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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 姚開屏 | zh_TW |
| dc.contributor.advisor | Grace Yao | en |
| dc.contributor.author | 林立中 | zh_TW |
| dc.contributor.author | Li-Chung Lin | en |
| dc.date.accessioned | 2023-01-06T17:06:34Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-01-06 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2022-12-23 | - |
| dc.identifier.citation | Albert, R., & Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of Modern physics, 74, 47. https://doi.org/10.1103/RevModPhys.74.47
Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509-512. https://doi.org/10.1126/science.286.5439.509 Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13, 139–161. https://doi.org/10.1016/0167-8116 (95) 00038-0 Bolland, J. M. (1988). Sorting out centrality: An analysis of the performance of four centrality models in real and simulated networks. Social Networks, 10, 233-253. https://doi.org/10.1016/0378-8733 (88) 90014-7 Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley. https://doi.org/10.1002/9781118619179 Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605-634. https://doi.org/10.1146/annurev.psych.53.100901.135239 Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27, 55-71. https://doi.org/10.1016/j.socnet.2004.11.008 Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28, 466-484. https://doi.org/10.1016/j.socnet.2005.11.005 Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social Networks, 28, 124-136. https://doi.org/10.1016/j.socnet.2005.05.001 Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. Thousand Oaks, SAGE. https://doi.org/10.1080/0022250X.2015.1053371 Borsboom, D. & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91-121. https://doi.org/10.1146/annurev-clinpsy-050212-185608 Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128, 892–903. https://doi.org/10.1037/abn0000446 Butts C. T. (2008). Social network analysis: A methodological introduction. Asian Journal of Social Psychology, 11, 13-41. https://doi.org/10.1111/j.1467-839X.2007.00241.x Carolan, B. V. (2013). Social network analysis and education: Theory, methods & applications. New York: SAGE. Carter, D. R., DeChurch, L. A., Braun, M. T., & Contractor, N. S. (2015). Social network approaches to leadership: An integrative conceptual review. Journal of Applied Psychology, 100, 597–622. https://doi.org/10.1037/a0038922 Clifton, A., & Webster, G. D. (2017). An introduction to social network analysis for personality and social psychologists. Social Psychological and Personality Science, 8, 442-453. https://doi.org/10.1177/1948550617709114 Clifton, A., Turkheimer, E., & Oltmanns, T. F. (2009). Personality disorder in social networks: Network position as a marker of interpersonal dysfunction. Social Networks, 31, 26-32. https://doi.org/10.1016/j.socnet.2008.08.003 Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159. Cooc, N., & Kim, J. S. (2017). Peer influence on children’s reading skills: A social network analysis of elementary school classrooms. Journal of Educational Psychology, 109, 727–740. https://doi.org/10.1037/edu0000166 Costantini, G., Epskamp, S., Borsboom D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. J. (2015). State of the art personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13-29. https://doi.org/10.1016/j.jrp.2014.07.003 Costenbaber, E. & Valente, T. W. (2003). The stability of centrality measures when networks are sampled. Social Networks, 25, 283-307. https://doi.org/10.1016/S0378-8733 (03) 00012-1 Costenbader, E., & Valente, T. W. (2003). The stability of centrality measures when networks are sampled. Social Networks, 25, 283-307. https://doi.org/10.1016/S0378-8733(03)00012-1 Croon, M. (2002). Using predicted latent scores in general latent structure models. In G. A. Marcoulides, & I. Moustaki (Eds.), Latent variable and latent structure models (pp. 207-236). Psychology Press. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1-9. Dekker, A. (2008). Centrality in social networks: Theoretical and simulation approaches. In Proceedings of the SimTect, 33–38. Dong, X., Castro, L., & Shaikh, N. (2020). fastnet: An R package for fast simulation and analysis of large-scale social networks. Journal of Statistical Software, 96, 1-23. http://dx.doi.org/10.2139/ssrn.3121725 Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195-212. https://doi.org/10.3758/s13428-017-0862-1 Erdős, P., & Rényi, A. (1959). Some further statistical properties of the digits in Cantor’s series. Acta Math. Acad. Sci. Hungar, 10, 21-29. Fan, X., & Sivo, S. A. (2005). Sensitivity of fit indexes to misspecified structural or measurement model components: rationale of two-index strategy revisited. Structural Equation Modeling, 12, 343-367. https://doi.org/10.1207/s15328007sem1203_1 Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832-842. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40, 35-41. https://doi.org/10.2307/3033543 Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239. Fried, E. I., & Cramer A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12, 999-1020. https://doi.org/10.1177/1745691617705892 Friedkin, N. E. (1990). Social networks in structural equation models. Social Psychology Quarterly, 53, 316-328. https://doi.org/10.2307/2786737 Fronczak P. (2018). Scale-free nature of social networks. New York: Springer Goldenberg, A., Zheng, A.X., Fienberg, S.E., & Airoldi, E.M. (2010). A survey of statistical network models. Foundations and Trends in Machine Learning, 2, 129–233. http://dx.doi.org/10.1561/2200000005 Grando, F., Noble, D., & Lamb, L. C. (2016). An analysis of centrality measures for complex and social networks. IEEE Global Communications Conference, 1-6 Guo, B., Perron, B. E., & Gillespie, D. F. (2009). A systematic review of structural equation modeling in social work research. Bristish Journal of Social Work, 39, 1556–1574. https://doi.org/10.1093/bjsw/bcn101 Hayes, T., & Usami, S. (2020). Factor score regression in connected measurement models containing cross-loadings. Structural Equation Modeling: A Multidisciplinary Journal, 27, 942-951. https://doi.org/10.1080/10705511.2020.1729160 Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2012). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090-1098. https://doi.org/10.1198/016214502388618906 Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26, 329-367. https://doi.org/10.1177/0049124198026003003 Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6, 53-60. Horn, J. L. (1965). A rationale and technique for estimating the number of factors in factor analysis. Psychometrika, 30, 179-185. https://doi.org/10.1007/BF02289447 Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. https://doi.org/10.1080/10705519909540118 Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24, 1-29. https://doi.org/10.18637/jss.v024.i03 Jackson, D. L., Gillaspy, J. A., Jr., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14, 6–23. https://doi.org/10.1037/a0014694 Kenny, D. A., Kashy, D. A., & Cook, W. L. (2006). Dyadic data analysis. New York, NY: Guilford. Kilduff, M., & Brass, D. J. (2010). Organizational social network research: Core ideas and key debates. The Academy of Management Annals, 4, 317-357. https://doi.org/10.1080/19416520.2010.494827 Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford. Koehly, L. M., & Shivy, V. A. (1998). Social network analysis: A new methodology for counseling research. Journal of Counseling Psychology, 45, 3–17. https://doi.org/10.1037/0022-0167.45.1.3 Kolaczyk, E. D. (2009). Sampling and estimation in network graphs. Statistical Analysis of Network Data. Springer, New York, NY. Kwok, N., Hanig, S., Brown, D. J., & Shen, W. (2018). How leader