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A Multidimensional Scaling Analysisof Students’ Attitudes about ScienceCareersAmy M. Masnick a , S. Stavros Valenti a , Brian D. Cox a &Christopher J. Osman ba Hofstra University, New York, USAb North Shore High School, New York, USA

Available online: 21 May 2009

To cite this article: Amy M. Masnick, S. Stavros Valenti, Brian D. Cox & Christopher J. Osman(2010): A Multidimensional Scaling Analysis of Students’ Attitudes about Science Careers,International Journal of Science Education, 32:5, 653-667

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International Journal of Science EducationVol. 32, No. 5, 15 March 2010, pp. 653–667

ISSN 0950-0693 (print)/ISSN 1464-5289 (online)/10/050653–15© 2010 Taylor & Francis DOI: 10.1080/09500690902759053

RESEARCH REPORT

A Multidimensional Scaling Analysis of Students’ Attitudes about Science Careers

Amy M. Masnicka*, S. Stavros Valentia, Brian D. Coxa and Christopher J. OsmanbaHofstra University, New York, USA; bNorth Shore High School, New York, USATaylor and FrancisTSED_A_376075.sgm10.1080/09500690902759053International Journal of Science Education0950-0693 (print)/1464-5289 (online)Original Article2009Taylor & Francis0000000002009Dr. AmyMasnickamy.m.masnick@hofstra.edu

To encourage students to seek careers in Science, Technology, Engineering and Mathematics(STEM) fields, it is important to gauge students’ implicit and explicit attitudes towards scientificprofessions. We asked high school and college students to rate the similarity of pairs of occupations,and then used multidimensional scaling (MDS) to create a spatial representation of occupationalsimilarity. Other students confirmed the emergent MDS map by rating each of the occupationsalong several dimensions. We found that participants across age and sex considered scientificprofessions to be less creative and less people-oriented than other popular career choices. Weconclude that students may be led away from STEM careers by common misperceptions thatscience is a difficult, uncreative, and socially isolating pursuit.

Keywords: Attitudes; Nature of science; Quantitative research; Science education; Secondary school

Finding ways to encourage students to seek careers in Science, Technology,Engineering and Mathematics (STEM) fields has been a continuing challenge toscience teachers. The choice of a career path depends on establishing a feedback loopbetween interest and competence: Interest in a subject leads to the acquisition ofknowledge in that domain, which leads to competence in the tasks in that area, whichin turn feeds continued interest. Eventually, students can come to identify with prac-titioners in science fields and commit to a career path. Although children beginschool with high interest in science (Simpkins, Davis-Kean, & Eccles, 2006) andmath (Jacobs, Lanza, Osgood, Eccles, & Wigfield, 2002), the results of longitudinal

*Corresponding author. Amy Masnick, Department of Psychology, Hofstra University, 207Hauser Hall, Hempstead, 11549, USA. Email: amy.m.masnick@hofstra.edu

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654 A. M. Masnick et al.

studies suggest that their attitude towards science decreases over time, with a signifi-cant drop at junior high (Eccles et al., 1993; Fredricks & Eccles, 2002).

To some degree, children’s increasingly negative attitudes towards STEMsubjects are related to their decreased feelings of competence in math from thebeginning of elementary school through high school, for both genders (Jacobs et al.,2002). However, the practice of science requires many competencies in addition tospecific math skills, such as analytical ability, skepticism, persistence, and problem-solving. Researchers in career choice have labeled this constellation of personalitytraits an “investigative personality type” (e.g., Holland, 1996). Others have found arelationship between science achievement and “mastery orientation” (Haydel &Roeser, 2002; Snow, 1994). But trait labeling begs the question, especially in devel-opmental studies, of whether and how such a set of skills is acquired. Somestudents undoubtedly move away from the science pathway because they perceivethemselves to lack science competence, but some students who do quite well inscience courses in school lose interest in science as well, either at the point ofchoosing higher-level high school science courses, or at the choice of major, or bythe end of their sophomore year in college (Barnes, McInerney & Marsh, 2005;Brainard & Carlin, 1998; George, 2006). Even the majority of students of bothsexes with high levels of preparation and skill in math and physics may not chooseSTEM careers, though there is some evidence this is more common for women(Dick & Rallis, 1991; Lubinski & Benbow, 2006).

