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Evaluation of the correlation matrix indicated that it was factorable: Kaiser-Meyer-Olkin Measure of Sampling Adequacy = .##, which is “marvelous” (> .90) according to Kasier’s criteria (Pett, Lackey, & Sullivan, 2003).

The factorability of the matrix was determined using the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (MSA). In our study, the MSAs for individual variables ranged from 0.89 to 0.97. The MSA for the entire matrix was 0.937. Each of these MSA values is well above the 0.80 meritorious level (Kaiser & Rice, 1974).

We examined the factorability through an inspection of the correlation matrix, and through conducting the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. The KMO test yielded a measure of 0.815, and Coakes and Steed recommend that this measure should exceed 0.6 to proceed with factoring.

The factorability of the matrix was determined using the Kaiser–Meyer–Olkin Measure of Sampling Adequacy (MSA). In our study, the MSAs for individual variables ranged from 0.89 to 0.97. The MSA for the entire matrix was 0.937. Each of these MSA values is well above the 0.80 meritorious level (Kaiser & Rice, 1974).

We examined the factorability through an inspection of the correlation matrix, and through conducting the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. The KMO test yielded a measure of 0.815, and Coakes and Steed recommend that this measure should exceed 0.6 to proceed with factoring.

Bartlett’s test of sphericity was significant well beyond the 0.001 level.

Coakes, S. J., & Steed, L. G. (1997). SPSS analysis without anguish. Brisbane: John Wiley. (不建議引用)

Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage Publications.

Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34, 111–117.

Factor analysis was performed with SPSS #, employing a cut-off eigenvalue of 1 and VARIMAX rotation.

Each was a principal components analysis with varimax rotation.

A principal component factor analysis (PCFA) with Varimax rotation was used to determine the underlying structure of the data.

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斜交

Oblique rotation methods allow for factors to be correlated, and the assumption was made that the 多少個 factors thought to be present in the 問卷 were related.

參考文獻

參考文獻

Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2, 13-43.

# factors were identified using the latent root criterion, which is the most common technique for determining the number of factors to extract (Hair, Anderson, Tatham, & Black, 1998). The initial eigenvalues were greater than 1, which are considered significant.

參考文獻

Hair, J. E., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in explor- atory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7, 191-205.

We adopted a factor loading criterion of 0.40 for inclusion of the item in the interpretation, more stringent than Tabachnik and Fidell (1996), who suggest 0.32, and consistent with Comrey and Lee (1992) who suggest that the criterion should be set a little higher than 0.32.

參考文獻

Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ: Erlbaum.

Hair, J. E., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Pallant, J. (2001). SPSS survival manual. Crows Nest, NSW: Allen & Unwin. (不建議引用)

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有因子load在兩個因素上

Where an item loaded on more than one factor, we have followed the advice of Arrindell et al. (1983) and have included the item in the factor on which it scored highest, provided the difference between the two-factor loadings was at least 0.2.

參考文獻

Arrindell, W. A., Emmelkamp, P. M. G., Brilman, E., & Monsma, A. (1983). Psychometric evaluation of an inventory for assessment of parental rearing practices. Acta Psychiatrica Scandinavica, 67, 163–177.

Hair, J. E., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall.

Pallant, J. (2001). SPSS survival manual. Crows Nest, NSW: Allen & Unwin. (不建議引用)

參考文獻

Arrindell, W. A., Emmelkamp, P. M. G., Brilman, E., & Monsma, A. (1983). Psychometric evaluation of an inventory for assessment of parental rearing practices. Acta Psychiatrica Scandinavica, 67, 163–177.

The reliability of the questionnaire is satisfactory, with a Cronbach alpha of 0.83. Both Coakes and Steed (1997) and Pallant (2001) suggest that alpha values above 0.7 are sufficient for reliability to be assumed.

參考文獻

Coakes, S. J., & Steed, L. G. (1997). SPSS analysis without anguish. Brisbane: John Wiley. (不建議引用)

Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Hillsdale, NJ:

Erlbaum.

Pallant, J. (2001). SPSS survival manual. Crows Nest, NSW: Allen & Unwin. (不建議引用)

Further inspection of the inter-item correlation matrix revealed that item A had low correlations (r < .40) with # other items in the subscale.

Artino, A., & McCoach, D. (2008). Development and initial validation of the online learning value and self-efficacy scale. Journal of Educational Computing Research, 38(3), 279-303. doi: 10.2190/EC.38.3.c

Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29-48. doi: 10.1080/01587910500081269

Smith, P. J., Murphy, K. L., & Mahoney, S. E. (2003). Towards identifying factors underlying readiness for online learning: An exploratory study. Distance Education, 24(1), 57-67. doi: 10.1080/01587910303043

Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52(4), 1003-1017. doi: 10.1177/0013164492052004025