ORIGINAL ARTICLE

Use of Self-perspectives and their Sources to Predictthe Mathematics Enrollment Intentions of Girls and Boys

Tara Stevens & Kun Wang & Arturo Olivárez Jr. &Doug Hamman

Published online: 28 February 2007# Springer Science + Business Media, LLC 2007

Abstract An understanding of the differences in the waysin which self-efficacy and interest influence boys’ and girls’intentions to continue enrollment in mathematics coursesmay provide insight into how to encourage continuedmathematics enrollment for all students. Two competingtheoretical models of mathematics self-efficacy and interestin predicting students’ enrollment intentions and achieve-ment were evaluated separately across samples of middlelevel boys and girls. One model was empirically supportedfor both groups and indicated the independent influence ofself-efficacy and interest in predicting enrollment inten-tions. Multi-group analyses revealed that self-efficacy’sinfluence in enrollment intentions and achievement wassignificantly stronger for boys than for girls, and interestwas significantly more important in the prediction ofenrollment intentions for girls than for boys.

Keywords Self-perspectives . Mathematics self-efficacy .

Mathematics interest . Gender . Ability . Achievement

In a technological society, employment fields, which at onetime did not seem to require much more than a basicmathematics background, are growing more dependentupon employees with mathematics knowledge (NationalCouncil of Teachers of Mathematics, 1991). Workersrecognize this need for mathematics not only in existing

positions but also in newly created jobs, which are often inthe high technology sector (Organization for Economic Co-Operation and Development [OECD], 1994). It is apparentthat those who avoid mathematics courses in their educa-tion will find themselves ill-prepared for competitiveemployment. As a result, the National Council of Teachersof Mathematics (2000) has emphasized the importance oflearning mathematics for all students.

Historically, mathematical proficiency has been utilizedas the basis for making decisions regarding the continuededucation and further job opportunities for students(Moreno & Muller, 1999; Rivera-Batiz, 1992; Schoenfeld,2002). In other words, students are often routed intodifferent instructional sequences in order to meet require-ments of graduation based on their level of mathematicalskill. Although four-fifths of American students take a firstalgebra course, fewer than one-half take a second course,and fewer than one-tenth of all students enroll in calculus(Bruning, Schraw, & Ronning, 1999; Rock & Pollack,1995). Enrollment in the mathematics sequences thatrequire students to take only the minimum number ofcourses often results in inequitable educational opportuni-ties and outcomes (Schneider, Swanson, & Riegle-Crumb,1998).

Ma and Willms (1999) identified this lack of studentexposure or failure to enroll in mathematics courses as animportant issue that is too often overlooked. For example,Lee and Bryk (1989) found that when students enrolledin a greater number of academic courses, higher levelsof achievement were documented. More specifically,Schneider et al. (1998) found that greater gains inmathematics were achieved by students in more advancedmathematics course sequences. Consequently, when seek-ing to address achievement concerns, it is important toinvestigate what factors influence enrollment behavior.

Sex Roles (2007) 56:351–363DOI 10.1007/s11199-006-9180-2

T. Stevens (*) :K. Wang :A. Olivárez Jr.Department of Educational Psychology and Leadership,Texas Tech University,Box 41071, Lubbock, TX 79409-1071, USAe-mail: tara.stevens@ttu.edu

D. HammanDepartment of Curriculum and Instruction, Texas Tech University,Lubbock, TX, USA

Self-efficacy, or individuals’ beliefs that they can usetheir skills and knowledge effectively to successfullycomplete a task, has been consistently identified as animportant factor in predicting mathematics interests (e.g.,Lapan, Boggs, & Morrill, 1989; Lent, Lopez, & Bieschke,1993; Post, Stewart, & Smith, 1991; Stevens, Olivárez, &Hamman, 2006; Stevens, Olivárez, Lan, & Tallent-Runnels,2004). Although these research teams shared an emphasison self-efficacy, only two of them evaluated students priorto or at the start of high school when enrollment decisionsare particularly important. As a result, little informationexists on the factors that may predict enrollment later inhigh school. If students in the middle level grades (e.g.,grades 8 and 9) fail to enroll in algebra at that time, theywill not have the prerequisites to take more advancedmathematics courses during their later high school years.Without information concerning early enrollment inten-tions, educators will have difficulty identifying studentswho plan to drop out of advanced mathematics coursesequences and intervening before important exposure tomathematics is lost. Thus, the purpose of the present studywas to evaluate two competing interpretations of Bandura’s(1986) Social Cognitive Theory, which have been em-ployed to describe the development of enrollment inten-tions (Lent et al., 1993; Stevens et al., 2006; Stevens et al.,2004), with data from a group of middle level students.

In present investigation we also evaluated separately forboys and girls the factors that lead to the intention to enrollin mathematics courses. Although girls are just as likely asboys to enroll in advanced mathematics courses (U.S.Department of Education and National Center for Educa-tional Statistics, 1995), women continue to be underrepre-sented in mathematics-related fields, which is likely to berelated to stereotypes documented over the past threedecades that have suggested the inferiority of women andgirls in mathematics activities (Brown & Josephs, 1999). Inaddition, the number of young women who pursue amathematics-related college degree is declining (Panteli,Stack, & Ramsay, 2001). Studies from several decades agosuggest differences in the predictors of mathematicsenrollment between boys and girls (e.g., Perl, 1982;Sherman, 1981); however current evaluation is necessaryas girls have made considerable progress in their mathe-matics achievement since that time, and most differenceshave declined significantly (Fennema & Hart, 1994), if notdisappeared (Kenney-Benson, Pomerantz, Ryan, & Patrick,2006; Reynolds & Conaway, 2003).

