The Interaction Effect between Level of Study and Level of Career Aspiration

Abstract

This study is aimed to investigate the differences between level of studies (First year and final year students of IIUM undergraduate students) in regards with their career aspirations level and the interaction effect between level of studies (First year and final year IIUM undergraduate students) and their level of career aspirations with regards with their problem-solving skills.

This present study has two different part of results, the first part of this study found that there is no significant difference between level of studies in regards with levels of career aspiration and in the second part of the result it was found out that there is a statistically significant interaction effect between level of studies and levels of career aspiration (CAS) with regards to problem-solving skills. However, level of career aspiration (CAS) is not a significant contributor to the interaction effect. This could be due to several limitations such as small number of sample size and other factors such as culture differences, emotional state, socio-economic status (SES) and more.

Keywords: Career aspiration, educational levels, level of studies, problem-solving

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Introduction

Career aspiration can be further defined as a path of what career that a person would like to pursue later in life. In the context of this study, career aspiration could also be associated with career readiness, career decision making, career goals and more. These different terms shared the same end goals. All of them would like to measure if the person has already decided or at least have a certain level of readiness in pursuing to a specific career path that could lead them to a better future life planning.

Earliest studies such as by Super, Thompson, Lindeman, Jordaan, & Myers (1981) had found a significant difference on career maturity scores between those who are in their 9th grade and 10th grade, and between 9th grade and 11th grade and 9th grade with 12th grade. Also, in another study, it is showed that there is an increase in career maturity from 9th grade to 12th grade (Crites, 1973; Herr & Enderlein, 1976). Moreover, there are other works that suggested students in higher grades have higher career maturity scores than those students who are in their lower grades (Neice & Bradley, 1979; Wallace-Broscious, Serafica, & Osipow, 1994).

However, in contrast with earlier mentioned studies, in a research by Fouad (1988), it is found that 9th graders did not score lower career maturity than 12th graders. In addition, a later study by Hollenbeck (2000) also suggested similar findings where there was no significant difference of career aspiration between 8th grade and 9th grade levels of students in his study. Another study by Danzinger & Eden (2006) found out that levels of career aspiration declined significantly from first year students up till postgraduate students. In another word, postgraduate students had a lower level of career aspiration in compare with first year and the rest of earlier level students of their research sample.

However, Earl & Bright (2003) had found a different outcome with their sample of 804 first year students and 353 third year students. They found out that age was the greatest predictor for the significant difference of career decision status among the two different level of study groups. It was suggested that students who are older and in their higher level of studies have the tendency to have a higher career decision making in compare with those who are in their early year of studies and with a lower age group. Thus, with a mixed of findings from past literature and mostly focused on school children. In the first phase of this present study, it would like to identify whether a new sample of university students could come out with a significant difference of career aspiration between first year and final year students of IIUM undergraduates.

Now moving to the second phase of the study where Holland and Holland (1977) proposed that career decision making processes does related and involved with one’s problem solving skills. This is further supported by study made by Chartrand, Rose, Elliot, Marmarosh, & Caldwell (1993) who found out that self-appraised problem-solving skills resulted in a better career decision making processes. Moreover, Tami Amir & Itamar Gati (2007) suggested that by having a higher ability in problem-solving, it will help in career decision making. In other words, it is easier for those who are equipped with high level of problem-solving skills to decide what career they would like to pursue or interested in. In the same study, they found out that problem-solving correlated the most with lack of career readiness. In addition, Larson and Heppner (1985) also suggested that problem solving is related to career decision making. While another study by Heppner et al. (2004) found out that by having a positive problem-solving appraisal, participants were more likely to have a better career transitions, better career decidedness and goal directedness.

