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| Title: | CB-SEM和PLS-SEM在估計交互作用效果之比較
The comparison of estimation accuracy in interaction effect between CB-SEM and PLS-SEM |
| Authors: | 李昭鋆
Lee, Chao-Yun |
| Contributors: | 余民寧
Yu, Ming-Ning
李昭鋆
Lee, Chao-Yun |
| Keywords: | CB-SEM
PLS-SEM
交互作用
CB-SEM
PLS-SEM
Interaction effect |
| Date: | 2018 |
| Issue Date: | 2018-07-27 12:41:33 (UTC+8) |
| Abstract: | 本研究研究目的主要在於瞭解CB-SEM和PLS-SEM之指標乘積法、正交法、二階段法、無限制法在結構方程式模型之交互作用中，對迴歸係數、因素負荷量、解釋量、因素分數估計結果之良窳；此外，並瞭解指標數目、人數、資料類型對估計之影響。故本研究之實驗情況，共分三種指標數目、九種人數、二種資料類型，合計五十四種類型，並在每一類型模擬五百次。研究結果顯示，以整體論，在大部份的情況下，CB-SEM在迴歸係數、解釋量、因素負荷量表現較佳，而PLS-SEM在估計因素分數上較佳。而精確來說，若研究目的乃欲精確估計因數分數，則三百人、四題以上，建議採取PLS-SEM二階段法；若研究目是在精確估計迴歸係數、解釋量，若人數在四百人以上，建議採用CB-SEM無限制法。另外，本研究亦發現在大部份的情況下，指標數目愈多，資料型態為連續型態者，其估計效果愈佳。
The purpose of this study is to find out the results of estimation about interaction effects. The estimations come from the product indicator, two stage, orthogonalizing, and unconstrained approach which are estimated by CB-SEM and PLS-SEM separately. In the research, regression coefficient, factor loading, factor score, and r square are calculated by eight kinds of methods. Besides, night kinds of sample sizes, three kinds of number of indicators, and two kinds of data types are also studied to realize how they influence the estimations. Therefore, there are fifty-four situations. The results show that the estimation of regression coffoeicient, r square and factor loading are excellent by CB-SEM, but the estimation of factor score is better by PLS-SEM under most situations. If the object is to estimate the factor score, two satge approach of PLS-SEM is suggested based on the condition of sample size larger than 300 and 4 indicators. However, if the aim is to estimate the regression coffoeicient , r square, or factor loading, the unconstrained method of CB-SEM is the best choice when sample size is larger than 400. In addition, the continuous data type and more numbers of indicators are good for estimation under most conditions. |
| Reference: | 朱經明(2007)。教育統計學 。臺北市:五南。
余民寧(2006)。潛在變項模式：SIMPLIS的應用。臺北市: 高等教育。
邱皓政(2003)。結構方程模式 : LISREL的理論、技術與應用。臺北
市: 雙葉書廊。
陳順宇 (2007)。結構方程模式 : Amos操作。臺北市: 心理總經銷.
黃文璋 (2003)。數理統計。臺北市: 華泰.
黃芳銘 (2010)。結構方程模式理論與應用。臺北市: 五南.
蕭文龍 (2015)。統計分析入門與應用-SPSS中文版+PLS-
SEM(SmartPLS)。臺北市:碁峰。
Aiken, L. S., & West, S. G. (1991). Multiple regression:
Testing and interpreting interactions. Newbury Park,
CA: Sage.
Bartholomew, D. J., & Knott, M. (1999). Latent variable
models and factor analysis. New York: Oxford University
Press.
Bollen, K. A. (1989). Structural equations with latent
variables. New York: Wiley.
Bollen, K. A., & Paxton, P. (1998). Two-stage least
squares estimation of interaction effects. In R. E.
Schumacker & G. A. Marcoulides (Eds.), Interaction and
nonlinear effects in structural equation modeling.
Mahwah, NJ: Lawrence Erlbaum Associates.
Brandt, H., Kelava, A., & Klein, A. (2014). A simulation
study comparing recent approaches for the estimation of
nonlinear effects in sem under the condition of
nonnormality. Structural Equation Modeling: A
Multidisciplinary Journal, 21(2), 181-195.
Chen, C. (2016). The role of resilience and coping styles
in subjective well-being among chinese university
students. Asia-Pacific Education Researcher, 25(3),
377-387.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A
partial least squares latent variable modeling approach
for measuring interaction effects: Results from a Monte
Carlo simulation study and an electronic-mail
emotion/adoption study. Information Systems Research,
14(2), 189-217.
