RESEARCH PAPER
Measuring Elementary Students’ Expectancies of Success in School Science: Psychometric Evaluation of the SUCCESS Instrument
 
 
More details
Hide details
1
University of Burgos, Department of Specific Didactics, SPAIN
Online publish date: 2019-04-11
Publish date: 2019-04-11
 
EURASIA J. Math., Sci Tech. Ed 2019;15(8):em1733
KEYWORDS
TOPICS
ABSTRACT
Background:
The importance of valid and reliable instruments for the assessment of factors affecting students’ interest in science encouraged the development and validation of a brief Spanish instrument for the measurement of expectancies of success in school science, named SUCCESS. In this study, the psychometric properties of the SUCCESS instrument are further evaluated using different psychometric tests and a different sample than the one included in the original validation study.

Material and methods:
A sample of 313 Spanish elementary school students enrolled in 4th to 6th grade was drawn by means of convenience sampling techniques. Responses were analyzed in terms of construct and criterion validity, and two reliability indices.

Results:
Results from confirmatory factor analysis established the unidimensional structure of the instrument, with great model fit indices. Correlation coefficients between the SUCCESS and external measures (i.e. intentions to enroll, enjoyableness, difficulty, auto-efficacy, utility and relevance of school science) provided evidence of criterion validity. Cronbach α and item-total correlation indices supported the internal consistency reliability of the instrument.

Conclusions:
Taken together, this study further provide evidence to consider the SUCCESS as a valid and reliable tool for the measurement of Spanish elementary school students expectancies of success in school science.

 
REFERENCES (61)
1.
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (AERA, APA, & NCME, 2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.
 
2.
Andersen, L., & Ward, T. J. (2014). Expectancy-value models for the STEM persistence plans of ninth-grade, high-ability students: A comparison between black, hispanic, and white students. Science Education, 98(2), 216–242. https://doi.org/10.1002/sce.21....
 
3.
Arbuckle, J. L. (2014). Amos (Version 23.0). Chicago: IBM SPSS.
 
4.
Archer, L., Dewitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2010). “Doing” science versus “being” a scientist: Examining 10/11-year-old schoolchildren’s constructions of science through the lens of identity. Science Education, 94(4), 617–639. https://doi.org/10.1002/sce.20....
 
5.
Aydeniz, M., & Kotowski, M. R. (2014). Conceptual and methodological issues in the measurement of attitudes towards science. Electronic Journal of Science Education Electronic Journal of Science Education, 18(3), 1–24.
 
6.
Bandura A, Barbaranelli C, Caprara G.V., & Pastorelli C. (2001). Self-efficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development, 72, 187–206. https://doi.org/10.1111/1467-8....
 
7.
Bandura A. (1997). Self-efficacy: The exercise of control. New York: Freeman.
 
8.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215. https://doi.org/10.1037/0033-2....
 
9.
Beatty, P. C., & Willis, G. B. (2007). Research synthesis: The practice of cognitive interviewing. Public Opinion Quarterly, 71(2), 287–311. https://doi.org/10.1093/poq/nf....
 
10.
Blalock, C. L., Lichtenstein, M. J., Owen, S., Pruski, L., Marshall, C., & Toepperwein, M. A. (2008). In pursuit of validity: A comprehensive review of science attitude instruments 1935-2005. International Journal of Science Education, 30(7), 961–977. https://doi.org/10.1080/095006....
 
11.
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.
 
12.
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (Vol. 2). New York: Routledge Taylor & Francis Group. https://doi.org/10.4324/978141....
 
13.
Ceci, S. J., & Williams, W. M. (2007). Why aren’t more women in science. Top researchers debate the evidence. Washington, DC: American Psychological Association. https://doi.org/10.1037/11546-....
 
14.
Chachashvili-Bolotin, S., Milner-Bolotin, M., & Lissitsa, S. (2016). Examination of factors predicting secondary students’ interest in tertiary STEM education. International Journal of Science Education, 38(3), 366–390. https://doi.org/10.1080/095006....
 
15.
de Pro, A., & Pérez Manzano, A. (2014). Actitudes de los alumnos de Primaria y Secundaria ante la visión dicotómica de la Ciencia. Enseñanza de Las Ciencias, 32(3), 111–132. https://doi.org/10.5565/rev/en....
 
16.
de Winter, J. C. F., & Dodou, D. (2012). Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics, 39(4), 695–710. https://doi.org/10.1080/026647....
 