role identity influences the process of leader emergence: A social network analysis. The Leadership Quarterly, 29, 648-662. https://doi.org/10.1016/j.leaqua.2018.04.003 Landherr, A., Friedl, B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2, 371-385. https://doi.org/10.1007/s12599-010-0127-3 Li, C. H. (2016). The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables. Psychological Methods, 21, 369-387. https://doi.org/10.1037/met0000093 Liu, H., Jin, H. I., & Zhang, Z. (2018). Structural equation modeling of social networks: Specification, estimation, and application. Multivariate Behavior Research, 53, 714-730. https://doi.org/10.1080/00273171.2018.1479629 Martin, C., & Niemeyer, P. (2019). Influence of measurement errors on networks: Estimating the robustness of centrality measures. Network Science, 7, 180-195. https://doi.org/10.1017/nws.2019.12 McKay, A. S., Grygiel, P., & Karwowski, M. (2017). Connected to create: A social network analysis of friendship ties and creativity. Psychology of Aesthetics, Creativity, and the Arts, 11, 284–294. https://doi.org/10.1037/aca0000117 Milardo, R. M., & Wellman, B. (1992). The personal is social. Journal of Social and Personal Relationships, 9, 339-342. https://doi.org/10.1177/0265407592093001 Niu, Q., Zeng, A., Fan, Y., & Di, Z. (2015). Robustness of centrality measures against network manipulation. Physical A: Statistical Mechanics and its Applications, 438, 124-131. https://doi.org/10.1016/j.physa.2015.06.031 Revelle, W., & Revelle, M. W. (2015) . Package ‘psych’. The Comprehensive R Archive Network, 337, 1-465. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1-36. https://doi.org/10.18637/jss.v048.i02 Salter-Townshend, M., White, A., Gollini, I., & Murphy, T. B. (2012). Review of statistical network analysis: Models, algorithms and software. Statistical Analysis and Data Mining, 5, 243-264. https://doi.org/10.1002/sam.11146 Selden, M., & Goodie, A. S. (2018). Review of the effects of five factor model personality traits on network structures and perceptions of structure. Social Networks, 52, 81–99. https://doi.org/10.1016/j.socnet.2017.05.007 Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24, 148-169. https://doi.org/10.1016/j.jom.2005.05.001 Shortreed, S., Handcock, M. S., & Hoff, P. (2006). Positional estimation within a latent space model for networks. Methodology, 2, 24-33. https://doi.org/10.1027/1614-2241.2.1.24 Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. T. (2006). The search for" optimal" cutoff properties: Fit index criteria in structural equation modeling. The Journal of Experimental Education, 74, 267-288. https://doi.org/10.3200/JEXE.74.3.267-288 Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66, 563-575. https://doi.org/10.1007/BF02296196 Snijders, TAB. (2011). Statistical models for social networks. Annual Review of Sociology, 37, 131-153. https://doi.org/10.1146/annurev.soc.012809.102709 Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321-327. https://doi.org/10.1007/BF02293557 Wang, D. J., Shi, X., McFarland, D. A., & Leskovec, J. (2012). Measurement error in network data: A re-classification. Social Networks, 34, 396-409. https://doi.org/10.1016/j.socnet.2012.01.003 Wasserman, S., & Faust, K. 1994. Social network analysis: Methods and applications. Cambridge University Press. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small world’ networks. Nature, 393, 440-442. https://doi.org/10.1038/30918 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83083 | - |
| dc.description.abstract | 心理學家主張個人是鑲崁在社會網絡之中，個人的潛在特質、狀態、與外顯行為受到社會網絡的影響，並建議研究者應在社會網絡脈絡中探討個體的行為表現。同時考量社會網絡與潛在變項資料至單一分析模型是一個重要的議題，然有關此議題之方法學相關研究仍有不足，因此本論文藉由執行兩個模擬研究，以探討此議題。研究一設計不同程度的因素負荷量、跨因素負荷量、樣本數、因素規模、與模型設定，以檢驗結合潛在因素資料與社會網絡資料之二階段最大概似估計程序（two-stage maximum likelihood estimation procedure）（Liu et al., 2018）的影響因子及其適用情境。整體而言，外顯預測變項之迴歸係數在所有模擬情境中皆有良好的表現。而潛在預測變項之迴歸係數及其估計標準誤的相對偏差程度隨跨負荷量越高，或因素負荷量越低而越為明顯。模擬研究二設計多元的網絡型態、節點連結程度、網絡規模、與模型設定，以評估該分析程序的實徵表現，以檢驗將社會網絡資料整合至SEM中的分析程序的實徵表現。研究結果顯示，此分析程序在所有模擬情境中皆表現良好，具有良好的模型適配度，以及可接受的參數估計及其標準誤的相對偏差。透過研究一與二的發現，除了可增加SNA與SEM在方法學知識的累積，填補文獻上的不足，並且藉由考量模擬近似於現實場域所面臨到的情境，可提供實徵研究者在實徵應用上的參考訊息。 | zh_TW |
