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| Title: | 形成性潛在變項於非線性效果之模型設定 : 限制式方法
Model Specification for Nonlinear Effects of Formative Latent Variables: The Constrained Approach |
| Authors: | 陳淑萍
Chen, Shu Ping |
| Contributors: | 鄭中平
Cheng, Chung Ping
陳淑萍
Chen, Shu Ping |
| Keywords: | 形成性潛在變項
潛在非線性效果
限制式方法
formatively-measured latent variables
latent nonlinear effects
the constrained approach |
| Date: | 2016 |
| Issue Date: | 2016-07-21 09:54:34 (UTC+8) |
| Abstract: | 社會行為科學領域中，潛在非線性關係常為研究者所關切，發展潛在變項間非線性效果方法有其重要性。近年來，已有許多統計方法致力於非線性結構方程模型之估計。就作者所知，大多數方法主要侷限在以反映性測量模式 (reflective measurement model) 為基礎之潛在非線性效果估計，而忽略以形成性測量模式 (formative measurement model) 為基礎之潛在非線性效果估計。本研究衍生Chen與Cheng (2014) 於反映性測量模式基礎下所建立的非線性效果方法，拓展至以形成性測量模式為基礎之非線性效果方法。本研究建立的六個廣義性非線性架構，可獨立或同時嵌入三種類型非線性效果，包含反映性潛在變項間交互作用與二次項效果、形成性潛在變項間交互作用與二次項效果和反映性與形成性潛在變項間交互作用效果。值得注意地，每個非線性架構皆保有Chen與Cheng矩陣分割技術，可簡化模型設定的過程，並類推至更多情境的非線性模型。整體來說，本研究促進限制式方法與交乘項指標方法的發展，希冀提升方法發展與研究者在實務研究的應用。
Modeling latent nonlinear effects is a significant issue in the social and behavioral sciences. A variety of approaches have recently been developed for the estimation of nonlinear structural equation modeling. To the best of our knowledge most of these approaches have been developed primarily to estimate interaction and/or quadratic effects of reflectively-measured latent variables, while leaving nonlinear effects of formatively-measured latent variables unaccounted for. The current study implements formatively-measured latent variables into Jöreskog and Yang’s (1996) constrained approach by extending Chen and Cheng’s (2014) research to create a unified set of six generalized nonlinear frameworks, each capable of differentially or collectively modeling the three types of latent nonlinear effects that have arisen in empirical applications (i.e., interaction and/or quadratic effects between reflective latent variables, between formative latent variables, and between reflective and formative latent variables). By preserving the inherent advantage of Chen and Cheng, i.e., the matrix partitioning technique, while at the same time further generalizing its applicability, it is expected that the current framework enhances the potential usefulness of the constrained approach as well as the entire class of product indicator approaches. |
| Reference: | Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Publications.
Algina, J., & Moulder, B. C. (2001). A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3. Structural Equation Modeling: A Multidisciplinary Journal, 8, 40-52. doi: 10.1207/S15328007SEM0801_3
Arminger, G., & Muthén, B. O. (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm. Psychometrika, 63, 271-300. doi: 10.1007/BF02294856
Bagozzi, R. P. (2011). Measurement and meaning in information systems and organizational research: Methodological and philosophical foundations. MIS Quarterly, 35(2), 261-292. Retrieved from http://www.misq.org/
Bagozzi, R. P., Moore, D. J., & Leone, L. (2004). Self-control and the self-regulation of dieting decisions: The role of prefactual attitudes, subjective norms, and resistance to temptation. Basic and Applied Social Psychology, 26, 199-213. doi: 10.1080/01973533.2004.9646405
Bauer, D. J. (2005). A semiparametric approach to modeling nonlinear relations among latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 12, 513-535. doi: 10.1207/s15328007sem1204_1
Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: John Wiley & Sons.
Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359-372. Retrieved from http://www.misq.org/
Bollen, K. A., & Bauldry, S. (2011). Three Cs in measurement models: Causal indicators, composite indicators, and covariates. Psychological Methods, 16, 265-284. doi: 10.1037/a0024448
Bollen, K. A., & Davis, W. R. (2009a). Causal indicator models: Identification, estimation, and testing. Structural Equation Modeling: A Multidisciplinary Journal, 16, 498-522. doi: 10.1080/10705510903008253
Bollen, K. A., & Davis, W. R. (2009b). Two rules of identification for structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 16, 523-536. doi: 10.1080/10705510903008261
Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110, 305-314. doi: 10.1037/0033-2909.110.2.305
Bollen, K. A., & Noble, M. D. (2011). Structural equation models and the quantification of behavior. Proceedings of the National Academy of Sciences, 108, 15639-15646. doi: 10.1073/pnas.1010661108
Bollen, K. A., & Ting, K.-f. (2000). A tetrad test for causal indicators. Psychological Methods, 5, 3-22. doi: 10.1037/1082-989X.5.1.3
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, 181-195. doi:10.1080/10705511.2014.882660
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
Camminatiello, I., Paletta, A., & Speziale, M. T. (2012). The effects of school-based management and standards-based accountability on student achievement: Evidence from PISA 2006. Electronic Journal of Applied Statistical Analysis, 5, 381-386. doi: 10.1285/i20705948v5n3p381
Cenfetelli, R. T., & Bassellier, G. (2009). Interpretation of formative measurement in information systems research. MIS Quarterly, 33(4), 689-707. Retrieved from http://www.misq.org/
Chang, C. (2010). Message framing and interpersonal orientation at cultural and individual levels: Involvement as a moderator. International Journal of Advertising, 29(5), 765-794. Retrieved from http://www.internationaljournalofadvertising.com/
Chen, S.-P., & Cheng, C.-P. (2014). Model specification for latent interactive and quadratic effects in matrix form. Structural Equation Modeling: A Multidisciplinary Journal, 21, 94-101. doi: 10.1080/10705511.2014.859509
Chen, S.-P., & Cheng, C.-P. (2016). Model specification of three-way latent nonlinear effects: The constrained approach (in Chinese). Organization and Management, 9, 127-158. doi:10.3966/199687602016020901004
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, 189-217. doi:10.1287/isre.14.2.189.16018
Coenders, G., Batista-Foguet, J. M., & Saris, W. E. (2008). Simple, efficient and distribution-free approach to interaction effects in complex structural equation models. Quality & Quantity, 42, 369-396. doi: 10.1007/s11135-006-9050-6
Cole, M. S., Bedeian, A. G., & Bruch, H. (2011). Linking leader behavior and leadership consensus to team performance: Integrating direct consensus and dispersion models of group composition. The Leadership Quarterly, 22, 383-398. doi: 10.1016/j.leaqua.2011.02.012
Cole, M. S., Walter, F., & Bruch, H. (2008). Affective mechanisms linking dysfunctional behavior to performance in work teams: A moderated mediation study. Journal of Applied Psychology, 93, 945-958. doi: 10.1037/0021-9010.93.5.945
Conner, M., McEachan, R., Jackson, C., McMillan, B., Woolridge, M., & Lawton, R. (2013). Moderating effect of socioeconomic status on the relationship between health cognitions and behaviors. Annals of Behavioral Medicine, 46, 19-30. doi: 10.1007/s12160-013-9481-y
Cudeck, R., Harring, J. R., & du Toit, S. H. C. (2009). Marginal maximum likelihood estimation of a latent variable model with interaction. Journal of Educational and Behavioral Statistics, 34, 131-144. doi: 10.3102/1076998607313593
Diamantopoulos, A. (2006). The error term in formative measurement models: Interpretation and modeling implications. Journal of Modelling in Management,1, 7-17. doi: 10.1108/17465660610667775
Diamantopoulos, A. (2011). Incorporating formative measures into covariance-based structural equation models. MIS Quarterly, 35(2), 335-358. Retrieved from http://www.misq.org/
Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17, 263-282. doi: 10.1111/j.1467-8551.2006.00500.x
Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61, 1203-1218. doi: 10.1016/j.jbusres.2008.01.009
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38, 269-277. doi: 10.1509/jmkr.38.2.269.18845
Diestel, S., & Schmidt, K.-H. (2009). Mediator and moderator effects of demands on self-control in the relationship between work load and indicators of job strain. Work & Stress, 23, 60-79. doi: 10.1080/02678370902846686
Diestel, S., & Schmidt, K.-H. (2010). Interactive effects of emotional dissonance and self-control demands on burnout, anxiety, and absenteeism. Journal of Vocational Behavior, 77, 412-424. doi: 10.1016/j.jvb.2010.05.006
Dijkstra, T. (1983). Some comments on maximum likelihood and partial least squares methods. Journal of Econometrics, 22, 67-90. doi:10.1016/0304-4076(83)90094-5
Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5, 155-174. doi: 10.1037/1082-989X.5.2.155
Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1-22. doi: 10.1037/1082-989X.12.1.1
Finkbeiner, C. (1979). Estimation for the multiple factor model when data are missing. Psychometrika, 44, 409-420. doi: 10.1007/BF02296204
Fornell, C., & Bookstein, F. L. (1982). A Comparative analysis of two structural equation models : LISREL and PLS applied to market data. In C. Fornell (Ed.), A second generation of multivariate analysis (Vol. 1, pp. 289-324). New York, NY: Praeger.
