metacognition, and risk behavior

Metacognition, Risk Behavior, and Risk Outcomes: The Role of PerceivedIntelligence and Perceived Knowledge

James Jaccard and Tonya DodgeUniversity at Albany, State University of New York

Vincent Guilamo-RamosColumbia University

The present study explores 2 key variables in social metacognition: perceived intelligence and perceivedlevels of knowledge about a specific content domain. The former represents a judgment of one’sknowledge at an abstract level, whereas the latter represents a judgment of one’s knowledge in a specificcontent domain. Data from interviews of approximately 8,411 female adolescents from a national samplewere analyzed in a 2-wave panel design with a year between assessments. Higher levels of perceivedintelligence at Wave 1 were associated with a lower probability of the occurrence of a pregnancy overthe ensuing year independent of actual IQ, self-esteem, and academic aspirations. Higher levels ofperceived knowledge about the accurate use of birth control were associated with a higher probability ofthe occurrence of a pregnancy independent of actual knowledge about accurate use, perceived intelli-gence, self-esteem, and academic aspirations.

Keywords: IQ, intelligence, risk behavior, adolescence

There is a growing body of literature in psychology on meta-cognition and the processes involved in “knowing what we know”(e.g., Metcalfe & Shimamura, 1994; Nelson, 1992). Most of thisresearch has evolved from developmental psychology and cogni-tive psychology. Developmental psychologists have examinedchildren’s perceptions of what they know, typically in the form ofcompetence and ability judgments, and how these judgmentschange as a function of age (e.g., Ruble, Boggiano, Feldman, &Loebl, 1980; Ruffman & Olson, 1989; Stipek & MacIver, 1989;

Wimmer, Hogrefe, & Perner, 1988). Cognitive psychologists havefocused on metainference with respect to lack of knowledge (Gen-tner & Collins, 1981), illusions of knowing (Epstein, Glenberg, &Bradley, 1984; Glenberg, Wilkinson, & Epstein, 1982), overcon-fidence in probability calibrations (Keren, 1991), and the bases of“do not know” responses to test questions (Glucksberg & McClos-key, 1981; Kolers & Palef, 1976). In addition, studies have exam-ined the “feeling-of-knowing” phenomenon and how this affectslearning in memory experiments (e.g., Nelson, Gerler, & Narens,1984; Schacter, 1983; Strack & Forester, 1999).

More recently, psychologists have turned their attention to meta-cognition in the social domain (Jost, Kruglanski, & Nelson, 1999;Kruger & Dunning, 1999; Nelson, Kruglanski, & Jost, 1998;Yzerbyt & Lories, 1999). The present research draws on the recentmetacognitive movement in psychology to explore two key vari-ables in metacognition as they relate to the prediction of adolescenthealth risk behavior. The first variable is perceived intelligence,which represents a judgment of one’s mental intellect and breadthof knowledge at an abstract level. The second variable is perceivedknowledge, which represents a judgment of one’s knowledge abouta specific content domain (e.g., knowledge about birth control oralcohol). These metacognitions are important because they mayimpact behavior independent of actual intelligence and actualknowledge. The present study describes competing psychologicalmodels relating these constructs to risk behavior and risk out-comes, empirically evaluates these models, and elucidates thedynamics by which perceived intelligence at the abstract level maycoalesce with perceived knowledge at the specific level to impactrisk behavior. In so doing, the research extends core constructs inmetacognition to the analysis of health risk behavior.

Perceived Intelligence and Risk Behavior

There is a large body of literature in psychology on constructsrelated to perceived intelligence. These include, for example, theconstruct of self-efficacy, which focuses on people’s judgments oftheir ability to perform certain behaviors (Bandura, 1986, 1989),

James Jaccard and Tonya Dodge, Department of Psychology, Universityat Albany, State University of New York; Vincent Guilamo-Ramos, Schoolof Social Work and Heilbrunn Department of Population and FamilyHealth, Mailman School of Public Health, Columbia University.

Tonya Dodge is now at the Department of Psychology, George Wash-ington University.

This research is based on data from the Add Health project, a programproject designed by J. Richard Udry (Principal Investigator) and PeterBearman and funded by National Institute of Child Health and HumanDevelopment Grant P01-HD31921 to the Carolina Population Center,University of North Carolina at Chapel Hill, with cooperative fundingparticipation by the National Cancer Institute; the National Institute ofAlcohol Abuse and Alcoholism; the National Institute on Deafness andOther Communication Disorders; the National Institute of Drug Abuse; theNational Institute of General Medical Sciences; the National Institute ofMental Health; the National Institute of Nursing Research; the Office ofAIDS Research, National Institutes of Health (NIH); the Office of Behav-ior and Social Science Research, NIH; the Office of the Director, NIH; theOffice of Research on Women’s Health, NIH; the Office of PopulationAffairs, Department of Health and Human Services (HHS); the NationalCenter for Health Statistics, Centers for Disease Control and Prevention,HHS; the Office of Minority Health, Centers for Disease Control andPrevention, HHS; the Office of Minority Health, Office of Public Healthand Science, HHS; the Office of the Assistant Secretary for Planning andEvaluation, HHS; and the National Science Foundation.

Correspondence concerning this article should be addressed to JamesJaccard, who is now at the Department of Psychology, Florida InternationalUniversity, University Park, Miami, FL 33199. E-mail: [email protected]

Health Psychology Copyright 2005 by the American Psychological Association2005, Vol. 24, No. 2, 161–170 0278-6133/05/$12.00 DOI: 10.1037/0278-6133.24.2.161

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and attribution theory, which examines how failure on easy ordifficult tasks influences judgments of ability, self-esteem, andfuture task performance (e.g., Weiner, 1994; Weiner, Russell, &Lerman, 1979). Despite this, the construct of perceived intelli-gence itself, namely how intelligent a person perceives himself orherself to be, has been studied with much less frequency. Per-ceived intelligence is potentially important because it represents abroad-based ability judgment that can serve as a foundation forinferences about more specific abilities (e.g., Parsons, 1983; Wood& Bandura, 1989). In addition, perceived intelligence has beenshown to be predictive of important behaviors in the achievementdomain (e.g., Bailey, 1971; Bailey & Mettetal, 1977; Lent, Brown,& Larkin, 1984).

