A confirmatory factor analysis of the attitude towards mathematics scale using multiply imputed datasets

Ayob, Aszunarni; Yassin, Ruhizan M.

International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 3 (March 2017), Pages: 7-12

Title: A confirmatory factor analysis of the attitude towards mathematics scale using multiply imputed datasets

Author(s): Aszunarni Ayob *, Ruhizan M. Yassin

Affiliation(s):

Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

https://doi.org/10.21833/ijaas.2017.03.002

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Abstract:

This study re-examined, via confirmatory factor analysis (CFA) method, construct validity of PISA 2012 attitude towards mathematics scale using multiply imputed datasets. Data for this study were drawn from the Malaysian sample of PISA 2012. Specifically, 4247 students from 135 Malaysian secondary schools were used as sample in this study. Prior to conducting the CFA, missing data resulted from questionnaire rotation design were multiply imputed using predictive mean matching (PMM) method via R-package Multiple Imputation by Chained Equations (MICE). Subsequently, Mardia’s multivariate normality test was performed using R-package MVN. Since the attitude towards Mathematics scale was hypothesized to consist of ten constructs, a ten-factor congeneric CFA model was then built using R-package lavaan.survey, which incorporate both multiply imputed data and survey weights as well as non-normality of data through its Maximum Likelihood Robust estimation. After a few series of theory-guided model specification, several items with low loadings or cross-loadings, and construct with low in both Composite Reliability (CR) and Average Variance Extracted (AVE) were eliminated. Through examination of various goodness-of-fit indices, results indicated that the final nine-factor congeneric CFA model provided good fit to the data.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Multiple imputation, Confirmatory factor analysis, Maximum likelihood robust, Validity, Reliability

Article History: Received 5 November 2016, Received in revised form 15 January 2017, Accepted 15 January 2017

Digital Object Identifier:

https://doi.org/10.21833/ijaas.2017.03.002

Citation:

Ayob A, Yassin RM (2017). A confirmatory factor analysis of the attitude towards mathematics scale using multiply imputed datasets. International Journal of Advanced and Applied Sciences, 4(3): 7-12

http://www.science-gate.com/IJAAS/V4I3/Ayob.html

References:

Abdul MA, Darmawan IGN, and Lynch P (2013). A confirmatory factor analysis of attitudes toward mathematics inventory (ATMI). The Mathematics Educator, 15(1): 121-135.

Aiken LR (1972). Research on attitudes toward mathematics. The Arithmetic Teacher, 19(3): 229-234.

Aiken LR (1974). Two scale of attitude toward mathematics. Journal for Research in Mathematics Education, 5(2): 657-671.

Aiken LR (1979). Attitudes toward mathematics and science in Iranian middle schools. School Science and Mathematics, 79(3): 229-234. https://doi.org/10.1111/j.1949-8594.1979.tb09490.x

Aiken LR and Dreger RM (1961). The effect of attitudes on performance in learning mathematics. Journal of Educational Psychology, 52(1): 19-24. https://doi.org/10.1037/h0041309

Behr N (1973). Achievement, aptitude and attitude in mathematics. The Two-Year College Mathematics Journal, 4(2): 72-74. https://doi.org/10.2307/3026447

Cheung KC (1988). Outcomes of schooling: mathematics achievement and attitudes towards mathematics learning in Hong Kong. Educational Studies in Mathematics, 19(2): 209-219. https://doi.org/10.1007/BF00751233

Fennema E and Sherman J (1976). Fennema-sherman mathematics anxiety scales: instruments designed to measure attitudes towards the learning of mathematics by females and males. Journal for Research in Mathematics Education, 7(5): 324-326. https://doi.org/10.2307/748467

Hair JF, Black WC, Babin BJ, and Anderson R (2010) Multivariate analysis. Pearson Prentice Hall, New Jersey, USA.

Huang YC and Lin SH (2015). Development and validation of an inventory for measuring student attitudes toward calculus. Measurement and Evaluation in Counseling and Development, 48(2): 109-123. https://doi.org/10.1177/0748175614563314

Kibrislioglu N (2015). An investigation about 6th grade students' attitudes towards mathematics. Procedia Social and Behavioral Sciences, 186: 64-69. https://doi.org/10.1016/j.sbspro.2015.04.024

Organisation for Economic Co-operation and Development (OECD) (2012). PISA 2012 technical report programme for international student assessment. OECD Publishing, Paris, France.

Palacios A, Arias V, and Arias B (2014). Attitudes towards mathematics: Construction and validation of a measurement instrument. Revista de Psicodidactica, 19(1): 67-91. https://doi.org/10.1387/RevPsicodidact.8961

Quaye J (2015), Exploring students' attitudes towards mathematics and mathematical achievement in secondary schools in England: The role of social class, gender and ethnicity. Research in Mathematics Education, 17(1): 59-60. https://doi.org/10.1080/14794802.2014.971340

Sellin N and Keeves JP (1997). Path analysis with latent variables. In Keeves JP (Eds.), Educational research, methodology, and measurement: An international handbook: 633–640. 2nd Edition, Pergamon Press, Oxford, UK.

Tabuk M and Haciömeroğlu G (2015). Turkish adaptation of attitude towards mathematics instrument. Journal of Theory and Practice in Education, 11(1): 245-260.

Tapia M and Marsh GE (2004). An instrument to measure mathematics attitudes. Academic Exchange Quarterly, 8(2): 16-21.

Tarim K and Akdeniz F (2008). The effects of cooperative learning on Turkish elementary students' mathematics achievement and attitudes towards mathematics using TAI and STAD methods. Educational Studies in Mathematics, 67(1): 79-91. https://doi.org/10.1007/s10649-007-9088-y

Tezer M and Ozcan D (2015). A study of the validity and reliability of a mathematics lesson attitude scale and student attitudes. Eurasia Journal of Mathematics, Science and Technology Education, 11(2): 371-379.

Van Buuren S and Groothuis-Oudshoorn K (2011). MICE: multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3): 1-67. https://doi.org/10.18637/jss.v045.i03