Alternatively, some of the most relevant influences on the choice of science careersinclude social factors, such as parent and teacher science knowledge or encourage-ment, the availability and commitment of mentors, the provision of materialresources (e.g., chemistry sets) experiences (e.g., trips to science museums) or, notsurprisingly, participation in after school science activities (Eccles et al., 1983;George, 2006). The relationship between parent influence and science interest inelementary school appears to be strongest because of parental support of scienceactivities (Simpkins, Davis-Kean, & Eccles, 2005; Simpkins et al., 2006). Later inthe adolescent years, parental support leads to science interest because of parentsand children who value achievement across all topics rather than because of specificscience-related activities (Jodl, Michael, Malanchuk, Eccles, & Sameroff, 2001;Simpkins et al., 2006).

Finally, some of the drop-off in the choice of STEM careers may be due simplyto students’ negative perceptions of the day-to-day activities of science, perceptionsacquired by the increase in rigor accompanied by a decrease in creativity of sciencecourses in the high school years (Osborne, Simon, & Collins, 2003). Students oftenchoose activities based on whether they believe them to be intrinsically motivatingand challenging (Larson & Borgen, 2002). There is considerable evidence thatscience, as taught in high school, focuses on isolated facts, and therefore is notperceived by many in high school as intrinsically motivating (e.g., Osborne &Collins, 2001). Not surprisingly, then, students who like such subjects are them-selves negatively stereotyped. For example, German high school students believethat those who like science are less attractive, popular, socially integrated, socially

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Students’ Attitudes about Science Careers 655

competent, and creative than those who like humanities (Hannover & Kessels,2004).

Perhaps the hard work of learning science techniques in the high school yearscauses students to lose sight of why scientists actually do science, whom they doit with, and what benefit it holds for others. If students are not made aware ofthe excitement of discovery, the creativity of experimentation, or the ubiquity ofscientific collaboration, they are unlikely to become scientists. Recent researchhas shown that creative self-efficacy—the ability to see oneself as being creative—is related to competence and interest in science (Beghetto, 2007). It is one thingfor students to be able to do science, but quite another to see oneself as a scien-tist or as someone who uses a high level of scientific problem-solving in a dailyjob. Some studies show that students still see science as quite useful to society,even as their attitudes towards science as a career declines (Fredricks & Eccles,2002). Overall, few high school students identify themselves as potentially pursu-ing STEM careers (Hannover & Kessels, 2004; Nosek, Banaji, & Greenwald,2002).

If we wish to increase students’ choice of STEM college majors and careers, weneed to know more about students’ beliefs about science as a profession. The currentstudy employs explicit and implicit measures to assess attitudes towards scientificprofessions as compared with other occupations. As with many prejudices, attitudesagainst science may only be partially conscious (Greenwald & Banaji, 1995). Inpart because students may know that science is highly valued, their direct responsesto questionnaires may give a skewed picture of their attitudes. In the few studieswhere implicit measures have been used, high school students associate physicsconcepts with difficulty, unpleasantness, and with not allowing free expression ofideas (Kessels, Rau, & Hannover, 2006); they also associate science words withwords denoting a lack of creativity (Osman, Masnick, Cox, & Valenti, 2008).These implicit beliefs may be driving personal choices in spite of students’ explicitstatements about the value of science.

In the first phase of our research, we employed multidimensional scaling (MDS)as an implicit measure of the subjective attributes of science occupations. MDS doesnot prejudge the relevant dimensions that differentiate various occupations; rather,the program converts participants’ ratings of the similarity of pairs of occupationsinto an n-dimensional space to best fit the participants’ similarity judgments. In thismanner, it can serve as an implicit measure of occupational attitudes. In the secondphase of our research, the dimensions of the occupation space were validated byhaving each participant rate occupations explicitly on characteristics that seem to berepresented by the dimensions.