The present research evaluates three different causal modelslinking perceived intelligence to negative risk outcomes in adoles-cents. Each model includes actual IQ in the theoretical system. Thefirst model, called the spurious effect model, views the associationbetween perceived intelligence and risk outcome as spurious innature and lacking theoretical import. According to this model,judgments of self-intelligence are based, in part, on the manyexperiences of success and failure that an individual incurs overthe course of life. To the extent that actual IQ influences theoccurrence of these success–failure experiences, a correlation be-tween perceived intelligence and actual IQ is expected. Indeed,small to moderate correlations between these constructs have beenreported in previous research (e.g., Bailey & Mettetal, 1977;Gabriel, Critelli, & Ee, 1994). Cliquet and Balcaen (1983) andMott (1983) reported inverse relations between measures of IQ andsexual risk-taking behavior. IQ scores also have been found to benegatively related to delinquency and truancy (e.g., Moffit, Gab-rielli, Mednick, & Schulsinger, 1981). If perceived intelligence ispositively correlated with one’s IQ and risk behavior is inverselycorrelated with IQ, then one might also expect an inverse relation-ship between perceived intelligence and risk behavior because ofthe common cause of IQ on both constructs. This is the essence ofthe spurious effect model, illustrated in Figure 1a.

The second model, called the independent effects model, rec-ognizes that part of the association between perceived intelligenceand risk behavior is spurious (because of the common cause of IQ),but the model also asserts that perceived intelligence has indepen-dent effects on risk behavior (see Figure 1b). There are severalmechanisms by which perceived intelligence may have indepen-dent effects on risk behavior. First, individuals who perceivethemselves as being intelligent may have higher levels of self-esteem, and this, in turn, may serve as a protective factor vis-a-visrisk behavior. For example, a number of studies have foundself-esteem to be negatively associated with sexual risk taking,alcohol use, smoking, and drug use (e.g., Jang & Thornberry,1998; Oetting, Deffenbacher, & Donnermeyer, 1998; Pederson,Koval, McGrady, & Tyas, 1998). Second, those who believe theyare of higher intelligence may have higher academic aspirations,and such aspirations may decrease the likelihood of risk behavior.It is well known that adolescents with high academic aspirationsand who do well in school are less prone to engage in a widevariety of risk behaviors, such as smoking, drinking, unprotectedsex, and drug use (e.g., Griffin, Botvin, Doyle, Diaz, & Epstein,1999; Jessor, Costa, Jessor, & Donovan, 1983; Kasen, Cohen, &Brook, 1997; Mott, 1983; Raine, Jenkins, Aarons, Woodward, &Fairfax, 1999). It seems logical that those who believe themselves

to be more intelligent than others also may have more loftyacademic aspirations, thereby lowering the probability of engagingin risk behaviors that threaten those aspirations. The present re-search tests this independent effects model and evaluates thepossible mediating roles of self-esteem and academic aspirations.

The third model, called the mediation model, assumes thatperceived intelligence is the primary determinant of risk behavior(with those who perceive themselves as intelligent being less likelyto engage in risk behavior) and that this construct, in turn, mediatesthe impact of IQ on risk behavior. IQ influences risk behavior, butit does so only because of its influence on perceived IQ.

The three models of Figure 1 can be differentiated empiricallyon the basis of competing predictions about partial coefficients inthe context of regression analysis or structural equation modeling.One purpose of the present research was to test these models.

Perceived Knowledge and Risk Behavior

Perceived intelligence represents individuals’ characterizationsof their mental abilities and knowledge at a general level. Peoplealso have perceptions of how knowledgeable they are about spe-cific content domains, and these judgments may, in turn, impacttheir risk behavior in that domain independent of their actualknowledge. A common finding in studies of confidence and testperformance is a weak association between confidence in perfor-mance and actual performance, with individuals showing a ten-dency to be overconfident in their ability to provide correct an-swers (e.g., Lichenstein & Fischoff, 1977; Quadrel, Fischoff, &Davis, 1993; Radecki & Jaccard, 1995). Kruger and Dunning(1999) found that individuals with lower levels of actual knowl-edge tended to overestimate their ability levels more than did thoseindividuals with higher levels of actual knowledge. In addition,individuals with lower levels of actual knowledge were limited intheir ability to recognize competence in others. These studies, aswell as others, suggest that perceptions of how knowledgeable oneis about a specific content domain may have only weak correspon-dence with one’s actual knowledge in that domain.

Few theorists have explored the relationship between perceivedknowledge and risk behavior or risk outcomes. In one of the fewrelevant studies we could locate, Quadrel et al. (1993) identifiedlow-risk and high-risk adolescents and found that at-risk adoles-cents had higher levels of confidence and lower levels of knowl-edge than did low-risk adolescents on questions about HIV, preg-nancy, alcohol use, and drug use. Unfortunately, this study waslimited because the knowledge items used for the two groups weredifferent, thereby obscuring the results. The present study exam-ined the issue more directly.

