Condom use, frequency of sex, and number of partners: multidimensional characterization of adolescent sexual risk-taking.

Adolescents' sexual risk-taking has been conceptualized in various ways: early age at first intercourse, number of partners, type of partner or length of relationship, frequency of intercourse, consistency of condom use, and use of other methods of birth control. Although each can be considered an aspect of risk taking, none by itself is valid as an operationalization of risky behavior (Metzler, Noell, & Biglan, 1992; Sieving et al., 1997). Rather, each is a proxy--a measure that captures some of the variance in risk-taking but does not in itself completely measure the construct.

The reason that none of these factors completely represents risk-taking is that sexual behavior always involves a combination of them. The characteristics of this combination determine whether a particular pattern of behavior is safe or risky with regard to disease prevention or pregnancy. For example, the risk of STD acquisition from inconsistent condom use may be minimal with one partner, like in a mutually monogamous relationship, if neither partner is infected. However, risk would become greater with larger partner numbers because the risk of coming across an infected individual increases. Similarly, disease risk from condom non-use increases with larger number of intercourse occasions, especially if occurring with multiple partners. In terms of pregnancy, each additional act of unprotected intercourse increases risk.

The use of risk-taking proxy measures may account for some inconsistent findings in studies of adolescent sexual risk-taking. For example, studies examining condom use and number of partners typically find that one, but not the other, is related to STD acquisition. Sometimes condom use, but not number of partners, is found to be a statistically significant predictor of getting an STD (Upchurch, Brady, Reichart, & Hook, 1990). At other times, number of partners, but not condom use, is a predictor (Joffe et al., 1992). Studies that account for more complex behavioral patterns may improve understanding of adolescent sexual risk-taking. Additionally, information about these patterns can inform development of interventions that are more reflective of the options that adolescents are comfortable adopting and more effective than simple prescriptions to use condoms or have fewer partners.

Previous Research Using Combined Risk Variables

Researchers have used two approaches for creating composite measures of risk-taking. Most often, they have used investigator-developed criteria to categorize participants (e.g., high vs. low risk) or developed scales representing degree of risk (e.g., Capaldi, Stoolmiller, Clark, & Owen, 2002; Metzler et al., 1992; Millstein & Moscicki, 1995; Sieving et al., 1997). In two studies (Capaldi et al., 2002; Millstein & Moscicki), the resulting measures were indeed correlated with STD acquisition, supporting the value of this approach. However, a drawback of this approach is that it does not necessarily provide descriptive information on the nature of the behaviors engaged in by individuals at the different risk levels. Also, because it is determined a priori, it may obscure or overlook important differences among subgroups of risk-takers.

In contrast to this approach, Newman and Zimmerman (2000) used a statistical method, Cluster Analysis, to derive risk profiles empirically in a sample of 15- to 18-year-old African Americans who reported having had sex. They identified four subgroups: low-risk youth who used condoms consistently and had fewer partners, high-risk youth who used condoms inconsistently with many partners, "monogamy strategy" youth who used condoms inconsistently but had few partners, and "condom strategy" partners who used condoms consistently with a moderate number of partners. This approach has two advantages. First, without imposing possibly incorrect a priori assumptions, the researchers could see how risk factors combined in the sample. Second, the method is informative in that examination of the subgroups provides understanding about the nature of the risks being taken and how membership is related to other variables. For example, the higher risk subgroup contained more males and the monogamy subgroup contained more females. High-risk males were found to use more alcohol and drugs during sexual activity than those in other subgroups.

Purpose and Hypotheses

In this study, condom use, frequency of sex, and number of partners were chosen as variables from which to derive empirical risk-taking profiles, or subgroups, in a sample of adolescents interviewed annually from the 8th to 12th grades. Each of these variables can be considered to reflect an aspect of risk-taking. Condom use, the factor most commonly used in research as a risk indicator, has been shown to reduce (but not necessarily eliminate) the risk of disease acquisition and pregnancy (Stone, Grimes, & Magder, 1986). For STDs, high frequency of intercourse is a risk factor because when unprotected, it offers increased opportunities for disease transmission. Frequent change in sexual partners is also an important component of risk-taking since it exponentially increases the possibility of contacting someone who is infected (Padian, Hitchcock, Fullilove, Kohlstadt, & Brunham, 1990).

This study tested two hypotheses concerning sexual risk behavior. In Hypothesis 1, we expected that profiles could be identified in each grade using a latent variable approach. Theoretically, this methodology allows the identification of subgroups engaging in lesser versus greater risk-taking, as well as a description of the source of risk for each. Hypothesis 2 predicted that the profiles would be related logically to other risk-taking variables. We chose variables that other studies have shown to be associates of risk-taking in adolescents: gender (Donald, Lucke, Dunne, O'Toole, & Raphael, 1994; MacKellar et al., 2000; Newman & Zimmerman, 2000), STD and pregnancy history (Crosby et al., 2002; Metzler et al., 1992; Millstein & Moscicki, 1995), and use of birth control methods other than condoms (Civic, 1999; Sieving et al., 1997).

In addition to these hypotheses, we planned to assess whether Newman and Zimmerman's (2000) results are generalizable when examined on a more ethnically diverse sample, and with a different statistical approach (Latent Profile Analyses) and methodology (deriving separate profiles for each age group rather than pooling age groups). Although the major focus of the study hypotheses was to derive and validate risk-taking profiles, we expected that the findings also would yield interesting patterns that further illustrate the utility of this methodological approach. For example, we expected that a larger proportion of students would be seen with higher risk profiles in later grades than in earlier grades, given that older adolescents are more likely to have sex (Brooks-Gunn & Paikoff, 1997) and less likely to use condoms (Grunbaum et al., 2002).

METHOD

All study procedures, measures, and protocols were reviewed and approved by the University of Washington's Human Subjects Review Board.

Sample

We conducted a longitudinal study of four cohorts of students (in grades 3 through 6 during the first data collection wave in 1992). Students in a large, urban northwest school district were recruited and then surveyed annually for seven years. To maximize geographic, socioeconomic, and racial/ethnic diversity, schools were selected based on information about percentage of free lunch-eligible students, racial/ethnic profiles, and geographic locations. Parents or guardians of the students received brochures that described the study and were asked for written consent for their children to participate. A total of 2,319 students were enrolled in the eligible grade levels at these schools. After removing 203 students who were non-English speakers, had changed schools, or could not be found, we obtained parental consent for 1,177 (56%) participants. This consent rate is similar to those obtained in other school-based studies requiring active parental consent (e.g., DeLoye, Henggeler, & Daniels, 1993). We then asked all children of consenting parents to assent to study participation. Four children of consenting parents refused to participate, resulting in an initial sample of 1,173 (a 55% consent rate for children). When the study was extended beyond the three years originally planned, we obtained re-consent for 1,084 (92% of the original sample).