17.
Eccles, J. S., & Wigfield, A. (1995). In the mind of the achiever: the structure of adolescents’ academic achievement related-beliefs and self-perceptions. Personality and Social Psychology Bulletin, 21, 215–225. https://doi.org/10.1177/014616....
 
18.
Eccles, J. S., Wigfield, A., Harold, R. B., & Blumenfeld, P. B. (1993). Age and gender differences in children’s self- and task perceptions during elementary school. Child Development, 64, 830–847. https://doi.org/10.2307/113122....
 
19.
Eccles, J., Adler, T., Futterman, R., Goff, S., Kaczala, C., Meece, J., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). San Francisco, CA: W. H. Freeman.
 
20.
Gardner, P. L. (1975). Attitudes to science: A review. Studies in Science Education, 2, 1–41. https://doi.org/10.1080/030572....
 
21.
Guo, J., Parker, P. D., Marsh, H. W., & Morin, A. J. S. (2015). Achievement, motivation, and educational choices: A longitudinal study of expectancy and value using a multiplicative perspective. Developmental Psychology, 51(8), 1163–1176. https://doi.org/10.1037/a00394....
 
22.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data anaysis (Vol 7.). Upper Saddle River: Pearson Prentice Hall.
 
23.
Hutchison, M. A., Follman, D. K., Sumpter, M., & Bodner, G. M. (2006). Factors influencing the self-efficacy beliefs of first-year engineering students. Journal of Engineering Education, 95(1), 39–47. https://doi.org/10.1002/j.2168....
 
24.
IBM, C. (2016). IBM SPSS Statistics for Windows. Version 24.0. Armonk, NY: IBM Corp.
 
25.
Kennedy, J. P., Lyons, T., & Quinn, F. (2014). The continuing decline of science and mathematics enrolments in Australian high schools. Teaching Science, 60(2), 34–46.
 
26.
Kennedy, J., Quinn, F., & Taylor, N. (2016). The school science attitude survey: A new instrument for measuring attitudes towards school science. International Journal of Research & Method in Education, 39(4), 422–445. https://doi.org/10.1080/174372....
 
27.
Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.
 
28.
Lent, R. W., & Brown, S. D. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79–122. https://doi.org/10.1006/jvbe.1....
 
29.
Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. In D. Brown, L. Brooks, & Associates (Eds.), Career choice and development (4th ed., pp. 255–311). San Francisco, CA: Jossey-Bass.
 
30.
Lindahl, B. (2007). Longitudinal study of students’ attitudes towards science and choice of career. Paper presented at the 80th session of the International Conference of the National Association for Research in Science Teaching. New Orleans.
 
31.
Linn, R. L., & Gronlund, N. E. (1995). Measurement and assessment in teaching. New Jersey: Prentice-Hall Inc.
 
32.
Lyons, T., & Quinn, F. (2010). Choosing science. Understanding the declines in senior high school science enrolments. Armidale: University of New England.
 
33.
Lyons, T., & Quinn, F. (2015). Understanding declining science participation in Australia: A systemic perspective. In E. K. Henriksen, J. Dillon, & J. Ryder (Eds.), Understanding student participation and choice in science and technology education (pp. 153–168). Springer Netherlands. https://doi.org/10.1007/978-94....
 
34.
Marbá-Tallada, A., & Márquez Bargalló, C. (2010). ¿Qué opinan los estudiantes de las clases de ciencias ? Un estudio tranversal de sexto de Primaria a cuarto de ESO. Enseñanza de Las Ciencias, 28(1), 19–30.
 
35.
MECD. (2016). Datos y cifras del sistema universitario español. Curso 2015/2016.
 
36.
Mokkink, L. B., Terwee, C. B., Knol, D. L., Stratford, P. W., Alonso, J., Patrick, D. L., … de Vet, H. C. (2010). The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: A clarification of its content. BMC Medical Research Methodology, 10(22), 1–8. https://doi.org/10.1186/1471-2....
 
37.
Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., … de Vet, H. C. W. (2010a). The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. Journal of Clinical Epidemiology, 63(7), 737–745. https://doi.org/10.1016/j.jcli....
 
38.
Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., … de Vet, H. C. W. (2010b). The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: An international Delphi study. Quality of Life Research, 19(4), 539–549. https://doi.org/10.1007/s11136....
 
39.
Munby, H. (1983). Thirty studies involving ‘Scientific Attitude Inventory’: What confidence can we have in this instrument? Journal of Research in Science Teaching, 20, 141–162. https://doi.org/10.1002/tea.36....
 