There is also research that had been made on level of studies or levels of education or age with regards to problem-solving skills. However, most of research focused on a larger group of age such as in a study by Hoyer, Rebok & Marx (1979) where they did a study on 3 different group age that is sixty, young (M = 20.6), middle-aged (M = 52.4), and elderly (M = 72.6) of men and women. They found that the young aged participants scored significantly higher in their problem-solving skills to be compared with the remaining age group. However, in another study by Denney & Palmer (1981), their results found out that the scores of problem-solving skills for those who are in group age 20-29 was (M = 29.63) but it increased for those who are in group age of 30-39 (M = 28.00) and 40-49 (M = 29.36) before it started to decreased gradually in the later age groups.

In other words, it could be seen that levels of problem-solving skills increased from a younger age to an older age but however it could stop at one period such as entering the old age as for example 50 years and above. This is in line with multiple past studies where at an old age, there is a high chance of cognitive decline in this group of people thus affecting their levels of problem-solving skills and many other cognitive aspects (Bjorklund, 2015). This is in line with an earlier study made by D’Zurilla, Maydeu-Olivares, & Kant (1998), where they suggested that problem-solving skills gradually increased from younger age to some period in middle adulthood before it started to decline when reaching late adulthood.

It can be seen that many past literature did not focused on several important aspects such as career aspiration and problem solving was mostly measured separately and little studies had been made on students especially university or college students that will be transiting into working life after finishing their studies and there is a smaller scope of age group or educational level of studies in identifying the levels of problem-solving skills especially among students whether in school, college and universities. Thus, as what past literatures are missing, this present study aimed to identify the interaction effect between level of studies and career aspiration regarding problem-solving skills among first year and final year undergraduate students of IIUM.

Significance of Study

Problem-solving is considered as one of the important skills that is needed in most of any working setting in overcoming the challenges it has (Carnevale, Gainer & Meltzer, 1990). By having a low level of problem-solving skills, a person might affect the performance of the institution or the company that they are working for. Thus, results from present and future studies such as this could help to raise an awareness that problem-solving skills is important in enhancing work performance in any organizational settings and studies found that problem-solving skills are not a natural gift to only certain people, however it can be trained and developed with an adequate amount of consistent trainings from even an early age (Carnevale, Gainer & Meltzer, 1990).

Hence, the reason why this study focused on finding the answers among university students and field such as positive psychology highlighted about enhancement of well-being can be achieved in workplace by having a better positive work performance such as good problem-solving skills (Compton & Hoffman, 2013).

Research Objectives and Hypotheses

Research Objectives

1. To study the differences between level of studies (First year and final year students of IIUM undergraduate students) in regards with their career aspirations level.
2. To study the interaction effect between level of studies (First year and final year IIUM undergraduate students) and their level of career aspirations with regards to their problem-solving skills.

Research Hypotheses

1. It is hypothesized that there is a significant difference between level of studies (First year and final year of IIUM undergraduate students) in regards with their career aspirations level.
2. It is hypothesized that there is an interaction effect between level of studies (First year and final year IIUM undergraduate students) and their level of career aspirations with regards to their problem-solving skills.

Method

Research Design

In this study, an experimental design was conducted to examine the differences between level of studies (First year and final year students of IIUM undergraduate students) in regards with their career aspirations level and to examine the interaction effect between level of studies (First year and final year IIUM undergraduate students) and their level of career aspirations with regards with their problem-solving skills. In this study a mixed of survey and experiment method were used. The survey contained the career aspirations test and the experiment later used a problem-solving test (Raven’s Standard Progressive Matrices) as the material.

Participants and Sampling

The participants of this study first started with 72 undergraduate students where 13 were males and 59 females from International Islamic University Malaysia which involved all programmes in IIUM Gombak campus, with ages ranged from 19 to 27 years old and 28 of them were in their first year while the remaining 44 of the students were in their final year of studies. A purposive sampling technique was used to select the participants of the study with only one criteria such as the participant must be in their first year or final year of undergraduate studies.

Later, for the second phase in answering the second research objective, we narrowed down to only 20 participants from the 72 participants. They were selected using a purposive sampling technique where the participants must fulfil several criteria(s), firstly the participant selected must among the top 10 highest scorers for career aspirations (5 participants for each level) and the remaining 10 participants must be among the lowest career aspirations scorers (5 participants for each level).

