Skip to main content

Advertisement

Log in

The ethical implications of collecting data in educational settings: discussion on the technology and engineering attitude scale (TEAS) and its psychometric validation for assessing a pre-engineering design program

  • Published:
International Journal of Technology and Design Education Aims and scope Submit manuscript

Abstract

K-12 Engineering Education has placed a lot of attention on students’ attitudes or predispositions towards science and technology. However, most assessment methods are focused on STEM as a whole or only on technology. In this article, we will discuss the instrument called Technology and Engineering Attitude Scale (TEAS) which focuses on attitudes towards technology. Previous studies and applications of this particular scale lacked proper statistical validation of the instrument. The following research looks at the application of an adapted version of the TEAS to assess a GEDC awarded pre-engineering design program in Chile. This version was psychometrically analyzed in 436 cases to validate the interpretations driven by a particular cultural context and specific to the discipline of engineering. The article focuses on the modifications applied to the instrument after the statistical validity process. The discussion is centered on the ethical importance of adapting an existing scale in a valid and reliable way to assess a pre-engineering design program in a local context. Lessons learned and recommendations for future research in this area are proposed based on this particular experience.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Ankiewicz, P. (2016). Perceptions and attitudes of pupils toward technology. In M. de Vries (Ed.), Handbook of technology education, international handbooks of education (pp. 1–15). Cham: Springer. doi:https://doi.org/10.1007/978-3-319-38889-2_43-1

  • Ankiewicz, P. (2019). Perceptions and attitudes of pupils towards technology: in search of a rigorous theoretical framework. International Journal of Technology and Design Education, 29(1), 37–56. https://doi.org/10.1007/s10798-017-9434-z.

    Article  Google Scholar 

  • Beaujean, A. A. (2014). Latent variable modeling using R: a step-by-step guide. London: Routledge.

    Book  Google Scholar 

  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guildford Press.

    Google Scholar 

  • Cerny, C. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research, 12(1), 43–47.

    Article  Google Scholar 

  • Cook, K. (2010). An investigation of middle school student interest, participation, and attitude toward technology and engineering (Masters Thesis). Brigham: Brigham Young University.

  • Cook, D. A., & Beckman, T. J. (2006). Current concepts in validity and reliability for psychometric instruments: theory and application. The American Journal of Medicine, 119(2), 166.e7-166.e16. https://doi.org/10.1016/j.amjmed.2005.10.036.

    Article  Google Scholar 

  • de Winter, J. C. F., Dodou, D., & Wieringa, P. A. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44(2), 147–181. https://doi.org/10.1080/00273170902794206.

    Article  Google Scholar 

  • Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Fort Worth, TX: Harcourt Brace Jovanovich.

    Google Scholar 

  • Ercan, I., Yazici, B., Sigirli, D., Ediz, B., & Kan, I. (2007). Examining cronbach alpha, theta, omega reliability coefficients according to sample size. Journal of Modern Applied Statistical Methods, 6(1), 291–303. https://doi.org/10.22237/jmasm/1177993560.

    Article  Google Scholar 

  • Fairhurst, C., Böhnke, J. R., Gabe, R., Croudace, T. J., Tober, G., & Raistrick, D. (2014). Factor analysis of treatment outcomes from a UK specialist addiction service: relationship between the leeds dependence questionnaire, social satisfaction questionnaire and 10-item clinical outcomes in routine evaluation. Drug and Alcohol Review, 33(6), 643–650. https://doi.org/10.1111/dar.12146.

    Article  Google Scholar 

  • Frank, J. (2013). Mitigating against epistemic injustice in educational research. Educational Researcher, 42(7), 363–370. https://doi.org/10.3102/0013189X12457812.

    Article  Google Scholar 

  • Fricker, M. (2007). Epistemic injustice: power and the ethics of knowing. New York: Oxford University Press.

    Book  Google Scholar 

  • Gaskin, C. J., Lambert, S. D., Bowe, S. J., & Orellana, L. (2017). Why sample selection matters in exploratory factor analysis: implications for the 12-item World Health Organization Disability Assessment Schedule 2.0. BMC Medical Research Methodology, 17(1), 40. https://doi.org/10.1186/s12874-017-0309-5.

    Article  Google Scholar 

  • Given, L. (2008). Perception. In The SAGE encyclopedia of qualitative research methods. Thousand Oaks: SAGE Publications Inc. doi:https://doi.org/10.4135/9781412963909.n314

  • Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176.