The data reported in this study come from assessments of the two oldest cohorts when they were in 8th through llth grades and from the oldest cohort in 12th grade. We did not include the younger two cohorts, who did not reach later years of high school during the study period and thus had lower rates of sexual activity, in the analyses. Out of the 627 participants originally recruited into the two oldest cohorts, 605 (96%) were included in the analyses, as each provided data for at least one time point from grades 8 through 12 (n = 597, 558, 560, 555, and 294 for grades 8 through 12, respectively). Of these, 261 (43%) and 344 (57%) were from the third and fourth cohort, respectively. See Table 1 for participant demographics.

A slight selection bias is likely in this sample, but there was no detectable bias resulting from attrition once enrolled. The sample contains a lower percentage of adolescents who were eligible for the federally funded or reduced price lunch program (35%), compared to the school district population (44%) for these grades, suggesting bias toward higher-income families. However, the sample's distribution of race and gender were not significantly different from the school district population in the same grades. In terms of study attrition, the 605 individuals included did not differ in terms of gender, ethnicity, or income from the overall sample of 627. Similarly, attrition did not appear to bias the sample in later years: there were no significant differences in gender, ethnicity, or income between the sample included in these analyses and the larger sample at study enrollment.

Research Assessments

We administered surveys at yearly intervals, in the spring, to small groups of 1 to 25 participants. Prior to survey administration, interviewers set up the classroom environment to prevent each student from being able to see other students' answers (e.g., arranging seats at a distance from each other and providing screens, when necessary, to block students' views of each others' responses). Trained interviewers read the questions aloud as the youth read along and recorded their answers in their own written copies; reading the survey aloud mitigated problems for those students who were not strong readers. Teachers were not present during survey administration, nor were students not taking the survey. The survey was designed so that each question had an answer that could be circled so that other students could not deduce behavior by observing whether an answer was being circled. Administration took about 45 minutes. Surveys occurred primarily in the schools. In years 2 through 7, we interviewed participants who had moved out of the area individually or, in rare cases, by phone. Each youth received a gift such as a t-shirt or gift certificate each year for participating.

Analytic Approach

Latent Profile Analysis. Latent Profile Analysis (LPA) is an extension of Latent Class Analyses (LCA) that is appropriate when the observed variables of interest are measured as continuous (Gibson, 1959; Lazarsfeld & Henry, 1968). Renewed interest in the use of this method has occurred as high-speed computers make such computationally-intensive methods practically applicable. Vermunt and Magidson (2002) have described the numerous labels used to describe this type of modeling, such as mixture-likelihood approach to clustering, mixture-model clustering, probabilistic clustering, Bayesian classification, and latent class cluster analysis. LPA is similar in intent to other clustering approaches, such as cluster analysis and Meehl's taxonomic procedures. Cleland, Routhschild, and Haslam (2000) describe the similarities and differences between these techniques and report on simulation studies which found LPA's accuracy to be superior to cluster analysis but similar to Meehl's taxonomic procedures.

Several authors have described the intent and estimation of LPA models (e.g., Gibson, 1959; Lazarsfeld & Henry, 1968; Muthen & Muthen, 2000; Vermunt & Magidson, 2002). As a latent variable approach, LPA assumes that the relationship between a set of indicator variables can be explained by a latent variable that is categorical. The categories are groups of people (referred to as classes) who are similar to each other but different from people in the other classes. The derivation of the latent class variable is based on the idea of local independence: the classes identified should be homogenous enough that the indicators are independent (i.e., uncorrelated) among members of any particular class. This approach is especially useful in deriving "empirically-based typologies such as personality categorization, psychiatric syndromes, job interest constellations, or modes of political behavior" (Gibson, 1962, p. 399). Although LPA bears similarity to another technique, factor analysis, these differ in their objectives. Factor analysis seeks to derive continuous latent variables that explain the relationships among a set of observed variables, while LPA seeks to derive one categorical latent variable representing groups of individuals (Muthen & Muthen, 2000).

This type of analysis is a model-based and probabilistic approach. Because it is model-based, a statistical model is postulated for the population from which the sample is recruited, measures of model fit are provided, and analyses can be exploratory or confirmatory (Vermunt & Magidson, 2002). Being probabilistic, LPA can take uncertainty about class membership into account even though each person is assumed to belong to one class. For each person, the probability of being in each class is computed based on model estimates and subjects' observed scores on indicators (Vermunt & Magidson). Class membership for an individual is then based on the highest probability class for that person.

In LPA, the researcher typically determines the number of subgroups that exist in a sample by performing the analyses repeatedly, each time specifying an increasing number. The solutions are compared, and the one chosen is that with the best fit to the data. The primary measure of fit used here was the Bayesian Information Criterion value (BIC), which balances two components of a model, the likelihood value and parsimony (Muthen & Muthen, 2000). Lower BIC values typically reflect better fit to the data, and reductions of 6 or greater are considered "strong" and l0 or greater "very strong" evidence of fit improvement (Raftery, 1995). In addition to BIC values, other factors for choosing the superior solution are the interpretability of the results, theoretical meaningfulness of the profiles, and the classification quality (Muthen & Muthen, 2000). The latter is reflected in the ability to distinguish membership in the latent profile groups given the model and the data, reflected in higher "average class probabilities" (ability to accurately classify individuals into their most likely subgroup).

Hypothesis testing. For Hypothesis 1, we performed LPA to identify categories of participants based on the three variables of interest: how many times sexual intercourse had occurred, how consistently condoms were used during acts of sexual intercourse, and the number of sexual partners. Five sets of analyses were performed, each for the entire sample when in a particular grade (8 through 12). The analyses were implemented using Mplus 2.02.

We tested Hypothesis 2 by comparing the subgroups identified in the LPA on gender, use of birth control (other than male condoms), and STD and pregnancy history. We expected that differences between subgroups would establish evidence for or against their validity. The Chi square statistic was used for all comparisons and, for each, Cramer's V and [V.sup.2] were calculated as effect size estimates. Cramer's V is a correlation coefficient, ranging from 0 to 1, based on the Chisquare statistic. Accordingly, Cramer's [V.sup.2], as reported in the tables, is similar to [r.sup.2] and here reflects how much of the variability in the dependent variable is explained by subgroup membership. All analyses were performed in SPSS 11.0.