Although significant differences between girls’ andboys’ mathematics performance scores, as well as thescores related to beliefs and motivation associated withmathematics, are becoming a thing of the past, girls do notappear to be benefiting to the expected degree. Forexample, Kenney-Benson et al. (2006) found that girls

displayed significantly less disruptive behavior and setsignificantly more mastery goals than performance goals incomparison to boys, which led to girls’ significantly highergrades. Even so, girls and boys did not differ in their levelsof mathematics self-efficacy or standardized mathematicstest scores. The authors cited the well-documented relation-ship between mathematics self-efficacy and performance asa possible explanation. The investigation of possible genderdifferences in the strengths of such associations in thecontext of Social Cognitive Theory is important to anyunderstanding of the psychological processes that influenceadolescents to continue studying mathematics. A betterunderstanding of the reasons for adolescents’ enrollmentintentions will allow us to generate practical recommenda-tions to encourage girls’ interest in enrollment goals that willfacilitate the entry of more women into pathways that canlead to mathematics-related careers.

Social Cognitive Theory

Social Cognitive Theory describes human developmentthrough the reciprocal interactions that occur betweenindividuals, their behavior, and the environment (Bandura,1986). Although the understanding of the behavioral andenvironmental factors is essential to describing, predicting,and influencing the mathematics enrollment intentions ofchildren, researchers frequently focus on one aspect of theindividual, that is, self-efficacy, due to its importance.Because Bandura (2001) has emphasized the agentic role ofindividuals in their own development, self-efficacy has acentral role. Students who believe that they are able to solvemathematics problems successfully or who display highlevels of mathematics self-efficacy are more likely thanthose who do not to express an interest in enrolling inadditional mathematics courses (Lent et al., 1993; Stevenset al., 2004). In other words, students who believe that theycan use their skills and knowledge effectively to succeedwill be more likely to persist in the face of a challenge thanwill those with doubts; therefore they will also be morelikely to select challenging tasks (Bandura, 1986). Studentstend to not choose to take a course in which they believethey will fail, even when they are aware of the benefits ofdoing so. “If they believe themselves incapable of requisiteattainments, they forsake the endeavor, and their beliefsabout the outcomes such accomplishments could bring havelittle effect on their behavior” (Bandura, 1986, p. 220).

Self-efficacy and enrollment intentions

Lent, Lopez, and Bieschke (1991) extended Bandura’s(1986) Social Cognitive Theory to explain career develop-ment and referred to this specific application as Social

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Cognitive Career Theory (SCCT). One of the earlier studiesthat contributed to the development of SCCT was con-ducted to investigate the relationships among prior achieve-ment, self-efficacy, outcome expectations, and interests inpredicting the mathematics enrollment intentions of maleand female college students (Lent et al., 1993). Unfortu-nately, the sample was comprised of undergraduate studentsenrolled in introductory psychology courses. The partic-ipants were predominately White, and their average highschool rank was at the 78th percentile. As a result, thesample was not likely to be representative of the majority ofpublic school students. Because students who are able topass college entrance exams are likely to have beenexposed to at least some advanced mathematics courses,the generalizability of SCCT to younger public schoolstudents is questionable.

Although Lent et al. (1993) did not specificallyinvestigate gender differences, the researchers did controlfor variations across gender after finding that the malestudents reported significantly higher mathematics self-efficacy, outcome beliefs, ACT scores, and course interestsand intentions than the female students did. Results from aseries of hierarchical linear regressions supported themediating role of self-efficacy in the relationship betweenprior performance and interests and the mediating role ofinterest in the relationship between self-efficacy andenrollment intentions. Lent et al. (1993) did not evaluatethese relationships with a path analysis but did include apath model to describe their theory. Support for similarmodels of SCCT have also been reported (e.g., Gainor &Lent, 1998; Lent et al., 2001).

In contrast to the aforementioned work, Stevens et al.(2004) actually evaluated a path model and found that self-efficacy was directly related to high school students’enrollment intentions. Similar to Lent et al. (1993), theauthors found that self-efficacy mediated the relationshipbetween prior mathematics achievement and mathematicsperformance. However, Stevens et al. (2004) did notinvestigate the role of interest in their model. Because theirsample included ninth and tenth grade students attending apublic high school, the results may be more useful inmaking recommendations that actually influence latermathematics enrollment. Also, their sample was consider-ably more diverse; 53% of the students described them-selves as Hispanic, 4.6% as African American, and 30% asEuropean American.

Stevens et al. (2006) built upon this theoretical model intwo ways. First, they included the sources of efficacy, and,second, they added a measure of interest. If self-efficacy isimportant in discriminating between students who intend tocontinue their enrollment in mathematics courses and thosewho do not, then studying the effects of the sources of self-efficacy is also valuable. Bandura (1986) identified four

sources; verbal persuasion, mastery experiences, vicariousexperiences, and physiological feedback; that influence thedevelopment of self-efficacy. In investigating the role ofself-efficacy in conjunction with ability, intrinsic motiva-tion, and mathematics interest in predicting mathematicsperformance, Stevens et al. found that the effects of self-efficacy, albeit significant, on intrinsic motivation wereweaker than the effects of its sources. They suggested thatthe difference between beliefs and the sources of thosebeliefs should be differentiated because of the practicalimplication this clarification holds for educators.

Such a strategy seems to be relevant in the investigationof enrollment intentions to help reveal if the intent tocontinue enrollment in mathematics courses is influencedby the belief itself or the factors that assist in developing it.Although educators should work to ensure that studentsexperience opportunities to benefit from the factors thatinfluence efficacy beliefs, an understanding that an actualchange in efficacy might not occur immediately is valuable.In addition, by evaluating both self-efficacy and its sources,measurement of this variable improves, which allowsresearchers to look at whether some sources might beparticularly more valuable for one group in comparison toanother. Thus, educators could be provided with informa-tion concerning what strategies are more effective atimproving efficacy specifically for boys and girls.