Materials

The demographic information is the first part of the questionnaire. It is used to answer the first research hypothesis where information about the participants including age, gender, Kulliyyah, level of study, and contact no. Apart from that, to collect the information on career aspiration, the study used the following scale:

Career Aspiration Scale – Revised

This scale was designed to measure career aspiration. It is comprised of 33 items that measure the career aspiration of participants using four points Likert type scales ranging from 0 = Not true at all of me until 4 = Very true of me. Career Aspiration Scale was developed by Gray & O’Brien (2007) and had been revised several times.

Later in the second phase of the study, to answer the second hypothesis, the 20 selected participants from the earlier study were given this test below to assess their levels of problem-solving:

Raven’s Standard Progressive Matrices (SPM)

Problem-solving was measured using Raven’s Standard Progressive Matrices. Raven’s Standard Progressive Matrices was developed by John C. Raven in 1936 to measure measuring abstract reasoning and regarded as a non-verbal estimate of fluid intelligence. It consisted of 5 sections with a total of 60 multiple choice questions that required the user to choose one possible pattern that fits the question. In terms of reliability, Powers, Barkan and Jones (1986) showed excellent internal consistency of Raven’s SPM for the total test of Anglo-America and Hispanic (r = .87) while study by Abdel-Khalek (2005), showed good to high internal consistency Cronbach’s coefficients alpha ranged from .88 to .93, displaying from acceptable to good temporal stability.

Test-retest reliability (N=968) of the Raven’s SPM when administered to a sample of children in Kuwait (N=6529) ranged between .69 and .85. While Moran (1986), suggested that Raven’s SPM has good moderate convergent validity as it has almost similar result when WAIS verbal IQ score were correlated with WAIS performance scores, r = .65.

Procedure

Firstly, a survey was given out online using Google Docs and only those who are in their first or final year of undergraduate studies were allowed to participate in the survey. Participants were stated about the nature of the study and proper instructions were given in the survey. At the beginning, the participants were asked to fill up the consent form to preserve their confidentiality. The online survey was designed by incorporating the demographic information and Career Aspiration Scale (CAS). The demographic information included a range of information such as age, gender, level of study, Kulliyyah and contact information. Participants took 5 to 10 minutes to submit their complete responses on the measures.

Later, 20 participants will be selected from the sample of the first phase of this study to undergo the second phase of this study. These 20 participants are consisted of 10 participants who scored the highest scores in CAS (5 participants for each first year and final year) and 10 other participants who scored the lowest scores in CAS (5 participants for each first year and final year). All 20 participants are required to go through a test named Raven’s Standard Progressive Matrices (SPM) to identify their levels of problem-solving skills.

Data Analysis and Result

For the first phase of this study, an independent-sample t-test was conducted to compare the career aspiration scores for first year and final year undergraduate students of IIUM. There was no significant difference in scores for first year students (M = 94.75, SD = 16.41) and final year students (M = 97.89, SD = 16.85; t (70) = .778, p = 0.44, two-tailed). The magnitude of the differences in the means (mean difference = 3.14, 95% CI: – 4.91 to 11.18) was moderate (eta squared = .009)

For the second phase of the study, a two-way between-groups analysis of variance (ANOVA) was conducted to examine the effect of level of study and level of career aspiration on problem-solving skills. Participants were divided into two groups according to their level of career aspiration scores (Low; High). There was a statistically significant interaction between the effects of level of study and level of career aspiration on problem-solving skills scores, F(1, 16) = 5.61, p = .031. The main effect of level of study is significant, F(1, 16) = 10.27, p = .006. However, lastly, the main effect of level of career aspirations (CAS) is not significant, F(1, 16) = 3.28, p = .089.