Cohen, J. (1978). Partialed products are interactions;
partialed powers are curve components. Psychological
Bulletin, 85(4), 858-866.
Cohen, J. (1988).Statistical power analysis for the
behavioral sciences. Hillsdale, NJ: Eribaum.
Falenchuk, O. (2006). A study of unidimensional IRT
models for items scored in multiple ordered response
categories. (Doctoral dissertation Ph.D.), University
of Toronto (Canada), Ann Arbor. Retrieved from
http://search.proquest.com/docview/304929555?
accountid=10067 ProQuest Dissertations & Theses A&I
database. (304929555)
Fox, J., Nie, Z., Byrnes, J., Culbertson, M., DebRoy, S.,
Friendly, M., . . . Monette, G. (2017). Package ‘sem’.
Retrieved from https://cran.r-
project.org/web/packages/lavaan/lavaan.pdf
Garson, G. D. (2016). Partial least squares: regression
and structural equation models. Asheboro, NC:
Statistical Publishing Associates.
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does
pls have advantages for small sample size or non-normal
data? Mis Quarterly, 36(3), 981-1001.
Gordon, M. K. (2016). Achievement Scripts: Media
Influences on Black Students` Academic Performance,
Self-Perceptions, and Career Interests. Journal of
Black Psychology, 42(3), 195-220.
Harwell, M., Stone, C., Hsu, T. & Kirisci, L. (1996).
Monte Carlo studies in item response theory. Applied
Psychological Measurement, 20,101-125.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt,
M. (2014). A primer on partial least squares structural
equations modeling (PLS-SEM). Thousand Oaks: SAGE
Publications.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-
SEM: Indeed a silver bullet. Journal of Marketing
Theory and Practice, 19(2), 139-152.
Henseler, J., & Chin, W. W. (2010). A comparison of
approaches for the analysis of interaction effects
between latent variables using partial least squares
path modeling. Structural Equation Modeling: A
Multidisciplinary Journal, 17(1), 82-109.
Henseler, J., & Fassott, G. (2010). Testing moderating
effects in pls path models: an illustration of
available procedures. In V. Esposito Vinzi, W. W. Chin,
J. Henseler, & H. Wang (Eds.), Handbook of partial
least squares: Concepts, methods and applications. New
York : Springer .
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for
fit inde xes in covariance structure analysis:
criteria versus new alternatives. Structural Equation
Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Hu, L. T., Bentler, P. M., & Kano, Y. (1992). Can test
statistics in covariance structure-analysis be trusted.
Psychological Bulletin, 112(2), 351-362.
Jöreskog, K. G., & Wallentin, F. Y. (1996). Nonlinear
structural equation models: The Kenny-Judd model with
interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling
(pp. 57-89). Mahwh, NJ: Lawrence Erlbaum
Jöreskog, K. G., Cudeck, R., Du Toit, S. H. C., & Sörbom,
D. (2001). Structural equation modeling, present and
future : A festschrift in honor of Karl Jöreskog.
Lincolnwood, IL: Scientific Software International.
Kaplan, D. (2009). Structural equation modeling :
Foundations and extensions (2nd ed.. ed.). Los Angeles:
Los Angeles : SAGE.
Kenny, D. A., & Judd, C. M. (1984). Estimating the
nonlinear and interactive effects of latent-variables.
Psychological Bulletin, 96(1), 201-210.
Klein, A., & Moosbrugger, H. (2000). Maximum likelihood
estimation of latent interaction effects with the LMS
method. Psychometrika, 65(4), 457-474.
Kraemer, H. C., & Blasey, C. M. (2004). Centring in
regression analyses: a strategy to prevent errors in
statistical inference. International Journal of Methods
in Psychiatric Research, 13(3), 141-151.
Leite, W. L., & Zuo, Y. Z. (2011). Modeling Latent
Interactions at Level 2 in Multilevel Structural
Equation Models: An Evaluation of Mean-Centered and
Residual-Centered Unconstrained Approaches. Structural
Equation Modeling: A Multidisciplinary Journal, 18(3),
449-464.
Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006).
On the merits of orthogonalizing powered and product
terms: Implications for modeling interactions among
latent variables. Structural Equation Modeling: A
Multidisciplinary Journal, 13(4), 497-519.
