Spanish High School Students’ Interests in Technology

Abstract

The authors have examined the relative contribution of personal (emotional state, gender-role attitudes), contextual (perceived social supports and barriers), and cognitive (self-efficacy beliefs, outcome expectations) variables to technological interests in a sample (N = 2,364) of 10th-grade Spanish students. The results of path analysis supported social cognitive career theory (SCCT), indicating that technological self-efficacy contributed to technological interests and technological outcome expectations. Perceived social support and perceived social barriers were related to technological self-efficacy, technological outcome expectations, and technological interests. However, the results did not support the hypothesis that outcome expectations contribute to interests. Contrary to expectations, there was no influence of gender-role attitudes on technological self-efficacy, but gender-role attitudes did determine technological interests. Finally, our study demonstrated that emotional state influenced technological self-efficacy beliefs, technological outcome expectations, and technological interests. This research extends previous work in this area by examining an understudied group, Spanish teenage students.

Keywords

social cognitive career theory self-efficacy beliefs outcome expectations path analysis high school students

The European Union considers it necessary to make greater efforts to convert science, technology, engineering, and mathematics (STEM) into a priority area of education. The demand for a qualified workforce in technology- and research-intensive sectors will remain at a high level, due to their continued need for skills related to STEM. Thus, technological competency is important for professional success in a growing number of occupations. In the case of the Spanish context, which has been influenced by a serious economic crisis, it is even more important to develop such technological competency and increase the numbers of STEM graduates, since these are key factors that may help the country to emerge from the crisis.

If our aim is to increase the number of graduates in technology and engineering, we need to analyze the factors that influence interest in technology. Therefore, in this study we examine the interests in this field of Spanish high school students, investigating several factors that have been shown to be salient in students from other countries. The identification of these factors would help teachers and career counselors to focus their work on such features.

In addition, data about Spanish university students show us that while women are in the majority in health sciences, social sciences, and humanities studies, their presence is very low in the STEM degrees (National Institute of Statistics, 2014). Assuming that not all girls and boys can have the same gender-role socialization experiences—including those relevant to career development—this study also considers the potential effect of these gender-role attitudes on their technological interests.

Interests Model and Social Cognitive Career Theory (SCCT)

SCCT can help us understand technology interests in Spanish High School students. SCCT has been widely used as theoretical framework to explain students’ academic choices, vocational preferences, and academic performance. Lent, Brown, and Hackett state that the configuration of vocational careers is influenced by both cognitive–personal variables and contextual and personal factors (Lent & Brown, 2006; Lent, Brown, & Hackett, 1994, 2000). The core of the SCCT model is formed by cognitive–personal variables: self-efficacy beliefs, outcome expectations, interests, and goals (Lent & Brown, 2006). The background variables (gender, ethnicity, etc.) and contextual variables (perceived social supports and barriers) remain outside the core, influencing it through their predictive value in relation to self-efficacy beliefs and other variables.

Since its publication, SCCT has stimulated a considerable body of empirical research. Verification of the model has mainly been carried out with samples of American college and high school students. The cross-cultural validity of SCCT has become an increasingly popular focus for career-related research in recent years, however, insufficient studies have been performed from a cross-cultural perspective. Some authors have found support for the potential usefulness of SCCT with Portuguese (Lent, Paixao, Da Silva, & Leitao, 2010) and Italian high school students (Lent, Brown, Nota, & Soresi, 2003). Our study belongs to this group of cross-cultural studies that intend to explore the predictive utility of SCCT for explaining technology interests in Spanish 10th-grade students. Thus, the main contribution of the present study would be the extension of previous findings on the SCCT model to a novel cultural and linguistic context. This extension could benefit the development of SCCT, since the studies, though carried out in different countries, have used a common set of measures and indicated the predictive utility of the model across cultural contexts.

As regards the relationship between cognitive–personal variables in the SCCT model, the research found strong positive paths between math and science self-efficacy and outcome expectations (N. A. Fouad & Smith, 1996; Navarro, Flores, & Worthington, 2007; Turner, Stewart, & Lapan, 2004). Likewise, the cited studies reveal other relationships between cognitive–personal variables. N. A. Fouad and Smith (1996), with 380 ethnically diverse middle school students, found that math and science interests had a fairly strong effect on intentions, and there were positive paths between outcome expectations and intentions (goals). Along similar lines, the study by Turner, Stewart, and Lapan (2004; see also Navarro et al., 2007) with 318 sixth-grade adolescents showed that adolescents’ math self-efficacy and math outcome expectations together affected their math and science career interests. Navarro, Flores, and Worthington (2007), with a sample of 426 Mexican American middle school students, found that math/science self-efficacy beliefs and math/science outcome expectations positively predict math/science goals. Moreover, they found that math/science self-efficacy seemed to have a greater total effect on interests than did math/science outcome expectations.

Regarding cross-cultural studies, Lent, Brown, Nota, and Soresi (2003), in their work with 796 Italian high school students, found that, across Holland types, self-efficacy was strongly predictive of outcome expectations; also, the paths from self-efficacy and outcome expectations to interests were significant for each theme. Similarly, Lent, Paixao, Da Silva, and Leitao (2010), with 600 Portuguese high school students, found that the integrated interests-choice model fitted the data well across Holland types and generally supported the hypothesis that self-efficacy and outcome expectations were individually and jointly predictive of interests. Furthermore, interests mediated the relations between self-efficacy and outcome expectations, on the one hand, and choice consideration on the other.