| dc.description.abstract | Psychologists claim that individuals are embedded in social networks, and their implicit traits and explicit behaviors are affected by social networks. They suggest that researchers should explore individual behaviors in the context of social networks. It is important to consider social network and latent variable data in a single statistical model. However, there still needs to be more methodological research on this issue. Therefore, the dissertation investigates this issue by performing two simulation studies. In study 1, various levels of factor loadings, cross-factor loadings, sample sizes, factor sizes, and model specifications were designed to test the impact factors and applicable conditions of the two-stage maximum likelihood estimation procedure that combines latent variable and social network data (Liu et al., 2018). Overall, the regression coefficients of observed predictors performed well in all simulated conditions. The degree of relative bias of the parameter estimates and their standard error are elevated as cross-factor loadings increase, or factor loadings decrease. In study 2, multiple network types, connected level between nodes, network sizes, and model specifications were designed to test the empirical performance of the analytic procedure proposed by this dissertation that integrates social network data into a structural equation model. The results showed that this analytical procedure performed well in all simulated conditions, with a good model fit and acceptable relative bias of parameter estimates and their standard errors. This dissertation not only improves the accumulation of methodological knowledge on social network analysis and structural equation modeling but also provides valuable suggestions for empirical researchers. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-06T17:06:34Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-01-06T17:06:34Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 論文口試委員審定書 i
中文摘要 ii 英文摘要 iii 第一章 緒論 1 第一節 研究背景 1 第二節 尚待處理的研究議題 2 第三節 研究目的與預期貢獻 3 第二章 社會網絡分析與結構方程模型 5 第一節 社會網絡分析 5 第二節 結構方程模型的基礎介紹 10 第三章 研究一 13 第一節 研究背景 13 第二節 模擬研究 15 第四章 研究二 28 第一節 研究背景 28 第二節 模擬研究 31 第五章 結論與建議 53 第一節 研究結果與實徵建議 53 第二節 限制與未來研究 54 第三節 研究總結 55 參考文獻 57 附錄 67 附錄一 Scale-free網絡模型之適配指數百分比摘要表 67 附錄二 小世界網絡模型之適配指數百分比摘要表 68 附錄三 結構網絡模型之適配指數百分比摘要表 69 附錄四 隨機網絡模型之適配指數百分比摘要表 70 附錄五 Scale-free網絡模型之參數估計相對偏差百分比摘要表 71 附錄六 小世界網絡模型之參數估計相對偏差百分比摘要表 72 附錄七 結構網絡模型之參數估計相對偏差百分比摘要表 73 附錄八 隨機網絡模型之參數估計相對偏差百分比摘要表 74 附錄九 Scale-free網絡模型之參數估計標準誤相對偏差百分比摘要表 75 附錄十 小世界網絡模型之參數估計標準誤相對偏差百分比摘要表 76 附錄十一 結構網絡模型之參數估計標準誤相對偏差百分比摘要表 77 附錄十二 隨機網絡模型之參數估計標準誤相對偏差百分比摘要表 78 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 檢驗與擴展社會網絡分析在結構方程模型的應用：蒙地卡羅模擬研究 | zh_TW |
| dc.title | Examining and Extending the Application of SNA in SEM: Monte Carlo Simulation Studies | en |
| dc.title.alternative | Examining and Extending the Application of SNA in SEM: Monte Carlo Simulation Studies | - |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 李宣緯;周婉茹;陳柏邑;游琇婷;謝志昇 | zh_TW |
| dc.contributor.oralexamcommittee | Hsuan-Wei Lee;Wan-Ju Chou;Po-Yi Chen;Hsiu-Ting Yu;Chih-Sheng Hsieh | en |
| dc.subject.keyword | 社會網絡分析,結構方程模型,中心性測量,潛在變項,模擬研究, | zh_TW |
| dc.subject.keyword | social network analysis,structural equation modeling,centrality measures,latent variables,simulation study, | en |
| dc.relation.page | 78 | - |
| dc.identifier.doi | 10.6342/NTU202210175 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2022-12-26 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 心理學系 | - |
| 顯示於系所單位： | 心理學系 | |
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