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    题名: 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
    作者: 李昭鋆
    Lee, Chao-Yun
    贡献者: 余民寧
    Yu, Ming-Ning
    李昭鋆
    Lee, Chao-Yun
    关键词: CB-SEM
    PLS-SEM
    交互作用
    CB-SEM
    PLS-SEM
    Interaction effect
    日期: 2018
    上传时间: 2018-07-27 12:41:33 (UTC+8)
    摘要: 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.
    參考文獻: 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.
    描述: 博士
    國立政治大學
    教育學系
    102152502
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G1021525021
    数据类型: thesis
    DOI: 10.6814/DIS.NCCU.EDU.013.2018.F02 
    显示于类别:[教育學系] 學位論文

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