There are many types of knowledge relevant to risk behavior,but one type that is particularly important is knowledge that helpsone avoid the negative consequences that can result from riskbehavior. For example, knowledge about the accurate use of birthcontrol methods represents knowledge that helps one prevent theoccurrence of an unintended pregnancy. Knowledge about whatconstitutes light, moderate, or heavy alcohol consumption isknowledge that may prevent one from ultimately developing adrinking problem, as one monitors one’s drinking behavior andmaintains an acceptably light consumption regimen. The conse-quences of misperceptions of this type of knowledge, we hypoth-esize, should depend on one’s actual knowledge. When actualknowledge is low, higher levels of perceived knowledge tend to

162 JACCARD, DODGE, AND GUILAMO-RAMOS

represent increasingly erroneous overestimations about one’sknowledge base. In this case, individuals think they know how toprotect themselves against the adverse consequences of a riskbehavior when, in fact, they do not. Such individuals should be ata higher risk of experiencing negative outcomes as a result of thatrisk behavior, everything else being equal. By contrast, whenactual knowledge levels are high, variations in perceived knowl-edge tend to reflect differing tendencies to underestimate one’sknowledge base. Such underestimations probably will be inconse-quential in terms of the individual’s ability to effectively avoid theadverse consequences of a risk behavior because the individual hasthe knowledge base to do so, independent of his or her perceptions.Another purpose of the present study was to test thisconceptualization.

An alternative model relating perceived knowledge to risk be-

havior is plausible. This model holds that higher levels of per-ceived knowledge lead to a higher probability of suffering riskconsequences independent of actual knowledge. According to thisformulation, individuals who believe they are knowledgeableabout how to avoid the adverse consequences of a risk behavior aremore likely to engage in risk behaviors because they believe thatthey have the requisite knowledge to avoid the adverse conse-quences of that behavior. In reality, these individuals are no morelikely than others to have such knowledge (because of the rela-tively low correlation between perceived knowledge and actualknowledge), and as a result, the individuals with higher levels ofperceived knowledge will be more likely to experience the adverseeffects of the risk behavior by virtue of the fact that they areengaging in it more often than others. The present study alsoevaluated this model.

Figure 1. Three models of the relationship among perceived intelligence, intelligence, and risk behavior. a:Spurious effect model. b: Independent effects model. c: Mediation model.

163METACOGNITION, RISK BEHAVIOR, AND RISK OUTCOMES

The Relationship Between Perceived Intelligence andPerceived Knowledge

We could locate no study that has examined the relationshipbetween perceived intelligence and perceived knowledge in agiven risk domain. Nor could we locate research that has examinedthe joint influence of the two constructs on risk outcomes. Onepossible prediction about the relationship of perceived intelligenceto perceived knowledge in a specific domain is that the twoconstructs should be positively correlated. For example, judgmentsof higher levels of perceived intelligence may produce a “haloeffect” (Anderson, 1981) that disposes people to overestimate theirknowledge relative to their actual knowledge in specific contentdomains. If individuals think that they are knowledgeable andsmart in general, then they may tend to think of themselves asknowledgeable in specific content domains. An alternative possi-bility is that individuals with higher levels of perceived intelli-gence may be less defensive about their lack of knowledge in aspecific content domain and, hence, be more willing to admit theirignorance. This, in turn, would lead them to report, and perhapsactually believe, that they have lower levels of perceived knowl-edge as they show a greater appreciation for the complexity of theworld. Individuals with low levels of perceived intelligence, bycontrast, may be more defensive about admitting their lack ofknowledge and therefore report and actually believe that they havehigher levels of perceived knowledge in a given content domain.The defensiveness model makes predictions opposite to the halo-effect model, with the former predicting an inverse relationshipbetween perceived intelligence and perceived knowledge and thelatter predicting a direct relationship between perceived intelli-gence and perceived knowledge. Another purpose of the presentstudy was to test these competing predictions.

In sum, there has been a great deal of attention in psychology tometacognition and processes involved in metacognition. Most ofthis research has been conducted in cognitive and developmentalpsychology, although the constructs have started to emerge inhealth psychology. Perceived knowledge about a given contentdomain represents metacognition at a concrete, specific level,whereas perceived intelligence represents metacognition at a moreabstract, general level. Both levels of metacognition may be re-lated to risk behavior. The present study tests several models of therelationship between these variables and risk outcomes. Specifi-cally, the theoretical mechanisms were tested in the context of alongitudinal study by using a nationally representative sample offemale adolescents to predict the occurrence of a pregnancy. Theknowledge constructs focused on actual and perceived knowledgeabout how to appropriately use contraception to avoid a pregnancy.

Method

Overview

The study used a two-wave longitudinal design to predict the occurrenceof a pregnancy between the two waves of assessment. Measures of per-ceived intelligence, IQ, perceived knowledge about the accurate use ofbirth control, and actual knowledge about the accurate use of birth controlas measured at Wave 1 of the survey were the primary predictor variables.

Respondents

The analysis used the National Longitudinal Study of Adolescent Health(Add Health) database collected by Harris et al. (2003; Udry, 1997). This

is a school-based sample of 20,745 adolescents in Grades 7 through 12.The sampling frame selected a random sample of 80 high schools. For eachschool, a set of feeder schools was identified that included seventh andeighth graders that sent their graduates to the high school. This resulted ina pair of schools in each of 80 communities. Because some high schoolsspanned Grades 7 to 12, they functioned as their own feeder school and thepair was a single school. Adolescents in Grades 7 through 11 wereinterviewed twice, with a 1-year interval between waves. Loss to follow-upfor unplanned reasons (e.g., refusals to be interviewed again, failure tolocate the respondent) was minimal (10%), with no significant biases.Analyses were restricted to never married female adolescents in Grades 7through 11 as reported at Wave 1 for a final sample size of 8,411. Adetailed description of the study is available at www.cpc.unc.edu/addhealth.

Procedure

Interviews were administered in the respondents’ homes. All data wererecorded on laptop computers. For less sensitive sections, the interviewerread the questions and entered the respondent’s answers. For more sensi-tive sections, respondents listened to questions through earphones andentered the answers directly on the computer. The topics covered in theinterviews were diverse, including health status, health utilization, nutri-tion, educational aspirations, substance use, and criminal activities.