If our results are consistent with previous research, we would expect that highschool and college students will associate negative attributes to science occupations.Students should find science occupations to be dissimilar to artistic occupations onthe dimension of creativity. They should also be seen as dissimilar to occupationsperceived as requiring social skills, and more similar to occupations requiring techni-cal, rather than social, competence.

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Method

Participants for Phase 1

Forty-five US high school students (22 male and 23 female, ages 16–18) and 58college students (26 male and 32 female, ages 18–25) participated in this phase ofthe study. The high school students were from a public high school in New YorkState, USA with 89% European-American, 5% Asian-American, and 4% Hispanic.The college students attended a private liberal arts institution in New York State,USA with 84% European-American, 7% African-American, 5% Asian-American,and 2% Latino students. Our samples approximated the ethnic proportions of theschools from which they were drawn.

Materials and Procedure for Phase 1

A list of 20 occupation names was created, including scientific occupations (biologist,chemist, geologist, physicist, psychotherapist), artistic occupations (interior designer,landscaper, musician, portrait painter, writer), technical professions (architect,builder, computer programmer, electrician, mechanic), and other common profes-sions (accountant, doctor, lawyer, teacher, police officer).

Participants were asked to compare pairs of occupations and rate how similarthe occupations were on a five-point scale. They were given an example ofcomparing Golden Retrievers and Labrador Retrievers: “If you feel that GoldenRetrievers and Labrador Retrievers are very similar to one another, you would ratethis pair as a 1 or 2. If you feel they are not very similar, you would rate this pairas a 4 or 5”.

All possible pairs of occupations were presented to participants, for a total of 190comparison ratings. The pairs were randomly ordered and participants completedthe ratings in one of two orders.

The second phase of the study was designed to help label the dimensions thatemerged from the MDS.

Participants for Phase 2

Seventy-five college students (ages 18–25), 38 males and 37 females, completed thesecond phase of the study. No participants from this phase completed the MDSratings measures. The ethnicity proportions of this sample approximated those ofthe college at large.

Materials and procedure. Participants were asked to rate each of the 20 occupationson a scale of 1 (low) to 10 (high) on six qualities: creative, people-oriented, techni-cal, talent essential, artistic, and scientific. We selected these qualities from speakingwith participants and by using our own judgments of qualities relevant for thinkingabout and comparing occupations.

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Students’ Attitudes about Science Careers 657

Results

We used a three-step method of analysis. First, participants’ ratings of the similari-ties of all possible pairs of occupations were reduced by MDS to an n-dimensionalspace. To portray the dimensions participants used to compare occupations, the 190average similarity judgments were represented as “distances” ranging from 1 (simi-lar) to 5 (dissimilar). Separate MDS analyses were conducted on each of the fourparticipant groups (female high school, male high school, female college, and malecollege) under the assumption of ordinal distances. This procedure creates a map inan abstract measurement space on which each occupation can be located, indexedby several dimensions. Second, the coherence of these dimensions across sex andage groups was justified by item intercorrelation and factor analysis procedures.Finally, we validated our interpretations of the meaning of the dimensions byregressing the dimensional scores on students’ explicit ratings of characteristics, andthen judging the resulting goodness of fit.

Dimensional Structure of the Career Space

To determine the number of dimensions that best fit the data, we followed therecommendations of Kruskal and Wish (1978, pp. 53–56) and examined the patternof S-stress (a measure of residual error) for solutions with dimensions from 1through 5. For all four groups, the stress began to asymptote around 0.1 for a three-dimensional solution; that is, after three dimensions, adding additional dimensionsto the model did not appreciably improve the amount of variance explained by thedata. Thus, we chose the three-dimensional solution as the best fit for the data foreach participant group, with each occupation located within a three-dimensional“career space”.

