal study of 255 adolescents. Results: Sexual-risk status did not differ statistically by gender (p = .654) or socioeconomic status (p = .590). However, adolescents who engaged in sexual-risk behaviors reported signicantly lower religiosity (p < .003), lower parental monitoring (p = .002), lower social connectedness (p = .007), and higher levels of peer inuence (p < .001) than those engaged in no sexual-risk behaviors. Adolescents engaged in sexual-risk Received 20 July 2011; accepted 23 August 2011. The study was funded by a grant to the rst author [R01-NR009856] from the National Institute of Nursing Research/National Institutes of Health. The content is the sole responsibility of the authors and does not necessarily reect the views of the funding institutions. The authors also wish to thank the adolescents and their parents who participated in this study, and the many graduate research assistants who assisted with data collection. Address Correspondence to L. Rew, EdD, RN, FAAN, School of Nursing, The University of Texas at Austin, 1700 Red River, Austin, TX 78701. E-mail: ellerew@mail.utexas.edu 189 190 L. Rew et al. behaviors were also engaged in signicantly more other health-risk behaviors such as smoking and drinking (p < .001). Discussion: Findings may be useful for developing interventions that focus on the social inuences of peers and parents on rural youth. Keywords: Resilience Rural, Sexual behavior Why do some adolescents engage in risky sexual activity at an early age while others do not? This question has plagued researchers for decades. A resilience framework (Rew & Horner, 2003) posits that protective factors at the intrapersonal (individual) and interpersonal (family and peers) levels mitigate the adverse effects of risk factors associated with healthrisk behaviors. The sociocultural context in which adolescents live and interact with parents and peers may be a risk or protective factor in shaping behaviors that affect their health. For example, socioeconomic status (SES) (e.g., higher parental income and education) protected inner-city adolescents from risky sexual behaviors (Oman, Vesely, Kegler, McLeroy, & Aspy, 2003) while the same high indicators of SES were associated with greater likelihood of oral or anal sex (Lindberg, Jones, & Santelli, 2008). Additionally, place of residence (e.g., rural vs. urban) was identied as a risk factor for sexual activity and other health-risk behaviors in a predominantly non-Hispanic sample (Atav & Spencer, 2002). Individual Risk and Protective Factors Previous studies have shown that individual factors such as gender and low religiosity are associated with early and risky sexual behavior. For example, Santelli's research team (Santelli, Lindberg, et al., 2000) found that males were more likely than females to initiate early sexual relationships. Others found that sexual-risk behaviors were higher in adolescent males than in females in urban settings (Evans et al., 2004; Rai et al., 2003; Roche & Leventhal, 2009). Social connectedness that aligns vulnerable children with caring adults is a critical individual resource that has been shown to protect adolescents from an array of poor developmental and health outcomes (Henrich, Brookmeyer, & Shahar, 2005; Resnick et al., 1997), buffering the effects of extreme conditions such as poverty and neighborhoods characterized by high crime and violence on adolescents (Frauenglass, Routh, Pantin, & Mason, 1997). High levels of religiosity are associated with delayed sexual debut (McCree, Wingood, DiClemente, Davies, & Harrington, 2003; Rostosky, Regnerus, & Wright, 2003). In an analysis of adolescent females who participated in the National Longitudinal Study of Adolescent Health (Add Early and Risky Sexual Behavior in Rural Adolescents 191 Health), Miller and Gur (2002) showed that religiousness was related to sexual responsibility. Adolescents with greater family religiosity had fewer sexual partners and used contraceptives consistently; thus, this factor served as protection (Manlove, Logan, Moore, & Ikramullah, 2008). Family Risk and Protective Factors Scholarly examination of the relationship between SES and adolescent sexual activity has produced mixed results. Whereas Lynch (2001) found that high SES protected against risky sexual behavior in 7th through 12th graders, Santelli and colleagues (Santelli, Lindberg, et al., 2000) found that the onset of sexual activity in Black adolescent females occurred several months earlier in the low SES group than in middle SES group. They also found that SES had no signicant inuence on sexual behavior. Although living in a single-parent family increased the risk for early sexual activity in one study of adolescents, sexual-risk behaviors also increased with lack of parent-adolescent communication among urban youth (McIntosh, Moore, & Elci, 2009). In contrast, Add Health data showed that adolescents in a nationally representative sample who communicated with their parents about sexual issues were more likely than those with little parental communication to engage in sexual-risk behaviors (Chen & Thompson, 2007). Thus, family factors may increase risk or provide protection. In a study of 446 Hispanic youth, lack of parental monitoring was associated with sexual-risk behaviors, including number of sexual partners (Kerr, Beck, Shattuck, Kattar, & Uriburu, 2003). Similarly, Wight and colleagues (2006) found that low parental monitoring predicted early sexual activity, and for females, additional sexual-risk behaviors. Data from the 1997 National Longitudinal Survey of Youth showed that parental monitoring mediated the relationship between family religiosity and sexual activity (Manlove et al., 2008). Adolescents with greater family religiosity had fewer sexual partners and used contraceptives consistently; thus, this family factor served as protection. Community Risk and Protective Factors Findings about the inuence of peers on sexual risk-taking behavior are equivocal. Oman et al. (2003) found that peer role models protected early adolescents from engaging in risky sexual behaviors; however, in a nationally representative sample, others found that peers who were sexually active had a strong inuence on increasing the sexual debut of adolescents in public high schools (Sieving, Eisenberg, Pettingell, & Skay, 2006). 192 L. Rew et al. Similarly, O'Donnell and colleagues (2003) found that peers had a signicant inuence on sexual initiation in 7th graders in disadvantaged neighborhoods in New York. Thus, ndings about the inuence of peers on sexual risk-taking behavior are equivocal. Health-Risk Behaviors Health-risk behaviors, including unprotected sexual activity, smoking, drinking, and using drugs, threaten the health and well being of adolescents (Rew, 2005). These behaviors are more likely to occur when adolescents have several risk factors and few protective resources (Hawkins, Cummins, & Marlatt, 2004). Studies have shown that many health-risk behaviors tend to cluster (Arbeau, Galambos, & Jansson, 2007; Brady, Tschann, Pasch, Flores, & Ozer, 2008; Elliott & Larson, 2004). Specically, Palen's research team (2006) reported that 8th graders who had engaged in sexual intercourse had inconsistent use of condoms and were more likely than their abstinent peers to have used alcohol or marijuana. Conceptual Framework The Youth Resilience Model (Rew & Horner, 2003) that guided this study was based on ndings from studies suggesting that both risk and protective factors at the individual, family, and community levels inuence early and risky sexual behavior and other health-risk behaviors in adolescents; however, this body of knowledge includes equivocal ndings. The majority of studies on factors that inuence sexual behavior have been done with older adolescents attending urban high schools, but some studies indicated that for many youth, sexual debut starts much younger and includes behaviors putting them at high-risk for adverse health outcomes. Moreover, few studies have included primarily rural, low-income Hispanic youth. Purpose The purpose of this study was to identify risk and protective factors associated with early and risky sexual behaviors in rural adolescents. Specic hypotheses were: 1. Males will report signicantly more sexual-risk behaviors than females. 2. Adolescents from low SES will engage in more sexual-risk behaviors than those from high SES. Early and Risky Sexual Behavior in Rural Adolescents 193 3. Adolescents engaged in sexual-risk behaviors will report signicantly lower scores on measures of religiosity, social connectedness, parental monitoring, and parent-adolescent communication than those not engaged in sexual-risk behaviors. 4. Adolescents engaged in sexual-risk behaviors will report signicantly greater peer inuence than those not engaged in sexual-risk behaviors. 5. Adolescents engaged in sexual-risk behaviors will report signicantly higher levels of smoking, drinking, using drugs and marijuana than those not engaged in sexual-risk behaviors. 6. There will be no statistically signicant differences in data obtained by method of collection (i.e., home computer, mailed paper survey, or home visit with laptop). METHOD Design and Setting This report is a cross-sectional analysis of data from a single cohort in an on-going longitudinal study of health behaviors in adolescents. Baseline data were collected from 2006-2009 from adolescents who attended 9th grade in public schools in three rural communities in the south central United States. Sample The non-probability sample consisted of 255 adolescents between the ages of 14 and 17 years (M = 15.1, SD = 0.63). Fifty-two percent (n = 133) were non-Hispanic, 43.9% (n = 112) were Hispanic, and 2% did not indicate ethnicity. The majority (n = 142, 55.7%) were White, 12.9% (n = 33) were African American, and the remaining reporting racial data were Asian, American Indian, Native Hawaiian/Pacic Islander, or multi-racial. There were 143 girls and 110 boys enrolled (two were missing data). The majority lived with married parents (n = 165, 65.1%), while the rest of the sample lived with parents who were divorced (n = 41, 16.1%), single parents (n = 11, 4.3%), separated