Self-efficacy, interest, and enrollment intentions

Stevens et al. (2006) also added the construct of interest, itsdirect relationship to motivation, and its indirect relation-ship to mathematics performance. Because the purpose ofthat investigation was to evaluate differences acrossethnicity that could potentially explain the existing achieve-ment gap, enrollment intentions were not included in themodel. It is interesting that the Stevens et al. model did notinclude a direct relationship between mathematics interestand mathematics self-efficacy. Instead the authors positedthat both interest and self-efficacy would predict intrinsicmotivation, or the perception that people engage in a taskbecause they simply choose to do so.

The interest measure utilized by Lent et al. (1993) reliedsimply on participants to indicate their degree of interest ina number of mathematics courses, whereas Stevens et al.(2006) utilized an instrument specifically developed toevaluate students’ knowledge, value, and emotion related tomathematics. By asking participants their interest in a course,Lent et al. seemed to tap into a construct more likely to betied to ability and enrollment, as students likely thought oftheir likelihood of taking such courses when they consideredhow to rate their interest. As a result, the relationship foundbetween self-efficacy and interest may be associated withthe measurement of the variables themselves.

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“In social cognitive theory, interest grows from satisfac-tions derived from fulfilling challenging standards and fromself-percepts of efficacy gained through accomplishments andother sources of efficacy information” (Bandura, 1986, p. 243).Thus, one would expect the presence of a meaningful rela-tionship between mathematics self-efficacy and mathematicsinterest to influence course enrollment intentions, whichcontradicts the findings of Stevens et al. (2006). Even so,we contend that interest is important in enrollment intentions;however we doubt its association to mathematics self-efficacyin this prediction of studying public school children’s inten-tions. In comparison to college students, public school childrenhave likely not yet experienced mastery across a variety ofmathematics courses, and, therefore, may not have had the timeto benefit fully from the self-satisfaction, which Bandura(1986) described as necessary for the development ofinterest. “If, in fact, effects follow such a temporal course,then increased interest would emerge as a later, rather than asan instant consequent of enhanced efficacy” (Bandura, 1986,p. 242). Bandura further acknowledged that an individualmight need at least a moderate degree of self-efficacy todevelop and maintain interest in any topic.

Enrollment intentions and gender

Although mathematics differences appear to be decliningbetween boys and girls, the manner in which affectivevariables influence behavior related to mathematics may stillvary across gender. Meyer and Koehler (1990) reported nosignificant differences between measures of confidence,usefulness, attitude, and attribution related to mathematicsbetween 151 grade school boys and girls; however they didfind the affective variables to be more important for the girlsthan for boys; confidence was found to be important for bothgroups in the prediction of mathematics participation andachievement. “The results show that, even when genderdifferences in the affective variables are not present, thevariables have differential effects on mathematics achieve-ment and participation for females and males” (Meyer &Koehler, 1990, p. 91).

More recently, Crombie et al. (2005) found that,although girls outperformed boys on a test of mathematicsperformance at grade 8, this statistically significant differ-ence disappeared at grade 9. This finding is consistent withearlier research that indicated that girls tend to not do aswell as boys in areas of advanced mathematics (e.g., Hyde,Fennema, & Lamon, 1990), which could also be related togirls’ reluctance to take more advanced mathematicscourses. Despite reporting a number of similarities betweenboys’ and girls’ utility and intrinsic value of mathematicsand grades, Crombie et al. found an important difference inthe theoretical model that described enrollment intentions.That is, competence beliefs were directly linked to

enrollment intentions for girls but not for boys. Crombieet al. concluded that “it is possible that the value of mathfor girls will no longer be the primary influence onenrollment intentions and that competence beliefs willassume an increasingly important role” (p. 363). Theauthors did caution that further research is needed toexplore this relationship. We concur, especially as Crombieet al. failed to cross validate their models after they mademodifications, which raises questions about the generaliz-ability of their findings. Also of concern is their four-itemmeasure of competence beliefs. Instead of looking atstudents’ beliefs concerning their ability to solve specifictypes of mathematics problems, the authors employed moregeneral items that related to students’ comparison to peersand their overall estimate of how good the students thoughtthey were at math.

Because there is evidence that the career choices of menand women are determined through different processes (e.g.,Trusty, 2002), and because differences also appear to bepresent in the psychological process that influence middlelevel students’ enrollment intentions, any investigation ofthe role of self-efficacy and interest in predicting enrollmentintentions should also be evaluated through separate modelsfor boys and girls. Even though boys and girls typicallyperform at the same mathematics level in the middle schoolyears, differences may still exist in advanced mathematicsperformance because girls may not continue in advancedmathematics course sequences. Furthermore, young womenstill do not enter fields that require advanced mathematicsat the same rate as young men do. Mathematics self-efficacy and interest may provide insight into the processesthat influence enrollment intentions, and the study of theseconstructs separately across gender appears warranted togenerate specific recommendations that will encourageequal opportunities for both men and women.

Purpose and predictions

The purpose of the present study was to evaluate the fit oftwo competing models to samples of middle level boys andgirls. The first model was developed based on the work ofLent et al. (1993), and it posited a direct relationshipbetween self-efficacy and interest in the prediction ofenrollment intentions (see Fig. 1). The second modelemerged from the work of Stevens et al. (2004, 2006),and it described self-efficacy and interest as separatepredictors of enrollment intentions (see Fig. 2). Bothmodels included the sources of self-efficacy in order betterto understand and describe the importance of behaviors thatare linked to the development and measurement of self-efficacy. Thus, more specific recommendations could bemade concerning how to influence the mathematics self-efficacy of students. Finally, both models included a

354 Sex Roles (2007) 56:351–363

measure of interest that was based on the theoreticalframework of interest researchers. We believe that theseimprovements allowed us better to evaluate the theoryrelated to the role of self-efficacy and interest in theenrollment intentions of boys and girls.

Method

Participants

Participants were students at schools in west and southTexas. Although the schools constituted conveniencesamples, classrooms were randomly selected to yield atotal of 438 participants. Students’ ages ranged from 13 to16 years; however only one student was age 16. Themajority of students were classified as eighth graders(89.7%), and 10.3% were classified as ninth graders.

