Efectos a largo plazo

Results from regression analyses at Time 1, where non-change motivational resources and personal characteristics were the predictor variables, and where mid-term exam scores were the criterion variable, are presented first. The study moves on to report the relationships between its non-change predictor variables measured at Time 2 and its criterion variables: coursework, final exam, and semester grades. Finally, the current section reports the relationships between the changes in motivational resources (where changes in motivational resources were calculated as the value at Time 2 minus the value at Time 1) and the criterion variables of coursework, final exam, and semester grades.

As a reminder, in study 2 the mid-term exam criterion variable was a summation of scores from standardized mid-term exams in all the skills (Reading, Writing, Listening/Speaking, and Grammar/Vocabulary). Coursework was a summation of teachers’ marks for participation and class work across all skills. The final exam was the summation of results across all skills in standardized final exams. Semester grades was the summation of all these indicators of academic achievement across all skills.

Time 1

The relationship between motivational resource variables measured at Time 1, the students’ personal characteristics, and the mid-term exams is now reported. Mid- term exams were standardized and are one element composing semester grades. Mid-term exam scores The first criterion variable to be predicted was mid-term exam scores. In model 1, (see Appendix, Table B.62), all the predictor variables, ex- cept IELTS scores and HSGPA (All), were entered into the regression analysis. The criterion variable was mid-term exam scores. Model 1 did not explain a statistically significant amount of the variance in the grades participants achieved for mid-term

scores, F(9, 59) = 1.751, p = 0.098, R2 _{= 0.211, R}2

5.4. Study 2 108 of the regression coefficients in the model indicated that none of the psychological variables at Time 1 statistically significantly predicted mid-term scores at the p ≤ 0.01 level. See Appendix, Table B.63 for model 2 results.

In model 3, the results of which are shown in Table 5.16, a statistically significant amount of variance in mid-term scores was not explained, F(6, 62) = 2.686, p =

0.022, R2 _{= 0.206, R}2

Adjusted = 0.13. Examination of the regression coefficients

revealed that perceived competence statistically significantly predicted mid-term scores at Time 1, (β = 0.3, p ≤ 0.01).

Table 5.16: Study 2, T1, model 3. Mid-term exam scores

Variable B Std. Error β t Sig.

(Constant) 92.390 15.940 - 5.796 0.000 SS -10.238 4.181 -0.331 -2.449 0.017 SE 0.118 0.065 0.245 1.833 0.072 GE -0.136 0.131 -0.122 -1.040 0.302 AU1 -2.204 1.926 -0.134 -1.144 0.257 MA1 1.553 1.127 0.163 1.378 0.173 PC1 3.033 1.184 0.300 2.561 0.013 N = 69

SS = SES (Scaled), SE = SES (Standardized and weighted), GE = GPA (English only), 1 = Time 1, AU = Autonomous motives, MA = Materialism, PC = Perceived competence

Time 2

The relationships between the study’s non-change motivational resource variables measured at Time 2, the students’ personal characteristics, and the study’s other criterion variables are now reported. This begins with coursework.

Coursework scores The second criterion variable to be predicted was coursework scores. In model 1 (see Appendix, Table B.64), all the predictor variables, apart from IELTS scores and HSGPA (All), were entered into the regression analysis. Model 1 did not explain a statistically significant amount of the variance in coursework

grades (F(9, 59) = 1.438, p = 0.193, R2 = 0.180, R2

Adjusted = 0.055). None of

the regression coefficents were statistically significant at the p ≤ 0.01 level. See Appendix, Table B.65 for model 2 results.

5.4. Study 2 109 In model 3a, the results of which are shown in Table 5.17, a parsimonious regres- sion model was constructed but without entering mid-term scores. Results revealed that the overall model did not predict a statistically significant amount of the vari-

ance in coursework scores, F(4, 67) = 3.175, p = 0.019, R2 _{= 0.159, R}2

Adjusted =

0.109. Examination of the regression coefficients indicated that none of the correla- tion coefficients were statistically significant predictors at the p ≤ 0.01 level.