40.
Munby, H. (1997). Issues of validity in science attitude measurement. Journal of Research in Science Teaching, 34(4), 337–341. https://doi.org/10.1002/(SICI)...<337::AID-TEA4>3.0.CO;2-S.
 
41.
Newman, I., & McNeil, K. (1998). Conducting survey research in the social sciences. New York: University Press of America.
 
42.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.
 
43.
Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079. https://doi.org/10.1080/095006....
 
44.
Pérez Manzano, A., & de Pro Bueno, A. (2018). Algunos datos sobre la visión de los niños y de las niñas sobre las ciencias y del trabajo científico. IQual. Revista de Género e Igualdad, 0(1), 18–31. https://doi.org/10.6018/iQual.....
 
45.
Polit, D. F. (2015). Assessing measurement in health: Beyond reliability and validity. International Journal of Nursing Studies, 52(11), 1746–1753. https://doi.org/10.1016/j.ijnu....
 
46.
Polit, D. F., & Yang, F. (2016). Meaurement and the measurement of change: A primer for health professionals. Philadelphia: Lippincott Williams & Wilkins.
 
47.
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/10.1080/030572....
 
48.
Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling. Routledge (Vol. 47). New York: Routledge Taylor & Francis Group.
 
49.
Sellami, A., El-Kassem, R. C., Al-Qassass, H. B., & Al-Rakeb, N. A. (2017). A path analysis of student interest in STEM, with specific reference to Qatari students. Eurasia Journal of Mathematics, Science and Technology Education, 13(9), 6045–6067. https://doi.org/10.12973/euras....
 
50.
Toma, R. B., & Greca, I. M. (2018). The effect of integrative STEM instruction on elementary students’ attitudes toward science. EURASIA Journal of Mathematics, Science & Technology Education, 14(4), 1383–1395. https://doi.org/10.29333/ejmst....
 
51.
Toma, R. B., & Meneses Villagrá, J. A. (2019a). Validation of the single-items spanish-School science attitude Survey (S-SSAS) for elementary education. PLoS ONE 14(1), e0209027. https://doi.org/10.1371/journa....
 
52.
Toma, R. B., & Meneses Villagrá, J. A. (2019b). Prefrencia por contenidos científicos de física o de biología en Educación Primaria: un análisis cluster, Revista Eureka sobre Enseñanza y Divulgación de las Ciencias, 1(1), 1104, https://doi.org/10.25267/Rev_E....
 
53.
Toma, R. B., & Meneses Villagrá, J. A. (in press). Development and validation of the SUCCESS instrument: towards a valid and reliable measure of expectancies of success in School Science.
 
54.
Van Dinther, M., Dochy, F., & Segers, M. (2011). Factors affecting students’ self-efficacy in higher education. Educational Research Review, 6(2), 95–108. https://doi.org/10.1016/j.edur....
 
55.
Vázquez-Alonso, Á., & Manassero–Mas, M. A. (2008). El declive de las actitudes hacia la ciencia de los estudiantes: un indicador inquietante para la educación científica. Revista Eureka sobre Enseñanza y Divulgación de Las Ciencias, 5(3), 274–292. https://doi.org/10.25267/Rev_E....
 
56.
Vázquez-Alonso, Á., & Manassero-Mas, M. A. (2004). Imagen de la ciencia y la tecnología al final de la educación obligatoria. Cultura y Educación, 16, 385-398. https://doi.org/10.1174/113564....
 
57.
Vázquez-Alonso, Á., & Manassero–Mas, M. A. (2011). El descenso de las actitudes hacia la ciencia de chicos y chicas en la Educación Obligatoria. Ciência & Educação, 17(2), 249–268. https://doi.org/10.1590/S1516-....
 
58.
Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: definitions, development, and relations to achievement outcomes. Developmental Review, 30(1), 1–35. https://doi.org/10.1016/j.dr.2....
 
59.
Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: a theoretical analysis. Developmental Review, 12, 265–310. https://doi.org/10.1016/0273-2....
 
60.
Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1....
 
61.
Wigfield, A., & Eccles, J. S. (2002). The development of competence beliefs, expectacies for success, and achievement values from childhood through adolescence. In A. Wigfield y J. S. Eccles (Eds.), Development of achievement motivation (pp. 91–120). San Diego: Academic Press. https://doi.org/10.1016/B978-0....
 
eISSN:1305-8223
ISSN:1305-8215