Discussion

Based on the result above, for the first phase of this study it was found that there was no significant difference between those first year and final year undergraduate students of IIUM in their levels of career aspiration. Thus, the first proposed hypothesis is not accepted. This is in line with several past researches that suggested findings stating that there are no significant differences between level of studies and levels of career aspiration (Fouad, 1988; Hollenbeck, 2000 & Danzinger & Eden, 2006). A much recent study by Jawarneh (2016) found out that there was no differences for career maturity based on educational levels but however he did highlighted that there was a significant difference between career planning and with levels of education where he found out that juniors and seniors (second year and third year of studies) were having a higher level of career planning in compare with those students who are in their sophomore year (first year of study).

There are many factors that could affect this result such as in a study made by Day & Allen (2004), found out that mentoring intervention could enhance a person level of career motivation regardless of their age. This showed that regardless of the level of study of a person, if he or she received this intervention later, they will improve their levels of career motivation despite their age or level of studies at that current time.

Moreover, a similar study was made earlier by King & Multon (1996) which they suggested that television role models gave an effect towards levels of career aspiration on high school students. This is in line with Bandura’s (1977) theory of learning where it can be associated with what had been shown to us especially in younger age. Hence, there is a need for parents to control and explain which one is a good career and which one is not since children especially younger one’s could not differentiate yet what is permissible and what is not in this life. For them, what is appealing and seems fun on the television is what they would like to be in the future (King & Multon, 1996).

In a different angle, a study made by Fouad, Cotter & Kantamneni (2009) suggested that career course or career intervention helped in increasing the career decision making among its participants. Gati et al. (1996) proposed three reasons for career indecision making such as lack of readiness, lack of information and inconsistent information. Hence, career course as proposed by Fouad, Cotter & Kantamneni (2009) helped to solve this problem by solving out the three reasons of career indecision by giving the participants readiness to venture into career life after studies, giving information of what career suits the best and what is their job description and it would help in giving reliable and consistent information. Other than that, there are several studies that had been made on the relationship of gender and career aspirations, some of the studies found out that boys reported greater career decision making than girls (Kishor, 1981).

While another part of the other studies claimed that it was the other way around (Vondracek, Hostetler, Schulenberg, & Shimizu, 1990; Wallace-Broscious et al., 1994). Another study found that a great career planning can be drive by high levels of social support and high levels of goal-setting (Walls, Covell, & Macintyre, 1999; Rogers, Creed, & Glendon, 2008). Other than that, culture also predict career aspiration where a study by Leung, Ivey, & Suzuki (1994) found different interest of career aspiration between Caucasian and Asian students.

Further suggestions for future studies could be to focus on the SES and ethnicity of the participants in relation to their career aspirations. A study made by Howard et al. (2011) found out that Socio Economic Status (SES) has no significant effect on career aspirations but however they found that ethnicity is a part of a predictor for career aspirations. In addition, career guidance must be well-equipped in any academic institutions and especially in family institution in order to foster a better well-being of a person. This is supported by a study made by Dudovitz et al. (2017) where career aspirations could become a sign for the health and well-being of any adolescent. Thus, it is advised for adults to consider of providing the necessary guidance and help in this matter.

For the second phase of this present study, results from above showed that there is an interaction effect between level of studies (First year and final year IIUM undergraduate students) and their level of career aspirations with regards to their problem-solving skills. However, level of career aspiration (CAS) was not considered to be a contributor for this interaction effect since it is not significant in compare to level of studies towards problem-solving skills. Hence, the second hypothesis of this study can be accepted for the interaction effect but still, the result of not significant of level of career aspiration score towards problem-solving skills also should be highlighted throughout the discussion of this study.

For participants who are in their first year, another part of the result showed that those who obtained low score of CAS, scored low in problem-solving (M = 39.6) while those who scored high in CAS, scored higher in problem-solving (M = 48.6). While it is a different situation for participants in their final year of undergraduate studies where those who scored low in CAS, they scored higher on problem-solving (M = 51.60) and those who scored high in CAS, but however they scored lower in problem solving (M = 50.4). In a way, this proved why level of career aspiration scores are not significant in the interaction effect between level of studies (First year and final year IIUM undergraduate students) and their level of career aspirations with regards to their problem-solving skills.