Measures

Perceived intelligence. Perceived intelligence was measured by askingindividuals to rate themselves on a scale in response to the followingquestion: “Compared with other people your age, how intelligent are you?”The measure was scored on a 6-point rating scale, with higher scoresindicating higher levels of perceived intelligence: 1 � moderately belowaverage, 2 � slightly below average, 3 � about average, 4 � slightlyabove average, 5 � moderately above average, and 6 � extremely aboveaverage. A category called extremely below average was not includedbecause pilot research revealed that no one would mark this category.

Actual knowledge. Actual knowledge was measured in three domains:knowledge about how to use condoms appropriately, knowledge about theaccurate use of the withdrawal method, and knowledge about the timing ofovulation and when it is “safe” to have sex relative to ovulation. The mostcommon methods of birth control used by adolescents are condoms andbirth control pills, with many adolescents also relying on informal judg-ments of ovulation timing. We selected the above three areas because wefelt that if an adolescent lacked knowledge about accurate use in these threedomains, the chances of an unintended pregnancy would be heightened. Anine-item true–false knowledge test was administered. The items were asfollows: “When a woman has sexual intercourse, almost all sperm dieinside her body after about 6 hr”; “when using a condom, the man shouldpull out of the woman right after he has ejaculated”; “natural skin (lambskin) condoms provide better protection against the AIDS virus than latexcondoms”; “when putting on a condom, it is important to have it fit tightly,leaving no space at the tip”; “Vaseline can be used with condoms and theywill work just as well”; “the most likely time for a woman to get pregnantis right before her period starts”; “even if a man pulls out before heejaculates (even if ejaculation occurs outside of the woman’s body), it isstill possible for the woman to become pregnant”; “as long as the condomfits over the tip of the penis, it does not matter how far down it is unrolled”;and “in general, a woman is most likely to get pregnant if she has sexduring her period, as compared to other times of the month.” The meannumber of correct responses was the final score.

Perceived knowledge. Perceived knowledge was assessed with thefollowing three items: “I am quite knowledgeable about the withdrawalmethod of birth control”; “I am quite knowledgeable about the rhythmmethod of birth control and when it is a ‘safe’ time during the month fora woman to have sex and not get pregnant”; and “I am quite knowledgeableabout how to use a condom correctly.” Individuals responded on 5-point


agree–disagree scales (1 � strongly disagree, 2 � disagree, 3 � neither,4 � agree, 5 � strongly agree), where each of the items was separated byfiller items. Responses to the three items were averaged (� � .78).1

Self-esteem. Add Health used a short-form version of the RosenbergSelf-Esteem Scale, which consisted of the following four items: I have a lotof good qualities; I have a lot to be proud of; I like myself just the way Iam; I feel like I am doing everything just about right (Rosenberg, 1965).Participants responded to each item on a 5-point agree–disagree scale (1 �strongly disagree, 2 � disagree, 3 � neither, 4 � agree, 5 � stronglyagree). The four items had an average intercorrelation of .50 and yieldedan alpha coefficient of .80. The measure has concurrent validity in that itwas predictive of pregnancy outcomes as well as binge drinking tendenciesin the Add Health data sets. Pilot research revealed that the 4-item versioncorrelated .91 with the full 10-item Rosenberg scale (which includes the 4items). A self-esteem score was based on the mean of the items such thathigher values indicated higher levels of self-esteem.

Academic aspirations. The academic aspirations of individuals wereassessed with the following two questions: “On a 1 to 5 scale where 1 islow and 5 is high, how much do you want to go to college?” and “On a 1to 5 scale where 1 is low and 5 is high, how likely is it that you will go tocollege?” Responses to the two items were averaged (� � .82).

IQ. IQ was measured with an abridged version of the Peabody PictureVocabulary Test—Revised (PPVT–R; Dunn & Dunn, 1981). The AddHealth version was computerized and involved the interviewer readingaloud a word with the respondent selecting from four illustrations the onethat best reflected the meaning of the word. The scale was half the lengthof the original Peabody Picture Vocabulary Test (PPVT), focusing onevery other item. The correlation between PPVT scores generated from astandard administration of the original measure and the scores based on theAdd Health version is .96 (Halpern, Joyner, Udry, & Suchindran, 2000).Scores on the scale were normed by age by using the traditional IQ metricof a mean of 100 and a standard deviation of 15.

Conceptual definitions of intelligence are controversial. The PPVT hasa median correlation of .62 with the Stanford–Binet Intelligence Scale anda median correlation of .64 with the Wechsler Intelligence Scale forChildren. Our primary motivation in measuring IQ was to allow us toevaluate the possibility that any correlation of perceived intelligence withrisk outcome is spurious because of the common influence of constructsreflected by more general measures of IQ that have been shown to bepredictive of adolescent risk behavior. The PPVT has been shown to bepredictive of adolescent risk behavior in a number of domains and, hence,fits the needs of the present research. The term IQ as used in this study,however, is restricted to reflect the skills and abilities measured by thePPVT.

