Interpreting SPSS

Interpreting SPSS

Bởi HUF04 Nguyễn Đăng Hải -

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Regression equation:
The regression equation based on the unstandardized coefficients is:
Perceived Stress = 50.828 − 0.621 (Total Mastery) − 0.175 (Total PCOISS).

Interpr...

tiếp...

image.png

Regression equation:
The regression equation based on the unstandardized coefficients is:
Perceived Stress = 50.828 − 0.621 (Total Mastery) − 0.175 (Total PCOISS).

Interpretation of one standardized Beta:
I think Total Mastery has a stronger effect on perceived stress because its standardized Beta (β = −.422) is larger in magnitude than that of PCOISS (β = −.358). This means that when mastery increases, perceived stress tends to decrease more noticeably compared to PCOISS.

Practical implication:
In real life, this suggests that helping students improve their sense of mastery or control over tasks may be an effective way to reduce their stress levels.

Interpreting SPSS

Bởi HUF04 Trần Huỳnh Gia Hân -
I think your interpretation is clear and mostly accurate. You correctly explained the regression equation and clearly interpreted the standardized Beta values. Your ...

tiếp...

I think your interpretation is clear and mostly accurate. You correctly explained the regression equation and clearly interpreted the standardized Beta values. Your conclusion that Total Mastery has a stronger effect (because β = −.422 is larger than β = −.358) is appropriate, and your practical implication about reducing stress is logical. One small suggestion: you could make it even clearer by mentioning that standardized Betas allow comparison because they are on the same scale, which strengthens your argument. Overall, your interpretation is easy to follow and well-supported by the results.

Interpreting SPSS

Bởi HUF04 Nguyễn Ngọc Quỳnh Như -
Your analysis is well-organized and easy to follow. You correctly used the stats and plots to prove that your model meets all the necessary requirements, like normality and...

tiếp...

Your analysis is well-organized and easy to follow. You correctly used the stats and plots to prove that your model meets all the necessary requirements, like normality and multicollinearity.