Participants were predominately Mexican American andEuropean American, 45.3% described themselves as Mex-ican American, and 45.6% described themselves asEuropean American. The remaining 7.6% described them-selves as either African American, Asian American, orNative American. A small number (1.6%) failed to respondto the item. Finally, the sample consisted of slightly moregirls than boys: with 237 (54.1%) girls, and 201 (45.9%)boys.

Procedures

Approximately 2 weeks prior to the anticipated test date,the mathematics teachers were invited to participate andassist in the collection of data, and they were trained in thecollection procedure. Teachers were given a script to beread verbatim, which provided an introduction explainedthe purpose of the testing to the students as well as

Fig. 1 Theoretical model basedon the work of Lent et al.(1993).

Fig. 2 Theoretical model basedon the work of Stevens et al.(2004, 2006).

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instructions for each of the included instruments. Themeasures presented to students were counterbalanced;however the measurement of the malleable affectivevariables prior to mathematics performance was necessary,as the measurement of mathematics self-efficacy andinterest would have been affected if students had had theopportunity to attempt to work the actual problems first.The test was administered during the students’ regularmathematics periods, and students completed the entireprocess in 1 h on average.

Instruments

Enrollment intentions

Students were grouped into one of three categories basedon their response to the question “Do you think you’ll takemore math classes in high school?” The three categoriesincluded “I hope to take a lot more math classes!,” “I’mwilling to take what is required to graduate,” and “I don’teven want to take the ones I have to take!” The participants’responses were coded from 1 to 3; 3 indicated greaterintention to take mathematics courses.

Sources of self-efficacy

The Mathematics Experiences Scale (MES) is an 18-item instrument created by the authors to assess the fourareas identified by Bandura (1986) to encourage thedevelopment of self-efficacy. Three of the four subscaleswere employed in the present study due to their particularrelevance. Vicarious experiences was assessed by sixitems that created the subscale “Math Models,” verbalpersuasion was assessed by four items that comprised thesubscale “Praise and Feedback,” and mastery experienceswas assessed by four items that comprised the subscale ofthe same name. Participants were asked to rate how wellstatements described them on a scale from 1—“not at alltrue of me” to 4—“very true of me.” Example itemsrepresenting each of the factors; Math Models, Praise andFeedback, and Mastery Experiences; respectively, are “Ihave friends who are good at math that I would want to belike,” “I have had teachers who tell me I am good atmath,” and “I usually make good grades on computationproblems.” Confirmatory factor analysis was conductedwith the total sample, and it supported the presence of thethree factors. Internal reliability estimates (Cronbach’salpha) were acceptable for the Math Models (.69), Praiseand Feedback (.81), and Mastery Experiences (.71)subscales for boys. Similar estimates were calculated forgirls: Math Models at .73, Praise and Feedback at .83, andMastery Experiences at .63.

Mathematics self-efficacy

Participants were asked to endorse on a seven-point linearscale that ranged from 1=extremely uncertain to 7=extremely certain their level of confidence that they couldcorrectly solve 19 grade-level appropriate mathematicsproblems. The mathematics items assessed were developedby the authors to be parallel to those that the students wouldencounter on the mathematics performance measure. Thus,the students solved problems that required knowledge oflong division; the addition, subtraction, multiplication, anddivision of fractions; and the understanding of negative andpositive numbers. Although students were presented withnumerical equations, they also given several word prob-lems, such as “How many inches is 75% of a one footruler?” Cronbach’s alpha for this scale was high: .94 forboys, and .93 for girls.

Mathematics interest

The Mathematics Interest Inventory (MII; Stevens &Olivárez, 2005) was utilized to assess participants’ interestin mathematics. Participants were presented with 27 state-ments about behavior and feelings about mathematics, suchas “I work more math problems than what I have to” and “Ifeel excited when a new math topic is announced” andasked to rate how well each described them on a scale of 1“not at all like me” to 4 “very much like me.” The internalconsistency estimate (Cronbach’s alpha) was .91 for boysand .93 for girls.

Mathematics performance

Standardized composite scores of two subtests (i.e.,Calculation and Mathematics Fluency) of the WoodcockJohnson III Tests of Achievement (Woodcock, McGrew, &Mather, 2001) were employed to assess mathematicsperformance. Because the test is standardized, appropriatecomparisons of mathematics performance could be madeacross students from varying grade levels. Students can takeas much time as they need to complete the Calculation test,and most finish in about 15–20 min. However, the fluencytest is timed, with students given 3 min to complete asmany problems as they can. The calculation test includesitems that start with basic calculation and then becomeincreasingly more difficult requiring knowledge of longdivision; the addition, subtraction, multiplication, anddivision of fractions; and the understanding of negativeand positive numbers. The final items require a basicknowledge of trigonometry and calculus. Students wereasked to simply work as many problems as they knew howto solve. The fluency test includes items that require only

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basic operations (i.e., addition, subtraction, multiplication,and division) on 1- and 2-digit numbers.

Statistical analyses

Subsequent to the calculation of descriptive statistics andmean comparisons, the first theoretical model’s fit to thesample of boys and then to the sample of girls wasevaluated via LISREL 8.52 (Joreskog & Sorbom, 2002)and the Simplis programming language. Goodness of fitindices were selected based on the recommendations of Huand Bentler (1999). A two-index presentation strategy thatinvolved an estimate of close to .09 for the maximumlikelihood (ML) based standardized root mean squaredresidual (SRMR) and close to .95 for the ML basedcomparative fit index (CFI) was employed. Modifications

to the structural model were not considered as this modelwas specified based on theory. The second theoreticalmodel was then tested with the same procedures.