The finding of this present study is not in line with most of previous studies that had been made on the relationship between level of career aspiration on problem-solving skills. Chartrand et al. (1993) mentioned that poor problem-solving skills is associated with career indecision. However, problem-solving skills is not necessarily the greatest predictor of career indecision where Peterson, Sampson, & Reardon, (1991) suggested that career knowledge could be more relevant as the greatest predictor for career indecision.

In other words, those who are equipped with sufficient knowledge of their desired career path or goals, they tend to have a lower level of career indecision later in the future. A study by Larson & Heppner (1985) also found that those who have good problem-solving skills, they are most likely to be more confident in deciding their career path or career aspirations.

However, this study also highlighted that career planning assistance is still needed including this group of people. In a study by Heppner et al. (2004), they created a pre and post study on their sample to see whether if career counselling improved the participant’s level of problem-solving appraisal and later lead to a better psychological well-being in career related matters. In their findings, it was found that participant’s levels of problem-solving increased after the career counselling intervention and later it was measured and found that if the participants increased positively in their levels of problem-solving they have a better direction of career goals and path.

However, the study was unsure which part of the career counselling that enhance the levels of problem-solving. Thus, from this past study above, career counselling intervention is one of the factor that could linked problem-solving skills and levels of career aspiration, but still further detailed studies are required to identify which part of the intervention in career counselling worked in enhancing problem-solving skills.

From other past studies, it showed that there are other factors that can significantly affect problem-solving skills such as in a study made by Mayer, Tajika & Stanley (1991) where they did a comparison study on problem-solving skills among Japan and United States schoolchildren. They found out that there is a difference in their levels of problem-solving skills although they are in the similar cohort or group of school grade. Their findings were that this is due to different kind and level of exposure.

This also could be another factor for different levels of problem-solving skills among different age group or even the same. It could be said that this is also a close example of another factor that could affect the findings of this present study where cultural differences and different upbriging could give a different affect problem-solving skills and levels of career aspiration. Moreover, Dobson & Dobson (1981) suggested that those who are depressive tend to have a less efficient problem-solving skills to be compared with those who are not depressive.

Moorthi (2018), found that there was no significant difference between age group of 20 years old, between 20-25 years old and above 25 years old in regard to their problem-solving skills. However, a study made by Macpherson (2002) found a contrary result where she found out that the year of study and academic qualifications are the predictors of differences in problem-solving skills. It was found out that education played a role in significantly predicting higher level of problem-solving skills (Denney and Palmer, 1981) and meanwhile, Mi & Kyungja (2016) suggested that leadership training helped in acquiring several skills including problem-solving skills into a person and they believed that this could improve the person career preparation behaviour.

In other words, problem-solving skills is considered as one of the important skills to enhance a person career preparation before entering the working life later. Talib & Aun (2009) suggested that there is no significant difference between age and career indecision making, there is no significant difference between gender and career indecision making and there is no significant relationship between academic achievement and career indecision among the Malaysian undergraduates.

The limitation of this present study is that there is lack of literature that mainly focused on the interaction effect of the variables in this study. Thus, it is suggested that a similar study with a larger sample is needed to see if there is a difference of results. Moreover, past studies that had been made had a larger sample size of participants which they obtained different results and it was suggested in previous study to also relate with other several factors such as socio-economic status (SES) and more. Problem-solving skills was mentioned as an important key factor of a person’s success, thus it is important to study the other external or even internal factors that could affect problem solving skills.