Table 5.17: Study 2, T2, model 3a. Course- work scores

Variable B Std. Error β t Sig.

(Constant) 70.087 11.887 - 5.896 0.000 SS -8.214 3.507 -0.334 -2.342 0.022 GE 0.176 0.102 0.197 1.719 0.090 PC2 1.323 0.754 0.198 1.754 0.084 FC 2.862 2.126 0.194 1.346 0.183 N = 72

SS = SES (Scaled), GE = GPA (English only), 2 = Time 2, PC = Perceived competence, FC = First and continuing students

In model 3b (see Appendix, Table B.66), a parsimonious regression model was constructed. This time, mid-term exam results were also entered. Results revealed that the overall model predicted a statistically significant amount of the variance in

coursework scores, F(4, 68) = 22.054, p < 0.001, R2 _{= 0.565, R}2

Adjusted = 0.539.

GPA (English) was a statistically significant predictor, after controlling for mid-term exams, where β = 0.353, p < 0.001.

Final exam scores The third criterion variable to be predicted was final exam scores. In model 1, (see Appendix, Table B.67), all the predictor variables – with the exception of IELTS scores and GPA (All) – were entered into regression anal- ysis. Model 1 did not explain a statistically significant amount of the variance in

final exam scores, F(9, 59) = 2.095, p = 0.044, R2 _{= 0.242, R}2

Adjusted = 0.127.

Examination of the regression coefficients revealed that none of the predictor vari- ables statistically significantly predicted final exam scores at the p ≤ 0.01 level. See Appendix, Table B.68 for model 2 results.

In model 3a (see Table 5.18), a parsimonious regression model was constructed but without entering mid-term scores. Results revealed that the overall model pre-

5.4. Study 2 110 dicted a statistically significant amount of the variance in final exam scores, F(6,

62) = 3.195, p = 0.008, R2 _{= 0.236, R}2

Adjusted = 0.162. Autonomous motives (at

T2), where β = -0.35, p = 0.004 was a statistically significant predictor of final exam scores.

Table 5.18: Study 2, T2, model 3a. Final exam scores

Variable B Std. Error β t Sig.

(Constant) 88.665 16.190 - 5.476 0.000 GN -4.626 2.392 -0.249 -1.934 0.058 SS -9.908 3.982 -0.330 -2.488 0.016 SE 0.044 0.065 0.093 0.666 0.508 GE 0.076 0.127 0.070 0.599 0.552 AU2 -5.465 1.823 -0.347 -2.999 0.004 PC2 1.833 0.922 0.226 1.989 0.051 N = 63

GN = Gender, SS = SES (Scaled), SE = SES (Standardized and weighted), GE = GPA (English only), 2 = Time 2, AU = Autonomous motives, PC = Perceived competence

In model 3b (see Appendix, Table B.69), a parsimonious regression model was again constructed but with mid-term scores entered. Results revealed that the overall model predicted a statistically significant amount of the variance in final exam

scores, F(4, 64) = 21.355, p < 0.001, R2 _{= 0.572, R}2

Adjusted= 0.545. Examination of

the regression coefficients revealed that none of the predictor variables statistically significantly predicted final exam scores at the p ≤ 0.01 level.

Semester grades The final criterion variable to be predicted was semester grades. Model 1 (see Appendix, Table B.70) did not explain a statistically significant amount

of the variance in semester scores, F(9, 59) = 1.981, p = 0.058, R2_{= 0.232, R}2

Adjusted

= 0.115. Examination of the regression coefficients indicated that only SES (Scaled) was a statistically significant predictor of semester grades. See Appendix, Table B.71 for model 2 results.