A Single Career Space across Age and Sex Groups

Further analyses suggest a high similarity among the four participant groups in thelocations of specific occupations in the abstract three-dimensional career space.There is no agreed-upon statistical test to confirm that multiple representations ofdimensions are similar or dissimilar. However, we reasoned that similarity could bedemonstrated in two complementary ways. First, we examined the intercorrelationsof the 20 occupations’ scores across all combinations of three dimensions and fourgroups. Table 1 indicates that intercorrelations were always higher within the samedimension, regardless of the participant group, and ranged from .91 to .99. Intercor-relations across dimensions were much lower, ranging from .01 to .24. Thus, there issome evidence that these three dimensions are similar across the four participantgroups.

As a complementary measure of similarity across the four groups, an exploratoryfactor analysis was performed. Entering 12 occupation score variables correspond-ing to three dimensions for each of four groups yielded three principal components

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658 A. M. Masnick et al.

Tab

le 1

.In

terc

orre

lati

on m

atri

x fo

r di

men

sion

s ac

ross

stu

dent

gro

ups

and

sex

Dim

ensi

onG

roup

Sex

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

1H

igh

Sch

ool

Fem

ale

(1)

Mal

e(2

)0.

97**

*C

olle

geF

emal

e(3

)0.

99**

*0.

96**

*M

ale

(4)

0.98

***

0.96

***

0.99

***

2H

igh

Sch

ool

Fem

ale

(5)

0.06

0.02

0.06

0.10

Mal

e(6

)−0

.02

−0.0

20.

000.

060.

92**

*C

olle

geF

emal

e(7

)0.

110.

100.

110.

160.

97**

*0.

93**

*M

ale

(8)

0.00

−0.0

10.

010.

050.

94**

*0.

98**

*0.

95**

3H

igh

Sch

ool

Fem

ale

(9)

0.06

−0.1

00.

120.

080.

110.

000.

040.

01M

ale

(10)

0.18

0.02

0.24

0.17

0.18

0.03

0.10

0.05

0.93

***

Col

lege

Fem

ale

(11)

0.02

−0.1

50.

080.

040.

140.

070.

060.

070.

94**

*0.

91**

*M

ale

(12)

0.10

−0.0

50.

170.

120.

200.

080.

130.

090.

96**

*0.

96**

*0.

97**

*

Not

e: *

**p

< .0

01 (

2-ta

iled)

.

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Students’ Attitudes about Science Careers 659

with eigenvalues between 3.5 and 4.4; all remaining eigenvalues were below 1.0.Direct oblimin rotation yielded a pattern matrix consistent with a single careerspace and occupation locations. Variables representing the same dimension loadedon one and only one component with values ranging from .98 to .99; all other load-ings were .24 or less. These results provide further evidence that all four participantgroups represented the occupations similarly across three dimensions. For allsubsequent analyses, we used a single MDS solution, collapsing the data across ageand sex.

Interpretation of the Career Space Dimensions

To help with the interpretation of the three dimensions, we used the mean ratings ofthe six qualities done by college-aged participants collected in the second phase ofthe study.1 We reasoned that the identity of each dimension may be representedamong one or more of the six occupational characteristics selected for the ratings:creativity, people-oriented, talent required, scientific, technical, and artistic. If this istrue, then some of these quality ratings would be correlated with the three dimen-sions of the occupations in the MDS solution. To assess how well these dimensionsare “named” by the occupational quality ratings, Kruskal and Wish (1978) recom-mend that MDS dimensions be regressed on independent ratings of the proposedconstructs—in this case, the occupational quality ratings. Using this procedure, themultiple R would assess the degree of fit between the dimensions and each ratedoccupational quality.

Spearman-Brown coefficients of effective reliability for the six mean occupa-tional quality ratings (creativity, people-oriented, talent required, scientific, techni-cal, and artistic) ranged from .86 to .99. Thus, we felt comfortable that thesequality ratings were reliable. The intercorrelations among the six rated qualitiesare presented in Table 2. It is interesting to note that occupations judged to be“scientific” were also judged to be “not creative” (r = −.50). Scientific occupationswere also rated as technical (r = .46) and not artistic (r = −.60). On the otherhand, there was an association of artistic occupations with creativity (r = .96) andtalent (r = .75).

