The aim of this study was to develop a scale assessing high school students’ selfefficacy beliefs in chemistry-related tasks and to assess psychometric properties of scores on this scale. A pilot study with a sample of 150 high school students provided initial evidence for two-factor structure of 16-item scale, named High School Chemistry Self-Efficacy Scale (HCSS). The final form of the HCSS was administered to 362 high school students in Turkey. Confirmatory factor analysis fit indices and factor pattern coefficients supported the proposed structure, with the two factors directly corresponding to the hypothesized dimensions. These dimensions were chemistry self-efficacy for cognitive skills (10 items, a reliability = .90) and self-efficacy for chemistry laboratory (6 items, a reliability = .92). In addition, the two-factor model for the HCSS was invariant across school types. Analyses of latent mean structure indicated that private school students were statistically more efficacious than public school students in chemistry laboratory skills. The HCSS is a promising tool to identify chemistry self-efficacy beliefs in high school students.
Andrew, S. ( 1998). Self-efficacy as a predictor of academic performance in science. Journal of Advanced Nursing, 27, 596-603.
2.
Arbuckle, J.L., & Wothke, W. ( 1999). Amos 4.0 User’s Guide. Chicago : SPSS.
3.
Bandura, A. ( 1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
4.
Bandura, A. ( 1997). Self-efficacy: The exercise of control. New York: W. H. Freeman.
5.
Bandura, A. ( 2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Selfefficacy beliefs of adolescents (pp. 307-337). Greenwich, CT: Information Age.
6.
Bentler, P.M. ( 1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238-246.
7.
Britner, S.L., & Pajares, F. ( 2001). Self-efficacy beliefs, motivation, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7, 271-285.
8.
Britner, S.L., & Pajares, F. ( 2006). Sources of science self-efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485-499.
9.
Browne, M.W., & Cudeck, R. ( 1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
10.
Byrne, B.M. ( 1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum.
11.
Byrne, B.M. ( 2001). Structural equation modeling with AMOS. Mahwah, NJ: Lawrence Erlbaum.
12.
Carter, C.S., & Brickhouse, N.W. (1989). What makes chemistry difficult? Alternate perceptions. Journal of Chemical Education , 66, 223-225.
13.
Chen, F.F. ( 2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464-504.
Dalgety, J., & Coll, R.K. ( 2006). Exploring first-year science students’ chemistry self-efficacy. International Journal of Science and Mathematics Education, 4, 1-20.
16.
Erdogan, I. (Ed.). (2002). Özel okullar ve eğitimde kalite [Private schools and quality in education] . Istanbul: Özel Okullar Derneği .
17.
Fan, X., & Thompson, B. ( 2001). Confidence intervals about score reliability coefficients, please: An EPM guidelines editorial. Educational and Psychological Measurement, 61, 517-531.
18.
Gopal, H., Kleinsmidt, J., Case, J., & Musonge, P. ( 2004). An investigation of tertiary students’ understanding of evaporation, condensation and vapour pressure. International Journal of Science Education, 26, 1597-1620.
19.
Graham, J.M., Guthrie, A.C., & Thompson, B. ( 2003). Consequences of not interpreting structure coefficients in published CFA research: A reminder. Structural Equation Modeling, 10, 142-153.
20.
Hancock, G.R. ( 1997). Structural equation modeling methods of hypothesis testing of latent variable means. Measurement and Evaluation in Counseling and Development, 30, 91-105.
21.
Hancock, G.R. ( 2001). Effect size, power, and sample size determination for structured means modeling and mimic approaches to between-groups hypothesis testing of means on a single latent construct. Psychometrika , 66, 373-388.
22.
Henson, R.K. ( 2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and Evaluation in Counseling and Development, 34, 177-189.
23.
Henson, R.K., & Roberts, J.K. ( 2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66, 393-416.
24.
Horn, J.L. ( 1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185.
Hu, L.T., & Bentler, P.M. ( 1995). Evaluating model fit. In R. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76-99). Thousands Oaks, CA: Sage.
27.
Hu, L., & Bentler, P.M. ( 1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
28.
Kennedy, E. ( 1996). What do they think of chemistry?Australian Science Teachers Journal, 42(2), 53-59.
29.
Kline, R.B. ( 1998). Principles and practice of structural equation modeling . New York: Guilford.
30.
Kupermintz, H. ( 2002). Affective and conative factors as aptitude resources in high school science achievement. Educational Assessment , 8, 123-137.
31.
Lau, S., & Roeser, R.W. ( 2002). Cognitive abilities and motivational processes in high school students’ situational engagement and achievement in science . Educational Assessment, 8, 139-162.
32.
MacCallum, R.C., & Austin, J.T. ( 2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201-226.
33.
MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
34.
MacCallum, R.C., Roznowski, M., Mar, C.M., & Reith, J.V. ( 1994). Alternative strategies for cross-validation of covariance structure models. Multivariate Behavioral Research , 29, 1-32.
35.
Ministry of Education (MEB). (2007). PISA 2006 Ulusal ön rapor [National Preliminary Report]. Ankara: Author. Retrieved May 10, 2008, from http://earged.meb.gov.tr/pisa/dokuman/2006/rapor/Pisa_2006_Ulusal_On_Rapor.pdf
36.
O’Connor, B.P. ( 2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments, & Computers, 32, 396-402.
37.
Pajares, F., Britner, S.L., & Valiante, G. ( 2000). Relation between achievement goals and self-beliefs of middle school students in writing and science. Contemporary Educational Psychology, 25, 406-422.
38.
Pajares, F., & Schunk, D.H. ( 2001). Self-beliefs and school success: Self-efficacy, self-concept, and school achievement. In R. J. Riding & S. G. Rayner (Eds.), Perception (pp. 239-266). London: Ablex.
39.
Schunk, D.H. ( 1989). Self-efficacy and cognitive achievement: Implications for students with learning problems. Journal of Learning Disabilities , 22, 14-22.
Thompson, B. ( 1997). The importance of structure coefficients in structural equation modeling confirmatory factor analysis. Educational and Psychological Measurement, 57, 5-19.
42.
Thompson, M.S., & Green, S.B. ( 2006). Evaluating between-group differences in latent variable means. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 119-169). Greenwich, CT: Information Age.
43.
Tsaparlis, G. ( 1997). Atomic orbitals, molecular orbitals, and related concepts: Conceptual difficulties among chemistry students. Research in Science Education, 27, 271-287.
44.
Watson, J., & McEwen, A. (1994). Sixth form A level students’ perceptions of the difficulty, intellectual freedom, social benefit and interest of science and arts subjects. Research in Science & Technological Education, 12, 43-49.
45.
Zwick, W.R., & Velicer, W.F. ( 1986). Comparison of the rules for determining the number of components to retain. Psychological Bulletin, 99, 432-442.