Pregnancy outcome and sexual behavior. Three dichotomous behav-ioral outcomes were measured at the second interview (approximately 1year following the first interview): whether the respondent had engaged insexual intercourse since the time of the first interview, whether the respon-dent used pregnancy protection at her most recent intercourse, and whetherthe respondent had become pregnant since the time of the first interview.The sexual intercourse measure was derived from responses to the follow-ing question asked at both the first and second interviews: “Have you everhad sexual intercourse? When we say sexual intercourse, we mean when amale inserts his penis into a female’s vagina.” If the respondent reportedthat he or she had never engaged in sexual intercourse at Wave 1 but hadengaged in sexual intercourse at Wave 2, then the respondent was scoredas having engaged in sexual intercourse since Wave 1. In addition, datesprovided in response to the question “In what month and year did you havesexual intercourse most recently?” at Wave 2 were used to determine ifsexual intercourse had occurred since the last interview for adolescentswho were already sexually active as of Wave 1. Use of birth control wasassessed by asking individuals who had reported having sexual intercourseto think of the last time they had done so and to indicate if they had useda method to prevent a pregnancy and if so, to indicate what it was (from alist of provided methods). Occurrence of a pregnancy between the first and

second interviews was derived from responses to the following itemmeasuring frequency of pregnancy: “How many times have you beenpregnant?” Responses were obtained to this item at both the first andsecond interviews, and if the number of pregnancies increased, a pregnancywas assumed to have occurred. Respondents also reported the date of theirmost recent pregnancy, and this information was used to determine if apregnancy had occurred since Wave 1. If the reported date of a pregnancyoccurred between the time the adolescent was initially interviewed at Wave1 and the time that she was interviewed at Wave 2, the adolescent also wasclassified as having become pregnant between waves. Disparities betweenthe two forms of assessment were rare, and if either report suggested apregnancy, we classified the adolescent as having experienced apregnancy.

The primary outcome in our analyses was the occurrence of a pregnancybetween waves. We report subsidiary analyses with respect to the other twoindices of sexual risk taking.

Analytic Strategy

Although many of the conceptual issues were framed in a form thatsuggests the use of traditional structural equation modeling, this approachwas not used because of the presence of a dichotomous endogenousvariable (the occurrence of a pregnancy). Traditional structural equationmodeling techniques cannot be applied in such cases. Instead, we usedlimited information estimation strategies in the context of logistic regres-sion (Bollen, 1996). The limited information estimation strategy relied ondirected logistic regressions that explicitly tested the predictions of themodel(s) for the causal paths in question.

Add Health used a stratified cluster sampling design in which schoolswere sampled from the Quality of Education database. Sampling weightswere derived for both waves of the design by the project statisticians(Tourangeau & Shin, 1999). These weights were used to calculate param-eter estimates and standard errors in the statistical models. The communityfrom which the school was sampled served as the primary sampling unit.Strata were defined in accord with the clustered sample design with aminimum of two primary sampling units per stratum. Standard errors wereestimated by using the jack-knifing methods in the WesVar statisticalpackage (WesVar, 1998). The use of weighted versus unweighted data iscontroversial among statisticians, with advocates in both camps (Lohr &Liu, 1994; Winship & Radbill, 1994). Our analytic approach was toanalyze the data by using both weighted and unweighted analyses and tofocus only on effects that were robust across both forms of analysis. All ofthe results were robust across the different forms of analysis. We report theunweighted results.

The sample size in the analyses described below was approximately8,411. Sample sizes varied slightly across analyses, depending on missingdata. Outlier analyses were performed for all analyses, as were checks formodel misspecification. Any analyses that included actual knowledgeabout the accurate use of birth control typically had sample sizes ofapproximately 5,339 because these questions were asked only of adoles-cents who were at least 15 years of age.

1 It is possible that the order of assessment of knowledge and perceivedknowledge affects the measures of perceived knowledge. For example, ifindividuals take a knowledge test prior to the assessment of perceivedknowledge, then this may alter their judgment about their knowledgelevels, depending on the difficulty of the test. Radecki and Jaccard (1995)explored this possibility for a task involving birth control knowledge thatwas similar to the present one and observed no such order effects. Alsoimportant is whether there are floor or ceiling effects for either the actualknowledge test or the perceived knowledge measures, as such base rateproblems may attenuate correlations between the constructs. We examinedthe means and standard deviations of all measures for this possibility andfound no evidence for this.


Results

Perceived Intelligence

Table 1 presents the frequency distribution of responses to theperceived intelligence item. The distribution is skewed, with mostadolescents believing they are above average in intelligence. Thecorrelation between perceived intelligence and actual intelligencewas .28 (95% confidence interval [CI] � 0.26–0.30, p � .05),suggesting a moderate correlation between the two constructs. Themean intelligence scores for respondents at each point of theperceived intelligence measure also appear in Table 1. In general,there is a monotonic relationship between the perceived intelli-gence ratings and the mean intelligence ratings, except at thehighest point on the scale, where there is a small decrease in meanintelligence.

The bivariate relationship between the occurrence of a preg-nancy and perceived intelligence was isolated by using logisticregression in which the dichotomous outcome variable (reported apregnancy vs. did not report a pregnancy) was regressed onto theperceived intelligence measure. The exponent of the logistic co-efficient for perceived intelligence was 0.81 (95% CI � 0.73–0.90, p � .05), indicating that for every one unit that perceivedintelligence increased, the predicted odds of a pregnancy decreasedby a factor of 0.81. For example, when perceived intelligence wasat its lowest value, the predicted odds of a pregnancy was 0.08(corresponding to a probability of .07), whereas when it was at itshighest value, the predicted odds of a pregnancy was only 0.03(corresponding to a probability of .029). The relationship betweenperceived intelligence and pregnancy occurrence was evaluatedwith grade, ethnicity, maternal education, and family income in-cluded as covariates. In all analyses reported hereafter, thesecovariates are included in the logistic analyses unless otherwisenoted. Results tended to be the same whether or not the covariateswere included in the model.

