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題名 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
貢獻者 余民寧
Yu, Ming-Ning
李昭鋆
Lee, Chao-Yun
關鍵詞 CB-SEM
PLS-SEM
交互作用
CB-SEM
PLS-SEM
Interaction effect
日期 2018
上傳時間 27-七月-2018 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
資料類型 thesis
dc.contributor.advisor 余民寧zh_TW
dc.contributor.advisor Yu, Ming-Ningen_US
dc.contributor.author (作者) 李昭鋆zh_TW
dc.contributor.author (作者) Lee, Chao-Yunen_US
dc.creator (作者) 李昭鋆zh_TW
dc.creator (作者) Lee, Chao-Yunen_US
dc.date (日期) 2018en_US
dc.date.accessioned 27-七月-2018 12:41:33 (UTC+8)-
dc.date.available 27-七月-2018 12:41:33 (UTC+8)-
dc.date.issued (上傳時間) 27-七月-2018 12:41:33 (UTC+8)-
dc.identifier (其他 識別碼) G1021525021en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/118984-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 教育學系zh_TW
dc.description (描述) 102152502zh_TW
dc.description.abstract (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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 第一章 緒論 1
     第一節 研究緣起 1
     第二節 待答問題 2
     第三節 名詞釋義 3
     第四節 研究貢獻 7
     第五節 研究限制 8
     第二章 文獻探討 9
     第一節 CB-SEM與PLS-SEM之比較 9
     第二節 交互作用 12
     第三節 模擬之相關研究 21
     第三章 研究方法 31
     第一節 研究步驟 31
     第二節 模擬因子與估計精準度 32
     第三節 資料產生方法 34
     第四節 資料處理與分析 35
     第四章 實驗結果 37
     第一節 全體模擬結果分析 37
     第二節 CB-SEM、PLS-SEM之迴歸係數結果分析 45
     第三節 CB-SEM、PLS-SEM之解釋力結果分析 65
     第四節 因素分數之結果分析 71
     第五節 因素負荷量之結果分析 94
     第六節 綜合討論 114
     第五章 結論與建議 145
     第一節 結論 145
     第二節 建議 146
     參考文獻 149
     附錄：虛擬程式碼 155		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1021525021en_US
dc.subject (關鍵詞) CB-SEMzh_TW
dc.subject (關鍵詞) PLS-SEMzh_TW
dc.subject (關鍵詞) 交互作用zh_TW
dc.subject (關鍵詞) CB-SEMen_US
dc.subject (關鍵詞) PLS-SEMen_US
dc.subject (關鍵詞) Interaction effecten_US
dc.title (題名) CB-SEM和PLS-SEM在估計交互作用效果之比較zh_TW
dc.title (題名) The comparison of estimation accuracy in interaction effect between CB-SEM and PLS-SEMen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (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.		zh_TW
dc.identifier.doi (DOI) 10.6814/DIS.NCCU.EDU.013.2018.F02 -