Measures

The questionnaire addressed knowledge of and attitudes about a variety of health-risk and health-promoting behaviors. Beginning in 8th grade, we asked a range of sexual history questions. To enhance reliability and validity, item development was informed by and tested in focus groups with the target population, and questions were designed to have direct and clear wording with simple response categories. Specific items used in these analyses are described below.

Sexual behavior. We asked students, "Since you've been in [present] grade, have you had sexual intercourse?" Sexual intercourse was defined as "vaginal or anal intercourse between any two people. Vaginal intercourse means the penis in the vagina. Anal intercourse means the penis in the rectum or butt." Responses were "no" or "yes," coded as 0 or 1.

Number of sexual partners. Students responded to the question, "Since you've been in [present] grade, how many people have you had sexual intercourse with?" by filling in the number of people.

Number of acts of intercourse. Students responded to, "Since you've been in [present] grade, how many times have you had sexual intercourse?" by filling in the number of times.

Consistency of condom use. We asked students, "Since you've been in [present] grade, how often have you or the person you were having sex with used a condom when you have had sexual intercourse?" Response options ranged from 1 (never) to 7 (every time). In previous research (Morrison, Baker, & Gillmore, 1998), this item was found to have good predictive validity.

Birth control use. We assessed birth control use by asking students to circle "yes" or "no" for each method in an exhaustive list of birth control methods: "Birth control pill; rhythm or natural family planning (having sex at a time of the month when you think pregnancy is not likely); jelly, foam or cream spermicides; contraceptive sponge; withdrawal (pulling out); condoms; Norplant; female condom; diaphragm with spermicide; Depo-Provera (the shot); other." The item stem asked youth to "circle yes if you have used the method since you've been in [present] grade. Otherwise, circle no. Please include methods used by the person you were having sex with." To avoid appearing to endorse questionable methods, a note at the bottom of the list said, "Some of the things in the last question help to prevent pregnancy, and some don't. Please talk with your parents, teacher, or school nurse if you need more information."

For the analyses, we created three dichotomous (did not use/used, coded as 0 or 1) variables: use of rhythm or natural family planning, withdrawal, and female-controlled methods. We constructed the latter based on whether a participant or a sexual partner used at least one method over which females typically have more control: birth control pill; jelly, foam, or cream spermicides; contraceptive sponge; Norplant; female condom; diaphragm with spermicide; or Depo-Provera. Pills and Depo-Provera were the methods most often used (reported by 21% and 11% of respondents, respectively, in the 12th grade), followed by douching (5%) and jellies (4%). No other female-controlled method was used by more than 2% of the sample at any grade.

STD history. We asked, "Have you ever been told by a doctor or other health care provider that you had a sexually transmitted disease?" Responses were "no" or "yes," coded as 0 or 1.

Pregnancy history. Females only were asked, "Have you ever been pregnant?" Responses were "no" or "yes," coded as 0 or 1.

RESULTS

Preliminary Analyses

We first performed analyses using LPA for each cohort separately. Because results for the two cohorts were virtually identical, we combined them into one sample and reanalyzed the data to produce what is reported here.

Identification of Profile Groups using LPA

We selected only the participants who reported having had sex and then performed LPA for each of five grades (8 through 12). For each grade, we first specified a 1-group model. Then we performed the analysis repeatedly, each time specifying an additional group (i.e., a 2-group model, then a 3-group model, and so on). For each model, we examined BIC scores. No further models were estimated once the BIC score for a model increased from that of the previous model. In certain models, we found that we had to fix some variances to 0 to achieve convergence, always in cases in which within-group variance was close to 0 for a specific variable. The smallest BIC values occurred for the 3-group models in grades 8, 9, and 10 (BIC = 977, 1171, and 1693, respectively), and for the 4-group model in grade 11 (BIC = 2027). Average class probabilities were above .90 for these solutions. Although the BIC value was lowest for the 5-group solution in grade 12 (BIC = 1344), the quality of classification in that model was substantially worse (two average class probabilities were below .90) than in the 4-group solution (in which all average class probabilities were above .90). Therefore, for grade 12, we selected the 4-group model (BIC = 1367) for subsequent analyses.

Tables 2 and 3 show the characteristics of members of each latent profile group for each grade. The characteristics of the three profiles identified for grades 8, 9, and 10 were markedly similar across grades. One profile (which we named Condom Users) consisted of participants who tended to use condoms consistently with approximately one to two partners; this group also had the fewest occasions of sex. We considered this group the lowest risk because of its combination of consistent condom use and small number of partners. Members of the two other groups had greater risk-taking because of inconsistent condom use either with one to two partners (Few Partners) or with a larger number of partners (Risk-Takers). In addition to a greater number of partners, Risk-Takers also had the most occasions of sex. Of the four profile groups identified in grades 11 and 12, two were like the Condom Users and Risk-Takers found in grades 8 through 10. Based on an examination of the means and standard deviations, two other groups appeared to be variations of the Few Partners group found in the earlier grades. Members of both groups used condoms inconsistently, either with exactly one partner (the group labeled One Partner) or with approximately two partners (the group labeled Two Partners).

Distributions of Participants Across Groups

Table 4 shows, for each grade separately, the distribution of participants across profile groups. For descriptive and theoretical purposes, we have included participants who did not have sex in the table as a separate group (No Sex) whose status was already known. Greater proportions of participants reported having sex in each subsequent grade. Among these, the Condom Users group was the largest in the first year (grade 8). Although the proportion of participants in this group became slightly larger over the years, it eventually became second in size to groups representing inconsistent condom use with small numbers of partners (the Few Partners group in grade 10 and the One and Two Partners groups in grades 11 and 12). The Risk-Takers group increased across grade levels but remained the smallest subgroup in grades 8 through 11 and was tied for the smallest in grade 12.

Associations of Profile Group Membership

We found statistically significant differences in the gender makeup of groups in each grade; see Tables 5 and 6. Although the gender balance switched back and forth for Condom Users across grades, more consistent findings were seen in the other groups. The No Sex subgroup had a fairly equal balance of males and females. However, groups in which condoms were used inconsistently with few partners (the Few Partners group in grades 8 through 10 and the One Partners and Two Partners groups in grades 11 and 12) typically had more females. Conversely, males had greater representation in the Risk-Takers group, except in grade 10, where the gender balance was roughly equal.