parents (n = 10, 3.9%) or widowed parents (n = 3, 1.2%). A few (n = 24, 9.4%) had missing data. Procedures After obtaining approval from the university institutional review board, written parental consent and adolescent assent were obtained. Data were 194 L. Rew et al. collected in one of three ways: a secure Web site from the family's home computer, a mailed survey, or computer-assisted self-interviewing (CASI) technology via laptops during an in-home visit. Method of data collection was a choice given to adolescents and their parents to enhance participation rates. Measures Descriptions of instruments to measure independent variables are shown in Table 1. Means, standard deviations, and Cronbach's for this sample are included. Individual factors of adolescent's age, gender, and race/ethnicity were measured using a demographic form designed for the larger study. Religiosity was measured using the Religious Commitment Inventory (Worthington et al., 2003), reporting how often one contributes time and money to a religious organization, spends time on religious activities, and Table 1. Measures of independent variables in Youth Resilience Model Variable Religious commitment Scale (Authors) and format Minimum Religious Commitment Inventory (Worthington et al., 2003) 10 items; 5-point Likert Social Family Integration connectedness Scale (Blum et al., 1989 10 items; 4-point Likert Parental Parental Monitoring monitoring Scale (Huebner & Howell, 2003) 8 items; 5-point Likert Parent-adolescent Parent-Adolescent Communication Communication Scale (Huebner & Howell, 2003) 7 items; 5-point Likert Peer inuence Peer Inuence Scale (Williams et al., 1995) 15 items; 5-point Likert Maximum Mean (SD) Cronbach's alpha 9.00 50.00 22.56 (11.40) .960 10.00 49.00 32.54 (5.047) .781 14.00 40.00 44.52 (5.35) .796 7.00 28.00 18.33 (4.43) .800 15.00 52.00 21/34 (7.36) .889 Early and Risky Sexual Behavior in Rural Adolescents 195 is inuenced by religious beliefs. Social connectedness (how much the adolescent felt that parents, teachers, and other adults cared about her/him) was measured by the Family Integration Scale (Blum, Harris, Resnick, & Rosenwinkel, 1989). Parental monitoring indicated that parents knew the adolescent's whereabouts, friends, and use of leisure time (Huebner & Howell, 2003). Parent-adolescent communication was based on how often adolescents communicated with parents in the last year about alcohol and drugs, personal problems, and sexual relationships (Huebner & Howell). High scores on each of these self-report measures indicated high levels of the construct. Family factors were parent's marital status, education, and SES (i.e., child received free or reduced price lunches, family on welfare, and income level), which were collected from the adolescents' parents. The community factor of peer inuence was measured using the Peer Inuence Scale, a self-report of how many friends smoke, use marijuana, drink alcohol, use other drugs, or asked the participant to engage in these behaviors (Williams, Toomey, McGovern, Wagenaar, & Perry, 1995). A high score indicates greater peer inuence. Other health-risk behaviors and sexual-risk behaviors were derived from the Youth Risk Behavior Surveillance Scale [YRBSS] (Centers for Disease Control and Prevention [CDC], 2004). Although the YRBSS was designed to use single-item variables of health-risk behaviors (Laura K. Kann, CDC, personal communication, August 11, 2009), for our analyses, we created a composite scale including self-reports of smoking cigarettes, smoking cigars, drinking alcohol, binge drinking, using marijuana, using cocaine, and snifng glue in the past 30 days. This scale had a Cronbach's alpha reliability coefcient of .759 for this sample. Sexual risk behavior, the outcome variable, was operationalized as self-reporting one or more of the following: (1) rst sex at or before age 14 years; (2) having two or more lifetime sexual partners; (3) lack of contraceptive use at last sexual intercourse; and (4) drinking alcohol or using drugs with last sexual intercourse. Data Analysis Descriptive statistics were computed for the individual variables of gender and SES. Means, standard deviations, and Cronbach's alpha coefcients of reliability were computed for all scales. Hypotheses were tested using one-tailed, independent t-tests and chi-squares. All data were analyzed using SPSS 17.0. One-way analysis of variance (ANOVA) and independent t-test analyses were done to determine if there were signicant differences in measures of theoretical variables by method of data collection. Because data were collected in a number of different ways, one-way 196 L. Rew et al. ANOVA and independent t-test analyses were done to determine if there were signicant differences in scores on theoretical variables by method of data collection. Cases of missing data were removed from descriptive analyses. FINDINGS More than one-third of the parents responding reported that their child received free or reduced price lunches at school (n = 93, 39.2%; n = 18 missing). The majority reported that nobody in the immediate