It should be borne in mind that some studies corroborate only specific aspects of the SCCT model. Thus, for example, Flores and O’Brien (2002), with a sample of 364 Mexican American adolescent girls, found that nontraditional career self-efficacy predicted nontraditional career interests. In Nauta and Epperson’s (2003) study of a sample of teenage girls with interests in science and technology, self-efficacy beliefs were found to influence interests in science, and these interests predicted disposition to persist with scientific and technological studies. This relationship between variables was not supported for technological interests, so that self-efficacy beliefs might not predict interests in technology, and such interests might not predict disposition to persist with science and technology studies. Similarly, Flores, Navarro, and Dewitz (2008) found no significant relationship between core variables in a sample of U.S. high school students of Mexican origin.

SCCT and Contextual Variables

As far as the influence of social supports and barriers is concerned, it should be pointed out that there are few studies with high school students and that research with college students is inconclusive. Previous studies have supported the assertion that high perceived social support and low perception of social barriers will increase people’s beliefs about their ability in a particular vocational area (Lent, Paixao, et al., 2010). However, in other studies the influence of social support has been found but not that of barriers (Flores, Navarro, Smith, & Ploszaj, 2006; Lent, Brown, Nota, et al., 2003). Other studies showed that supports or social barriers directly affect goals (Lent, Brown, Sheu, et al., 2005; Lent, Lopez, Lopez, & Sheu, 2008; Lent, Lopez, Sheu, & Lopez, 2011; Sheu et al., 2010), outcome expectations (Lent, Brown, Brenner, et al., 2001; Sheu et al., 2010), and interests (Lent, Brown, Brenner, et al., 2001). Finally, in the study by Constantine, Wallace, and Kindaichi (2005), perception of barriers to career development was significantly and positively related to career indecision in a sample of African American teenagers.

Outside SCCT, we should mention some qualitative studies that analyze the influence of the perception of social barriers and supports on the vocational development of secondary school students. In Hill, Ramirez, and Dumka (2003), adolescents with more clearly defined goals tended to perceive fewer social barriers. Furthermore, students categorized as members of families that provided little or no support tended to have less clear goals and to perceive greater barriers in their academic aspirations. In the work by Kenny, Blustein, Chaves, Grossman, and Gallagher (2003), perceived social support contributed to the school attitudes of high school students, helping them to feel engaged in their academic and vocational goals. In addition, the perception of barriers emerged as a negative predictor of educational and vocational attitudes, even though less robust and consistent than social support.

Gender-Role Attitudes and SCCT

The importance of gender-role formation and its influence on vocational choices have also been studied. Technological fields have always been considered “boys’ subjects,” and research has shown that adolescent girls show less interests in them (Papastergiou, 2008). In the case of Spain, similar results have been obtained in the research conducted by Vazquez and Manassero (2009a, 2009b). These authors found that girls scored significantly lower than boys on the item “to have a technological job.” Similarly, when asked if they would like to get a job in technology, boys valued this positively and girls negatively. For these reasons, we believe that the “gender-role attitudes” variable may play a relevant role within the SCCT model for explaining Spanish high school students’ interests in technology.

In our study, gender-role attitudes refer to beliefs and expectations about what is appropriate for males and females in career-related technological fields. For example, an adolescent boy who holds traditional (vs. egalitarian) gender-role attitudes would be more likely to pursue a career in a technological field. From an SCCT perspective, gender-role attitudes would be considered a personal variable, resulting from the interaction of more basic person-related inputs (sex) and social influences. Therefore, we propose that gender-role attitudes would be associated with technological self-efficacy, outcome expectations, and interests.

In relation to this variable, Gushue and Whitson (2006), with a sample of Black and Latina high school girls, showed that gender-role attitudes predicted career decision self-efficacy. The results suggested that the more successfully a Black girl incorporates egalitarian gender-role attitudes into her self-understanding, the stronger her beliefs in her ability to negotiate the tasks associated with career decision making. Similarly, Flores, Robitschek, Celebi, Andersen, and Hoang (2010) demonstrated that the gender-role variable was a significant predictor of career self-efficacy. Tokar, Thompson, Plaufcan, and Williams (2007) extended the research on SCCT and corroborated that conformity to masculine/feminine role norms related positively to learning experiences. They suggested that conformity to gender-role norms was an important individual difference that contributed uniquely to career-related learning experiences. However, in Flores and O’Brien’s work with Mexican American adolescent women (2002), there was no significant association between feminist attitudes and nontraditional career self-efficacy.

Purpose of the Study

The purpose of the present study was to examine influences on technological interests in Spanish high school students. This analysis allows us to evaluate the generalizability of SCCT to a novel cultural and linguistic context as there is a need for additional research on SCCT, principally in the area of cross-cultural and cross-national investigation. Our own research replicated and extended SCCT propositions with a sample of Spanish high school students. To this end, we administered measures of several cognitive–personal (self-efficacy beliefs, outcome expectations), contextual (perceived social support and barriers), personal (gender-role attitudes, emotional state), and outcome (interests) variables.

In accordance with SCCT, we hypothesized that (a) technological self-efficacy would be related to technological outcome expectations and (b) technological self-efficacy and technological outcome expectations would influence technological interests. In addition, we investigated the influence of contextual supports and barriers. Thus, we hypothesized that (c) perceived social support and barriers would influence technological self-efficacy, outcome expectations, and interests. Finally, we analyzed the effects of gender-role attitudes on SCCT and proposed that (d) gender-role attitudes would be associated with technological self-efficacy, outcome expectations, and interests.

