Abstract
Abstract. This study proposed an improved representation of the factor structure of the Gaspard et al. (2015) value beliefs about math scale relying on bifactor exploratory structural equation modeling (B-ESEM). Using a convenience sample of 537 Italian students (327 males; Mage = 18.2), our results supported the superiority of a B-ESEM solution including nine specific factors (intrinsic, importance of achievement, personal importance, utility for school/job, utility for life, social utility, effort required, opportunity cost, and emotional cost) and one global value factor. The results further revealed that the specific factors (with the exception of personal importance) retained meaning over and above participants’ global levels of value. Finally, our results confirmed that global value beliefs predicted career aspirations, whereas expectancies of success remained the strongest predictor of math achievement.
References
2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397–438.
(2015). Bayesian Structural equation modeling with cross-loadings and residual covariances. Journal of Management, 41, 1561–1577.
(2006). Task values and ability beliefs as predictors of high school literacy choices: A developmental analysis. Journal of Educational Psychology, 98, 382–393.
(2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44, 78–89.
(1983).
(Expectancies, values and academic behaviors . In J. T. SpenceEd., Achievement and achievement motives (pp. 75–146). San Francisco, CA: Freeman.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.
(2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.
(2010). Applied missing data analysis. New York, NY: Guilford Press.
(2015). More value through greater differentiation: Gender differences in value beliefs about math. Journal of Educational Psychology, 107, 663–677.
(2017). Assessing task values in five subjects during secondary school: Measurement structure and mean level differences across grade level, gender, and academic subject. Contemporary Educational Psychology, 48, 67–84.
(2018). Dimensional comparisons: How academic track students’ achievements are related to their expectancy and value beliefs across multiple domains. Contemporary Educational Psychology, 52, 1–14.
(2016). The higher-order model imposes a proportionality constraint: That is why the bifactor model tends to fit better. Intelligence, 55, 57–68. https://doi.org/10.1177%2F0149206316645653
(2009). Guidelines for translating and adapting psychological instruments. Nordic Psychology, 61, 29–45.
(2019). A systematic evaluation and comparison between exploratory structural equation modeling and Bayesian structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, https://doi.org/10.1080/10705511.2018.1554999
(2017). Extending expectancy-value theory predictions of achievement and aspirations in science: Dimensional comparison processes and expectancy-by-value interactions. Learning and Instruction, 49, 81–91.
(2015). Expectancy-value in mathematics, gender and socioeconomic background as predictors of achievement and aspirations: A multi-cohort study. Learning and Individual Differences, 37, 161–168.
(2016). Probing the unique contributions of self-concept, task values, and their interactions using multiple value facets and multiple academic outcomes. AERA Open, 2, 1–20.
(2015). Achievement, motivation, and educational choices: A longitudinal study of expectancy and value using a multiplicative perspective. Developmental Psychology, 51, 1163–1176.
(2002). Changes in children’s self-competence and values: Gender and domain differences across grades on through twelve. Child Development, 73, 509–527.
(2017). Evidence of a continuum structure of academic self-determination: a two-study test using a bifactor-ESEM representation of academic motivation. Contemporary Educational Psychology, 51, 67–82.
(2018). Comparing Exploratory Structural Equation Modeling and existing approaches for multiple regression with latent variables. Structural Equation Modeling, 25, 737–749.
(1992). Self-Description Questionnaire III: A theoretical and empirical basis for the measurement of multiple dimensions of adolescent self-concept: A test manual and research monograph. Penrith, NSW, Australia: University of Western Sydney, SELF Research Centre.
(2013). Factorial, convergent, and discriminant validity of TIMSS math and science motivation measures: A comparison of Arab and Anglo-Saxon countries. Journal of Educational Psychology, 105, 108–128. https://doi.org/10.1037/a0029907
(1998). Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33, 181–220. https://doi.org/10.1207/s15327906mbr3302_1
(2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110.
(2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275–300. https://doi.org/10.1037/1082-989X.9.3.275
(2014). Bifactor models of personality and college student performance: A broad versus narrow view. European Journal of Personality, 26, 604–619.
(1970). Theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical & Statistical Psychology, 23, 1–21.
(2016). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling, 23, 116–139.
(2016). Exploring sources of construct-relevant multidimensionality in psychiatric measurement. International Journal of Methods in Psychiatric Research, 25, 277–288. https://doi.org/10.1002/mpr.1485
(2017). Complementary variable and person-centered approaches to the dimensionality of psychometric constructs: Application to psychological wellbeing at work. Journal of Business and Psychology, 32, 395–419.
(2019).
(Modern factor analytic techniques: Bifactor models, exploratory structural equation modeling (ESEM) and bifactor-ESEM . In G. TenenbaumR. C. EklundEds., Handbook of Sport Psychology (4th ed.). New York, NY: Wiley.2014). Mplus user’s guide. Los Angeles, CA: Muthén & Muthén.
(2011). Who took the “x” out of expectancy-value theory? Psychological Science, 22, 1058–1066.
(2006). Evolution of student interest in science and technology studies. Paris, France: Author.
. (2007). PISA-2006 science competencies for tomorrow’s world. Paris, France: Author.
. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106, 315–329.
(2018). The short form of the Workplace Affective Commitment Multidimensional Questionnaire (WACMQ-S): A bifactor-ESEM approach among healthcare professionals. Journal of Vocational Behavior, 106, 62–83. https://doi.org/10.1016/j.jvb.2017.12.004
(2013). The general factor of personality: A general critique. Journal of Research in Personality, 47, 493–504.
(2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42, 70–83.
(2013).
(Expectancy-value theory revisited . In D. M. McInerneyH. W. MarshR. G. CravenF. GuayEds., Theory driving research: New wave perspectives on self-processes and human development (pp. 233–249). Charlotte, NC: Information Age.