探索性因素分析英文寫作

學統計的時候學的是觀念,但到了寫paper的時候,如何寫出來,還得在學學這些方面如何用英文來描述。另外,有時候分析都作了,但寫文章的時候又忘了,紀錄下這些步驟,也是提高文章的完整性。

下面針對探索性因素分析的部分,給出一些文章用的說法,大家自行修改,並附上一些寫作時可用的文獻。至於驗證性分析,目前還在整理,請大家靜待下一篇。

有些部分還不太全,這有空再補,或是請大家提供這方面有詳細描述的文章。文章也還有點亂,請大家包涵。如果有缺漏的地方,請大家指正。

探索性因素分析

可執行因素分析與否 (factorability)

這又叫作因子可分解性。

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.

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

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.

探索性因素之間的關係

正交

A principal axis factor (PAF) analysis was carried out on the 28 items from the 問卷名 using 統計軟體.

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.

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

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

決定探索性因子的方法

The number of factors to extract was determined on the basis of several criteria, including parallel analysis, examination of the resulting scree plot, and eigen- values greater than 1.0 (i.e., the K1 criterion; Hayton, Allen, & Scarpello, 2004).

Eigenvalue

The K1 criterion suggested # initial factors.

# 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.

Parallel analysis

The parallel analysis suggested that two factors should be retained.

Scree plot

Inspection of the scree plot, although subjective, seemed to suggest two or three factors.

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.

Factor loadings

A cutoff for statistical significance of the factor loadings of 0.5 was used, because loadings of 0.5 or greater are also considered practically significant (Hair et al., 1998).

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)

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.


探索性因素分析的效度

Cronbach’s alpha

The Cronbach’s alpha for these ## items was .##.

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)

Correlation matrix

Further inspection of the inter-item correlation matrix revealed some considerable redundancy in items A and B.Each of these items was highly correlated (r > .70) with four other items in the subscale. Therefore, these two items were deleted.

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

範例文章

這些文章是我用關鍵字從我的文獻管理軟體找出來的,上面很多英語也是從下面的文章節出來的。大家可以參考一下。如果有更具有代表性的文章,歡迎在下面留言。

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

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