family received welfare (n = 214, 89.9%; n = 17 missing). Total income level ranged from less than $20,000 to over $150,000. The mode was less than $20,000 (n = 36, 15.6%); the mean income was $50,001 to $60,000 (n = 25 missing). Nearly three-fourths (71.5%) of the sample had not yet experienced sexual intercourse. Table 2 shows the frequency of each type of sexual-risk behavior by age and gender. Hypothesis 1 On average, males reported fewer sexual-risk behaviors (M = 1.56, SE = 0.037) than females (M = 1.59, SE = 0.060). This difference was not statistically signicant (X 2 (1) = .200, p = .381). The hypothesis that males would report signicantly more sexual-risk behaviors than females was not supported. Table 2. Frequency of sexual risk behaviors by gender Age at Sexual Debut: Never had sexual intercourse 11 years 12 years 13 years 14 years 15 years 16 years Number of lifetime sexual partners: 1 person 2 people 3 people 4 or more people Condom at last itercourse No Alcohol or drugs at last intercourse Yes Males (%) Females (%) 80 (72.7) 2 (1.8) 1 (<1.0) 4 (3.6) 15 (13.6) 4 (3.6) 3 (2.7) 101 (70.6) 1 (<1.0) 0 (0) 6 (4.2) 12 (8.4) 14 (9.8) 9 (6.3) 16 (14.5) 9 (8.2) 1 (<1.0) 5 (4.3) 23 (16.1) 8 (5.6) 4 (2.8) 7 (4.9) 6 (5.5) 11 (7.7) 3 (2.7) 9 (6.3) Early and Risky Sexual Behavior in Rural Adolescents 197 Hypothesis 2 There was no statistically signicant difference between adolescents' sexual-risk behaviors and SES, dened as the adolescent received free or reduced price lunches at school, (X 2 (1) = 0.290, p = .349). Although adolescents in the sexual-risk group came from families with lower incomes (M = 4.86, SE = 0.445) than those in the no-risk group (M = 4.98, SE = 0.229), this difference was not statistically signicant (t (227) = 0.248, p = .805). The hypothesis that adolescents from low SES would engage in more sexual-risk behaviors than those from high SES was not supported by these data. Hypothesis 3 Adolescents engaged in sexual-risk behaviors (Table 3) reported significantly lower religiosity, lower social connectedness, and lower parental monitoring than those not engaged; however, there was no statistically signicant difference in the measure of parent-adolescent communication. The hypothesis that adolescents who engaged in sexual-risk behaviors would report signicantly lower scores on measures of social connectedness, religiosity, parental monitoring, and parent-adolescent communication than those with lower levels of sexual-risk behaviors was partially supported by the data. Mean scores on the Religious Commitment Index (RCI) differed signicantly depending on method of data collection (ANOVA, F(2, 64) = 37.869, p < .001). Games-Howell post-hoc comparisons revealed that Table 3. Differences in selected psychosocial variables and sexual-risk behaviors in young adolescents Sexual risk (n = 68) No sexual risk (n = 185) Outcome variable M SE M SE t df p Social connectedness Religiosity Parent-adolescent communication Parental monitoring Peer inuence Health-risk behaviors 31.01 0.627 32.99 .352 2.856 250 .005 19.26 18.25 1.222 0.527 23.8 18.36 0.865 0.332 2.842 248 0.170 248 .005 .866 30.88 25.46 9.50 0.756 0.978 0.528 33.52 20.02 7.49 0.358 0.520 0.112 3.541 247 5.076 229 5.439 251 <.001 <.001 <.001 M = mean; SE = standard error; t = t-test; df = degrees of freedom; p = signicance (values in bold are statistically signicant. 198 L. Rew et al. participants who took the survey on the study's laptop computer during a home visit (M = 12.57) scored signicantly lower on the RCI than participants who took the survey on paper (M = 22.76, p < .001) or on the Internet from their home computer (M = 24.17, p < .001). The method of data collection had no signicant effect on social connectedness (ANOVA, F (2,250) = .787, p = .456), parental monitoring (ANOVA, F (2, 34) = 1.759, p = .188), parent-adolescent communication (ANOVA, F (2,247) = .305, p = .737), or peer inuence [t (228) = 0.228, p = .820]). Hypothesis 4 On average, adolescents engaged in sexual-risk behaviors reported signicantly higher scores on peer inuence and engaged in signicantly more additional health-risk behaviors than those not engaged in sexual-risk behaviors (Table 3). This hypothesis was supported. Hypothesis 5 On average, adolescents engaged in sexual-risk behaviors also engaged in signicantly more additional health-risk behaviors than those not engaged in sexual-risk behaviors (Table 3). This hypothesis was supported. DISCUSSION The rst hypothesis that males would engage in signicantly more sexualrisk behaviors than females was not supported. This contrasts with the ndings of Evans et al. (2004) and may reect contextual differences in rural vs. urban samples. Perhaps males in this rural setting are engaged in other activities that support abstinence. The lack of statistical signicance could also be due to the relatively small sample and/or the relatively small number of adolescents who had already engaged in sexual activity. Another possible explanation is that although there was not a signicant difference, the mean age of females engaged in sexual-risk