Table 2. Intercorrelations among the 6 ratings of occupation qualities (N = 20)

Rating Creative People-oriented Talent Scientific Technical Artistic

CreativePeople-oriented 0.12Talent 0.79*** 0.10Scientific −0.50* −0.40 −0.26Technical −0.34 −0.40 −0.13 0.46*Artistic 0.96*** 0.10 0.75*** −0.60** −0.30

Note: *p < .05; **p < .01; ***p < .001.

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660 A. M. Masnick et al.

Following the recommendations of Kruskal and Wish (1978), we then performedseparate regressions of the MDS dimensions on each of the six rated qualities of theoccupations. Table 3 presents the standardized regression coefficients for eachdimension across the six rated qualities. The regression weights for Dimension 1suggest that “scientific” (Beta = .67), “artistic” (Beta = −.70), and “creativity” (Beta= −.55) are all reasonable interpretations.

Figure 1 presents a graphical representation of the occupations plotted alongDimension 1 and Dimension 2. Using the coefficient for Dimension 1 regressed onartistic ratings (.70), a 46° clockwise rotation of that dimension [cos(45.57) = .70]yields the axis “artistic-not artistic” which is presented in the MDS plot. A similarprocedure suggests that Dimension 2, with rotation, has several possible labels, suchas “people-oriented” (Beta = .63) or “scientific” (Beta = −.65) or “technical” (Beta= −.63). While there may be several ways to describe Dimension 2, we reason that“people-oriented” yields the most satisfactory label for this dimension because“artistic” and “people-oriented” are orthogonal, that is, not correlated (r = .10, notsignificant). Therefore, the “people-oriented” axis is drawn perpendicular (orthogo-nal) to the “artistic” axis.Figure 1. Two-dimensional representation of Dimension 1 and Dimension 2 for occupation similarity ratings for all participantsNote that scientific occupations in Figure 1 such as biologist, chemist, andphysicist are located opposite the artistic occupations of writer, painter, and musi-cian. Midway between these are a varied set of professions, some of which areviewed as people-oriented (e.g., psychologist, teacher, police officer) and othersthat are viewed as opposite of people-oriented (e.g., builder, electrician, land-scaper). This structure was replicated with four different subject samples, acrosstwo age groups and two sex groups. Therefore these two dimensions are bothdistinct and robust.

The fact that several regression coefficients in Table 2 are significant for Dimen-sion 3 does not help us reach an easy interpretation of this dimension. We reasonthat “technical” provides an acceptable fit with this dimension because it is theonly rated quality that covaries with Dimension 3 (Beta = .46) but it does notcorrelate significantly with the rated qualities “artistic” and “people-oriented”. In

Table 3. Standardized regression of 3 dimensions on each of 6 ratings of occupations

Dimension

Scale 1 2 3 RCreativity −0.55*** 0.40** −0.56*** 0.88***People 0.16 0.63** 0.10 0.69*Talent −0.31 0.43* −0.59** 0.76**Scientific 0.67*** −0.65*** −0.17 0.90***Technical −0.15 −0.63** 0.46* 0.74**Artistic −0.70*** 0.38** −0.48*** 0.93***

Note: *p < .05; **p < .01; ***p < .001.

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Students’ Attitudes about Science Careers 661

other words, these three ratings—artistic, people-oriented, and technical—appearto form an orthogonal set that maps reasonably well after rotation onto the careerspace.