Method

Participants

Students participating in this study (N = 2,364) were 10th graders, 1,197 of whom were girls (50.7%) and 1,162 boys (49.3%; 5 people failed to indicate their sex), recruited from public (61.4%) and private (38.6%) high schools in the Principality of Asturias, a region in Northwestern Spain. The age range was 16–18 years (M = 15.65, SD = 0.74), but age data were lacking for 84 students. Age distribution was normal, with absolute skewness and kurtosis values under 1. Analysis of age by gender yielded statistically significant differences (F = 10.80, p = .001). Mean age for girls was 15.52 (SD = 0.73) and for boys, 16.66 (SD = 0.75). Race/ethnicity was not recorded, as the presence of immigrants in the Principality of Asturias Autonomous Region (compared to others, such as Andalusia or the Canary Islands) is very low. The sampling technique used was probability proportional to size. This procedure is highly appropriate when the sampling units are very different in magnitude, insofar as it ensures that all have the same probability of inclusion in the sample, regardless of their size. Furthermore, researchers can easily plan their fieldwork because a specific number of respondents are interviewed in each of the selected units.

Procedure

All participants filled out the questionnaires in the spring of 2012, during normal school hours. A total of 45 schools participated in the study. Students were told that they could leave the room if they did not want to participate in the study, and a few did so. After a brief presentation in which one of the researchers spoke about the purpose of the study, the students were asked to begin filling out the questionnaire, which took around 30–45 min. Participation in the study was voluntary, with no remuneration or award of course credits. No parents withheld consent.

Instruments

The questionnaire answered by participants was designed after taking into account the following instruments: First of all, four scales translated into Spanish, namely, F. Fouad and Smith’s (1997) Math/Science Outcome Expectations Scale and Math/Science Intentions and Goals Scale (MSIGS), and Britner and Pajares’s (2001, 2006) Science Grade Self-Efficacy Scale and Sources of Science Self-Efficacy Scale (all translated with the authors’ authorization). The Technology Interests Scale (Inda, Rodríguez, & Peña, 2013) was adapted from Lent, Brown, Nota, et al.’s (2003) Engineering Fields Questionnaire, and the author gave us permission to translate the measures. The procedure set out by Hambleton, Merenda, and Spielberg (2005) was followed for the adaptation of the instruments from source (English) to target language (Spanish). The Spanish to English back translation could not be done by the original authors because they do not read Spanish, but a native Spanish speaker with an excellent command of English was able to carry out the Spanish to English translation.

Technology Grade Self-Efficacy Scale

This scale is a Spanish translation and adaptation of the Science Grade Self-Efficacy Scale (Britner & Pajares, 2001, 2006). The scale consists of 3 items assessing high school students’ confidence in their abilities to successfully perform technology-related tasks. Participants rated each item (e.g., “How confident are you that you will pass technology subjects?”) on a 6-point scale ranging from 1 (not confident at all) to 6 (completely confident). Total mean scores ranged from 1 to 6, with high scores indicating high levels of technology self-efficacy. For the full sample, the scale yielded an internal consistency (reliability; Cronbach’s α) of .88. Britner and Pajares (2006) found an α coefficient of .85, an earlier study by the same authors (Britner & Pajares, 2001) having yielded an α of .86.

Sources of Technology Self-Efficacy Scale

The Sources of Technology Self-Efficacy Scale is a Spanish translation and adaptation of the Sources of Science Self-Efficacy Scale (Britner & Pajares, 2006). The scale consists of 59 items measuring the effects of mastery experience, vicarious experience, social persuasion, and psychological states (1 = strongly disagree to 6 = strongly agree). Britner and Pajares (2006) reported α coefficients of .90 for mastery experience, .80 for vicarious experience, .88 for social persuasion, and .91 for emotional state. In our study, three factors were obtained from factor analyses: social persuasion, vicarious experience, and emotional state. The mastery experience factor appears to be somewhat unclear, with some items being found in the social persuasion factor and others in vicarious learning (Table 1).

Table 1.
Summary of Exploratory Factor Analysis.

χ² df TLI-NNFI CFI GFI RMSR^a

Nine factor 23,268.78* 3,561 .79 .83 .99 .03

Eight factor 14,762.53* 2,406 .84 .87 .99 .03

Seven factor 13,703.90*** 2,136 .85 .90 1.00 .02

Note. TLI-NNFI =Tucker–Lewis index–nonnormed fit index; CFI = comparative fit index; GFI = goodness-of-fit index; RMSR = root mean square of residuals.

^aKelly’s criterion = .021.***p < .001.

Social persuasion items refer to the support students perceive from important people “My mother thinks I’m good at things to do with technology and computers” and “My friends tell me I’m good at technology and computers.” This factor has been called “perceived social support.” For the full sample, the factor has an α coefficient of .95. The second factor, vicarious experience, measures variables that work as barriers for students’ careers. Examples of items in this category would be “Most of my friends dislike technology/computers” and “Technology has always been a very difficult subject for me.” This factor has been called “perceived social barriers,” and its α coefficient is .85.

The third and last factor has been defined as emotional states, rather than psychological states, since we consider it measures more constructs than just psychological states. Within this factor there are 2 items for assessing (a) whether young people show an absence of perseverant behavior (regarding activities and planning one’s career) and (b) the anxiety felt by students in relation to technology/computers. That is, psychological states are a part of emotional states. The loads of the items in this factor can be seen in Table 1.

Two items are related to perseverant behavior (“My friends—men and women—have told me to consider a science and technology career” and “When I come across a tough science question, I work on it until I find a solution”). The first one does not refer, strictly speaking, to perseverant behavior but rather to an insistence on the part of one’s friends to consider a university course related to science and technology. We can explain this result as follows: when students feel uncomfortable or nervous about technology and science activities, they do not persist in trying to solve them and are less likely to consider a career that involves science and technology. The α coefficient for the full sample was .86.