Ganzach, Y. (1997). Misleading interaction and curvilinear terms. Psychological Methods, 2, 235-247. doi: 10.1037/1082-989X.2.3.235
Gefen, D., Straub, D. W., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-78. Retrieved from http://aisel.aisnet.org/cais/
Ghazal, G. A., & Neudecker, H. (2000). On second-order and fourth-order moments of jointly distributed random matrices: A survey. Linear Algebra and its Applications, 321, 61-93. doi: 10.1016/S0024-3795(00)00181-6
Harring, J. R., Weiss, B. A., & Hsu, J.-C. (2012). A comparison of methods for estimating quadratic effects in nonlinear structural equation models. Psychological Methods, 17, 193-214. doi:10.1037/a0027539
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis. New York, NY: Guilford Press.
Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 713-735). Heidelberg, Germany: Springer.
Henseler, J., Fassott, G., Dijkstra, T. K., & Wilson, B. (2012). Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. European Journal of Information Systems, 21, 99-112. doi: 10.1057/ejis.2011.36
Hoyle, R. H. (2011). Structural equation modeling for social and personality psychology. London, England: Sage Publications.
Hukkelberg, S. S., Hagtvet, K. A., & Kovac, V. B. (2014). Latent interaction effects in the theory of planned behaviour applied to quitting smoking. British Journal of Health Psychology, 19, 83-100. doi: 10.1111/bjhp.12034
Isserlis, L. (1918). On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables. Biometrika, 12, 134-139. doi: 10.1093/biomet/12.1-2.134
Jaccard, J., & Wan, C. K. (1995). Measurement error in the analysis of interaction effects between continuous predictors using multiple regression: Multiple indicator and structural equation approaches. Psychological Bulletin, 117, 348-357. doi: 10.1037/0033-2909.117.2.348
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199-218. doi: 10.1086/376806
Jeffers, P. I., Muhanna, W. A., & Nault, B. R. (2008). Information technology and process performance: An empirical investigation of the interaction between IT and Non-IT Resources. Decision Sciences, 39, 703-735. doi: 10.1111/j.1540-5915.2008.00209.x
Jokela, M., & Keltikangas-Järvinen, L. (2011). The association between low socioeconomic status and depressive symptoms depends on temperament and personality traits. Personality and Individual Differences, 51, 302-308. doi: 10.1016/j.paid.2010.05.004
Jöreskog, K. G., & Goldberger, A. S. (1975). Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association, 70, 631-639. doi: 10.1080/01621459.1975.10482485
Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8 user`s reference guide. Chicago, IL: Scientific Software International.
Jöreskog, K. G., & Yang, F. (1996). Nonlinear structural equation models: The Kenny-Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 57-88). Mahwah, NJ: Lawrence Erlbaum.
