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Asparouhov, T., & Muthén, B. O. (2014a). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling: A Mutidisciplinary Journal, 21(3), 329-341. doi:10.1080/10705511. 2014.915181

Asparouhov, T., & Muthén, B. O. (2014b). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary second model. Retrieved from http://www.statmodel.com/download/asparouhov_muthen_2014.pdf

Bakk, Z., Tekle, F. B., & Vermunt, J. K. (2013). Estimating the association between latent class membership and external variables using bias adjusted three-step approaches. Sociological Methodology, 43(1), 272-311. doi:10.1177/0081175012470644

Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic function of Markov chains. Annals of Mathematical Statistics, 41(1), 164-171. doi:10.1214/aoms/1177697196

Biagi, F., & Loi, M. (2013). Measuring ICT use and learning outcomes: Evidence from recent econometric studies. European Journal of Education, 48(1), 28-42. doi:10.1111/ejed.12016 Boeschoten, L., Oberski, D. L., Waal, T. D., & Vermunt, J. K. (2018). Updating latent class

imputations with external auxiliary variables. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 750-761. doi:10.1080/10705511.2018.1446834

Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13(2), 195-212. doi:10.1007/BF01246098

Chang, F.-C., Chiu, C.-H., Miao, N.-F., Chen, P.-H., Lee, C.-M., Chiang, J.-T., & Pan, Y.-C. (2015).

The relationship between parental mediation and Internet addiction among adolescents, and the association with cyberbullying and depression. Comprehensive Psychiatry, 57, 21-28.

doi:10.1016/j.comppsych.2014.11.013

Chen, S.-Y., & Tzeng, J.-Y. (2010). College female and male heavy internet users’ profiles of practices and their academic grades and psychosocial adjustment. Cyberpsychology Behavior and Social Networking, 13(3), 257-262. doi:10.1089/cyber.2009.0023

Cho, S. J., Cohen, A. S., Kim, S. H., & Bottge, B. (2010). Latent transition analysis with a mixture item response theory measurement model. Applied Psychological Measurement, 34(7), 483-504.

doi:10.1177/0146621610362978

Choi, H. J., & Temple, J. R. (2016). Do gender and exposure to interparental violence moderate the

56 網路行為影響學業成就 曾明基

stability of teen dating violence? Latent transition analysis. Prevention Science, 17(3), 367-376.

doi:10.1007/s11121-015-0621-4

Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral and health sciences. Hoboken, NJ: John Wiley & Sons. doi:10.1002/

9780470567333

Collins, L. M., & Wugalter, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27(1), 131-157. doi:10.1207/s15327906mbr 2701_8

Geng, J., Han, L., Gao, F., Jou, M., & Huang, C.-C. (2018). Internet addiction and procrastination among Chinese young adults: A moderated mediation model. Computers in Human Behavior, 84, 320-333. doi:10.1016/j.chb.2018.03.013

Kam, C., Morin, A. J. S., Meyer, J. P., & Topolnytsky, L. (2016). Are commitment profiles stable and predictable? A latent transition analysis. Journal of Management, 42(6), 1462-1490.

doi:10.1177/0149206313503010

Kiyici, M., Kiyici, F. B., & Franklin, T. (2012). Examining freshmen believe concerning ICT usage in K-12 and university settings. Turkish Online Journal of Educational Technology, 11(4), 427-431. doi:10.1016/j.sbspro.2011.04.403

Koukounari, A., Donnelly, C. A., Moustaki, I., Tukahebwa, E. M., Kabatereine, N. B., Wilson, S., …van Dam, G. J. (2013). A latent markov modelling approach to the evaluation of circulating cathodic antigen strips for schistosomiasis diagnosis pre- and post-praziquantel treatment in Uganda. PLoS Computational Biology, 9(12), e1003402. doi:10.1371/journal.pcbi.