behaviors was slightly higher than the mean age of males, thus they may have had more opportunities to engage in sexual-risk behaviors especially if these females had older friends. The second hypothesis that adolescents from lower SES levels would engage in signicantly more sexual-risk behaviors than those of higher SES levels also was not supported. When family income was used in the analysis, the ndings were in the predicted direction. Adolescents in families with lower incomes were engaged in slightly more sexual-risk behaviors than those in families with higher incomes. Again, the small Early and Risky Sexual Behavior in Rural Adolescents 199 sample size of adolescents who engaged in sexual-risk behaviors may have attenuated these ndings. However, this nding is congruent with studies that found limited inuence of SES on sexual behaviors in adolescents (Santelli, Lowry et al., 2000). The majority of adolescents in this study lived in rural areas, which may have mitigated the potential negative effects of low SES. For example, Lammers, Ireland, Resnick, and Blum (2000) found that rural residence was a factor in delaying adolescent sexual activity. Adolescents engaged in sexual-risk behaviors reported signicantly lower religiosity (religious commitment) than those not engaged, providing partial support for the third hypothesis. This nding is similar to ndings reported by McCree et al. (2003) and Rostosky et al. (2003), indicating that an individual's religiosity supports delaying sexual activity in adolescents. Adolescents may incorporate religious principles into their decision-making processes regarding sexual activity. This underscores the importance of peer inuence on adolescent sexual behavior. Exposure by way of frequent attendance in religion-oriented groups to peer groups who hold traditional views may act to deter risky sexual behavior. Holder et al. (2000) also found that adolescents with a higher degree of interconnectedness with spiritual peer groups engaged in less sexual activity. This nding has implications for developing interventions in religious settings where adolescents are likely to make commitments that inuence their sexual behavior. Adolescents engaged in sexual-risk behaviors reported signicantly lower social connectedness than adolescents not engaged in sexual-risk behaviors. This nding supports other studies that showed that adolescents who felt they had caring adults in their lives were not as likely to engage in health-risk behaviors as adolescents who did not have such support (Frauenglass et al., 1997; Henrich et al., 2005; Resnick et al., 1997). This individual protective factor may be especially important for rural adolescents who have limited opportunities to interact with other adults because of their place of residence. Adolescents engaged in one or more sexual-risk behaviors had lower self-reported parental monitoring, supporting the ndings of McIntosh et al. (2009) and Kerr et al. (2003). Parental monitoring may induce greater feelings of responsibility in adolescents, but when it is lacking, youth may be poorly equipped to make decisions about behaviors that give immediate gratication but have long-term consequences. Encouraging parental monitoring could be an important component of interventions that prevent or reduce adolescent health-risk behaviors such as early and risky sexual activity. The nding that there was no signicant difference in parent-adolescent communication between those engaged in sexual-risk behaviors and those 200 L. Rew et al. not engaged was not too surprising, as previous studies have yielded equivocal ndings with respect to parent-adolescent communication and risky sexual behaviors (Chen & Thompson, 2007 v. McIntosh et al., 2009). It may be that the measure of parent-adolescent communication was not sensitive enough to detect differences in this sample. Further study is warranted to extend the understanding of how parent-adolescent communication inuences early and risky sexual behavior in adolescents. Peer inuence in this sample was strongly associated with sexual-risk behaviors, a nding that supports those of other studies of adolescents (O'Donnell et al., 2003; Sieving et al., 2006). Although previous studies have examined the relationship between peers who were involved in sexual activity and sexual risk-behaviors, this study examined the inuence of peers who were engaged in other health-risk behaviors on the sexual behavior of young adolescents. This new nding suggests that even if peers are not themselves engaged in sexual activity, the clustering of health-risk behaviors places their friends at risk. This nding may be a critical factor in developing interventions that address groups of adolescents and their awareness of the inuence that peers may have on them. Sexual-risk behaviors were strongly associated with other health-risk behaviors, consistent with the ndings of Palen et al. (2006), Kiene, Barta, Tennen, and Armeli (2009), and Brodbeck, Matter, and Moggi (2006) that alcohol and substance use were signicantly related to having multiple sexual partners, low