一、中文文獻:
王美蓉(2005)。網路後設認知閱讀策略訓練對南台灣高中生英語閱讀成效及網路學習認知之研究。未出版之博士論文，國立高雄師範大學英語學系，高雄市。王瓊珠、洪儷瑜、張郁雯、陳秀芬(2008)。一到九年級學生國字識字量發展。教育心理學報，39(4)，555-568李茂能(2008)。SEM適配度指標的潛藏問題：最佳模式難求。測驗統計年刊，16，17-29。李茂能(2010)。虛無假設顯著性考驗的演進、議題與迷思 。測驗統計年刊，18，1-22。周怡君、伊彬(2008)。電子童書之互動程度對三年級與五年級兒童閱聽成效之影響。教育資料與圖書館學，45(3)，371-401。林尹千(2011)國中學生社會領域線上閱讀素養評量工具之發展。未出版之碩士論文，國立臺南大學測驗統計研究所，臺南市。林淑惠(2006)。國小學習障礙學生在網路閱讀與紙本閱讀之閱讀理解、行為、介面、策略偏好之研究。未出版之碩士論文，國立新竹教育大學特殊教育學系碩士班，新竹市。林寶貴、錡寶香(1999)。中文閱讀理解測驗指導手冊。臺北市：教育部。
俞永嘉(2009)。探討整合式線上閱讀理解策略對特定任務線上閱讀理解歷程之影響。未出版之碩士論文，國立臺灣師範大學資訊教育研究所，臺北市。柯華葳(2006)。國民中學閱讀理解測驗。臺北市：教育部。
柯華葳、詹益綾(2007)。國民小學二-六年級閱讀理解測驗二-六年級閱讀篩選測驗。臺北市：教育部。
柯華葳、詹益綾、張建妤、游婷雅(2009)。臺灣四年級學生閱讀素養(PIRLS 2006 報告)第二版。
洪儷瑜、王瓊珠、張郁雯、陳秀芬(2006)。識字量評估測驗。臺北市：教育部。
洪儷瑜、王瓊珠、張郁雯、陳秀芬(2008)。學童“識字量評估測驗”之編製報告。測驗學刊，55(3)，489-508。洪儷瑜、王瓊珠、張郁雯、陳秀芬、陳慶順(2007)。識字量評估測驗(國字測驗)使用手冊。臺北市：國立台灣師範大學特殊教育中心。
張偉豪、鄭時宜(2012)。與結構方程模型共舞: 曙光初現。新北市：前程文化。
張貴琳(2011)。國中學生線上閱讀素養構念之探討。未出版之博士論文，國立臺南大學測驗統計所，臺南市。教育部(2012)。中華民國教育現狀：國民教育。檢索日期2013/5/17，取自http://www.edu.tw/userfiles/url/國民教育.pdf
陳文俊(譯)(2005)。E. R. Babbie。社會科學研究方法(The Practice of Social Research)。臺北市：雙葉書廊總經銷。
陳正昌、程炳林、陳新豐、劉子鍵(2009)。多變量分析方法－統計軟體應用。
台北市：五南。
陳海泓(2008)。紙本閱讀和電腦閱讀文章對國小五、六年級學生閱讀理解之影響。教育學誌(20)，121-168。陳新豐(2014)。結構方程模式-Mplus的應用。台北市：心理。
陳慧卿(2003)。國小二年級學童對電子童書與紙本童書之閱讀能力研究。未出版之碩士論文，朝陽科技大學幼兒保育系碩士班，臺中市。曾雅瑛(2012)。國民小學高年級學生線上閱讀理解測驗編製與實驗教學驗證之分析。未出版之博士論文，國立彰化師範大學教育研究所，彰化市。黃惠芬(2010)。國小六年級學生資訊素養評量發展之研究。未出版之碩士論文，國立臺中教育大學區域與社會發展學系，臺中市。黃齡嫻(2008)。紙本與電子繪本教學對國小輕度智能障礙學生閱讀理解學習成效之比較研究。未出版之碩士論文，國立臺灣師範大學特殊教育學系，臺北市。葉雅鏽(2009)。國中學生線上閱讀素養與策略調整特徵之探討。未出版之碩士論文，國立臺南大學測驗統計研究所，臺南市。簡志明 (2011) 。線上閱讀對偏遠地區六年級學生閱讀理解的影響。未出版之碩士論文，國立臺灣師範大學資訊教育學系在職進修碩士班，臺北市。二、英文文獻:
Akbulut, Y. (2007). Variables predicting foreign language reading comprehension and vocabulary acquisition in a linear hypermedia environment. Turkish Online Journal of Educational Technology, 6(2), 53-60.