Jun, L., Qiuzhen, W., & Qingguo, M. (2011). The effects of project uncertainty and risk management on IS development project performance: A vendor perspective. International Journal of Project Management, 29, 923-933. doi:10.1016/j.ijproman.2010.11.002
Kankanhalli, A., Pee, L. G., Tan, G. W., & Chhatwal, S. (2012). Interaction of individual and social antecedents of learning effectiveness: A study in the IT research context. Engineering Management, IEEE Transactions on, 59, 115-128. doi: 10.1109/TEM.2011.2144988
Kelava, A., & Brandt, H. (2009). Estimation of nonlinear latent structural equation models using the extended unconstrained approach. Review of Psychology, 16(2), 123-131. Retrieved from http://psihologija.ffzg.unizg.hr/review
Kelava, A., Moosbrugger, H., Dimitruk, P., & Schermelleh-Engel, K. (2008). Multicollinearity and missing constraints: A comparison of three approaches for the analysis of latent nonlinear effects. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 4, 51-66. doi:10.1027/1614-2241.4.2.51
Kelava, A., Werner, C. S., Schermelleh-Engel, K., Moosbrugger, H., Zapf, D., Ma, Y., . . . West, S. G. (2011). Advanced nonlinear latent variable modeling: Distribution analytic LMS and QML estimators of interaction and quadratic effects. Structural Equation Modeling: A Multidisciplinary Journal, 18, 465-491. doi: 10.1080/10705511.2011.582408
Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201-210. doi: 10.1037/0033-2909.96.1.201
Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457-474. doi: 10.1007/BF02296338
Klein, A. G., & Muthén, B. O. (2007). Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects. Multivariate Behavioral Research, 42, 647-673. doi: 10.1080/00273170701710205
Koring, M., Richert, J., Lippke, S., Parschau, L., Reuter, T., & Schwarzer, R. (2012). Synergistic effects of planning and self-efficacy on physical activity. Health Education & Behavior, 39, 152-158. doi: 10.1177/1090198111417621
Lee, S.-Y., Song, X.-Y., & Lee, J. C. K. (2003). Maximum likelihood estimation of nonlinear structural equation models with ignorable missing data. Journal of Educational and Behavioral Statistics, 28, 111-134. doi: 10.3102/10769986028002111
Lee, S.-Y., Song, X.-Y., & Tang, N.-S. (2007). Bayesian methods for analyzing structural equation models with covariates, interaction, and quadratic latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 14, 404-434. doi:10.1080/10705510701301511
Lee, S.-Y., & Zhu, H.-T. (2000). Statistical analysis of nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 53, 209-232. doi: 10.1348/000711000159303
Lee, S.-Y., & Zhu, H.-T. (2002). Maximum likelihood estimation of nonlinear structural equation models. Psychometrika, 67, 189-210. doi: 10.1007/BF02294842
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, 497-519. doi: 10.1207/s15328007sem1304_1
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, Mass: Addison-Wesley.
Luszczynska, A., Cao, D. S., Mallach, N., Pietron, K., Mazurkiewicz, M., & Schwarzer, R. (2010). Intentions, planning, and self-efficacy predict physical activity in Chinese and Polish adolescents: Two moderated mediation analyses. International Journal of Clinical and Health Psychology, 10(2), 265-278. Retrieved from http://www.aepc.es/ijchp/
Lyhagen, J. (2007). Estimating nonlinear structural models: EMM and the Kenny-Judd model. Structural Equation Modeling: A Multidisciplinary Journal, 14, 391-403. doi: 10.1080/10705510701301487
MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin, 114, 533-541. doi: 10.1037/0033-2909.114.3.533
MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90, 710-730. doi: 10.1037/0021-9010.90.4.710
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293-334. Retrieved from http://www.misq.org/
Magnus, J. R., & Neudecker, H. (1979). The commutation matrix: Some properties and applications. The Annals of Statistics, 7, 237-466. doi: 10.1214/aos/1176344621
Magnus, J. R., & Neudecker, H. (1980). The elimination matrix: Some lemmas and applications. SIAM Journal on Algebraic Discrete Methods, 1, 422-449. doi: 10.1137/0601049
Magnus, J. R., & Neudecker, H. (1988). Matrix differential calculus with applications in statistics and econometrics. New York, NY: John Wiley & Sons.