    Article  Google Scholar 

  • Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Medical Care, 44(Suppl 3), S78–S94. https://doi.org/10.1097/01.mlr.0000245454.12228.8f.

    Article  Google Scholar 

  • Guzey, S. S., Harwell, M., & Moore, T. (2014). Development of an instrument to assess attitudes toward science, technology, engineering, and mathematics (STEM). School Science and Mathematics, 114(6), 271–279. https://doi.org/10.1111/ssm.12077.

    Article  Google Scholar 

  • Hammer, M. J. (2017). Ethical considerations for data collection using surveys. Oncology Nursing Forum, 44(2), 157–159. https://doi.org/10.1188/17.ONF.157-159.

    Article  Google Scholar 

  • Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than cronbach’s alpha for estimating reliability. But Communication Methods and Measures, 14(1), 1–24. https://doi.org/10.1080/19312458.2020.1718629.

    Article  Google Scholar 

  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53–60.

    Google Scholar 

  • Hynes, M. M., Mathis, C., Purzer, S., Rynearson, A., & Siverling, E. (2017). Systematic review of research in P-12 engineering education from 2000–2015. International Journal of Engineering Education, 33(1B).

  • Instituto Nacional de Estadísticas de Chile (INE). (2018). Síntesis de Resultados CENSO 2017. http://www.censo2017.cl/descargas/home/sintesis-de-resultadoscenso2017.pdf

  • Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.

    Article  Google Scholar 

  • Kaufman, T. (2019). Exploring changes in STEM career aspirations through participation in a weather unit engineering design challenge. Masters Thesis. Ann Arbor: Hofstra University

  • Kurban, E. R., & Cabrera, A. F. (2019). Building readiness and intention towards STEM fields of study: using HSLS: 09 and SEM to examine this complex process among high school students. The Journal of Higher Education 1–31.

  • Kvale, S. (1995). The social construction of validity. Qualitative Inquiry, 1(1), 19–40. https://doi.org/10.1177/107780049500100103.

    Article  Google Scholar 

  • Lyons-Thomas, J. (2014). Interscale correlations. in encyclopedia of quality of life and well-being research, pp. 3352–3353. Dordrecht: Springer. Doi:https://doi.org/10.1007/978-94-007-0753-5_1519

  • McFarlane, T. A., Green, K. E., & Hoffman, E. R. (1997). Teachers’ attitudes toward technology: psychometric evaluation of the technology attitude survey. Chicago, IL: In Annual Meeting of the American Educational Research Association.

    Google Scholar 

  • Murris, K. (2013). The epistemic challenge of hearing child’s voice. Studies in Philosophy and Education, 32(3), 245–259. https://doi.org/10.1007/s11217-012-9349-9.

    Article  Google Scholar 

  • National Research Council. (2010). Standards for K-12 engineering education? Washington: National Academies Press. https://doi.org/10.17226/12990.

    Book  Google Scholar 

  • Perugini, M. (2005). Predictive models of implicit and explicit attitudes. British Journal of Social Psychology, 44(1), 29–45. https://doi.org/10.1348/014466604X23491.

    Article  Google Scholar 

  • Pleasants, J., & Olson, J. K. (2019). What is engineering? Elaborating the nature of engineering for K-12 education. Science Education, 103(1), 145–166. https://doi.org/10.1002/sce.21483.

    Article  Google Scholar 

  • Potvin, P., & Hasni, A. (2014). Interest, motivation and attitude towards science and technology at K-12 levels: a systematic review of 12 years of educational research. Studies in Science Education, 50(1), 85–129. https://doi.org/10.1080/03057267.2014.881626.

    Article  Google Scholar 

  • Rencher, A. (1995). Methods of multivariate analysis. New York: Wiley.

    Google Scholar 

  • Sousa, V. D., & Rojjanasrirat, W. (2011). Translation, adaptation and validation of instruments or scales for use in cross-cultural health care research: a clear and user-friendly guideline. Journal of Evaluation in Clinical Practice, 17(2), 268–274. https://doi.org/10.1111/j.1365-2753.2010.01434.x.

    Article  Google Scholar 

  • Steenkamp, J. B. E., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of consumer research, 25(1), 78–90.

    Article  Google Scholar 

  • Sun, J. (2005). Assessing goodness of fit in confirmatory factor analysis. Measurement and Evaluation in Counseling and Development, 37, 240–256. https://doi.org/10.1080/07481756.2005.11909764.