Tables 7 and 8 show the proportion of participants in each group, and in each grade, who reported using birth control methods other than male condoms. For each grade, only participants who had sex were included in the analyses. Statistically significant differences were seen in each grade for at least one method. In all such cases, a greater proportion of inconsistent condom use groups reported using the birth control method. This was true for withdrawal in all grades, rhythm in grades 10 and 12 (but not 8, 9, and 11), and female-controlled methods in later grades (11 and 12), but not in earlier ones.

Profile group membership in many instances was associated with STD and pregnancy history for grades in which these questions were asked (9 through 12). For this analysis, we collapsed into one group those that were characterized by inconsistent condom use; otherwise, there would be multiple cells with expected frequencies less than 5. Table 9 shows the number of participants in the consistent condom use and inconsistent condom use groups who reported ever having an STD or becoming pregnant. For STD history, relationships were statistically significant, with effect sizes ranging from small to medium in magnitude, for all grades except 12. When statistically significant, the findings were notable: except for two persons in grade 11, all participants who reported ever having an STD were in the inconsistent condom use groups. Among females, inconsistent condom use was associated with pregnancy history reported in grades 11 and 12, with medium effect sizes, but not in grade 10. Due to low statistical power, results are not shown for grade 9.

DISCUSSION

We empirically identified sexual behavior profiles of teenagers, each of which reflected a different type or level of risk-taking, and compared the subgroups on several variables. Many of the findings discussed here illustrate the utility of the latent profile approach to risk characterization while also providing substantively meaningful information. This study provides new information by identifying risk subgroups in teens at different ages, showing the complex and at times non-linear nature of relationships among adolescent sexual risk behaviors, validating and allowing greater generalizability of previous research, illustrating the utility of the profile approach to risk measurement, and providing ideas for targeted interventions.

Three profiles were identified in each of grades 8 through 10. Of the students who reported having sex, the lowest risk group (Condom Users) used condoms consistently with a small number of partners. The other profile subgroups represented higher risk because of inconsistent condom use, either with a small number of partners (Few Partners) or with multiple partners (Risk-Takers). In grades 11 and 12, two profiles emerged (One Partner and Two Partners) seeming to represent a greater differentiation of individuals who would have been grouped together in the Few Partners profile in earlier years.

It is important to note that the inconsistent condom use subgroups typically had more instances of sex then the consistent condom use subgroups. More frequent sex would add to pregnancy risk since each act of unprotected intercourse would be an opportunity for pregnancy to occur. In terms of STDs, more frequent sex combined with condom non-use would provide greater opportunities for disease transmission, especially due to a particular partner being infected (for any of the subgroups). This risk can be compounded for subgroups with multiple partners (the Two Partner and Risk-Taker subgroups) due to the greater likelihood of having at least one of multiple partners be infected.

Validity of Profile Groupings

Evidence supported the validity of these profile groupings. First, they were conceptually sensible in that they represented profiles that are reasonable, given existing theoretical and empirical evidence. Second, the number and characteristics of the profiles were markedly similar across the years, and grade 11's increase in the number of profile subgroups was replicated in grade 12. Third, the results were markedly similar to those of Newman and Zimmerman (2000) who, as previously mentioned, found four clusters among their sample of 15- to 18-year-old African Americans. Despite differences between these studies in methodology and sample characteristics, the profiles they identified were similar to those found in this study's grades 11 and 12. This suggests that their findings are robust and generalizable beyond African Americans.

The findings concerning gender, given their consistency with previous research, also provided evidence of profile subgroup validity. For example, females typically had greater representation in the Few Partners group (grades 8 through 10) and the One Partner and Two Partner groups (grades 11 and 12). The only exception was the equivalent representation in the Two Partners group in grade 11. This is consistent with research showing reports of less condom use and fewer partners on the part of female teens (Donald et al., 1994; Durbin et al., 1993; Grunbaum et al., 2002; MacKellar et al., 2000). Similarly, the Risk-Takers group had more males than females in all but one grade, consistent with findings of greater partner numbers on the part of adolescent males (MacKellar et al.). In addition to their consistency with previous research on adolescent risk-taking, both of these findings concerning gender replicate the findings from Newman and Zimmerman's (2000) study.

Other findings are consistent with previous research and provide support for the validity of the profile groups derived in the analyses. The finding that inconsistent condom use was associated with pregnancy and STD histories is not only logical, but also similar to research on the protective effects of condom use (Morrison, 1985; Stone et al., 1986; Upchurch et al., 1990). Predictable from what is known about teens was that the groups characterized by inconsistent condom use or few partners reported higher use of birth control methods other than male condoms. Members of these groups probably had greater concern about pregnancy than disease transmission due to low perceived risk for STD acquisition (for some, because of having a steady partner). This would be consistent with previous research showing that adolescents' condom use varies as a function of whether their primary goal is STD/HIV prevention or pregnancy prevention (Crosby et al., 2001), and that the relationship between condom use and other contraceptive use is related to type of partner, length of relationship, and perception of risk (Kershaw, Niccolai, Ethier, Lewis, & Ickovics, 2003; Ott, Alder, Millstein, Tschann, & Ellen, 2002; Roye & Seals, 2001).

A question arises as to why an additional profile group was found in grades 11 and 12 as compared to earlier grades. Two explanations seem most likely. One is that the greater number of participants having sex in the later years allowed statistical differentiation into a larger number of profile groups. However, this is not as one might expect, given that the grade 12 data is based on a smaller sample. The more likely explanation is that developmental changes occurred that involved greater solidifying of romantic relationships, making stable monogamy more possible. Table 4 shows that a greater proportion of participants in each subsequent grade were in the groups with fewer partners (3%, 10%, and 17% in the Few Partners group in grades 8 through 10; 27% and 32% in the One Partner and Two Partner groups thereafter). This may reflect a transition from initial "exploration of sex for sex's sake toward more intimacy and commitment" found in qualitative interviews with adolescents (Brooks-Gunn & Paikoff, 1997) and is consistent with findings of greater closeness, intimacy, and commitment on the part of older adolescents (Adams, Laursen, & Wilder, 2001).