Figure 2 shows the 20 occupations mapped with reference to the axes “artistic”and “technical” (i.e., Dimensions 1 and 3). Figure 3 completes the set of MDS maps,showing the occupations mapped with reference to the axes “people-oriented” and“technical” (i.e., Dimensions 2 and 3). These maps suggest that students see highsimilarity among a group of technical occupations: Architect, Builder, ComputerProgrammer, Electrician, and Mechanic. This subgroup received the highest explicitratings on technical (M = 8.5 on a scale of 1 to 10). One difficulty with interpretingDimension 3 is that the group of science occupations (i.e., Biologist, Chemist, Phys-icist, Geologist) appears opposite of the technical occupations in Figure 2, but alsoappears close to the technical occupations in Figure 3. Hence, “technical” should beconsidered a provisional interpretation of this dimension.

Figure 1. Two-dimensional representation of Dimension 1 and Dimension 2 for occupation similarity ratings for all participants

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662 A. M. Masnick et al.

Figure 2. Two-dimensional representation of Dimension 1 and Dimension 3 for occupation similarity ratings of all participantsFigure 3. Two-dimensional representation of Dimension 2 and Dimension 3 for occupation similarity ratings for all participants

Discussion

Interpreting the Dimensions of the MDS Analysis

The key goal of this study was to assess perceptions of scientific and nonscientificcareers by exploring how students rate occupations relative to one another and howthey rate occupations on a series of characteristics. Examining the results of theMDS ratings, we found that high school and college students differentiate occupa-tions using three dimensions. The first dimension, when rotated, produces an axis ofhow scientific the occupation appears to be, which was inversely related to how artis-tic or creative it was rated. Thus, students perceive science and creativity to beessentially opposite; professions such as biologist and physicist were perceived ashighly scientific but noncreative, and professions such as interior designer and musi-cian were perceived as creative but not scientific. This clear distinction between the

Figure 2. Two-dimensional representation of Dimension 1 and Dimension 3 for occupation similarity ratings for all participants

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Students’ Attitudes about Science Careers 663

characteristics of science and creativity may play a role in career choices, particularlybecause creative self-efficacy is related to competence in science (Beghetto, 2007).

The second dimension students used in comparing occupations results in an axisof how “people-oriented” the occupations were. Occupations such as police officerand musician were perceived as highly people-oriented, while science occupationssuch as biologist and physicist were perceived as not people-oriented. The MDSdimensions and the direct ratings indicated that scientific occupations clustered veryclosely together on the low end of the people-oriented axis while the different typesof artistic occupations had more varied ratings. For example, musicians were ratedhigher on the people-oriented dimension than landscapers.

The third dimension was least clear-cut from the MDS analysis. We suspect thatmore fine-grained categorizations, such as applied versus more abstract vocations,may be some of what is reflected in Dimension 3, but this could not be confirmedwith the current and somewhat limited occupational set.

Figure 3. Two-dimensional representation of Dimension 2 and Dimension 3 for occupation similarity ratings for all participants

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664 A. M. Masnick et al.

Closing the Gender Gap

In the past, there has been great concern over a gender gap in science involvementfavoring boys (see Osborne et al., 2003). In contrast to such findings, we found goodevidence that the males and females in our sample had similar perceptions ofoccupations; the patterns of responses from each group led to very similar spatialrepresentations of the dimensions. Males and females also gave the same dimen-sional ratings in assessing the professions. Thus, male and female adolescents weresimilar in their understanding of relative characteristics of scientific and nonscientificoccupations, but there is considerable evidence in the literature that women may notchoose the same professions as men.