    Article  Google Scholar 

  • Taherdoost, H. (2016). Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. International Journal of Academic Research in Management, 5(3), 28–36.

    Google Scholar 

  • Tronto, J. C. (1993). Moral Boundaries, a political argument for an ethic of care. New York: New York University Press.

    Google Scholar 

  • Tronto, J. C. (2013). Caring democracy. New York: New York University Press.

    Google Scholar 

  • United Nations Development Programme. (2019). Human development report 2019: Beyond income, beyond averages, beyond today: inequalities in human development in the 21st century. http://hdr.undp.org/sites/default/files/hdr2019.pdf

  • van Rensburg, S., Ankiewicz, P., & Myburgh, C. (1999). Assessing South Africa learners’ attitudes towards technology by using the PATT (Pupils’ Attitudes Towards Technology) questionnaire. International Journal of Technology and Design Education, 9(2), 137–151. https://doi.org/10.1023/A:1008848031430.

    Article  Google Scholar 

  • Vygotsky, L. (1987). Thinking and speech. In: R. W. Rieber & A. S. Carton (Eds.), The collected works of L. S. Vygotsky (Vol. 1, pp. 39–285). New York: Plenum Press.

  • Weisbuch, M. (2007). Dual attitudes. In R. Baumeister & K. Vohs (Eds.), Encyclopedia of social psychology (pp. 267–268). Thousand Oaks: SAGE Publications, Inc. doi:https://doi.org/10.4135/9781412956253.n163

  • Wilson, T. D., Lindsey, S., & Schooler, T. Y. (2000). A model of dual attitudes. Psychological Review, 107(1), 101–126. https://doi.org/10.1037/0033-295X.107.1.101.

    Article  Google Scholar 

  • Wilson Van Voorhis, C. R., & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43–50. https://doi.org/10.20982/tqmp.03.2.p043.

    Article  Google Scholar 

  • Wright, G. (2014). A Blended STEM curriculum: using ROVs, programming, and robotics to teach K-8 students core concepts of science, technology, engineering and math. In: T. Bastiaens (Ed.), Proceedings of World Conference on E-Learning (pp. 2098–2108). New Orleans: Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/148765/.

  • Wright, G. & Terry, R. (2010). Emerging research-pathways: assessing teacher technology literacy and student interest in engineering and technology in K-12 education. In J. Herrington & C. Montgomerie (Eds.), Proceedings of ED-MEDIA 2010-World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 2597–2604). Toronto: Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/primary/p/35003/.

  • Wright, G. A. (2018). Engineering attitudes: an investigation of the effect of literature on student attitudes toward engineering. International Journal of Technology and Design Education, 28(3), 653–665. https://doi.org/10.1007/s10798-017-9417-0.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Constanza Miranda.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A: English version of the TEAS Survey with additional items

1

I am not interested in technology and engineering

2

Boys are better at being engineers than girls

3

Engineering and technology have nothing to do with our lives

4

To be good at engineering or technology you have to be very smart

5

Engineers and technologists solve problems

6

The engineers that studied in Santiago are better at their jobs than the ones that studied in other cities in Chile

7

I think engineering and technology are often used in science

8

Engineers and technologists help make people’s lives better

9

Girls can be as successful doing engineering and technology as boys

10

I am good at problems that can be solved in many different ways

11

The engineers from other regions in Chile are as good as the ones that studied in Santiago

12

I would like a job that lets me do a lot of engineering and technology

13

Engineers and technologists use a lot of math and science

14

I think I could do well in an advanced technology and engineering class

15

To be an engineer you need be rich

16

I think that having a job in engineering or technology would be fun

17

I think there should be a class at my school related to technology and engineering

18

I would be nervous to take a technology and engineering class

19

Science has nothing in common with technology and engineering

20

I would like to be a technologist when I grow up

21

A person that doesn’t live in Santiago can have a job as a technologist

22

You do not have to problem solve to be an engineer or technologist

23

I would like to learn more about technology and engineering at school

24

If there was a technology and engineering club at my school, I would like to join

25

A girl can have a technical job

26

Rich people know more about engineering and technology

27

In my everyday life, I am able to solve problems well

28

Societal issues, like water and air pollution, influence the jobs of technologists and engineers

29

Solving problems is hard

30

Technology and engineering has brought about more bad things than good things

31

People from Santiago know more about engineering than the ones in other regions of Chile