To address whether perceived intelligence was related to theoccurrence of a pregnancy independent of IQ, we performed alogistic regression analysis to predict pregnancy outcome from theperceived intelligence scores and the PPVT scores. Model diag-nostics suggested a curvilinear effect for PPVT in the form of aninverted U shape, and the addition of a squared PPVT score to theinitial equation yielded a statistically significant logistic coeffi-cient. In general, the predicted odds of a pregnancy tended to belower at the low and high ends of the PPVT and tended to peak ata PPVT score of approximately 90. For this reason, all analysesthat include IQ as a covariate used both the PPVT scores and thesquared PPVT scores as predictors. For the equation regressing

pregnancy outcome onto perceived intelligence, intelligence, andthe demographic covariates, the logistic coefficient for perceivedintelligence was statistically significant, with the exponent of thecoefficient equaling 0.84 (95% CI � 0.75–0.95, p � .05). Thissuggests that perceived intelligence is related to the occurrence ofa pregnancy independent of its association with actual intelligence(as measured by the PPVT), with higher levels of perceivedintelligence leading to lower odds of a pregnancy. The IQ scorealso was a statistically significant predictor of pregnancy occur-rence independent of perceived intelligence. These data are incon-sistent with the spurious effect model and the mediator model inFigure 1 and are consistent with the independent effect model.

It is possible that perceived intelligence merely reflects thedynamics of self-esteem (with higher levels of perceived intelli-gence being associated with higher levels of self-esteem). Toevaluate the potential mediating role of self-esteem in the inde-pendent effects model, we included the measure of self-esteem inthe logistic regression that used perceived IQ, actual intelligence,and the covariates as predictors. If the influence of perceivedintelligence derives from its association with self-esteem, then thelogistic coefficient for perceived intelligence should change tononsignificance or be trivial in value. The exponent of the coeffi-cient for perceived IQ in this equation was 0.86 (95% CI �0.76–0.97, p � .05), and it maintained its statistical significance.This suggests that the bases of the predictive power of perceivedintelligence are not necessarily captured by self-esteem. Parenthet-ically, self-esteem also made an independent contribution to pre-dicting the log odds of a pregnancy, yielding a coefficient with anexponent of 0.83 (95% CI � 0.69–0.99, p � .05). Higher levelsof self-esteem were associated with lower levels of pregnancy.

A second analysis evaluated whether the effects of perceivedintelligence could be attributed to its relationship to academicaspirations. The most likely model is one involving mediation suchthat those who believe themselves to be more intelligent set higheracademic aspirations as a result of their supposed higher intellect.These higher aspirations, in turn, impact the occurrence of apregnancy. When academic aspirations were substituted for self-esteem in the logistic analyses, the exponent of the coefficient forperceived intelligence was 0.87 (95% CI � 0.78–0.98, p � .05).Perceived intelligence maintained its independent effect on theoccurrence of a pregnancy, suggesting that academic aspirationswere not the source of its effects. Parenthetically, academic aspi-rations were predictive of pregnancy occurrence independent ofperceived intelligence and intelligence (exponent of logistic coef-ficient � 0.78, 95% CI � 0.70–0.87, p � .05), such that studentswith higher academic aspirations had a lower probability of apregnancy.

Taken together, these analyses suggest that the effects of per-ceived intelligence are not simply attributable to confounds withself-esteem or academic aspirations. The data are consistent witheffects of perceived intelligence independent of these mechanisms.

Perceived Knowledge

The correlation between perceived and actual knowledge was.10 (95% CI � 0.07–0.13, p � .05). These data suggest a statis-tically significant but weak association between perceived knowl-edge and actual knowledge and are consistent with the results ofRadecki and Jaccard (1995). To test if perceived knowledge wasassociated with the occurrence of a pregnancy independent of

Table 1Perceived Intelligence Frequency Distribution and MeanIntelligence Scores

Category Frequency % Mean PPVT–R

Moderately below average 100 1.2 83.7Slightly below average 376 4.5 91.7About average 3,423 40.7 95.0Slightly above average 1,773 21.2 101.8Moderately above average 2,160 25.7 104.6Extremely above average 551 6.6 100.4

Note. PPVT–R � Peabody Picture Vocabulary Test—Revised.


actual knowledge, we performed a logistic regression that re-gressed the occurrence of a pregnancy onto the covariates, per-ceived knowledge, and actual knowledge. The exponent of thelogistic coefficient was 1.53 (95% CI � 1.28–1.81, p � .05),suggesting that higher levels of perceived knowledge were asso-ciated with a higher probability of a pregnancy during the ensuingyear. For every one unit that perceived knowledge changed (on thefive-category scale on which it was measured), the predicted oddsof a pregnancy increased by a multiplicative factor of 1.53, holdingactual knowledge constant. When perceived knowledge was at itshighest level, the predicted odds of a pregnancy was over sevenand a half times higher than when perceived knowledge was at itslowest value. The exponent of the coefficient for actual knowledgewas 2.70 (95% CI � 1.38–5.31, p � .05), indicating that higherlevels of actual knowledge were associated with higher probabil-ities of a pregnancy. This latter result was unexpected, as we hadhypothesized that those with higher levels of knowledge about theaccurate use of birth control would be less likely to experience apregnancy.

To test if the impact of perceived knowledge on pregnancyoccurrence was moderated by actual knowledge, we reestimatedthe logistic regression equation but included a product term be-tween perceived knowledge and actual knowledge. The productterm was not statistically significant and was trivial in magnitude(exponent of logistic coefficient � 1.78, 95% CI � 0.83–3.84).

The results of these analyses are inconsistent with the model thatholds that the impact of perceived knowledge is moderated by theeffects of actual knowledge. Instead, higher levels of perceivedknowledge are associated with a higher probability of pregnancyirrespective of the levels of actual knowledge. The model thatpredicted this outcome posited that the effect was due to the factthat those who thought they were more knowledgeable about theaccurate use of birth control would be more likely to engage in sex.To test this possibility, we performed a logistic regression analysisthat regressed whether the individual had engaged in sex betweenWave 1 and Wave 2 onto the covariates, perceived knowledge (asmeasured at Wave 1), and actual knowledge (as measured at Wave1). The exponent of the logistic coefficient for perceived knowl-edge was 1.66 (95% CI � 1.54–1.80, p � .05), indicating thatthose with higher levels of perceived knowledge about the accurateuse of birth control had a higher probability of engaging in sexualintercourse. The exponent of the logistic coefficient for actualknowledge was 4.03 (95% CI � 2.93–5.54, p � .05), indicatingthat more knowledge about the accurate use of birth control atWave 1 was associated with a higher probability of sexual inter-course during the ensuing year.