1003402

Lin, M.-P., Wu, J.-Y. W., You, J., Hu, W.-H., & Yen, C.-F. (2018). Prevalence of internet addiction and its risk and protective factors in a representative sample of senior high school students in Taiwan. Journal of Adolescence, 62, 38-46. doi:10.1016/j.adolescence.2017.11.004

Luu, K., & Freeman, J. G. (2011). An analysis of the relationship between information and communication technology (ICT) and scientific literacy in Canada and Australia. Computers &

Education, 56(4), 1072-1082. doi:10.1016/j.compedu.2010.11.008

Machin, S., McNally, S., & Silva, O. (2007). New technology in schools: Is there a payoff? The Economic Journal, 117(522), 1145-1167. doi:10.1111/j.1468-0297.2007.02070.x

Meeus, W., van de Schoot, R., Klimstra, T., & Branje, S. (2011). Personality types in adolescence:

Change and stability and links with adjustment and relationships: A five-wave longitudinal study. Developmental Psychology, 47(4), 1181-1195. doi:10.1037/a0023816

曾明基 網路行為影響學業成就 57

Mullis, I. V. S., Martin, M. O., Foy, P., & Arora, A. (2012). TIMSS 2011 international results in mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.

Mullis, I. V. S., Martin, M. O., Foy, P., & Drucker, K. T. (2012). PIRLS 2011 international results in reading. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.

Muthén, B. O. (2001a). Latent variable mixture modeling. In G. A. Marcoulides & R. E.

Schumacker (Eds.), New developments and techniques in structural equation modeling (pp.

1-33). Mahwah, NJ: Lawrence Erlbaum Associates.

Muthén, B. O. (2001b). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In L. M. Collins & A. Sayer (Eds.), New methods for the analysis of change (pp.

291-322). Washington, DC: American Psychological Association.

Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29(1), 81-117. doi:10.2333/bhmk.29.81

Muthén, B. O. (2008). Latent variable hybrids: Overview of old and new models. In G. R. Hancock

& K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 1-24). Charlotte, NC: Information Age. doi:10.1080/10705511003661595

Muthén, B. O., & Asparouhov, T. (2011). LTA in Mplus: Transition probabilities influenced by covariates. Retrieved from http://www.statmodel.com/examples/LTAwebnote.pdf

Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA:

Muthén & Muthén.

Papanastasiou, E. C., Zembylas, M., & Vrasidas, C. (2003). Can computer use hurt science achievement? The USA results from PISA. Journal of Science Education and Technology, 12(3), 325-332. doi:10.1023/A:1025093225753

Park, J., & Yu, H.-T. (2018). A comparison of approaches for estimating covariate effects in nonparametric multilevel latent class models. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 778-790. doi:10.1080/10705511.2018.1448711

Spiezia, V. (2010). Does computer use increase educational achievements? Student-level evidence from PISA. OECD Journal: Economic Studies, 1, 1-22. doi:10.1787/eco_studies-2010-5km33 scwlvkf

Tsai, M.-J., & Tsai, C.-C. (2010). Junior high school students internet usage and self-efficacy: A re-examination of the gender gap. Computers & Education, 54(4), 1182-1192. doi:10.1016/j.

58 網路行為影響學業成就 曾明基

compedu.2009.11.004

Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches.

Political Analysis, 18(4), 450-469. doi:10.1093/pan/mpq025

Vermunt, J. K., Langeheine, R., & Böckenholt, U. (1999). Discrete-time discrete-state latent Markov models with time-constant and time varying-covariates. Journal of Educational and Behavioral Statistics, 24(2), 179-207. doi:10.3102/10769986024002179

Wainer, J., Dwyer, T., Dutra, R. S., Covic, A., Magalhaes, V. B., Ferreira, L. R. R., …Claudio, K.

(2008). Too much computer and internet use is bad for your grades, especially if you are young and poor: Results from the 2001 Brazilian SAEB. Computers & Education, 51(4), 1417-1429.

doi:10.1016/j.compedu.2007.12.007

Wiggins, L. M. (1973). Panel analysis. Amsterdam, NL: Elsevier.

Wittwer, J., & Senkbeil, M. (2008). Is students’ computer use at home related to their mathematical performance at school? Computers & Education, 50(4), 1558-1571. doi:10.1016/j.compedu.

2007.03.001

曾明基 網路行為影響學業成就 59

Journal of Research in Education Sciences 2019, 64(4), 31-59

doi:10.6209/JORIES.201912_64(4).0002

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