The Development of the STEM Career Interest Survey (STEM-CIS)

Abstract

Internationally, efforts to increase student interest in science, technology, engineering, and mathematics (STEM) careers have been on the rise. It is often the goal of such efforts that increased interest in STEM careers should stimulate economic growth and enhance innovation. Scientific and educational organizations recommend that efforts to interest students in STEM majors and careers begin at the middle school level, a time when students are developing their own interests and recognizing their academic strengths. These factors have led scholars to call for instruments that effectively measure interest in STEM classes and careers, particularly for middle school students. In response, we leveraged the social cognitive career theory to develop a survey with subscales in science, technology, engineering, and mathematics. In this manuscript, we detail the six stages of development of the STEM Career Interest Survey. To investigate the instrument's reliability and psychometric properties, we administered this 44-item survey to over 1,000 middle school students (grades 6–8) who primarily were in rural, high-poverty districts in the southeastern USA. Confirmatory factor analyses indicate that the STEM-CIS is a strong, single factor instrument and also has four strong, discipline-specific subscales, which allow for the science, technology, engineering, and mathematics subscales to be administered separately or in combination. This instrument should prove helpful in research, evaluation, and professional development to measure STEM career interest in secondary level students.

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Notes

  1. 1.

    AMOS is the structural equation modeling software produced with IBM/SPSS.

  2. 2.

    Modification indices are reported in AMOS as chi square values with one degree of freedom. As such, a significant improvement in the model is signified by a chi square in excess of 3.84. In order to be very conservative with this process, and to model inter-item correlations that contributed substantially to model fit, we used a rule that we would examine inter-item correlations that resulted in a modification index (MI) of 8.00 or greater. Further, note that because these models were all single latent variable models with multiple measured variables that legitimately should be allowed to correlate, there is no conceptual issue with allowing items designed to measure a particular latent variable to correlate. We cannot model all possible correlations between items on a scale in CFA due to lack of degrees of freedom, and thus, the use of modification indices is merely an efficient method of identifying those items that, by allowing a nonzero correlation, significantly and substantially (and appropriately) improve model fit.

  3. 3.

    Mahalanobis D 2 is a common index of whether an individual score is aberrant within the multivariate distribution of scores; higher numbers indicate the score is farther from the center of the multivariate distribution. Guidelines for assessing Mahalanobis D 2 suggest that a reasonable cutoff score is a chi square value that would be significant at p < .05 for the number of df equal to the number of variables or parameters estimated in the model.

  4. 4.

    Note that Marsh et al. (2004) argue against strict cutoff scores or “golden rules,” and thus, these indices should be treated as the continuous variables they are, and interpreted in context.

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Acknowledgments

The authors wish to thank Michael D. Cobb for his helpful suggestions on the initial development of this instrument and all of the collaborators on this project who participated in this research. This research was funded by an ITEST grant (2010–2013) from the National Science Foundation (award number 1031118). The opinions expressed are those of the authors and do not represent the views of the National Science Foundation or North Carolina State University.

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Correspondence to Meredith W. Kier.

Appendix

Appendix

STEM Career Interest Survey (STEM-CIS)

Optional Demographic Questions

  1. 1.

    Date

  2. 2.

    First name

  3. 3.

    Last name

  4. 4.

    Grade

  5. 5.

    Gender

  6. 6.

    Teacher

  7. 7.

    Race

  8. 8.

    Period

  9. 9.

    School

Directions: Students will complete the STEM-CIS online via iPod Touches or computers. Each question is a Likert scale with the following choices:

Strongly Disagree (1), Disagree (2), Neither Agree nor Disagree (3), Agree (4), Strongly Agree (5)

Science

  1. S1

    I am able to get a good grade in my science class.

  2. S2

    I am able to complete my science homework.

  3. S3

    I plan to use science in my future career.

  4. S4

    I will work hard in my science classes.

  5. S5

    If I do well in science classes, it will help me in my future career.

  6. S6

    My parents would like it if I choose a science career.

  7. S7

    I am interested in careers that use science.

  8. S8

    I like my science class.

  9. S9

    I have a role model in a science career.

  10. S10

    I would feel comfortable talking to people who work in science careers.

  11. S11

    I know of someone in my family who uses science in their career.

Mathematics

  1. M1

    I am able to get a good grade in my math class.

  2. M2

    I am able to complete my math homework.

  3. M3

    I plan to use mathematics in my future career.

  4. M4

    I will work hard in my mathematics classes.

  5. M5

    If I do well in mathematics classes, it will help me in my future career.

  6. M6

    My parents would like it if I choose a mathematics career.

  7. M7

    I am interested in careers that use mathematics.

  8. M8

    I like my mathematics class.

  9. M9

    I have a role model in a mathematics career.

  10. M10

    I would feel comfortable talking to people who work in mathematics careers.

  11. M11

    I know someone in my family who uses mathematics in their career.

Technology

  1. T1

    I am able to do well in activities that involve technology.

  2. T2

    I am able to learn new technologies.

  3. T3

    I plan to use technology in my future career.

  4. T4

    I will learn about new technologies that will help me with school.

  5. T5

    If I learn a lot about technology, I will be able to do lots of different types of careers.

  6. T6

    My parents would like it if I choose a technology career.

  7. T7

    I like to use technology for class work.

  8. T8

    I am interested in careers that use technology.

  9. T9

    I have a role model who uses technology in their career.

  10. T10

    I would feel comfortable talking to people who work in technology careers.

  11. T11

    I know of someone in my family who uses technology in their career.

Engineering

  1. E1

    I am able to do well in activities that involve engineering.

  2. E2

    I am able to complete activities that involve engineering.

  3. E3

    I plan to use engineering in my future career.

  4. E4

    I will work hard on activities at school that involve engineering.

  5. E5

    If I learn a lot about engineering, I will be able to do lots of different types of careers.

  6. E6

    My parents would like it if I choose an engineering career.

  7. E7

    I am interested in careers that involve engineering.

  8. E8

    I like activities that involve engineering.

  9. E9

    I have a role model in an engineering career.

  10. E10

    I would feel comfortable talking to people who are engineers.

  11. E11

    I know of someone in my family who is an engineer.

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Kier, M.W., Blanchard, M.R., Osborne, J.W. et al. The Development of the STEM Career Interest Survey (STEM-CIS). Res Sci Educ 44, 461–481 (2014). https://doi.org/10.1007/s11165-013-9389-3

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Keywords

  • STEM interest
  • Instrument
  • Survey
  • Social cognitive career theory
  • STEM careers
  • Confirmatory factor analysis