Lohmöller, J.-B. (1989). Latent variable path modeling
with partial least squares. Heidelberg, Germany:
Physica-Verlag.
Marquardt, D. W. (1980). You should standardize the
predictor variables in your regression model. Journal
of the American Statistics Association, 75, 87-91.
Marsh, H. W., Wen, Z. L., & Hau, K. T. (2004). Structural
equation models of latent interactions: Evaluation of
alternative estimation strategies and indicator
construction. Psychological Methods, 9(3), 275-300.
Martínez-Ruiz, A., & Aluja-Banet, T. (2013). Two-step PLS
path modeling mode B: Nonlinear and interaction effects
between formative constructs. In H. A. W. W. Chin, V.
E. Vinzi, G. Russolillo, & L. Trinchera (Eds.), New
perspectives in partial least squares and related
methods. Springer: New York.
Monecke, A. (2013). Package ‘semPLS’. Retrieved from
https://cran.r-
project.org/web/packages/semPLS/index.html
Moulder, B. C., & Algina, J. (2002). Comparison of
methods for estimating and testing latent variable
interactions. Structural Equation Modeling, 9(1), 1-19.
Mueller, R. O. (1996). Basic principles of structural
equation modeling : An introduction to LISREL and EQS.
New York: Springer.
Muthén, L. K., & Muthén, B. O. (2017). Mplus user`s
guide: Version 8. Los Angeles, CA: Muthen & Muthen.
Park, H. (2000). Comparison of IRT models for ordered
polytomous response data(Order No. 9983591). Available
from ProQuest Dissertations & Theses A&I. (304612642).
Retrieved from
http://search.proquest.com/docview/304612642?
accountid=10067
Quintana, S. M., & Maxwell, S. E. (1999). Implications of
recent developments in structural equation modeling for
counseling psychology. Counseling Psychologist, 27(4),
485-527.
Qureshi, I., & Compeau, D. (2009). Assessing between-
group differences in information systems research: A
comparison of covariance- and component-based sem. Mis Quarterly, 33(1), 197-214.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An
empirical comparison of the efficacy of covariance-
based and variance-based SEM. International Journal of
Research in Marketing, 26(4), 332-344.
Rossee, Y. (2012). lavaan: An R package for structural
equation modeling. Journal of Statistical Software,
48(2), 1-36.
Rosseel, Y., Oberski, D., Byrnes, J., Vanbrabant, L.,
Savalei, V., Merkle, E., . . . Barendse, M. (2015).
Package ‘lavaan’. Retrieved from https://cran.r-
project.org/web/packages/lavaan/lavaan.pdf
Schumacker, R. E. (2002). Latent variable interaction
modeling. Structural Equation Modeling, 9(1), 40-54.
Schumacker, R. E. (2016). A beginner`s guide to
structural equation modeling. New York : Routledge.
Sharma, P. N., & Kim, K. H. (2013). A comparison of PLS
and ML bootstrapping techniques in SEM: A Monte Carlo
Study. In H. Abdi, W. W. Chin, V. Esposito Vinzi, G.
Russolillo, & L. Trinchera (Eds.), New perspectives in
partial least squares and related methods (pp. 201-
208). New York, NY: Springer New York.
Wall, M. M., & Amemiya, Y. (2001). Generalized appended
product indicator procedure for nonlinear structural
equation analysis. Journal of Educational and
Behavioral Statistics, 26(1), 1-29.
Wang, L. J., & Preacher, K. J. (2015). Moderated
mediation analysis using Bayesian methods. Structural
Equation Modeling: A Multidisciplinary Journal, 22(2),
249-263.
Wen, Z. L., Marsh, H. W., & Hau, K. T. (2010). Structural
equation models of latent interactions: An appropriate
standardized solution and its scale-free properties.
Structural Equation Modeling: A Multidisciplinary
Journal, 17(1), 1-22.
Yang-Wallentin, F. (2001). Comparisons of the ML and TSLS
estimators for the Kenny-Judd. In D. Sörbom, S. H. C.
Du Toit, & R. Cudeck (Eds.), Structural equation
modeling present and future : A festschrift in honor of
Karl Jöreskog (pp. 425-442). Lincolnwood, IL Scientific
Software International. |
| Description: | 博士
國立政治大學
教育學系
102152502 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G1021525021 |
| Data Type: | thesis |
| DOI: | 10.6814/DIS.NCCU.EDU.013.2018.F02 |
| Appears in Collections: | [教育學系] 學位論文
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