Technology Outcome Expectations Scale

The Technology Outcome Expectations Scale is a modified version of the Mathematics/Science Outcome Expectations Scale (MSOES; Fouad, Smith, & Enochs, 1997). Consisting of 9 items, this scale assesses high school students’ beliefs about the potential consequences of studying technology subjects in high school (1 = strongly disagree to 6 = strongly agree; e.g., “Studying technology subjects in high school will improve my job prospects”). The α coefficient in the present sample was .95, while N. A. Fouad and Smith’s (1996) study yielded an α of .71, and F.Fouad and Smith (1997) obtained an α of .70.

Technology Intentions and Goals Scale

This is an adaptation of the MSIGS (Fouad et al., 1997) and includes 4 items assessing high school students’ intentions to pursue and persist in technology-related courses at college (1 = strongly disagree to 6 = strongly agree). (e.g., “I plan to do a technology-related degree at university”). N. A. Fouad and Smith’s (1996) study yielded an internal consistency (α coefficient) of .66, while F.Fouad and Smith (1997) obtained an α of .74. In our study, factor analyses reveal one factor, “outcome expectations.” Three items measure goals, but their loadings are no higher than .35.

Technology Interests Scale

This scale included 9 Likert-type items and was an adaptation of the Engineering Fields Questionnaire (Lent, Brown, Nota, et al., 2003). The original scale was intended for university students, but we adapted it to test adolescents’ interests in technological activities. Item examples would be “I find technology boring” and “I’m interested in learning about technology.” Participants rated each item on a 6-point scale ranging from 1 (very low interests) to 6 (very high interests). Reliability of this scale was .94. In Lent, Brown, Nota, et al. (2003), this scale obtained an α coefficient of .83, in Lent, Brown, Sheu, et al. (2005) the α coefficient was .80.

Gender-role attitudes

This scale consisted of 7 Likert-type items, also developed by the research team, for assessing the individual’s gender stereotypes in relation to technology studies. Examples of these items are “Boys are better than girls at solving technological problems” and “Girls are as good as boys at technology subjects.” Participants rated each item on a 6-point scale ranging from 1 = strongly disagree to 6 = strongly agree. The α coefficient in the present sample was .85. We evaluated the content validity using Lawshe’s (1975) content validity ratio (CVR). Twenty-three experts took part in this evaluation, all of them had expert knowledge about gender differences in a range of scientific areas (education, sociology, psychology, and technology). CVR values ranged from 0.82 to 1.00.

Results

Missing Data

We screened the data to analyze missing cases. Percentages missing ranged from 1.1% to 63%. In our analysis of missing data patterns, we first displayed patterns of missing data by individual case and then carried out t-tests with groups made up of indicator variables. In order to further analyze the data, an analysis of variance was carried out to determine whether the data for these variables were missing completely at random or missing at random by comparing indicator variables in age and sex. We found no significant differences (p > .05) by age, and there were no relationships by sex between groups of variables. Therefore, we decided to remove four variables where the proportion of missing data was 61–63%. These variables were “I have brothers who have completed high school science and technology,” “I have sisters who have completed high school science and technology,” “My brothers encouraged me to do high school science and technology,” and “My sisters encouraged me to do high school science and technology.”

Factor Analyses

We carried out an exploratory factor analysis (EFA) with all the scales since this instrument had not been validated for the population of Asturias (a region in northern Spain). We set out to test the SCCT model, but in this case, in contrast to other studies, we used several scales from a range of authors, all of which had been designed for evaluating the SCCT model. Once we checked whether the data were suitable for carrying out EFA, we used the FACTOR program (Lorenzo-Seva & Ferrando, 2006). Item normality was assessed by skewness and kurtosis (values among −1 and 1). The Bartlett and Kaiser–Meyer–Olkin (KMO) tests were checked to determine whether the items correlated among themselves. The obtained values were Bartlett’s statistic = 96,771.50, df = 2,775, p =.000010, and KMO = .96. We used the scree plot and “minimum partial test” to determine how many factors were retained. Unweighted least squares was used as factor extraction method. This method has been shown as the more effective with relatively small samples (Ferrando & Anguiano-Carrasco, 2010). Promin was the oblique rotation method employed (Lorenzo-Seva, 1999), because it does not consider factors as pure measures of a single dimension.

The initial solution was a nine-factor one (mastery experience, vicarious experience, social persuasion, emotional states, self-efficacy, outcome expectations, goals, interests, and gender-role attitudes). However, this model did not show acceptable fit, χ²(3,561, 2,136) = 23,268.784 (p =.000010), Tucker–Lewis index–nonnormed fit index (TLI-NNFI) = .79, comparative fit index (CFI) = .83, goodness-of-fit index (GFI) = 0.9, and root mean square of residuals (RMSR) = .03. Kelly’s criterion value for acceptance of the model was .02. Consequently, we carried out a refinement of items, removing those with extreme skewness and kurtosis and/or a discrimination index of under .30. We forced the entry of the following items: “Most of my friends (boys) dislike technology/computers” and “Most of my friends (girls) dislike technology/computers” into a factor called perceived social barriers (vicarious experience), and when these items were forced into a single factor (eight-factor solution), the factor structure fit was poorer. This eight-factor structure also included the items “my friends (boys) tend to take science/technology courses” and “my friends (girls) tend to take science/technology courses.” Finally, the seven-factor structure was accepted as the most appropriate for the sample (Table 1). It is interesting to note that the RMSR value decreased from the nine-factor to the seven-factor model, this being an indicator of the amount of error in the factor structure. As this value decreases, error also decreases.