Implications

Identifying classes of individuals with varying risk levels allows for the creation of new research questions and hypotheses. Pursuing these can deepen the understanding of adolescent risk behavior and contribute to theory-building. For example, these analyses identified a group of Risk-Takers, young people with more sex partners, higher frequency of sex, and relatively low condom use. In some grades, this group showed considerable reliance on withdrawal as a birth control method and was disproportionately composed of males. Researchers might try to identify this group's cultural, intrapersonal, and interpersonal influences. Another salient question is whether individuals tend to remain in the same profile over time, and if not, what factors are associated with transitions from lower to higher risk, or vice versa. Such information can inform the timing and content of intervention approaches.

Through its use of a profile approach, this study also helps to resolve conflicting findings in the literature. For example, some studies have found that teens less likely to use condoms tend to have only one partner (e.g., MacKellar et al., 2000), while others have found them to have higher numbers of partners (e.g., Diclemente et al., 1992). This study suggests that both findings have validity: of the individuals who used condoms inconsistently, some had few partners (the Few Partners, One Partner, and Two Partners groups) and, though a smaller percent of the sample, some had multiple partners (the Risk-Takers group). Thus, the relationship between number of partners and condom use was not linear. Person-centered analytic techniques sometimes yield more informative findings compared to variable-centered methods, especially those based on an assumption of linear relationships.

As noted in the introduction, it is difficult to represent sexual risk-taking using single behavioral measures, because sexual activity always involves a combination of them. However, in more commonly used regression models collinearity, or correlations between risk factors used as predictors, may lead to misleading conclusions. As an example, the bivariate findings of one study (Boyer et al., 2000) differed from its regression findings. In bivariate analyses, both condom use and number of lifetime partners were related to STD history. However, only number of lifetime partners, and not condom use, was related to STD history when both were entered simultaneously in a regression analysis with demographic and psychosocial variables. In light of the present study's finding that inconsistent condom use and higher numbers of partners sometimes co-occur, it seems plausible that the role of condom non-use may have been obscured due to its correlation with other variables in the equation, when, in fact, it was an important factor.

Finally, these results, and results from similar future studies, can inform intervention development. For example, given that full abstinence among all teens is unlikely, it is useful to distinguish teens who have sex but whose risk level is low (i.e., the Condom Users group) from those whose behavior carries a higher level of risk. Particularly high-risk subgroups can then be targeted for additional or tailored interventions. In this study, Risk-Takers in some grades relied on withdrawal as a contraceptive method and might therefore benefit from emphasis on its inefficacy, in addition to encouragement to increase condom use or decrease partner numbers. Consistent condom users had lower rates of female-controlled methods, especially in older grades when the frequency of sex was increasing. Frank discussions of the greater contraceptive protection afforded by dual method use would be indicated for these teens. Similarly, those who limited partners to one or a few appeared to put greater reliance on the rhythm method. We know, however, that young users of rhythm typically cannot correctly identify their "safe periods" (Morrison, 1985), suggesting the usefulness of increased education about this technique's lack of efficacy among teens.

Study Limitations

It is important to consider the characteristics of this study when interpreting its results. It has the strength of being based on teens representative of the local public school population. Additionally, we did not detect bias due to attrition. However, several limitations must be kept in mind. First, the sample can only be generalized to the urban geographic area in which it was collected. Second, we did not gather information on, and so could not account for, whether participants' sexual partners were already infected with an STD, an important factor for judging risk, especially in steady relationships. Third, our measures of STD acquisition and pregnancy were cumulative, as opposed to solely covering the grade in which they were reported, and relied completely on self-report. Finally, while some conclusions can be drawn concerning developmental issues, a fuller examination was beyond the scope of this study.

Conclusion

The methodology used in this study is one of several approaches for conceptualizing and identifying risk. As seen here, a person-centered latent variable approach can make unique contributions to research on sexuality. Future uses of this technique might include other risk factors, such as participant and partner STD status, and other known or suspected partner risk factors. Both basic and applied research can be enriched by taking the interrelated aspects of sexual risk-taking into account.

Note. Preparation of this article was facilitated by a research grant from the National Institute of Mental Health (MH63274-01) and a grant from the National Institute on Drug Abuse (DA07047).

Manuscript accepted October 12, 2004

REFERENCES

Adams, R. E., Laursen, B., & Wilder, D. (2001). Characteristics of closeness in adolescent romantic relationships. Journal of Adolescence, 24, 353-363.

Boyer, C. B., Sharer, M. A., Wibbelsman, C. J., Seeberg, D., Teitle, E., & Lovell, N. (2000). Assocations of sociodemographic, psychosocial, and behavioral factors with sexual risk and sexually transmitted diseases in teen clinic patients. Journal of Adolescent Health, 27, 102-111.

Brooks-Gunn, J., & Paikoff, R. (1997). Sexuality and developmental transitions during adolescence. In J. Schulenberg, J. Maggs, & K. Hurrelmann (Eds.), Health risks and developmental transitions during adolescence. New York: Cambridge University Press.

Capaldi, D. M., Stoolmiller, M., Clark, S., & Owen, L. D. (2002). Heterosexual risk behaviors in at-risk young men from early adolescence to young adulthood: Prevalence, prediction and association with STD contraction. Developmental Pyschology, 38, 394-406.

Civic, D. (1999). The association between characteristics of dating relationships and condom use among heterosexual young adults. AIDS Education and Prevention, 11, 343-352.

Cleland, C. M., Rothschild, L., & Haslam, N. (2000). Detecting latent taxa: Monte carlo comparison of taxometric, mixture model, and clustering procedures. Psychological Reports, 87, 37-47.

Crosby, R. A., DiClemente, R. J., Wingood, G. M., Sionean, C., Cobb, B. K., Harrington, K., et al. (2001). Correlates of using dual methods for sexually transmitted diseases and pregnancy prevention among high-risk African American female teens. Journal of Adolescent Health, 28, 410-414.

Crosby, R. A., DiClemente, R. J., Wingood, G. M., Sionean, C., Harrington, K., Davies, S. L., et al. (2002). Pregnant African-American teens are less likely than their nonpregnant peers to use condoms. Preventive Medicine, 34, 524-528.

DeLoye, G. H., Henggeler, S. W., & Daniels, C. M. (1993). Developmental and family correlates of children's knowledge and attitudes regarding AIDS. Journal of Pediatric Psychology, 18, 209-219.