References

1. Abdel-Khalek, A. M. (2005). Reliability and factorial validity of the standard progressive matrices among Kuwaiti children ages 8 to 15 years. Perceptual and motor skills, 101(2), 409-412.
2. Amir, T., & Gati, I. (2006). Facets of career decision-making difficulties. British Journal of Guidance & Counselling, 34(4), 483-503.
3. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall.
4. Bjorklund, B. R., & Bee, H. L. (2015). The journey of adulthood. Florida: Pearson.
5. Carnevale, A. P. (1990). Workplace basics: The essential skills employers want. astd best practices series: training for a changing work force. Jossey-Bass Inc., Publishers, 350 Sansome Street, San Francisco, CA 94104.
6. Chartrand, J. M., Rose, M. L., Elliott, T. R., Marmarosh, C., & Caldwell, S. (1993). Peeling back the onion: Personality, problem solving, and career decision-making style correlates of career indecision. Journal of Career Assessment, 1(1), 66-82.
7. Compton, W., & Hoffman, E. (2012). Positive psychology: The science of happiness and flourishing. Nelson Education.
8. Crites, J. O. (1973). Career Maturity. NCME Measurement in Education, 4(2).
9. Danziger, N., & Eden, Y. (2006). Student career aspirations and perceptions: The case of Israeli accounting students. Accounting Education: an international journal, 15(2), 113-134.
10. Day, R., & Allen, T. D. (2004). The relationship between career motivation and self-efficacy with protégé career success. Journal of Vocational Behavior, 64(1), 72-91.
11. Denney, N. W., & Palmer, A. M. (1981). Adult age differences on traditional and practical problem-solving measures. Journal of gerontology, 36(3), 323-328.
12. Dobson, D. J., & Dobson, K. S. (1981). Problem-solving strategies in depressed and nondepressed college students. Cognitive Therapy and Research, 5(3), 237-249.
13. Dudovitz, R. N., Chung, P. J., Nelson, B. B., & Wong, M. D. (2017). What do you want to be when you grow up? Career aspirations as a marker for adolescent well-being. Academic pediatrics, 17(2), 153-160.
14. D’Zurilla, T. J., Maydeu-Olivares, A., & Kant, G. L. (1998). Age and gender differences in social problem-solving ability. Personality and individual differences, (25), 241-252.
15. Earl, J. K., & Bright, J. E. (2003). Undergraduate level, age, volume and pattern of work as predictors of career decision status. Australian Journal of Psychology, 55(2), 83-88.
16. Earl, J. K., & Bright, J. E. (2003). Undergraduate level, age, volume and pattern of work as predictors of career decision status. Australian Journal of Psychology, 55(2), 83-88.
17. Fouad, N., Cotter, E. W., & Kantamneni, N. (2009). The effectiveness of a career decision-making course. Journal of Career Assessment, 17(3), 338-347.
18. Fouad, N. A. (1988). The construct of career maturity in the United States and Israel. Journal of Vocational Behavior, 32(1), 49-59.
19. Gati, I., Krausz, M., & Osipow, S. H. (1996). A taxonomy of difficulties in career decision-making. Journal of Counseling Psychology, 43, 510-526.
20. Herr, E. L., & Enderlein, T. E. (1976). Vocational maturity: The effects of school, grade, curriculum and sex. Journal of Vocational Behavior, 8(2), 227-238.
21. Heppner, M. J., Lee, D. G., Heppner, P. P., McKinnon, L. C., Multon, K. D., & Gysbers, N. C. (2004). The role of problem-solving appraisal in the process and outcome of career counseling. Journal of Vocational Behavior, 65(2), 217-238.
22. Hollenbeck, K. (2000). Career aspirations and knowledge about career and technical education of Kalamazoo County 8th and 9th grade students.
23. Howard, K. A., Carlstrom, A. H., Katz, A. D., Chew, A. Y., Ray, G. C., Laine, L., & Caulum, D. (2011). Career aspirations of youth: Untangling race/ethnicity, SES, and gender. Journal of Vocational Behavior, 79(1), 98-109.