The fit indices support the seven-factor solution as the best, χ²(2,349, 2,136) = 13,703.9, p = .000010, TLI-NNFI = .85, CFI = .90, GFI = 1, and RMSR = .02. Kelly’s criterion value for acceptance of the model was .02. The α coefficient for the whole scale was .96. All factor loadings were over .30, with reliability values of .80 or more in all factors, indicating that the scale’s reliability was generally adequate. The structure solution obtained suggests the need to refine Pajares’s model, mainly with regard to mastery experience, whose items were distributed among the other three factors: perceived social supports (social persuasion), perceived social barriers (vicarious experience), and emotional states (Table 2).

Table 2.
Factorial Structure Obtained.

Items 1 2 3 4 5 6 7

Reliability .88 .95 .94 .95 .86 .85 .85

Number of Items 3 10 8 21 11 5 5

How confident are you that you will get a B into majority of technological subjects this year? (computer, technology) .75

How confident are you that you will get an A in the majority of technological subjects this year? (computer, technology) .63

How confident are you that you will get a C in the majority of technological subjects this year? (computer, technology) .58

Subjects of the next 11th grade will likely allow me to get a good job .91

Subjects of the next 11th grade will likely allow me to earn an attractive salary when I work .90

Subjects of the next 11th grade will likely allow me to have a career that is valued by society .89

Subjects of the next 11th grade will likely allow me to have a career that is valued by my family .87

Subjects of the next 11th grade will likely allow me to make an educational plan that will prepare me for college .81

Subjects of the next 11th grade will likely allow me to choose the degree I would like .75

Subjects of the next 11th grade will likely allow me to go into a field with high employment demand .75

I will study the career my teachers want for me is .35

I intend to enter a career that will use technological/computer .33

I will study the career my parents want for me is .31

I enjoy learning things technology/computer .93

Subjects of technology/computer are fun .90

I have interested in learning about technology/computer .88

I would like working on involving technology/computer .80

My favorites subjects are from technology/computer .66

Technology/computer is boring to me −.57

Subjects of Technology/computer are useful to daily life .55

I am not interested in technology/computer −.43

My teachers believe that I have high technology/computer ability .84

My mother believes that I have high technology/computer ability to get good grades .80

People often tell me I am good in technology/compute. .82

My father believes that I have high technology/computer ability to get good grades .79

Students in my class believe I am good in technology/computer .76

I have always had a natural talent for technology /computer .72

My teachers have told me I have good technology/computer skills .62

My counselor has told me I have good technology/computer skills .61

My friends tell me that I am good in technology/computer .60

My family believes that I can do well in technology/computer subjects .57

People I look up to have told me to consider a career that involves technology/computer .57

I am able to help my classmates with difficult technology/computer .57

People I look up to are good at technology/computer .53

I am often encouraged to participate in school activities involving technology/computer .52

I have been congratulated for my grades in the subjects technology/computer .50

My friends often come to me for help in technology/computer .47

Among my friends, I am usually the one who finishes a technology/computer activity first .46

My favorite teachers are usually technology/computer teachers .37

When I see my teachers explain technology/computer, it makes me think that I could be a technology/computer teacher .35

I got a high grade in last year’s technology/computer class .34

Many of the adults I know have jobs that require a good understanding of technology .32

Most of my friends (boys) dislike technology/computer .68

Most of my friends (girls) dislike technology/computer .68

Technology/computer has always been a very difficult subject for me .53

Most of my friends (boys) do poorly in technology/computer .47

Most of my friends (girls) do poorly in technology/computer .43

I am not as good in technology/computer .46

I get really uptight during technology/computer tests .45

My close friends believe I have poor technology/computer skills .43

I am not as good in technology/computer as most of the kids in my class .40

My mind goes blank and I am unable to think clearly during technology/computer activities .41

Technology/computer makes me feel uneasy and confused .36

Science makes me feel uneasy and confused .82

Science makes me feel uncomfortable and nervous .82

When I come across a tough science question, I work on it until I solve it −.51

My friends (men and women) I look up to have told me to consider a career that involves science and technology −.45

Technology makes me feel uncomfortable and nervous .31

My mother believes that technology/computer is for boys .86

My father believes that technology/computer is for boys .82

Few girls studying technology / computer subjects because women have more skills for arts than science .53

Boys get better grades in technological/computer subjects than girls .51

Girls are as good as boys in technology −.32

Note. N = 2,345. Kaiser–Meyer–Okin index = .96. Loadings lower than absolute 0.30 omitted. 1 = self-efficacy; 2 = outcomes expectations/goals; 3 = interests; 4 = perceived social supports; 5 = perceived social barriers; 6 = emotional state; 7 = gender-role attitudes.

Furthermore, we should point out that in the factor analysis, the loads of the items that evaluate “goals” show better results when they are in conjunction with the items of the “outcome expectations” factor than when they are considered as two different factors. However, we have also studied this model with high school students (to be reported in our next paper) and with college students (Inda et al., 2013), and in this case the model fits better when goals is considered as an independent factor. In future research we would like to assess this variable in more depth with another sample of secondary school students and to test the hypothesis about the later development of the psychological construct goals in relation to the rest of the dimensions of the model.

Path Analysis of the Social Cognitive Model Applied to the Sample of High School Students

Data fit of the SCCT structural model was also studied. It has been suggested that model fit should be assessed using a series of indices to ensure more reliable and accurate decisions regarding this aspect. In the present study, the chi-square test of significance (χ²), the ratio of χ² statistics to degrees of freedom, the CFI, the GFI, the standardized root mean square residual (SRMSR), and Steiger’s root mean square error of approximation (RMSEA) were used to assess the fit of the hypothesized SCCT structural model. Fit of the model was obtained using the M-PLUS program (Muthén & Muthén, 2010). Standard deviations and Pearson correlations were calculated, and the estimation method employed was maximum likelihood (ML; Table 3).