Akyel, A., & Erçetin, G. (2009). Hypermedia reading strategies employed by advanced learners of English. System, 37(1), 136-152. doi: 10.1016/j.system.2008.05.002
AASL/AECT(1998). Information Literacy Standards For Student Learning. Chicago: American Library Association, Washington, DC/ Association for Educational Communications and Technology, Chicago, IL.
Allan, N. P., Lonigan, C. J., & Wilson, S. B. (2013). Psychometric evaluation of the children's behavior questionnaire-very short form in preschool children using parent and teacher report. Early Childhood Research Quarterly, 28(2), 302-313. doi: 10.1016/j.ecresq.2012.07.009
Amadieu, F., Tricot, A., & Mariné, C. (2009). Prior knowledge in learning from a non-linear electronic document: Disorientation and coherence of the reading sequences. Computers in Human Behavior, 25(2), 381-388. doi: 10.1016/j.chb.2008.12.017
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two step approach. Psychological Bulletin, 103, 411-423.
Anderson-Inman, L., & Horney, M. (2007). Supported Etext: Assistive technology through text transformations. Reading Research Quarterly, 42(1), 153-160.
Anderson, S., Sanderson, J., & Košutić, I. (2011). Therapist use-of-self orientations questionnaire: A reliability and validity study. Contemporary Family Therapy, 33(4), 364-383. doi: 10.1007/s10591-011-9146-6
Antonenko, P. D., & Niederhauser, D. S. (2010). The influence of leads on cognitive load and learning in a hypertext environment. Computers in Human Behavior, 26(2), 140-150. doi: 10.1016/j.chb.2009.10.014
Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397-438.
Aud, S., Hussar, W., Johnson, F., Kena, G., Roth, E., Manning, E., . . . Zhang, J. (2012). The Condition of Education 2012. (NCES 2012-045). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved from http://nces.ed.gov/pubsearch.
Babbie, E. R. (1998). The practice of social research. Belmont, CA : Wadsworth.
Babin, B. J., Hair, J. F., & Boles, J. S. (2008). Publishing research in marketing journals using structural equation modeling. The Journal of Marketing Theory and Practice, 16(4), 279-286.
Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. doi: 10.1007/BF02723327
Bandalos, D. L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 9(1), 78-102. doi: 10.1207/S15328007SEM0901_5
Beauducel, A., & Herzberg, P. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling: A Multidisciplinary Journal, 13(2), 186-203. doi: 10.1207/s15328007sem1302_2
Beaujean, A. A. (2013). Factor analysis using R. Practical Assessment, Research & Evaluation, 18.
Bentler, P. M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78-117. doi: 10.1177/0049124187016001004
Bilal, D. (2000). Children's use of the Yahooligans! Web search engine: I. Cognitive, physical, and affective behaviors on fact-based search tasks. Journal of the American Society for Information Science, 51(7), 646-665. doi: 10.1002/(sici)1097-4571(2000)51:7<646::aid-asi7>3.0.co;2-a
Blank, R., & Strickling, L. (2010). Exploring the digital nation: Home broadband Internet adoption in the United States. Washington, DC: US Department of Commerce. Retrieved from http://www.ntia.doc.gov/files/ntia/publications/esa_ntia_us_broadband_adoption_report_11082010_1.pdf
Boomsma, A. (2000). Reporting Analyses of Covariance Structures. Structural Equation Modeling, 7, 461-483.