To evaluate which parameters might be more importantfor one group in comparison to the other, multiple-groupmodeling was utilized. We initially tested a baseline modelthat included the boys’ and girls’ second theoretical modelswith no parameters constrained. We next compared subse-quent restricted models with the baseline model, and weconstrained each regression coefficient one at a time. Weused chi-square difference tests to compare each restrictivemodel with the baseline model. Non-significant chi-squaredifference tests suggested that the constrained regressioncoefficient remained consistent across gender, whereassignificant chi-square tests suggested that the regression

Table 1 Means, standard deviations, and independent t-tests between groups.

Variable Mean SD t-value p value

Boys Girls Boys Girls

Enrollment intentions 1.93 1.91 .56 .58 −.35 .724Mathematics performance 103.83 107.89 16.05 14.04 2.82 .005Mathematics self-efficacy 102.46 101.34 24.22 23.32 −.49 .624Sources of self-efficacyModels 14.47 15.02 4.14 4.19 1.38 .170Praise 10.28 10.20 3.49 3.39 −.26 .796Mastery experiences 10.23 9.93 3.05 2.78 −1.08 .280

Mathematics interest 52.91 51.51 12.40 13.57 −1.11 .266

Table 2 Correlations for the total scores across the seven study subscales: upper triangle displays the correlations for the girls’ sample.

Total scores across the seven study subscales

S1 S2 S3 S4 S5 S6 S7S1 .288** .276** .396** .354** .608** .265**S2 .298** .083 .184** .160* .149* .301**S3 .194** −.121 .447** .466** .385** .152*S4 .353** .261** .387** .650** .521** .227**S5 .400** .187** .397** .640** .502** .435**S6 .486** .193** .312** .382** .490** .200**S7 .416** .450** .094 .308** .386** .297**NF 235 237 237 237 237 237 237M 198 201 201 201 201 201 201

S1=Do you think you’ll take more math classes in high school S4=Total score of praise subscale S7=Self-efficacy total score.S2=Math ability comprehensive score S5=Total score of mastery subscale.S3=Total score of models subscale S6=Math feelings total score.** p<.01 level.* p<.05 level.

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coefficients differed evidenced by the degradation in themodel.

Results

Initial comparisons and correlations

Independent measures t tests were conducted to evaluatedifferences between boys and girls for each of the variablesincluded in the analyses. No statistically significant differ-ences were present in boys’ and girls’ sources of mathe-matics self-efficacy, mathematics self-efficacy, mathematicsinterest, or enrollment intentions. However, a statisticallysignificant difference was present between boys’ and girls’mathematics performance score, t(436)=2.82, p<.01; thegirls’ mean score (M=107.89, SD=14.04) was higher than

that of the boys (M=103.83, SD=16.05). All mean scores, tvalues, and significance levels are reported in Table 1.Table 2 presents the correlations among the variables,which were all related to the expected degree and in theexpected directions for both samples.

Evaluation of the first theoretical model

To evaluate the first model, maximum likelihood estimationwas utilized, and parameter estimation matrices werepositive definite, with no parameter estimates outside theirpermissible range. The first theoretical model presented inFig. 1 did not provide adequate fit to the data for the sampleof boys (CFI=.81; SRMR=.18) or for the sample of girls(CFI=.82; SRMR=.19). A review of parameter estimateswas inappropriate due to the poor fit of the model. Even so,these are reported in Figs. 3 and 4. Modifications of the

Fig. 3 Parameter estimates forboys: Model 1.

Fig. 4 Parameter estimates forgirls: Model 1.

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model were not made as the purpose of the study was toevaluate two a priori models; however modification indicesdid suggest the addition of a path to enrollment intentionsfrom self-efficacy. This relationship is present in the secondtheoretical model, and indicates that mathematics self-efficacy may have a direct impact on students’ enrollmentintentions rather than one that is mediational in nature.

Evaluation of the second theoretical model

To evaluate the second theoretical model, maximumlikelihood estimation was utilized, and parameter estima-tion matrices were positive definite, with no parameterestimates outside their permissible range. In contrast to thefirst theoretical model, the second model presented inFig. 2 did provide adequate fit to the data for the sample ofboys (CFI=.95; SRMR=.07) and for the sample of girls

(CFI= .94; SRMR=.07). Parameter estimates for thesecond theoretical model with data from the sample ofboys were all statistically significant at the p<.01 level.Mathematics self-efficacy accounted for 19% of thevariance in mathematics performance. Mathematics self-efficacy and mathematics interest accounted for 42% ofthe variance in students’ enrollment intentions. Finally, thesources of efficacy accounted for 18% of the variance inmathematics self-efficacy. All parameter estimates arepresented in Figs. 5 and 6.

Parameter estimates for the second theoretical modelwith data from the sample of girls were all statisticallysignificant at the p<.01 level. Mathematics self-efficacyaccounted for 9% of the variance in mathematics perfor-mance. Mathematics self-efficacy and mathematics interestaccounted for 52% of the variance in students’ enrollmentintentions. Finally, the sources of efficacy accounted for

Fig. 5 Parameter estimates forboys: Model 2.

Fig. 6 Parameter estimates forgirls: Model 2.

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15% of the variance in mathematics self-efficacy. Allparameter estimates are presented in Fig. 5.

Evaluation of the multiple-group model

A multiple-group model was tested to allow comparisonsbetween individual regression coefficients to aid in inter-preting the importance of the individual predictors ofenrollment across gender. Evaluation of the baseline modelthat included the boys’ and girls’ second theoretical modelswith all parameters free to vary revealed an adequate fit tothe data (CFI=.94, SRMR=.09). Subsequent models thatconstrained one regression coefficient at a time wereevaluated and compared to the initial baseline model. Testsof chi-square differences between each constrained modeland the baseline model are reported in Table 3. Resultsindicated that three of the four parameters significantlydegraded the invariant model when constrained. In otherwords, these parameters were different between the twogroups.