Table 3.
Correlations Between the Seven Factors of the Model and Descriptive Statistics for Each Factor.

M SD 1 2 3 4 5 6

Self-efficacy 3.77 1.58

Outcome expectations 3.92 1.24 .24*

Interests 2.17 1.38 .46* .19*

Perceived social supports 3.66 1.22 .53* .23* .76*

Perceived social barriers 2.99 0.87 −.28* −.03 −.43* −.36*

Emotional state 0.18 1.23 −.27* −.31* −.39* −.41* .32*

Gender-role attitudes 1.17 1.24 .07 −.04* .11* .14* .00 .09*

Note. N = 2,364.

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

The model shown in Figure 1 yielded indicators of good fit: χ²(2,364, 2) = 4.20, p = .12, CFI = .99, TLI-NNFI = .99 and RMSEA = .02 (Hoyle, 1995; Hooper, Coughlan, & Mullen, 2008). Percentage of variance explained is 29% for self-efficacy, 14% for outcome expectations, and 61% for interests (Lent, Brown, Nota, et al., 2003; Lent, Paixao, et al., 2010). We estimated the paths among the factors hypothesized by the theoretical model, finding support for the specific contribution of social supports and social barriers in the determination of self-efficacy levels. Social supports are the major influence on self-efficacy and interests, and social barriers show a significant path to interests. Emotional state is an important factor in relation to outcome expectations, ahead of self-efficacy and social supports. Social barriers have an indirect influence on outcome expectations mediated by self-efficacy. Although the influence of gender-role attitudes in interests is not statistically significant, we considered the incorporation of this factor into the model because in spite of being an experimental factor, its internal consistency was good (.85). Moreover, when it was omitted from the model the fit was poorer, so it seemed necessary to take into account its role.

Figure 1.
Parameter estimates from the path analysis (N = 2,364). Dotted line indicates indirect effects of perceived social barriers to outcome expectations through self-efficacy. ^†p < .10. p < .05; p < .01; p < .001.

Discussion

This study was the first to test aspects of Lent et al.’s (1994, 2000) SCCT with a sample of high school students in the domain of technology. Consistent with the SCCT’s propositions, our findings suggest that technology self-efficacy determines technology interests and technology outcome expectations. However, we also note that the path from self-efficacy beliefs to interests was low. Previous research had contributed with strong evidence for these direct relations within the mathematics/science domains (N. A. Fouad & Smith, 1996; Turner et al., 2004) and in the context of the Holland Themes (Lent, Brown, Nota, et al., 2003; Lent, Paixao, et al., 2010) with samples of middle school and high school students. It would seem that greater confidence in their capacity for technology increases students’ expectations about the potential consequences of choosing technology subjects. Moreover, technology confidence increases their interests in technology-related activities. These findings support the SCCT propositions that people develop interests in areas in which they have a strong sense of agency.

Despite the numerous SCCT propositions supported in the present study, one of them was not corroborated. Previous findings with samples of middle school and high school students suggested that outcome expectations determined interests (N. A. Fouad & Smith, 1996; Lent, Brown, Nota, et al., 2003; Lent, Paixao, et al., 2010; Navarro et al., 2007; Turner et al., 2004), but we failed to find such a relationship. In our study, while technology self-efficacy has a significant effect on technology interests and outcome expectations, technology outcome expectations do not have the expected effects on technology interests. The meta-analysis developed by Sheu et al. (2010), organizing the literature in line with Holland’s broad occupational themes, found that not only outcome expectations but also self-efficacy usefully contribute to the prediction of interests. In that study, “although self-efficacy sometimes accounts for more predictive variance than do outcome expectations in individual studies, the study suggests that outcome expectations constitute a worthy conceptual partner when results are aggregated over studies” (Sheu et al., 2010, p. 262). Further research is necessary to examine the conditions that affect the relative contributions of self-efficacy and outcome expectations to predictive criteria.

In line with previous findings from samples of high school and middle school students, perceived social support (Flores et al., 2006; Lent, Brown, Nota, et al., 2003; Lent, Paixao, et al., 2010) and perceived social barriers (Lent, Paixao, et al., 2010) significantly predicted technology self-efficacy. Furthermore, our findings support the assertion that perceived social support and perceived social barriers have an influence (direct or indirect) on technology outcome expectations and technology interests, although their influence on outcome expectations was low. Previous research with samples of college students has provided evidence for these direct relations between perceived social support/social barriers and outcome expectations (Sheu et al., 2010) as well as interests (Lent, Brown, Brenner, et al., 2001). Our study demonstrates the potential value of contextual variables for influencing all the cognitive–personal variables. N. A. Fouad et al. (2010) explain that the majority of the research examining SCCT has focused on the individual cognitive variables, rather on their interaction with the environmental variables. Our study represents an effort to follow their recommendation that future empirical investigation should examine the dimensionality of the contextual supports and barriers measures and assess how particular types of supports and barriers relate to choice behaviors.

Furthermore, our study fails to corroborate the findings previously obtained in relation to gender-role attitudes and their influence on self-efficacy beliefs (Gushue & Whiston, 2006; Tokar, Thompson, Plaufcan, & Williams, 2007). In the present work, there is no influence of gender-role attitudes on technology self-efficacy (see too Flores & O’Brien, 2002). Gender-role attitudes were found to influence significantly outcome expectations but not technology interests. Further exploration of the importance of the gender-role attitudes factor in the SCCT model is required. One possible explanation may be that the lack of variability in scores assessing gender-role attitudes contributed to the lack of validity of this variable as regards confidence in technology activities.