Marsh, H. W., Hau, K.-T., Wen, Z., Nagengast, B., & Morin, A. J. S. (2013). Moderation. In T. D. Little (Ed.), The Oxford handbook of quantitative methods in psychology (Vol. 2, pp. 361-386). New York, NY: Oxford University Press.
Marsh, H. W., Wen, Z., & Hau, K.-T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275-300. doi: 10.1037/1082-989X.9.3.275
Marsh, H. W., Wen, Z., Hau, K.-T., & Nagengast, B. (2013). Structural equation models of latent interaction and quadratic effects. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course, 2nd Edition (pp. 267-308). Charlotte, NC: Information Age Publishing.
Marsh, H. W., Wen, Z., Nagengast, B., & Hau, K.-T. (2012). Structural equation models of latent interaction. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 436-458). New York, NY: Guilford Press.
Martínez-Ruiz, A., & Aluja-Banet, T. (2013). Two-step PLS path modeling mode B: Nonlinear and interaction effects between formative constructs. In H. Abdi, W. W. Chin, V. E. Vinzi, G. Russolillo, & L. Trinchera (Eds.), New perspectives in partial least squares and related methods (pp. 187-199). New York, NY: Springer.
Mooijaart, A., & Bentler, P. M. (2010). An alternative approach for nonlinear latent variable models. Structural Equation Modeling: A Multidisciplinary Journal, 17, 357-373. doi: 10.1080/10705511.2010.488997
Moosbrugger, H., Schermelleh-Engel, K., Kelava, A., & Klein, A. G. (2009). Testing multiple nonlinear effects in structural equation modeling: A comparison of alternative estimation approaches. In T. Teo & M. S. Khine (Eds.), Structural equation modelling in educational research: Concepts and applications (pp. 103-136). Rotterdam, NL: Sense Publishers.
Morgan-Lopez, A. A., Castro, F. G., Chassin, L., & MacKinnon, D. P. (2003). A mediated moderation model of cigarette use among Mexican American youth. Addictive Behaviors, 28, 583-589. doi: 10.1016/S0306-4603(01)00262-3
Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115-132. doi: 10.1007/BF02294210
Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus User`s Guide (7th ed.). Los Angeles: CA: Muthén & Muthén.
Parade, S. H., Leerkes, E. M., & Blankson, A. N. (2010). Attachment to parents, social anxiety, and close relationships of female students over the transition to college. Journal of Youth and Adolescence, 39, 127-137. doi: 10.1007/s10964-009-9396-x
Pek, J., Sterba, S. K., Kok, B. E., & Bauer, D. J. (2009). Estimating and visualizing nonlinear relations among latent variables: A semiparametric approach. Multivariate Behavioral Research, 44, 407-436. doi: 10.1080/00273170903103290
Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623-656. Retrieved from http://www.misq.org/
Ping, R. (2007). Second-order latent variables: Interactions, specification, estimation, and an example. Proceedings of the AMA Winter Educators` Conference, 18, 286-293. Retrieved from https://www.ama.org/Documents/arc_ama_winter2007.pdf
Podsakoff, N. P., Shen, W., & Podsakoff, P. M. (2006). The role of formative measurement models in strategic management research: Review, critique, and implications for future research. In D. J., Ketchen, & D. D. Bergh (Eds.), Research methodology in strategy and management (Vol. 3, pp. 197-252). Oxford, England: Elsevier.
Pollack, J. M., Vanepps, E. M., & Hayes, A. F. (2012). The moderating role of social ties on entrepreneurs` depressed affect and withdrawal intentions in response to economic stress. Journal of Organizational Behavior, 33, 789-810. doi: 10.1002/job.1794
Popan, J. R., Kenworthy, J. B., Frame, M. C., Lyons, P. A., & Snuggs, S. J. (2010). Political groups in contact: The role of attributions for outgroup attitudes in reducing antipathy. European Journal of Social Psychology, 40, 86-104. doi: 10.1002/ejsp.612
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. doi: 10.1080/00273170701341316
Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41, 293-305. doi: 10.2307/30162340
Seber, G. A. F. (2007). A Matrix Handbook for Statisticians. New York, NY: John Wiley & Sons.