24. Hoyer, W. J., Rebok, G. W., & Sved, S. M. (1979). Effects of varying irrelevant information on adult age differences in problem solving. Journal of gerontology, 34(4), 553-560.
25. Howard, K. A., Carlstrom, A. H., Katz, A. D., Chew, A. Y., Ray, G. C., Laine, L., & Caulum, D. (2011). Career aspirations of youth: Untangling race/ethnicity, SES, and gender. Journal of Vocational Behavior, 79(1), 98-109.
26. Jawarneh, M. (2016). Career maturity among university students in Jordan: The case for social studies. Australian Journal of Career Development, 25(3), 110-116.
27. Kishor, N. (1981). Effect of self-esteem and locus ofcontrol in career decision making in Fiji. Journal of Vocational Behavior, 19, 227-232.
28. King, M. M., & Multon, K. D. (1996). The effects of television role models on the career aspirations of African American junior high school students. Journal of Career Development, 23(2), 111-125.
29. Leung, S. A., Ivey, D., & Suzuki, L. (1994). Factors affecting the career aspirations of Asian Americans. Journal of Counseling & Development, 72(4), 404-410.
30. Larson, L. M., & Heppner, P. P. (1985). The relationship of problem-solving appraisal to career decision and indecision. Journal of Vocational Behavior, 26(1), 55-65.
31. Mayer, R. E., Tajika, H., & Stanley, C. (1991). Mathematical problem solving in Japan and the United States: A controlled comparison. Journal of Educational Psychology, 83(1), 69.
32. Macpherson, K. (2002). Problem-solving ability and cognitive maturity in undergraduate students. Assessment & Evaluation in Higher Education, 27(1), 5-22.
33. Mi, Y., & Kyungja, K. (2016). The factors affecting nursing students’ career preparation behavior: Focusing on participation in a self-leadership program. Indian Journal of Science and Technology, 9(25).
34. Moorthi, S. (2018). Problem Solving Skills Among College Students. International Journal of Innovative Research Explorer, 5(4).
35. Neice, D. E., & Bradley, R. W. (1979). Relationship of age, sex, and educational groups to career decisiveness. Journal of Vocational Behavior, 14(3), 271-278.
36. Peterson, G. W., Sampson Jr, J. P., & Reardon, R. C. (1991). Career development and services: A cognitive approach. Thomson Brooks/Cole Publishing Co.
37. Patton, W., & Creed, P. A. (2001). Developmental issues in career maturity and career decision status. The Career Development Quarterly, 49(4), 336-351.
38. Powers, S., & Barkan, J. H. (1986). Concurrent Validity of the standard progressive matrices for Hispanic and non Hispanic and seventh-grade students. Psychology in the Schools, 23(4), 333–336.
39. Powers, S., Barkan, J. H., & Jones, P. B. (1986). Reliability of the Standard Progressive Matrices Test for Hispanic and Anglo-American Children. Perceptual and Motor Skills, 62(2), 348–350.
40. Rogers, M. E., Creed, P. A., & Glendon, A. I. (2008). The role of personality in adolescent career planning and exploration: A social cognitive perspective. Journal of Vocational Behavior, 73(1), 132-142.
41. Super, D. E., Thompson, A. S., Lindeman, R. H., Jordaan, J. P., & Myers, R. A. (1981).
42. Career Development Inventory. Palo Alto, CA: Consulting Psychologists Press.
43. Talib, M. A., & Aun, T. K. (2009). Predictors of career indecision among Malaysian undergraduate students. European Journal of Social Sciences, 8(2), 215-224.
44. Vondracek, F. W., Hostetler, M., Schulenberg & Shimizu, K. (1990). Dimensions
45. of career indecision. Journal of Counseling Psychology, 37, 98-106.
46. Wall, J., Covell, K., & Macintyre, P. D. (1999). Implications of social supports for adolescents’ education and career aspirations. Canadian Journal of Behavioural Science, 31(2), 63.
47. Wallace-Broscious, A., Serafica, F. C., & Osipow, S. H. (1994). Adolescent career development: Relationships to self-concept and identity status. Journal of Research on Adolescence, 4, 127-149.