Finally, our study demonstrates that emotional state influences technology self-efficacy beliefs, technology outcome expectations, and technology interests. More specifically, stress and anxiety in relation to technology activities give rise to low self-efficacy beliefs regarding technology studies (see too Gainor & Lent, 1998; Lent, Lopez, & Bieschke, 1991; Williams & Subich, 2006) and lack of interests in technology. In addition, people expect negative consequences from studying technology-related subjects. However, we also note that the path from emotional state to self-efficacy was low. There has been very little study of the influence of the personal variable “emotional states” in the SCCT model. Britner and Pajares (2001, 2006), following Bandura’s model, identified four resources that influence self-efficacy beliefs: learning experiences, social persuasion, vicarious learning, and emotional states. In our study, we used the scales proposed by these authors, so that we incorporated the variable emotional states in the statistical analysis. The results show that this variable had greater influence on the cognitive–personal variables of the SCCT model than the other personal variable analyzed (gender-role attitudes). That is, the anxiety people experience on carrying out a technological task has more influence on cognitive–personal variables than their beliefs and expectations about what is appropriate for males and females in career-related technological fields.

Directions for Professional Practice and Future Research

Studies with larger and more diverse samples are needed to allow for intergroup comparisons (Gushue & Whitson, 2006). In this regard, our findings can help to check some of the key assumptions of the SCCT model in other cultural contexts. Additional studies on SCCT within cross-cultural contexts are necessary so as to permit a fuller appreciation of SCCT’s range of generalizability.

Complementing the findings of earlier research with middle school and high school samples, our results suggest that SCCT can help to explain the academic interests of high school students at a developmental stage which can be a key one for their later career options in technological fields. The majority of studies that have validated the SCCT model with adolescents were carried out in the context of math/science domains (N. A. Fouad & Smith, 1996; Navarro et al., 2007) and Holland themes (Lent, Brown, Nota, et al., 2003; Lent, Paixao, et al., 2010), and our own work explored the influence of several factors on technological interests. Given the stated importance of highlighting STEM as a priority area of education, future research would need to analyze and identify effective approaches for increasing student engagement and interests in this area.

The obtained results and conclusions indicate the need for further research into social barriers and support. One way to enrich our understanding of these experiences would be to carry out qualitative investigation which may allow students to express in detail how contextual factors influenced their career decisions. Future studies should help researchers in this field to improve our understanding and explanations of the mechanisms of different social support and barriers.

Sheu et al. (2010) point out that since the introduction of social cognitive theory to the field of vocational psychology, more empirical attention has been given to self-efficacy than to outcome expectations. This differential focus may reflect the relative weight given to the two constructs within Bandura’s broader social cognitive theory. Therefore, Fouad and Guillén (2006; as cited in Navarro et al., 2007, p. 331), “pointed to the lack of attention given to outcome expectations in the vocational literature and the need to further attend to the operational definition of outcome expectations, their precursors, and their role in SCCT.”

It is important to stress that the present study incorporates a personal variable: emotional states. In our research, this variable can determine self-efficacy beliefs regarding technology studies, technology outcome expectations, and technology interests. Nevertheless, there is a need for supplementary research to analyze the role of this variable in the SCCT model and test whether the results obtained in our study can be corroborated in cross-national and cross-cultural studies.

The present study has practical implications and suggests the utility of the SCCT model in the context of career development and technology. Professionals could find support from this research for developing career interventions focused on increased self-efficacy beliefs, outcome expectations, and interests in technology studies. If the aim is to increase graduation rates in science and technology (a fundamental objective of the European Union), interventions—especially in high school—should promote the development of technology self-efficacy beliefs, technology outcome expectations, and technology interests. Since low technology interests impact on the goals involved in completing an engineering degree (Inda et al., 2013), high-school counselors should encourage optimal technological career development in adolescents. It would also be advantageous to design interventions for helping students to avoid stress and anxiety in relation to technology studies. Furthermore, it is necessary to influence the design of career development programs that allow educators and counselors to address those factors within the social and educational environment (social supports and barriers) that may be discouraging high school students from pursuing technological careers. In this regard, vocational guidance programs should encourage parents, counselors, and teachers to value the importance of their opinions in relation to students’ choice of degree course as well as to provide support to students at different relevant points of their vocational development.

Limitations and Conclusion

Like all research, the present study has some clear limitations. One of these is that the sample population was high school students in a specific geographical and cultural context (Asturias, Spain). Caution should be exercised in generalizing our findings to other contexts and other ages. Secondly, our study was tested with a racially homogeneous sample, and it would be important in future to test SCCT in other, more ethnically diverse Spanish regions. Future research in this line, then, should be carried out with more racially and geographically diverse Spanish samples to check whether similar findings are obtained.

Thirdly, the cross-sectional nature of this research is a methodological limitation. The study can indicate statistical relationships among particular variables but cannot support causal inferences. Longitudinal designs are needed to validate causal relations in the SCCT model. Fourth, it is important to note that SCCT posits that background variables affect the formation of self-efficacy beliefs and outcome expectations which, in turn, affect interests. The present study examined only two types of personal input (gender-role attitudes and emotional state) and did not assess background contextual factors (e.g., mother and father’s educational level, and current occupation).