DiClemente, R. J., Durbin, M., Siegel, D., Krasnovsky, F., Lazarus, N., & Comacho, T. (1992). Determinants of condom use among junior high school students in a minority, inner-city school district. Pediatrics, 89, 197-202.

Donald, M., Lucke, J., Dunne, M., O'Toole, B., & Raphael, B. (1994). Determinants of condom use by Australian secondary school students. Journal of Adolescent Health, 15, 503-510.

Durbin, M., DiClemente, R. J., Siegel, D., Krasnovsky, F., Lazarus, N., & Camacho, T. (1993). Factors associated with multiple sex partners among junior high school students. Journal of Adolescent Health, 14, 202-207.

Gibson, W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis. Psychometrika, 24, 229-252.

Gibson, W. A. (1962). Class assignment in the latent profile model. Journal of Applied Psychology, 46, 399-400.

Grunbaum J. A., Kann, L., Kinchen, S., Williams, B., Ross J. G., Lowry R., et al. (2002 June 28). Youth Risk Behavior Surveillance--United States, 2001. In Centers for Disease Control, Morbidity and Mortality Report, 51(SS-4), 1-64.

Joffe, G. P., Foxman, B., Schmidt, A. J., Farris, K., Carter, R. J., Neumann, S., et al. (1992). Multiple partners and partner choice as risk factors for sexually transmitted disease among female college students. Sexually Transmitted Disease, 19, 272-278.

Kershaw, T. S., Niccolai, L. M., Ethier, K. A., Lewis, J. B., & Ickovics, J. R. (2003). Perceived susceptibility to pregnancy and sexually transmitted disease among pregnant and nonpregnant adolescents. Journal of Community Psychology. 4, 419-434.

Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analyses. Boston, MA: Houghton Mifflin.

MacKellar, D. A., Valleroy, L. A., Hoffmann, J. P., Glebatis, D., Lalota, M., McFarland, W., et al. (2000). Gender differences in sexual behaviors and factors associated with nonuse of condoms among homeless and runaway youths. AIDS Education and Prevention, 12, 477-491.

Metzler, C. W., Noell, J., & Biglan, A. (1992). The validation of a construct of high-risk sexual behavior in heterosexual adolescents. Journal of Adolescent Research, 7, 233-249.

Millstein, S. G., & Moscicki, A. (1995). Sexually transmitted disease in female adolescents: Effects of psychosocial factors and high risk behaviors. Journal of Adolescent Medicine, 17, 83-90.

Morrison, D. M. (1985). Adolescent contraceptive behavior: A review. Psychological Bulletin, 98, 538-568.

Morrison, D. M., Baker, S. A., & Gillmore, M. R. (1998). Condom use among high-risk heterosexual teens: a longitudinal analysis using the Theory of Reasoned Action. Psychology and Health, 13, 207-222.

Muthen, B., & Muthen, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.

Newman, P. A., & Zimmerman, M. A. (2000). Gender differences in HIV-related sexual risk behavior among urban African American youth: A multivariate approach. AIDS Education and Prevention. 12, 308-25.

Ott, M. A., Adler, N. E., Millstein, S. G., Tschann, J. M., & Ellen, J. M. (2002). The trade-off between hormonal contraceptives and condoms among adolescents. Perspectives on Sexual and Reproductive Health, 34, 6-14.

Padian, N., Hitchcock, E, Fullilove, R., Kohlstadt, V., & Brunham, R. (1990). Report on the NIAID Study Group. Part I: Issues in defining behavioral risk factors and their distribution. Sexually Transmitted Diseases, 17, 200-204.

Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111-164.

Roye, F., & Seals, B. (2001). A qualitative assessment of condom use decisions by female adolescents who use hormonal contraception. Journal of the Association of Nurses in AIDS Care, 12(6), 78-87.

Sieving, R., Resnick, M. D., Bearinger, L., Remafedi, G., Taylor, B. A., & Harmon, B. (1997). Cognitive and behavioral predictors of sexually transmitted disease risk behavior among sexually active adolescents. Archives of Pediatric and Adolescent Medicine, 151, 243-251.

Stone, K. M., Grimes, D. A., & Magder, L. S. (1986). Primary prevention of sexually transmitted diseases. Journal of the American Medical Association, 255, 1,763-1,766.

Upchurch, D. M., Brady, E. W., Reichart, C. A., & Hook, E. W. (1990). Behavioral contributions to acquisition of gonorrhea in patients attending an inner city sexually transmitted disease clinic. Journal of Infectious Diseases, 61,938-941.

Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis. New York: Cambridge University Press.

Blair Beadnell, Diane M. Morrison, Anthony Wilsdon, Elizabeth A. Wells, Elise Murowchick, Marilyn Hoppe, Mary Rogers Gillmore, and Deborah Nahom University of Washington School of Social Work

Address correspondence to Blair Beadnell, University of Washington School of Social Work, 4101 15th Avenue NE, Seattle, WA, 98105; e-mail: [e-mail].

Table 1. Demographic Characteristics of the Sample (n = 605)

Characteristic            %       n

Gender
  Female                 50%     304
  Male                   50%     301
Race
  African American       19%     115
  Asian American         21%     125
  European American      47%     283
  Other                  14%      82
Family Income (1)
  < $17,420               7%      43
  $17,420-$24,790        27%     165
  > $24,790              66%     397

(1) Based on eligibility categories for the federal free lunch program.

Note. Percents may not equal 100% due to rounding.

Table 2. Risk-Taking Profiles: Means (and Standard
Deviations) of Latent Profiles in Grades 8
Through 10

                                     Latent Profile Group (a,b)

                               Condom Users  Few Partners  Risk-Takers

8th Grade (n = 88)
  How often use condoms (c)     7.0 (0.2)      3.1 (1.5)    4.8 (2.0)
  Number of sex partners        1.4 (0.6)      1.5 (0.7)    8.4 (3.9)
  Number of times had sex       2.6 (1.5)      3.5 (1.6)    7.5 (4.5)
9th Grade (n = 145)
  How often use condoms (c)     7.0 (0.0)      3.5 (2.1)    5.4 (1.6)
  Number of sex partners        1.4 (0.6)      1.3 (0.5)    5.4 (2.8)
  Number of times had sex       4.7 (4.3)      7.3 (5.4)   10.6 (4.6)
10th Grade (n = 196)
  How often use condoms (c)     7.0 (0.0)      3.7 (2.0)    4.6 (1.9)
  Number of sex partners        1.5 (0.8)      1.2 (0.4)    5.4 (4.9)
  Number of times had sex       3.8 (2.6)     12.1 (8.4)   16.1 (7.1)

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; Few Partners: Medium number of
sexual occasions with a small number of partners, using condoms
inconsistently; Risk-Takers: Larger number of sexual occasions with
multiple partners, using condoms inconsistently.