Although the above results are consistent with the propositionthat higher levels of perceived knowledge about birth controldispose the individual toward engaging in sexual intercourse, it ispossible that the causal influence is in the reverse direction, withindividuals who engage in more sexual activity becoming moreconfident of their knowledge about the accurate use of birthcontrol by virtue of their greater experience with sex. The per-ceived knowledge measure was obtained at Wave 1, whereas thesexual behavior measure was obtained 1 year later, thereby ques-tioning this interpretation. In addition, we tested if the effects ofperceived knowledge on sexual activity persisted if virgin status atWave 1 was introduced as a covariate into the equation (therebycontrolling for initial levels of sexual activity), and this was indeedthe case. Finally, perceived knowledge at Wave 1 was predictive

of sexual activity between the two waves even when perceivedknowledge at Wave 2 was included as a covariate in the equation(exponent of the logistic coefficient for Wave 1 perceived knowl-edge � 1.26, 95% CI � 1.15–1.37, p � .05). Taken together, theseresults question (but do not rule out definitively) the interpretationthat the causal direction is strictly from sexual activity to perceivedknowledge.

Another possible source of spuriousness is that adolescentsacquire information about birth control when they are about tobecome sexually active and that the information acquisition activ-ities that they engage in during this time increase their perceivedknowledge about birth control. Higher levels of perceived knowl-edge do not bias the individual to engage in sex. Rather, the higherlevels of perceived knowledge reflect the fact that the individual ispreparing for sex and has participated in some information-acquisition activities. One way to address this issue is to includecovariates in the analysis that serve as indicators of whether anindividual at Wave 1 is preparing for sex. If the effects of per-ceived knowledge on sexual activity are merely the result ofpreparing for sex, then the logistic coefficient for perceived knowl-edge should vanish when these covariates are included. We addedthree such indicators to the logistic regression (in addition to thestandard covariates, perceived knowledge, and actual knowledge):(a) whether the adolescent was currently involved in a romanticrelationship (on the assumption that those who are involved in arelationship are more likely to be preparing for sex than those whoare not), (b) an index of physical development (on the assumptionthat those who are more physically mature are more likely to bepreparing for sex than those who are less physically mature), and(c) whether the adolescent was a virgin at Wave 1 (on the assump-tion that nonvirgins would be more likely to be preparing for futuresex). The logistic coefficient for perceived knowledge remainedstatistically significant in this analysis (exponent of logistic coef-ficient � 1.22, 95% CI � 1.12–1.34, p � .05).

In sum, the analyses suggest that those with higher levels ofperceived knowledge about the accurate use of birth control are atgreater risk for a pregnancy independent of their actual knowledgeabout the accurate use of birth control and that the source of thismay be their tendency to greater exposure to sexual situations.

Perceived Intelligence and Perceived Knowledge

To explore the relationship between perceived intelligence andperceived knowledge, we calculated the correlation between thetwo constructs. The correlation was �.05 (95% CI � �0.07 to�0.03, ns). This correlation suggests a trivial relationship betweenthe constructs and is counter to both the halo-effects and defen-siveness models. It is not surprising (in light of these correlations)that perceived knowledge was predictive of the occurrence of apregnancy even when perceived intelligence was included as acovariate in the logistic regression equation (exponent of logisticcoefficient � 1.52, 95% CI � 1.28–1.81, p � .05), and this wasalso true when perceived intelligence, actual intelligence, self-esteem, academic aspirations, academic performance, and actualknowledge were included as covariates (exponent of logistic co-efficient � 1.44, 95% CI � 1.20–1.73, p � .05).

Mediators of the Occurrence of a Pregnancy

The occurrence of a pregnancy between waves is influenced, inpart, by two behavioral mediators: the amount of sexual activity


that an adolescent engages in and the extent to which pregnancyprotection is used during sexual intercourse. The Add Health datahad limited measures of these constructs, such that we could onlydefine (a) whether an individual had engaged in sex betweenwaves and (b) whether the individual had used some form ofpregnancy protection at her most recent intercourse. The constructof perceived intelligence was related to both of these outcomes, asreflected in two separate logistic regressions. In general, higherperceived intelligence was associated with lower levels of sexualactivity (exponent of coefficient � 0.88, 95% CI � 0.84–0.92,p � .05) and higher levels of birth control use (exponent ofcoefficient � 1.16, 95% CI � 1.07–1.26, p � .05). Perceivedknowledge was only statistically significantly related to sexualactivity, such that higher perceived knowledge was associated withhigher levels of sexual activity (exponent of coefficient � 1.68,95% CI � 1.59–1.79, p � .05). These results were robust whendemographic covariates were included in the model as well aswhen perceived intelligence, perceived knowledge, actual knowl-edge, and actual IQ all were included in the equations.

Discussion

Perceptions of one’s knowledge as well as more abstract per-ceptions of one’s intelligence represent central constructs in meta-cognitive theory. The present study was one of the first empiricaldemonstrations of a link between these constructs and risk behav-ior and risk outcomes. The results suggest that both constructs areindependently tied to risk behavior and risk outcomes and exerteffects on such behavior over and above actual IQ and actualknowledge.

With respect to perceived intelligence, we found that individualstended to perceive themselves as above average in intellect, at leastas reflected by labels on our rating scales. Perceived intelligenceserved a protective function such that higher levels of perceivedintelligence were associated with lower probabilities of adverserisk outcomes. The effects of perceived intelligence could not beaccounted for by IQ, as measured by the PPVT–R, nor could theeffects be accounted for by self-esteem or academic aspirations.Perceived intelligence seems to contribute explanatory power overand above these more traditional constructs.