intention to use protection, and having unprotected sex with casual partners. Reducing substance and alcohol use might mitigate adolescents' sexual-risk behaviors, thereby promoting their physical, psychological, and sexual health. Method of Data Collection Religiosity scores collected during home visits were signicantly lower than were those collected by the other two methods, an interesting new nding that may be related to the unexpected nding that sexual-risk behaviors were not related to SES. In this study, home visits were made primarily to families with fewer economic resources and without easy access to computers. Thus, the lack of a computer in the home may have served as a proxy for low SES, thus providing more support for the second hypothesis. Limitations and Strengths This study had several limitations, including a relatively small number of sexually-active adolescents in the sample, a non-probability sample, use of Early and Risky Sexual Behavior in Rural Adolescents 201 self-report measures, and outcome variables that were single-item indicators of behaviors. Findings, therefore, cannot be generalized to all rural 9th graders in the United States. Another limitation was the various ways in which data were collected (i.e., home visit vs. home computer vs. paperpencil). Comparison of ndings by method of data collection pointed to those variables that were more or less sensitive to method of data collection, suggesting the need for further study of how such methods may inuence ndings and their interpretation. A strength of the study, however, was the sampling frame that focused on adolescents living in three rural areas and the over-representation of Hispanics relative to this community and nationally. Other strengths were the theoretical approach and use of valid scales to measure independent variables. These ndings add to the mounting evidence that peers and families both inuence sexual health behaviors of young adolescents. IMPLICATIONS Individual, family, and peers are important factors associated with sexualrisk behaviors in young rural adolescents. Sexual-risk behaviors are also related to other health-risk behaviors such as tobacco and alcohol use. Social connectedness, individual religiosity, parental monitoring, and peer inuence were signicantly related to sexual-risk behaviors in this sample of adolescents. Therefore, this study has implications for parents, religious leaders, counselors, healthcare providers, social workers, and educators, especially those who work with younger adolescents. In future studies, researchers should continue to investigate how differences in gender and SES inuence sexual-risk behaviors, perhaps even exploring how gender differences in sexual attitudes affect adolescents' sexual behaviors. With this body of research, a more complete picture of the relationships between individual, family, and community factors and health-risk behaviors can continue to be developed so that adolescents' sexual health can be promoted. 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However, users may print, download, or email articles for individual use. Directions Summarize the article you have identified in a maximum of 600 words using the DAA Template document located in the Resources, under the Required Resources heading. Specific instructions for completing each section of the DAA Template are listed as follows. You may use some of the author's own words to summarize the article, but avoid lengthy direct quotes (for example, copying multiple sentences or paragraphs verbatim). You should not exceed the limit of 600 words. This is a situation where more is not better. Step 1: Write Section 1 of the DAA Provide a context of the journal article. Include a definition of the specified variables (predictor, outcome) and corresponding scales of measurement. Specify the sample size of the data set. Discuss why the journal article is relevant to your career specialization. Step 2: Write Section 2 of the DAA Discuss the assumptions of the statistical test used in the journal article. If possible, identify information in the article that provides information on how these assumptions were tested. If no information on assumptions is provided, consider this as a limitation of the reported study. Step 3: Write Section 3 of the DAA Specify a research question related to the journal article. Articulate the null hypothesis and alternative hypothesis. Specify the alpha level if it is provided in the article. Step 4: Write Section 4 of the DAA Report the results of the statistical test using proper APA guidelines. This includes: o The statistical notation (for example, r, t, or F). o The degrees of freedom. o The statistical value of r, t, or F, and the p value. o The effect size (if one is provided) and an interpretation of it. Interpret the test statistic against the null hypothesis. Step 5: Write Section 5 of the DAA Discuss the conclusions of the statistical test as it relates to the research question. Conclude with an analysis of the strengths and limitations of the study reported in the journal article