(b) Latent profile groups were derived through Latent Profile Analysis
that included only sexually active participants.

(c) Condom use was measured on a scale of 1 (never) through
7 (every time).

Table 3. Risk-Taking Profiles: Means (and Standard Deviations) of
Latent Profiles in Grades 11 and 12

                                  Latent Profile Group (a,b)

                                  Condom Users    One Partner

11th Grade (n = 257)
  How often use condoms (c)        7.0 (0.0)       3.7 (2.1)
  Number of sex partners           1.6 (0.9)       1.0 (0.0)
  Number of times had sex          6.8 (9.9)      30.0 (28.0)
    (logged) (d)                   0.6 (0.4)       1.2 (0.5)
12th Grade (n = 164)
  How often use condoms (c)        7.0 (0.0)       3.4 (2.0)
  Number of sex partners           1.3 (0.5)       1.0 (0.0)
  Number of times had sex          7.0 (7.9)      32.2 (32.4)
    (logged) (d)                   0.6 (0.5)       1.3 (0.5)

                                  Latent Profile Group (a,b)

                                  Two Partners    Risk-Takers

11th Grade (n = 257)
  How often use condoms (c)        3.7 (1.8)       4.7 (1.9)
  Number of sex partners           2.3 (0.4)       7.4 (3.8)
  Number of times had sex         24.0 (22.1)     34.3 (25.0)
    (logged) (d)                   1.2 (0.5)       1.4 (0.4)
12th Grade (n = 164)
  How often use condoms (c)        3.2 (1.8)       4.2 (l.9)
  Number of sex partners           2.0 (0.0)       4.7 (2.2)
  Number of times had sex         49.1 (37.1)     40.3 (37.8)
    (logged) (d)                   1.5 (0.5)       1.4 (0.5)

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; One Partner: Larger number of
sexual occasions with one partner, using condoms inconsistently; Two
Partners: Larger number of sexual occasions with approximately two
partners, using condoms inconsistently; Risk-Takers: Larger number
of sexual occasions with multiple partners, using condoms
inconsistently.

(b) Latent profile groups were derived through Latent Profile Analysis
that included only sexually active participants.

(c) Condom use was measured on a scale of 1 (never) through 7 (every
time).

(d) Log transformed scores on "Numer of times had sex" were used in
analyses due to skewed distributions.

Table 4. Percentage Distribution of Participants Across
Latent Profile Groups in Each Grade

                             Latent Profile Group (a)

                 Condom          Few          Risk-
Grade            Users         Partners     Takers   No Sex (b)

8th (n = 597)      9%             3%             2%        85%
9th (n = 558)     12%            10%             4%        74%
10th (n = 560)    12%            17%             6%        65%

                            One      Two
                          Partner  Partners

11th (n = 555)    15%       18%       9%        5%        54%
12th (n = 294)    15%       23%       9%        9%        44%

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; Few Partners: Medium number of
sexual occasions with a small number of partners, using condoms
inconsistently; One Partner: Larger number of sexual occasions with
one partner, using condoms inconsistently; Two Partners: Larger
number of sexual occasions with approximately two partners, using
condoms inconsistently; Risk-Takers: Larger number of sexual occasions
with multiple partners, using condoms inconsistently; No sex: Did not
have sex in that grade.

(b) Participants who were not sexually active were not included in
Latent Profile Analysis because their status was already known.

Note. Row percentages may not add to 100 due to rounding.

Table 5. Percentage of Sexually Active Males and Females in Each
Latent Profile Group, Grades 8 Through 10

                                 Latent Profile Group (a)

                    Condom Users  Few Partners  Risk-Takers   No Sex

8th grade             (n = 56)      (n = 18)     (n = 14)    (n = 509)
  Female (n = 299)      30%           78%           29%         52%
  Male (n = 298)        70%           22%           71%         48%
9th grade             (n = 68)      (n = 54)     (n = 23)    (n = 413)
  Female (n = 286)      60%           65%           39%         49%
  Male (n = 272)        40%           35%           61%         51%
10th grade            (n = 69)      (n = 94)     (n = 33)    (n = 364)
  Female (n = 290)      54%           67%           52%         48%
  Male (n = 270)        46%           33%           49%         53%

                     [chi square] df=3   Cramer's [V.sup.2] (b)

8th grade                 17.5 ***                .03
  Female (n = 299)
  Male (n = 298)
9th grade                  8.7 *                  .02
  Female (n = 286)
  Male (n = 272)
10th grade                11.5 **                 .02
  Female (n = 290)
  Male (n = 270)

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; Few Partners: Medium number of
sexual occasions with a small number of partners, using condoms
inconsistently; Risk-Takers: Larger number of sexual occasions with
multiple partners, using condoms inconsistently; No sex: Did not have
sex in that grade.

(b) Interpretation of Cramer's [V.sup.2]: .01 = small effect size,
.06 = medium, .16 = large.

* p. < .05 ** p. < .01 *** p. < .001

Table 6. Percentage of Sexually Active Males and Females in Each
Latent Profile Group, Grades 11 and 12

                                   Latent Profile Group (a)

                      Condom     One       Two      Risk-
Gender                Users    Partner   Partners   Takers    No Sex

11th grade           (n = 84)  (n = 98)  (n = 48)  (n = 27)  (n = 298)
  Female (n = 288)     37%       75%       50%       41%        50%
  Male (n = 267)       63%       26%       50%       59%        50%
12th grade           (n = 44)  (n = 67)  (n = 26)  (n = 27)  (n = 130)
  Female (n = 149)     55%       63%       58%       30%        46%
  Male (n = 145)       46%       37%       42%       70%        54%

Gender                  [chi square] df=4   Cramer's [V.sup.2] (b)

11th grade                   29.4 ***                .05
  Female (n = 288)
  Male (n = 267)
12th grade                   10.5 *                  .04
  Female (n = 149)
  Male (n = 145)

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; One Partner: Larger number of
sexual occasions with one partner, using condoms inconsistently; Two
Partners: Larger number of sexual occasions with approximately two
partners, using condoms inconsistently: Risk-Takers: Larger number
of sexual occasions with multiple partners, using condoms
inconsistently; No sex: Did not have sex in that grade.