Overall, the gender gap in choosing science careers has been narrowing; womenare now at near exact parity in obtaining bachelor’s degrees in overall science-related fields (Farmer, Wardrop, Anderson, & Reisinger, 1995; National ScienceBoard, 2008). After the undergraduate years, however, the gender disparityincreases, especially in the physical sciences and engineering professions (NationalScience Board, 2008). Recent research suggests that one of the key gender differ-ences in career choice is likely in what day-to-day activities they want in a profes-sion (Rosenbloom, Ash, Dupont, & Coder, 2008). If both men and womenperceive science professions to be nonsocial, but women value the social dimen-sion more in their jobs, they will either avoid science, or if competent in science,choose different science careers. When women do choose scientific careers, theyare more likely to choose careers in socially valued helping professions such asdoctor or veterinarian (Dick & Rallins, 1991). Indeed, in 2006, the number ofmen enrolling in US medical schools exceeds the number of women by barely1%, on an overall increase in enrollment (American Association of MedicalColleges, 2006). Recently, the number of practicing veterinarians who are womenequaled the number of men; in some top veterinary schools, first year classes arenow over 80% women (Schweitzer, 2007). Both of these professions, of course,are likely perceived to involve more social interaction than the physical sciences.One theory of occupation choice suggests that the reason that a gender differenceremains in engineering, computer science, and physics is that women scienceachievers may be also highly competent in other fields, whereas men in thesefields may have expertise strongly concentrated only in math and science. Smartwomen therefore have more options and may still not choose math or engineer-ing, even as gender discrimination in science dissipates with time in society(Rosenbloom et al., 2008).

Consistent with our findings, Rosenbloom et al. (2008) found that consideringpersonality preferences can explain a sizeable portion of the gender variance in careerchoice. Using a career typology developed by Holland (1996), Rosenbloom et al.found that one of the strongest predictors of career choice is the “Realistic” personalitycharacteristic, which is similar to the characteristic of “Technical” that we assessed.This personality characteristic refers to a “preference for activities that entail theexplicit, ordered, or systematic manipulation of objects, tools, and machines”

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Students’ Attitudes about Science Careers 665

(Rosenbloom et al., 2008, p. 546). Combining the ratings on the realistic personalitycharacteristic with Holland’s other characteristics such as the “Investigative” person-ality characteristic, or scientific focus, the “Artistic” characteristic and the “Social”characteristic accounted for most of the gender differences in career choice. Thus, thefactors students are using to distinguish occupations are some of the same factors theyare using in making their own career choices.

Although preferences may account for some career choice variance, the source ofthe preferences remains unclear. In the current cross-sectional sample, we found noevidence of age differences in the dimensional solutions between high school andcollege students, suggesting that male and female students’ conceptions of careers arestable through at least several years of adolescence and early adulthood. One impor-tant future direction involves following students longitudinally to see if preferenceschange with time. Some longitudinal work looking at mathematically precociousyouth suggests that preferences do play a role in leading to gender differences amongmales and females with exceptionally high mathematics ability (Lubinski & Benbow,2006). Thus, at least for these gifted students, ability was not a determining factor infinal career choice.

Of course, the specific dimensional solution is contingent on the initial set ofoccupations participants were asked to rate. Other occupations that were notincluded, such as sports occupations, business occupations, and humanities occu-pations, could have changed the solution to a degree. Also, even the specific occu-pation categories used encompass a broad range of types of careers. However, thecurrent study demonstrates that perceptions of some science occupations are quitestable and widely shared in late adolescence. Specifically, students see science asnot creative and not social. Undoubtedly, most scientists value the creative andsocial dimensions in their choice of professions, as do the students in our study. Butthe scientists, unlike the students, know that their chosen professions do containthose attributes. Our study suggests that if we want to increase the likelihood thatboth men and women will choose scientific professions, we need to address thesemisapprehensions before their high school and college years, when these attitudesare developing.

Conclusions

This survey demonstrated that high school and college students have a strongperception that scientific careers are not particularly creative and do not involvemuch interaction with others. The high stability across age groups in expressingthese beliefs was surprising, suggesting the beliefs are entrenched before moststudents make substantial career training choices. Students seem to be aware of thebias, in that both the direct ratings and the paired comparisons yielded the samegeneral pattern of results. Students are likely to rely on their perceptions in makingcareer choices. Therefore, further understanding of these concepts becomes impor-tant both for predicting career choices and for informing science educators aboutissues to address in encouraging the next generation of scientists.

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Note

1. Due to time restrictions for collecting data in the high school, we were not able to collect acomparable set of occupation ratings for the high school participants. However, the strongsimilarity between the occupational similarity ratings of high school and college studentsindicates that the pattern of ratings of occupational qualities would be similar as well.

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