Whereas higher levels of perceived intellect were protectivewhen considered at the abstract level, just the opposite was foundfor perceived knowledge at the specific level. As perceived knowl-edge about strategies for avoiding the negative consequences of arisk behavior increased (holding actual knowledge constant), thelikelihood of experiencing those consequences also increased.Three different models relating perceived knowledge to risk out-comes were evaluated. The most viable model seemed to be onethat argued that higher levels of knowledge about strategies toavoid negative outcomes result in increased performance of riskbehaviors, perhaps because the threat of the negative consequenceshas been lessened. This increased exposure to risk, in turn, raisesthe probability of experiencing an adverse outcome.

The overall picture that emerges is one of diametrically opposedprocesses operating for metacognition and risk behavior that de-pend on the level of abstractness of the metacognition. Theseprocesses are independent of one another, as reflected by the weakand inconsistent correlation between perceived intelligence on theone hand and perceived knowledge about the strategies for avoid-ing risk behavior on the other, as well as by the fact that perceived

intelligence failed to mediate any of the effects of perceivedknowledge on risk outcomes and vice versa. At the abstract level,metacognitive constructs such as perceived intelligence may im-pact broad-based lifestyles (e.g., working hard in school) that serveto push the individual toward constructive activities and awayfrom counterproductive risk behaviors. At the specific level, per-ceptions that one has the requisite knowledge base to avoid neg-ative consequences of risk behavior may encourage the individualto engage in such behaviors, thereby raising the risk to the indi-vidual. Future research needs to further explore this potentiallyinteresting dynamic of opposing risk and protective functions.

The results of this study have applied implications. For example,an emerging strategy for the prevention of risk behavior in ado-lescents is one based on increasing communication between par-ents and children about problem behaviors (Jaccard, Dittus, &Litardo, 1999). Studies of reasons parents provide for not engagingin conversations with their adolescents about a given risk behaviorsuggest that a common reason is that their adolescents profess toalready know what they need to know about the behavior (e.g.,drugs, sex, alcohol; see Jaccard & Dittus, 1991). The present datasuggest that rather than be deterred by such statements, parentsshould be all that more concerned, because adolescents who claimhigher levels of perceived knowledge actually may be at greaterrisk. Another potential ramification focuses on the presentation ofeducational information about risk behavior and ways of avoidingrisk outcomes. As adolescents are exposed to such information,their perceptions of how knowledgeable they are may increase.The effect of such information provision may actually have ad-verse effects if the information is not directly acted upon by theadolescent because the increased levels of perceived knowledgeabout how to avoid the consequences of risk behavior may disposethe individual to engage in more risk behavior out of the belief thatshe or he can avoid the adverse consequences.

Several ancillary findings were observed in the present researchthat, though not the focus of the study, are worthy of futureinvestigation. The first result was the observation of a curvilinearrelationship between IQ as measured by the PPVT–R and theoccurrence of a pregnancy between Wave 1 and Wave 2. Theprobability of a pregnancy tended to be lowest for individuals oflow and high intelligence and tended to peak for individuals whowere near or just below average intelligence. Using the Add Healthdatabase, Halpern et al. (2000) found that intelligence was curvi-linearly related to sexual behavior in a fashion similar to this study.Our results extend this finding to pregnancy occurrence. Themechanisms that can account for this interesting relationship needto be further explored. A second result of interest was the findingthat increasing levels of knowledge about the accurate use of birthcontrol were associated with an increased probability of bothsexual behavior and a pregnancy between Wave 1 and Wave 2.This finding has potential policy implications as it suggests a linkbetween the acquisition of information about birth control andsubsequent sexual activity. This is a complex issue, and the anal-yses reported here are insufficient to gain perspectives on the manypossible alternative explanations of this result. Future researchshould explore this finding in greater depth.

The present analysis, though provocative, has limitations thatmust be borne in mind. The study used only a single risk behavior,and it is unclear if the social–psychological dynamics will replicatewith other risk behaviors. We relied on a school-based sample thatdoes not, strictly speaking, permit generalizations beyond such


populations. It is possible that variation in the knowledge test wasdue, in part, to a failure of some of the adolescents to understandthe questions rather than to the lack of knowledge per se. The AddHealth questionnaire was pilot tested for comprehension, and theknowledge questions were only asked of adolescents who wereolder than 14 or who were sexually active, thus lessening thispossibility.2 The research also relied on self-report measures ofpregnancy, which also represents a cause for caution because suchreports may contain some error. Questions about sex and preg-nancy were posed to adolescents through headphones on a laptopcomputer, and the adolescent entered her response so that no onecould see it. The respondent also knew that her name would neverbe associated with the data in any way. Adolescents were giveninstructional sets that emphasized the importance of honest re-sponding. Adolescents had the option of skipping questions theyfelt uncomfortable answering. We correlated the self-report of apregnancy with a measure of social-desirability response tenden-cies, and the correlation was trivial and not significant. Althoughwe doubt that there is sufficient error to undermine the conclusionsof the present study, one still must be cautious. The design of thestudy was correlational in nature, and many of the constructs wererepresented by a single measure, introducing potential bias inparameter estimates because of measurement error. If a constructis not adequately represented by a measure, then that constructmay not be fully controlled for in the statistical analyses. Despitethese caveats, the results are suggestive and set the stage for furtherresearch on metacognition and risk behavior.

2 The correlation between the knowledge scores and scores on the IQ testwas .27, suggesting that adolescents with higher levels of receptive vocab-ulary were more likely to obtain higher knowledge scores. One mightexpect on logical grounds that adolescents with higher IQs should indeedbe more knowledgeable about pregnancy prevention than adolescents withlower IQs, but we cannot rule out conclusively that some of this correlationreflects the possibility that adolescents with lower receptive vocabularywere less able to understand some of the knowledge items.

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