32

To me, the field of science is related to the field of technology and engineering

33

Working in engineering and technology as a job would be boring and dull

34

Engineering and technology make our lives more comfortable

35

When I think of engineering and technology, I mostly think of solving problems

36

A student from a public school can have a job in engineering

37

To become an engineer or technologist, you have to take hard classes

38

Boys know more about engineering and technology than girls

39

You don’t have to be smart to study engineering and technology

40

In engineering and technology, you use math

41

Students from public schools can be as successful as students from private schools in engineering

42

I’m capable of being accepted in an engineering school

Appendix B: Spanish version of the TEAS Survey with additional items

1

No estoy interesado en la tecnología e ingeniería

2

Los hombres son mejores ingenieros que las mujeres

3

La ingeniería y la tecnología no tienen impacto en mi vida

4

Para ser bueno en ingeniería y tecnología tienes que ser muy inteligente

5

Los ingenieros y técnicos ligados a la tecnología resuelven problemas

6

Los ingenieros que estudiaron en Santiago son mejores que los ingenieros que estudiaron en otras regiones

7

Creo que la ingeniería y la tecnología son usadas frecuentemente en las ciencias

8

Los ingenieros ayudan a hacer mejor la vida de las personas

9

Las mujeres pueden ser tan exitosas como los hombres en tecnología

10

Soy bueno con los problemas que pueden ser resueltos de muchas maneras diferentes

11

Los ingenieros que trabajan en otras regiones pueden ser tan buenos como los ingenieros de Santiago

12

Me gustaría tener un trabajo donde pueda hacer ingeniería y usar mucha tecnología

13

Ingenieros y técnicos en tecnología usan mucho las matemáticas y las ciencias

14

Creo que me iría bien en una clase relacionada con ingeniería y tecnología

15

Para ser un ingeniero hay que tener dinero

16

Creo que sería entretenido vincularme con la ingeniería o temas relacionados a la tecnología

17

Creo que debiera haber una clase en mi escuela relacionada con ingeniería o tecnología

18

Tomar una clase de ingeniería o tecnología avanzada me pondría nervioso/a

19

Las ciencias no tienen nada que ver con la ingeniería y la tecnología

20

Me gustaría dedicarme a la tecnología cuando salga del colegio

21

Una persona que no vive en Santiago puede tener un trabajo de tecnología avanzada

22

El trabajo de un ingeniero o de un técnico ligado a la tecnología no involucra resolver problemas

23

Me gustaría aprender más de ingeniería y tecnología

24

Si hubiera una capacitación en mi liceo sobre tecnología e ingeniería, me gustaría unirme

25

Una mujer puede ser una experta en tecnología

26

Las personas con dinero saben más de ingeniería y tecnología

27

En mi vida diaria soy capaz de resolver bien problemas de diferentes ámbitos

28

Los trabajos de ingenieros y expertos en tecnología están relacionados a problemas sociales, como el agua, la contaminación, etc

29

En general, resolver problemas me resulta difícil

30

La tecnología y la ingeniería han traído más cosas malas que buenas

31

Los santiaguinos saben más de ingeniería y tecnología que las personas de otras regiones

32

Para mí, las ciencias están relacionadas a la tecnología y la ingeniería

33

Un trabajo en ingeniería y tecnología sería aburrido para mí

34

La ingeniería y la tecnología hacen nuestra vida más cómoda

35

Cuando pienso en ingeniería y en tecnología, la mayoría del tiempo pienso en resolver problemas

36

Un estudiante de liceo técnico puede tener un trabajo de ingeniería

37

Para ser un ingeniero tienes que hacer cursos difíciles en la universidad

38

Los hombres saben más de ingeniería y tecnología que las mujeres

39

No tienes que ser inteligente para estudiar ingeniería o algo relacionado con la tecnología

40

En ingeniería y en tecnología tienes que usar matemáticas

41

En ingeniería, los estudiantes de liceo técnico pueden ser tan exitosos como los estudiantes de colegio privado

42

Soy capaz de entrar y estudiar ingeniería

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miranda, C., Goñi, J., Pickenpack, A. et al. The ethical implications of collecting data in educational settings: discussion on the technology and engineering attitude scale (TEAS) and its psychometric validation for assessing a pre-engineering design program. Int J Technol Des Educ 32, 1495–1513 (2022). https://doi.org/10.1007/s10798-021-09653-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10798-021-09653-x

Keywords

Navigation