(b) Interpretation of Cramer's [V.sup.2]: .01 = small effect size,
.06 = medium, .16 = large.

* p. < .05 *** p. < .001

Table 7. Percentage of Sexually Active Participants in Each Latent
Profile Group Using Each Non-Condom Birth Control Method, Grades 8
Through 10

                                         Latent Profile Group (a)

                                   Condom         Few          Risk-
Birth Control Methods             Users (b)   Partners (b)   Takers (b)

8th grade                         (n = 53)      (n = 18)      (n = 14)
  Female-Controlled Methods (d)      48%          50%           43%
  Withdrawal                         19%          33%           64%
  Rhythm                             06%          22%            7%
9th grade                         (n = 68)      (n = 54)      (n = 23)
  Female-Controlled Methods (d)      47%          43%           61%
  Withdrawal                         21%          54%           39%
  Rhythm                             7%           17%           09%
10th grade                        (n = 69)      (n = 94)      (n = 33)
  Female-Controlled Methods (d)      39%          56%           53%
  Withdrawal                         12%          62%           82%
  Rhythm                             04%          16%           19%

                                                         Cramer's
Birth Control Methods             [chi square] df=2    [V.sup.2] (c)

8th grade
  Female-Controlled Methods (d)          0.2                .00
  Withdrawal                            11.5 **             .13
  Rhythm                                 4.5                .05
9th grade
  Female-Controlled Methods (d)          2.2                .01
  Withdrawal                            14.1 ***            .10
  Rhythm                                 2.8                .02
10th grade
  Female-Controlled Methods (d)          5.1                .03
  Withdrawal                            58.9 ***            .30
  Rhythm                                 6.5 *              .03

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; Few Partners: Medium number of
sexual occasions with a small number of partners, using condoms
inconsistently; Risk-Takers: Larger number of sexual occasions with
multiple partners, using condoms inconsistently.

(b) Percentages reflect the proportion of members of each latent
profile group who use that birth control method. In a particular group,
the sum of percentages for the three methods may be greater or less
than 100 because some individuals used more than one method, and some
no method at all.

(c) Interpretation of Cramer's [V.sup.2]: .01 = small effect size,
.06 = medium, .16 = large.

(d) Includes birth control pill; jelly, foam, or cream spermicides;
contraceptive sponge; Norplant; female condom; diaphragm with
spermicide; and/or Depo-Provera.

* p <. .05 ** p < .01 *** p. < .001

Table 8. Percentage of Sexually Active Participants in Each Latent
Profile Group Using Each Non-Condom Birth Control Method,
Grades 11 and 12

                                      Latent Profile Group (a)

Birth Control Methods             Condom Users (b)   One Partner (b)

11th grade                            (n = 84)          (n = 98)
  Female-Controlled Methods (d)         43%                61%
  Withdrawal                            17%                59%
  Rhythm                                08%                18%
12th grade                            (n = 44)          (n = 67)
  Female-Controlled Methods (d)         34%                61%
  Withdrawal                            16%                64%
  Rhythm                                02%                18%

                                      Latent Profile Group (a)

Birth Control Methods             Two Partners (b)   Risk-Takers (b)

11th grade                            (n = 48)          (n = 27)
  Female-Controlled Methods (d)         63%                70%
  Withdrawal                            52%                63%
  Rhythm                                15%                15%
12th grade                            (n = 26)          (n = 27)
  Female-Controlled Methods (d)         62%                67%
  Withdrawal                            65%                44%
  Rhythm                                19%                07%

                                                         Cramer's
Birth Control Methods              [chi square] df=4   [V.sup.2] (b)

11th grade
  Female-Controlled Methods (d)         10.1 *              .04
  Withdrawal                            39.8 ***            .15
  Rhythm                                 3.8                .01
12th grade
  Female-Controlled Methods (d)         10.7 *              .07
  Withdrawal                            28.5 ***            .17
  Rhythm                                 7.9 *              .05

(a) Condom Users: Fewer sexual occasions with a small number of
partners, using condoms consistently; One Partner: Larger number of
sexual occasions with one partner, using condoms inconsistently; Two
Partners: Larger number of sexual occasions with approximately two
partners, using condoms inconsistently; Risk-Takers: Larger number of
sexual occasions with multiple partners, using condoms inconsistently.

(b) Percentages reflect the proportion of members of each latent
profile group who use that birth control method. In a particular
group, the sum of percentages for the three methods may be greater
or less than 100 because some individuals used more than one method,
and some no method at all.

(c) Interpretation of Cramer's [V.sup.2]: .01 = small effect size,
.06 = medium, .16 = large.

(d) Includes birth control pill; jelly, foam, or cream spermicides;
contraceptive sponge; Norplant; female condom; diaphragm with
spermicide; and/or Depo-Provera.

* p <. .05 *** p. < .001

Table 9. Percentage of Consistent and Inconsistent Condom Users
Reporting Lifetime STD and Pregnancy

                                  STD

        Consistent   Inconsistent   [chi square]     Cramer's
Grade     Users       Users (b)         df=1       [V.sup.2] (c)

9th      (n = 66)      (n = 74)
           00%           12%           8.6 **           .06
10th     (n = 69)     (n = 126)
           00%           11%a          8.3 **           .04
11th     (n = 84)     (n = 146)
           02%           10%           4.3 *            .02
12th     (n = 44)      (n = 93)
           05%           08%           0.4             <.01

                              Pregnancy (a)

        Consistent   Inconsistent   [chi square]     Cramer's
Grade     Users       Users (b)         df=1       [V.sup.2] (c)

9th           not tabled (d)

10th     (n = 37)      (n = 79)
           16%           27%           1.5              .01
11th     (n = 31)      (n = 96)
           07%           33%           8.6 **           .07
12th     (n = 24)      (n = 57)
           08%           28%           3.8 *            .05

Note. STD and pregnancy history items were not asked of 8th graders.

(a) Pregnancy applies only to female participants.

(b) Because of multiple cells with expected frequencies less than 5,
latent profile groups characterized by inconsistent condom use were
collapsed into one group.

(c) Interpretation of Cramer's [V.sup.2]: .01 = small effect size,
.06 = medium, .16 = large.

(d) Pregnancy history is not shown for 9th graders due to low
statistical power due to a relatively small number of sexually
active females.

* p. < .05 ** p. < .01