Fifth, in our study the significance of the gender-role attitudes factor within the SCCT model was low. One possible explanation for this is that we did not use gender-role attitude scales that have been validated (e.g., attitudes toward feminism and the women’s movement scale). In this regard, future research should include measurement of this variable through standardized instruments.

To conclude, and despite these limitations, this study provides additional support for the predictive utility of SCCT in a cross-cultural context. Our research extends previous work in this area by examining an understudied group, Spanish teenage students, in a new area: the technological domain. Preliminary analyses show that social cognitive, personal, and contextual variables explain technological interests in Spanish high school students. These results suggest a variety of future research directions and offer some important points for career counselors working with middle school and high school students. In particular, they suggest that efforts to promote adolescents’ technological self-efficacy may provide a viable means of consolidating their academic interests in technology.

	χ²	df	TLI-NNFI	CFI	GFI	RMSR^a
Nine factor	23,268.78***	3,561	.79	.83	.99	.03
Eight factor	14,762.53***	2,406	.84	.87	.99	.03
Seven factor	13,703.90***	2,136	.85	.90	1.00	.02

	M	SD	1	2	3	4	5	6
Self-efficacy	3.77	1.58
Outcome expectations	3.92	1.24	.24***
Interests	2.17	1.38	.46***	.19***
Perceived social supports	3.66	1.22	.53***	.23***	.76***
Perceived social barriers	2.99	0.87	−.28***	−.03	−.43***	−.36***
Emotional state	0.18	1.23	−.27***	−.31***	−.39***	−.41***	.32***
Gender-role attitudes	1.17	1.24	.07**	−.04***	.11***	.14***	.00	.09*

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article is based on work supported by the Ministry of Economy and Competitiveness from Spain (EDU- 2010- 17233). We gratefully acknowledge the assistance of Shari Britner (Bradley University) and R. W. Lent (University of Maryland) for sending the scales used in this study.

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Items	1	2	3	4	5	6	7
Reliability	.88	.95	.94	.95	.86	.85	.85
Number of Items	3	10	8	21	11	5	5
How confident are you that you will get a B into majority of technological subjects this year? (computer, technology)	.75
How confident are you that you will get an A in the majority of technological subjects this year? (computer, technology)	.63
How confident are you that you will get a C in the majority of technological subjects this year? (computer, technology)	.58
Subjects of the next 11th grade will likely allow me to get a good job		.91
Subjects of the next 11th grade will likely allow me to earn an attractive salary when I work		.90
Subjects of the next 11th grade will likely allow me to have a career that is valued by society		.89
Subjects of the next 11th grade will likely allow me to have a career that is valued by my family		.87
Subjects of the next 11th grade will likely allow me to make an educational plan that will prepare me for college		.81
Subjects of the next 11th grade will likely allow me to choose the degree I would like		.75
Subjects of the next 11th grade will likely allow me to go into a field with high employment demand		.75
I will study the career my teachers want for me is		.35
I intend to enter a career that will use technological/computer		.33
I will study the career my parents want for me is		.31
I enjoy learning things technology/computer			.93
Subjects of technology/computer are fun			.90
I have interested in learning about technology/computer			.88
I would like working on involving technology/computer			.80
My favorites subjects are from technology/computer			.66
Technology/computer is boring to me			−.57
Subjects of Technology/computer are useful to daily life			.55
I am not interested in technology/computer			−.43
My teachers believe that I have high technology/computer ability				.84
My mother believes that I have high technology/computer ability to get good grades				.80
People often tell me I am good in technology/compute.				.82
My father believes that I have high technology/computer ability to get good grades				.79
Students in my class believe I am good in technology/computer				.76
I have always had a natural talent for technology /computer				.72
My teachers have told me I have good technology/computer skills				.62
My counselor has told me I have good technology/computer skills				.61
My friends tell me that I am good in technology/computer				.60
My family believes that I can do well in technology/computer subjects				.57
People I look up to have told me to consider a career that involves technology/computer				.57
I am able to help my classmates with difficult technology/computer				.57
People I look up to are good at technology/computer				.53
I am often encouraged to participate in school activities involving technology/computer				.52
I have been congratulated for my grades in the subjects technology/computer				.50
My friends often come to me for help in technology/computer				.47
Among my friends, I am usually the one who finishes a technology/computer activity first				.46
My favorite teachers are usually technology/computer teachers				.37
When I see my teachers explain technology/computer, it makes me think that I could be a technology/computer teacher				.35
I got a high grade in last year’s technology/computer class				.34
Many of the adults I know have jobs that require a good understanding of technology				.32
Most of my friends (boys) dislike technology/computer					.68
Most of my friends (girls) dislike technology/computer					.68
Technology/computer has always been a very difficult subject for me					.53
Most of my friends (boys) do poorly in technology/computer					.47
Most of my friends (girls) do poorly in technology/computer					.43
I am not as good in technology/computer					.46
I get really uptight during technology/computer tests					.45
My close friends believe I have poor technology/computer skills					.43
I am not as good in technology/computer as most of the kids in my class					.40
My mind goes blank and I am unable to think clearly during technology/computer activities					.41
Technology/computer makes me feel uneasy and confused					.36
Science makes me feel uneasy and confused						.82
Science makes me feel uncomfortable and nervous						.82
When I come across a tough science question, I work on it until I solve it						−.51
My friends (men and women) I look up to have told me to consider a career that involves science and technology						−.45
Technology makes me feel uncomfortable and nervous						.31
My mother believes that technology/computer is for boys							.86
My father believes that technology/computer is for boys							.82
Few girls studying technology / computer subjects because women have more skills for arts than science							.53
Boys get better grades in technological/computer subjects than girls							.51
Girls are as good as boys in technology							−.32