Results of the multiple-group model revealed that therelationship between mathematics self-efficacy and mathe-matics achievement was stronger for boys than it was forgirls. In addition, the relationship between mathematicsself-efficacy and enrollment intentions was stronger forboys than it was for girls. Finally, the association betweenmathematics interest and enrollment intentions was strongerfor girls than it was for boys.

Discussion

Theoretical models have the potential to provide insightinto how gender differences develop. Because multiplemodels often can explain data equally well (McCallum,Roznowski, & Necowitz, 1992), we evaluated two com-peting models that had both been evaluated and docu-

mented in the current research literature. We found supportfor the second theoretical model that emphasized theindependence of mathematics self-efficacy and mathematicsinterest as well as the direct influence of self-efficacy onmathematics enrollment intentions. This model provided agood fit to data from both the sample of boys and thesample of girls. This suggests that mathematics self-efficacy and interest may not be related for middle levelstudents. Perhaps these students have not yet had enoughsuccessful mathematics experiences and resulting satisfac-tion, described by Bandura (1986) to be important in thedevelopment of interest. Also, middle level students arebeing confronted with more challenging mathematical tasksthat require more of their abstract reasoning and spatialskills. These greater demands may result in lower overalllevels of mathematics self-efficacy. Without the presence ofmoderate levels of self-efficacy, a high degree of interestmay not be observed. This means that mathematics self-efficacy would directly influence enrollment intentions ofthe students, as would their mathematics interest.

The present findings provide interesting insight intogender issues in mathematics enrollment intentions as well.We found that boys and girls did not significantly differ intheir mathematics self-efficacy or mathematics interest.Furthermore, the girls performed significantly better thanthe boys did on standardized tests of mathematics achieve-ment. Finally, we did not find significant differences inmathematics enrollment intentions between the boys andgirls in the sample. These findings indicate positiveadvancements for girls in the area of mathematics. Girlsappear to be entering high school with the mathematicsskills necessary to be successful. Even so, evidencesuggests that many of these girls will not be likely tocontinue in mathematics-related fields (Brown & Josephs,1999; Panteli et al., 2001). Thus, girls ostensibly make thedecision to avoid advanced mathematics courses subse-quent to the middle level years.

Several important differences were observed when wecompared the present findings to recent research. First,mathematics self-efficacy scores did not significantly differbetween the boys and girls in the current study; howeverresearchers have consistently documented boys’ greaterconfidence and stronger beliefs in their ability in stereotyp-ically masculine fields (for a review, see Ruble, Greulich,Pomerantz, & Gochberg, 1993). Kenney-Bensen et al.(2006) also failed to find a significant gender differencein mathematics self-efficacy, and they noted that this wasunusual as the girls in their sample had significantly highermathematics grades than the boys did. In contrast, the girlsin their sample did not perform significantly better than theboys on standardized tests of mathematics achievement.Kenney-Bensen et al. discussed several explanations forthis phenomenon including “stereotype threat” (Spencer,

Table 3 Test of chi square differences between constrained andbaseline models.

Parameter constrained DF χ2 ΔDF Δχ2

pvalue

Baseline model (noconstraints)

27 95.75

Interest to enrollmentintentions

28 105.44 1 9.69 <.005

Ability to mathematicsperformance

28 103.58 1 7.83 <.01

Prior achievement to self-efficacy

28 100.22 1 4.47 <.05

360 Sex Roles (2007) 56:351–363

Steele, & Quinn, 1999), girls’ greater levels of anxiety intesting situations (see Maccoby & Jacklin, 1974), anddifferences in the format and type of exams given inclassrooms in comparison to standardized tests.

The second important difference then is that the girls inthe present study outperformed the boys on the standard-ized tests of mathematics achievement. This result may berelated to the type of standardized tests administered. Weused a test of calculation, and research has indicated thatgirls often perform better than boys in this aspect ofmathematics (Brandon, Newton, & Hammond, 1987; D.Sabers, Cushing, & D. Sabers, 1987). Although it ispossible that a “stereotype threat” or heightened anxietyrelated to girls’ fears that they will be judged by culturalstereotypes that indicate their lack of ability in mathematics(Spencer et al., 1999) is limited to advanced mathematics,this explanation seems quite speculative. In addition, wealso utilized a fluency test, which requires students to solveas many addition, subtraction, multiplication, and divisionproblems as they can in a specific time period. This lattertest likely would have elicited some level of anxiety forstudents prone to such difficulties. Therefore, girls mayapproach mathematics with confidence during the middlelevel years. For example, Crombie et al. (2005) found thateighth and ninth grade girls reported greater intentions toenroll in mathematics than eighth and ninth grade boys did.Although our results did not indicate the presence of asignificant difference between the enrollment intentions ofboys and girls, girls were not at a disadvantage. Importantdifferences apparently emerge when students reach highschool. In fact, when the present sample was split by gradelevel, we found that the significant difference in mathemat-ics performance between boys and girls was not present forninth graders, only eighth graders. That is, girls no longerhad an advantage, which suggests that something may bechanging in their beliefs and interests about mathematics.

Although the second theoretical model described bothboys and girls well, differences were found in theimportance of the variables, which could provide a basisfor the development of later gender disparities. In thetheoretical model, mathematics self-efficacy providedgreater predictive power for both enrollment intentions andmathematics performance for boys than it did for girls.However, mathematics interest was a stronger predictor ofenrollment intentions for girls than it was for boys. It isinteresting that levels of mathematics self-efficacy andinterest were similar for boys and girls. These findingssuggest that, although girls possess mathematics self-efficacy, this efficacy is not as important to them as theirinterest in the task when they are asked to predict theirenrollment intentions. Interest was also important to boys’intentions to continue their mathematics enrollment. Al-though their interest was a slightly stronger predictor than

their mathematics self-efficacy, their mathematics self-efficacy was much more important than what was observedfor girls.

