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Table 2.Decomposition of total effect for the structural equation model The other important variable in the model is intrinsic interest value. Intrinsic interest value is the enjoyment individual gains from doing a task. In the model, interest value has a direct and positive effect on expectancy (6=.38, t=8.94, p<.01) and math performance (f=.22, t=5.50, p<.01). Also, interest value has an indirect and positive effect on math performance (f=.23). Total effect of intrinsic interest value on expectancy is .38 and math performance is .44. Finally it is assumed in the model that, task difficulty, required effort, extrinsic utility value and attainment value/importance affecting intrinsic interest value account for approximately 27% of the variation in intrinsic interest value.

Table 2 Decomposition of total effect for the structural equation model The other important variable in the model is intrinsic interest value. Intrinsic interest value is the enjoyment individual gains from doing a task. In the model, interest value has a direct and positive effect on expectancy (6=.38, t=8.94, p<.01) and math performance (f=.22, t=5.50, p<.01). Also, interest value has an indirect and positive effect on math performance (f=.23). Total effect of intrinsic interest value on expectancy is .38 and math performance is .44. Finally it is assumed in the model that, task difficulty, required effort, extrinsic utility value and attainment value/importance affecting intrinsic interest value account for approximately 27% of the variation in intrinsic interest value.

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Y urt, E. (2015). Understanding middle school students’ motivation in math class: The expectancy-value model perspective. International Journal of Education in Mathematics, Science and Technology, 3(4), 288-297.
Y urt, E. (2015). Understanding middle school students’ motivation in math class: The expectancy-value model perspective. International Journal of Education in Mathematics, Science and Technology, 3(4), 288-297.
Table 1. Means, standard deviations and Pearson product-moment correlation coefficients, for the subscales of the STPQ and math performance
Table 1. Means, standard deviations and Pearson product-moment correlation coefficients, for the subscales of the STPQ and math performance
In the present study, structural equation modeling procedures are used to explore the relationships that exis among the variables. SEM analysis is a statistical approach to test a theoretical model to reveal the causal relationships between the observed and latent variables (Shumacker & Lomax, 2004). The hypothesized model is shown in Figure 1. According to the analysis results the fit indices (y’/sd=1.86, RMSEA=.07, SRMR=.02, CFI=.99, GFI=.98, AGFI=.93, TLI=.97 and IFI=.99) showed that the hypothesized model fits the data perfectly (Bollen, 1990; Browne & Cudeck, 1993; Byrne, 2006; Hu & Bentler, 1999; Steiger, 2007; Tanaka & Huba, 1985).
In the present study, structural equation modeling procedures are used to explore the relationships that exis among the variables. SEM analysis is a statistical approach to test a theoretical model to reveal the causal relationships between the observed and latent variables (Shumacker & Lomax, 2004). The hypothesized model is shown in Figure 1. According to the analysis results the fit indices (y’/sd=1.86, RMSEA=.07, SRMR=.02, CFI=.99, GFI=.98, AGFI=.93, TLI=.97 and IFI=.99) showed that the hypothesized model fits the data perfectly (Bollen, 1990; Browne & Cudeck, 1993; Byrne, 2006; Hu & Bentler, 1999; Steiger, 2007; Tanaka & Huba, 1985).
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Mathematics PerformanceExpectancy-value model
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