In model 3a, the results of which are shown in Table 5.19, a parsimonious re- gression model was constructed but without mid-term scores. The overall model predicted a statistically significant amount of the variance in semester grades, F(6,

62) = 2.971, p = 0.013, R2 _{= 0.223, R}2

5.4. Study 2 111 sion coefficients revealed that autonomous motives (at Time 2) and SES (Scaled) were statistically significant negative predictors of semester grades, where β = -0.303,

p = 0.012, and where β = -0.363, p = 0.009 respectively.

Table 5.19: Study 2, T2, model 3a. Semester grades

Variable B Std. Error β t Sig.

(Constant) 91.642 13.831 - 6.626 0.000 GN -2.679 2.044 -0.171 -1.311 0.195 SS -9.234 3.402 -0.363 -2.714 0.009 SE 0.050 0.056 0.126 0.890 0.377 GE 0.030 0.108 0.033 0.280 0.781 AU2 -4.046 1.557 -0.303 -2.598 0.012 PC2 1.875 0.788 0.273 2.380 0.020 N = 69

GN = Gender, SS = SES (Scaled), SE = SES (Standardized and weighted), GE = GPA (English only), 2 = Time 2, AU = Autonomous motives, PC = Perceived competence

No model 3b was produced as semester grades are a composite of mid-term scores. Having examined the relationships that the non-change variables have with the study’s criterion variables, the results of the analyses using the change variables are now presented.

The ‘change’ predictor variables

Results are now presented showing the relationship that the change predictor vari- ables – autonomous motivation (T2-T1), perceived competence (T2-T1), controlled motivation (T2-T1), and materialism (T2-T1) – have with coursework, final exam, and semester grades, beginning with coursework. Once again, three models are made use of.

Coursework scores The first criterion variable to be predicted was coursework scores. In model 1 (see Appendix, Table B.72), all the predictor variables were entered into the regression analysis. Model 1 did not explain a statistically significant

amount of the variance in coursework scores, F(9, 59) = 1.553, p = 0.151, R2= 0.192,

R2

5.4. Study 2 112 the predictor variables statistically significantly predicted final exam scores at the p

≤0.01 level. See Appendix, Table B.73 for model 2 results.

In model 3a, the results of which are shown in Table 5.20, a parsimonious regres- sion model was constructed but without entering mid-term scores. Results revealed that the overall model predicted a statistically significant amount of the variance

in coursework scores, F(4, 67) = 3.386, p = 0.014, R2 _{= 0.168, R}2

Adjusted = 0.118.

Examination of the regression coefficients revealed that only SES (Scaled) was a statistically significant predictor of coursework scores, where β = -0.367, p ≤ 0.01.

Table 5.20: Study 2, T2-1, model 3a. Course- work scores

Variable B Std. Error β t Sig.

(Constant) 82.425 10.559 - 7.806 0.000 SS -9.039 3.458 -0.367 -2.614 0.011 GE 0.129 0.104 0.144 1.238 0.220 CO2-1 -4.330 2.216 -0.223 -1.954 0.055 FC 3.431 2.108 0.232 1.627 0.108 N = 72

GN = Gender, SS = SES (Scaled), SE = SES (Standardized and weighted), GE = GPA (English only), 2 = Time 2, AU = Autonomous motives, PC = Perceived competence

In model 3b (see Appendix, Table B.74), a parsimonious regression model was again constructed but with mid-term scores added as a predictor. Results revealed that the overall model predicted a statistically significant amount of the variance in

coursework scores, F(3, 69) = 27.085, p < 0.001, R2 = 0.541, R2

Adjusted = 0.521.

Examination of the regression coefficients revealed that apart from mid-term results, only GPA (English) was a statistically significant predictor, where β = 0.277, p = 0.002.

Final exam scores The second criterion variable to be predicted was final exam scores. In model 1, (see Appendix, Table B.75), all the predictor variables were entered into the regression analysis. Model 1 did not explain a statistically significant

amount of the variance in final exam scores, F(9, 59) = 1.352, p = 0.231, R2_{= 0.171,}

R2

Adjusted = 0.044. Examination of the regression coefficients revealed that none of

5.4. Study 2 113

≤0.01 level. See Appendix, Table B.76 for model 2 results.