Some researchers have documented that boys and girlsapproach schoolwork differently; boys tend to be moreperformance-oriented than girls, and girls tend to be moremastery-oriented than boys (Kenney-Benson et al., 2006).In other words, boys may be more likely to attend to andemphasize their performance in comparison to others. Thisemphasis may result in the saliency of mathematics self-efficacy for boys, an emphasis that is not present for girls.Girls may instead focus on understanding and learning,which tend to be associated with interest. Although girlsmay not possess a higher level of interest in mathematicsthan boys do, they may place a greater importance onexperiencing interest, as interest is something they associatewith academic outcomes.

A second explanation involves the dissociation ofoutcome expectations from self-efficacy judgments. Out-come expectations refer to consequences of an action(Bandura, 1986). In some situations, improvements inperformance make little or no difference to the outcomes.For example, if girls perceive that they will have little to nochance to gain entrance into mathematics-related careers, orif they do not perceive these careers as future options, thentheir self-efficacy in mathematics will be likely to fail tolead to action. This dissociation may “permit social biasesto come into play, so that the same performance attainmentsmay produce variable and often inequitable outcomes”(Bandura, 1986, p. 393). Future research is necessary toexplore this interpretation.

Limitations

Future researchers should also work to avoid or improve uponthe limitations of the present study. First, the problem ofequivalent models is always present when working withtheoretical models. MacCallum, Roznowski, and Necowitz(1992) warned that multiple models can fit the data equallywell. Although we worked to address this issue by selectingtwo competing models that had both been tested by others,the possibility exists that other theoretical explanations areappropriate. The inclusion of other important variables, suchas outcome expectations, would also be of value inunderstanding gender issues. Continued evaluation of thesetheoretical models across samples divided by gender alsoappears warranted. The psychological constructs related toenrollment intentions appear to be similar across boys andgirls; however their importance differs, which indicatessubtle underlying differences that require further exploration.

Second, the measures selected for future studies shouldinclude reports from others as well as self-reports. The

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reliance on self-report, although important due to the natureof the self-perspective and intention variables under study,may have resulted in certain individuals or possibly groupsattempting to portray themselves in either a positive ornegative light. In addition, a greater variety of data sourcesmay provide more information concerning the schoolcontext and home environment, which could result in aneven better understanding of the development of mathe-matics self-efficacy and interest as well as differences inoutcomes.

Finally, only enrollment intentions, and not actualenrollment, were studied. Although the emphasis wasplaced on younger students so that suggestions that couldcircumvent later drops in enrollment could be generated, alongitudinal design would have been beneficial. Longitudi-nal analyses would have allowed a better understanding ofwhether the differences in the importance of mathematicsself-efficacy and interest are related to the actual behaviorof boys and girls. Furthermore, a longitudinal study wouldhave allowed for the evaluation of any changes in therelative importance of these variables. Despite theselimitations, we believe the findings do indicate severalimportant practical applications.

Practical implications

The present findings clearly indicate that girls are makingpositive advancements in the academic domain of mathe-matics. In fact, the results suggest that girls may even havesome advantages prior to entering high school. Althoughthe importance of self-efficacy is not as strong for girls as itis for boys, girls still possess similar levels of mathematicsself-efficacy. Furthermore, mathematics interest is moreimportant for girls than it is for boys. Because interest isalso associated with emotion as the person interacts with anobject, in the present case mathematics, a greater depen-dence upon interest might initially appear to be anadvantage in the mathematics classroom. However, thismay only result in a poor fit between what girls need andthe context in which mathematics is taught.

Mathematics has long been criticized for its “mile wide,inch deep” curriculum; educators are encouraged to cover alot of content but not to work on deeper levels ofunderstanding. This strategy would likely fail to catch andmaintain the interest of many students. Because girls appearto rely on interest more so than boys do, they may be morelikely to make the decision to avoid continued enrollmentin mathematics courses when given the choice. Therefore,educators should make a concentrated effort to infuseinteresting activities in mathematics content. Mathematicscertainly has multiple applications to everyday life. High-lighting these applications would likely facilitate theinterest of all students, both boys and girls, but might

influence girls in particular to continue in mathematicscourses.

Acknowledgement This work was sponsored by an AERA-OERIresearch grant to Drs. Stevens and Olivárez.

References

Bandura, A. (1986). Social foundations of thought and action: Asocial cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (2001). Social cognitive theory: An agentic perspective.Annual Review of Psychology, 52, 1–26.

Brandon, P. R., Newton, B. J., & Hammond, O. W. (1987). Children’smathematics achievement in Hawaii: Sex differences favoringgirls. American Educational Research Journal, 24, 437–463.

Brown, R. P., & Josephs, R. A. (1999). A burden of proof: Stereotyperelevance and gender differences in math performance. Journalof Personality and Social Psychology, 76, 246–257.

Bruning, R. H., Schraw, G. J., & Ronning, R. R. (1999). Cognitivepsychology and instruction (3rd edn.). Upper Saddle River, NJ:Merrill.

Crombie, G., Sinclair, N., Silverthorn, N., Byrne, B. M., DuBois, D.L., & Trinneer, A. (2005). Predictors of young adolescents’ mathgrades and course enrollment intentions: Gender similarities anddifferences. Sex Roles, 52, 351–367.

Fennema, E., & Hart, L. E. (1994). Gender and the JRME. Journal forResearch in Mathematics Education, 25, 648–659.

Gainor, K. A., & Lent, R. W. (1998). Social cognitive expectationsand racial identity attitudes in predicting the math choiceintentions of Black college students. Journal of CounselingPsychology, 45, 403–413.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes incovariance structure analysis: Conventional criteria versus newalternatives. Structural Equation Modeling, 6, 1–55.

Hyde, J. S., Fennema, E., & Lamon, S. J. (1990). Gender differencesin mathematics performance: A meta-analysis. PsychologicalBulletin, 107, 139–155.

Joreskog, K. G., & Sorbom, D. (2002). LISREL 8.52. Chicago, IL:Scientific Software International.