Simonson, H. (1992). Interaction effects of television and socioeconomic status on teenage aggression. International Journal of Adolescence and Youth, 3, 333-343. doi:10.1080/02673843.1992.9747713
Slater, M. D., Hayes, A. F., & Ford, V. L. (2007). Examining the moderating and mediating roles of news exposure and attention on adolescent judgments of alcohol-related risks. Communication Research, 34, 355-381. doi: 10.1177/0093650207302783
Spearman, C. (1904). "General intelligence," objectively determined and measured. The American Journal of Psychology, 15, 201-292. doi: 10.2307/1412107
Takeuchi, R., Yun, S., & Wong, K. F. E. (2011). Social influence of a coworker: A test of the effect of employee and coworker exchange ideologies on employees` exchange qualities. Organizational Behavior and Human Decision Processes,115, 226-237. doi: 10.1016/j.obhdp.2011.02.004
Treiblmaier, H., Bentler, P. M., & Mair, P. (2011). Formative constructs implemented via common factors. Structural Equation Modeling: A Multidisciplinary Journal, 18, 1-17. doi: 10.1080/10705511.2011.532693
Tucker-Drob, E. M., & Briley, D. A. (2012). Socioeconomic status modifies interest-knowledge associations among adolescents. Personality and Individual Differences, 53, 9-15. doi: 10.1016/j.paid.2012.02.004
Van Rompay, T. J. L., De Vries, P. W., & Van Venrooij, X. G. (2010). More than words: On the importance of picture-text congruence in the online environment. Journal of Interactive Marketing, 24, 22-30. doi: 10.1016/j.intmar.2009.10.003
Wall, M. M. (2009). Maximum likelihood and Bayesian estimation for nonlinear structural equation models. In R. E. Millsap & A. Maydeu-Olivares (Eds.), The SAGE handbook of quantitative methods in psychology (pp. 540-567). London, England: Sage Publications.
Wall, M. M., & Amemiya, Y. (2001). Generalized appended product indicator procedure for nonlinear structural equation analysis. Journal of Educational and Behavioral Statistics, 26, 1-29. doi: 10.3102/10769986026001001
Wall, M. M., & Amemiya, Y. (2003). A method of moments technique for fitting interaction effects in structural equation models. British Journal of Mathematical and Statistical Psychology, 56, 47-63. doi: 10.1348/000711003321645331
Wall, M. M., & Amemiya, Y. (2007). Nonlinear structural equation modeling as a statistical method. In S.-Y. Lee (Ed.), Handbook of computing and statistics with applications (Vol. 1, pp. 321-343). Amsterdam, the Netherlands: Elsevier.
Wiedemann, A. U., Schüz, B., Sniehotta, F., Scholz, U., & Schwarzer, R. (2009). Disentangling the relation between intentions, planning, and behaviour: A moderated mediation analysis. Psychology and Health, 24, 67-79. doi: 10.1080/08870440801958214
Williams, L. J., Edwards, J. R., & Vandenberg, R. J. (2003). Recent advances in causal modeling methods for organizational and management research. Journal of Management, 29, 903-936. doi: 10.1016/S0149-2063(03)00084-9
Yang-Wallentin, F., & Jöreskog, K. G. (2001). Robust standard errors and chi-squares for interaction models. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 159-171). Mahwah, NJ: Erlbaum.
Ye, C., & Potter, R. (2011). The role of habit in post-adoption switching of personal information technologies: An empirical investigation. Communications of the Association for Information Systems, 28(35), 585-610. Retrieved from http://aisel.aisnet.org/cais/
Yu, S., Mishra, A. N., Gopal, A., Slaughter, S., & Mukhopadhyay, T. (2015). E-procurement infusion and operational process impacts in MRO procurement: Complementary or substitutive effects? Production and Operations Management, 24, 1054-1070. doi:10.1111/poms.12362 |
| Description: | 博士
國立政治大學
心理學系
97752501 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0097752501 |
| Data Type: | thesis |
| Appears in Collections: | [心理學系] 學位論文
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Files in This Item:
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| 250101.pdf | 1993Kb | Adobe PDF2 | 388 | View/Open |
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