In model 3a, the results of which are shown in Table 5.21, a parsimonious regres- sion model was constructed but without entering mid-term scores into the analysis. Results revealed that the overall model did not predict a statistically significant

amount of the variance in final exam scores, F(5, 63) = 2.327, p = 0.053, R2 =

0.156, R2

Adjusted = 0.089. Examination of the regression coefficients revealed that

none of the predictor variables statistically significantly predicted final exam scores at the p ≤ 0.01 level.

Table 5.21: Study 2, T2-1, model 3a. Final exam scores

Variable B Std. Error β t Sig.

(Constant) 78.853 14.131 - 5.580 0.000 GN -2.769 2.476 -0.149 -1.118 0.268 SS -8.698 4.105 -0.290 -2.119 0.038 SE 0.059 0.068 0.127 0.869 0.388 GE 0.055 0.134 0.051 0.413 0.681 CO2-1 -5.715 2.790 -0.244 -2.049 0.045 N = 69

GN = Gender, SS = SES (Scaled), SE = SES (Standardized and weighted), GE = GPA (English only), 2 = Time 2, AU = Autonomous motives, PC = Perceived competence

In model 3b,(see Appendix, Table B.77), a parsimonious regression model was constructed. This time, mid-term scores were entered into the analysis. Results revealed that the overall model predicted a statistically significant amount of the

variance in final exam scores, F(4, 64) = 21.389, p < 0.001, R2 _{= 0.572, R}2

Adjusted

= 0.545. Examination of the regression coefficients revealed that controlling for mid-term exam results, only GPA (English) statistically significantly predicted final exam results, where β = 0.234, p = 0.006.

Semester grades The final criterion variable to be predicted was semester grades. In model 1 (see Appendix, Table B.78), all the predictor variables were entered into the regression analysis. However, model 1 did not explain a statistically significant

amount of the variance in semester grades, F(9, 59) = 1.448, p = 0.189, R2 = 0.181,

R2

5.4. Study 2 114 the predictor variables statistically significantly predicted final exam scores at the p

≤0.01 level. See Appendix, Table B.79 for model 2 results.

In model 3a, the results of which are shown in Table 5.22, a parsimonious regres- sion model was constructed in order to predict semester grades, but without entering mid-term scores into the analysis. Results revealed that the overall model did not predict a statistically significant amount of the variance in semester grades, F(4, 64)

= 3.129, p = 0.021, R2 = 0.164, R2

Adjusted = 0.111. Examination of the regression

coefficients revealed that none of the predictor variables statistically significantly predicted final exam scores at the p ≤ 0.01 level.

Table 5.22: Study 2, T2-1, model 3a. Semester grades

Variable B Std. Error β t Sig.

(Constant) 85.577 10.777 - 7.940 0.000 SS -8.494 3.435 -0.334 -2.473 0.016 SE 0.076 0.053 0.193 1.437 0.156 GE 0.021 0.108 0.023 0.192 0.848 CO2-1 -5.725 2.320 -0.289 -2.468 0.016 N = 69

SS = SES (Scaled), SE = SES (Standardized and weighted), GE = GPA (English only), 2 = Time 2, CO = Controlled motives

As for model 3b, this was not constructed because the variable semester grades is not independent of mid-term results.

Chapter 6

Discussion

6.1

Answering the research questions (RQs)

The current section addresses six main research questions. These were: 1. What is the relationship between the study’s non-change motivational and personal charac- teristics variables? 2. What is the relationship between the personal characteristics variables and academic performance? 3. What is the relationship between the current study’s non-change motivational resources variables and academic perfor- mance? 4. Are changes in motivational resources predictive of grades? 5. What is the relationship between generational status and academic performance? 6. What differences exist between the cohorts in study 1 and 2? An overview of the study’s results is now given. This is followed by further discussion of its main findings.