Kenney-Benson, G. A., Pomerantz, E. M., Ryan, A. M., & Patrick, H.(2006). Sex differences in math performance: The role of children’sapproach to schoolwork. Developmental Psychology, 42, 11–26.

Lapan, R. T., Boggs, K. R., & Morrill, W. H. (1989). Self-efficacy as amediator of investigative and realistic general occupationalthemes on the Strong-Campbell Interest Inventory. Journal ofCounseling Psychology, 36, 176–182.

Lee, V. E., & Bryk, A. S. (1989). A multilevel model of the socialdistribution of high school achievement. Sociology of Education,62, 172–192.

Lent, R. W., Brown, S. D., Brenner, B., Chopra, S. B., Davis, T.,Talleyrand, R., et al. (2001). The role of contextual supports andbarriers in the choice of math/science educational options: A testof social cognitive hypotheses. Journal of Counseling Psychol-ogy, 48, 474–483.

Lent, R. W., Lopez, F. G., & Bieschke, K. J. (1991). Mathematics self-efficacy: Sources and relation to science-based career choice.Journal of Counseling Psychology, 38, 424–430.

Lent, R. W., Lopez, F. G., & Bieschke, K. J. (1993). Predictingmathematics-related choice and success behaviors: Test of anexpanded social cognitive model. Journal of Vocational Behav-ior, 42, 223–236.

Ma, X., & Willms, J. D. (1999). Dropping out of advancedmathematics: How much do students and schools contribute to

362 Sex Roles (2007) 56:351–363

the problem? Educational Evaluation and Policy Analysis, 21,365–383.

MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992).Model modifications in covariance structure analysis: Theproblem of capitalization on chance. Psychological Bulletin,111, 490–504.

Maccoby, E. E., & Jacklin, C. N. (1974). The psychology of sexdifferences. Stanford, CA: Stanford University Press.

Meyer, M. R., & Koehler, M. S. (1990). Internal influences on genderdifferences in mathematics. In E. Fennema & G. C. Leder (Eds.),Mathematics and gender (pp. 60–95). New York, NY: TeachersCollege Press.

Moreno, S. E., & Muller, C. (1999). Success and diversity: Thetransition through first-year calculus in the university. AmericanJournal of Education, 108, 30–57.

National Council of Teachers of Mathematics (1991). Professionalstandards for teaching mathematics. Reston, VA: Author.

National Council of Teachers of Mathematics (2000). Principles andstandards for school mathematics. Retrieved October 9, 2004,from http://standards.nctm.org/document/.

Organization for Economic Co-Operation and Development (1994).The jobs study, vols. I-II. Paris: Author.

Panteli, N., Stack, J., & Ramsay, H. (2001). Gendered patterns incomputing work in the late 1990s. New Technology, Work andEmployment, 16, 3–11.

Perl, T. H. (1982). Discriminating factors and sex differences inelecting mathematics. Journal for Research in MathematicsEducation, 13, 66–74.

Post, P., Stewart, M. A., & Smith, P. L. (1991). Self-efficacy, interest,and consideration of math/science and non-math/science occupa-tions among Black freshmen. Journal of Vocational Behavior, 38,179–186.

Reynolds, N. G., & Conaway, B. J. (2003). Factors affectingmathematically talented females’ enrollment in high schoolcalculus. Journal of Secondary Gifted Education, 14, 218–228.

Rivera-Batiz, F. L. (1992). Quantitative literacy and the likelihood ofemployment among young adults in the United States. Journal ofHuman Resources, 27, 313–328.

Rock, D., & Pollack, J. (1995). Psychometric report for the NELS: 88base year through second follow-up. Washington, DC: U.S.

Department of Education, Office of Educational Research andImprovement.

Ruble, D. N., Greulich, J., Pomerantz, E. M., & Gochberg, B.(1993). The role of gender-related processes in the developmentof sex differences in self-evaluation and depression. Journal ofAffective Disorders, 29, 97–128.

Sabers, D., Cushing, K., & Sabers, D. (1987). Sex differences inreading and mathematics achievement for middle level students.Journal of Early Adolescence, 7, 117–128.

Schneider, B., Swanson, C. B., & Riegle-Crumb, C. (1998).Opportunities for learning: Course sequences and positionaladvantages. Social Psychology of Education, 2, 25–53.

Schoenfeld, A. (2002). Making mathematics work for all children:Issues of standards, testing, and equity. Educational Researcher,31, 13–25.

Sherman, J. (1981). Girls’ and boys’ enrollments in theoreticalmathematics courses: A longitudinal study. Psychology of WomenQuarterly, 5, 681–689.

Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threatand women’s math performance. Journal of Experimental SocialPsychology, 35, 4–28.

Stevens, T., & Olivárez, A., Jr. (2005). Development and evaluation ofthe Mathematics Interest Inventory. Measurement & Evaluationin Counseling & Development, 38, 141–152.

Stevens, T., Olivárez, A., Jr., & Hamman, D. (2006). The role ofcognition, motivation, and emotion in explaining the mathemat-ics achievement gap between Hispanic and White students.Hispanic Journal of Behavioral Sciences, 28, 161–186.

Stevens, T., Olivárez, A., Jr., Lan, W. Y., & Tallent-Runnels, M. K.(2004). Role of mathematics self-efficacy and motivation inmathematics performance across ethnicity. Journal of Education-al Research, 97, 208–221.

Trusty, J. (2002). Effects of high school course-taking and othervariables on choice of science and mathematics college majors.Journal of Counseling and Development, 80, 464–474.

U.S. Department of Education, National Center for EducationalStatistics (1995). The condition of education 1995. RetrievedOctober 9, 2004, from http://nces.ed.gov/pubs/96768.pdf.

Woodcock, R. W., McGrew, K. S., & Mather, N. (2001). WoodcockJohnson III tests of achievement. Itasca, IL: Riverside.

Sex Roles (2007) 56:351–363 363