Brown, G. T. (2003). Searching informational texts: Text and task characteristics that affect performance. Reading Online. Retrieved from http://www.readingonline.org/articles/brown/
Bush, V.(1945). As We May Think. The Atlantic Monthly. 176 (1), 101-108
Byrne, B. M. (2011). Structural equation modeling with Mplus: Basic concepts, applications, and programming. New York, NY: Rutledge.
Cain, K., & Oakhill, J. (2009). Reading comprehension development form 8 to 14 years: The contribution of component skills and processes. In R. K. Wagner, C. Schatschneider & C. Phythian-Sence (Eds.), Beyond decoding : The behavioral and biological foundations of reading comprehension (pp. 143-175). New York, NY: Guilford Press.
Carlisle, J., & Rice, M. S. (2002). Improving reading comprehension: Research-based principles and practices. Baltimore, MD: York Press.
Castek, J., Zawilinski, L., McVerry, J. G., O’Byrne, W. I., Leu, D. (2011). The new literacies of online reading comprehension: New opportunities and challenges for students with learning difficulties. In C. Wyatt-Smith, J. Elkins & S. Gunn (Eds.), Multiple perspectives on difficulties in learning literacy and numeracy (pp. 91-110): Springer Netherlands.
Chall, J. S. (1983). Stages of reading development. New York, NY: McGraw-Hill.
Chall, J. S., & Jacobs, V. A. (2003). The classic study on poor children's fourth-grade slump. American Educator, (Spring, 2003). Retrieved from http://www.aft.org/newspubs/periodicals/ae/spring2003/hirschsbclassic.cfm
Chang, K. E., Sung, Y. T., & Chen, I. D. (2002). The effect of concept mapping to enhance Text Comprehension and Summarization. The Journal of Experimental Education. 71(1), 5-23.
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464-504.
Chen, H.-Y. (2009). Online reading comprehension strategies among general and special education elementary and middle school students. Unpublished doctoral dissertation, Michigan State University, East Lansing. Retrieved from http://www.eric.ed.gov/ERICWebPortal/detail?accno=ED506429
Cohen, J. (1998). The analysis of variance. In J. Cohen (Ed.), Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Coiro, J. (2007). Exploring changes to reading comprehension on the Internet: Paradoxes and possibilities for diverse adolescent readers. Unpublished doctoral dissertation, University of Connecticut, Storrs.
Coiro, J. (2011a). The online reading comprehension assessment (ORCA) project: Preparing students for common core standards and 21st century literacies. Retrieved from http://www.orca.uconn.edu/orca/assets/File/Research%20Reports/CCSS%20ORCA%20Alignment%20June%202011.pdf
Coiro, J. (2011b). Talking about reading as thinking: Modeling the hidden complexities of online reading comprehension. Theory Into Practice, 50(2), 107-115. doi: 10.1080/00405841.2011.558435
Coiro, J. (2011c). Predicting Reading Comprehension on the Internet: Contributions of Offline Reading Skills, Online Reading Skills, and Prior Knowledge. Journal of literacy research, 43(4), 352-392.
Coiro, J. (2012). The new literacies of online reading comprehension: Future directions. The Educational Forum, 76(4), 412-417. doi: 10.1080/00131725.2012.708620
Coiro, J., & Dobler, E. (2007). Exploring the online reading comprehension strategies used by sixth-grade skilled readers to search for and locate information on the Internet. Reading Research Quarterly, 42(2), 214-257.
Coiro, J., Knobel, M., Lankshear, C., & Leu, D. J. (Eds.). (2008). Handbook of research on new literacies. NY: Lawrence Erlbaum.
Costello, A., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(4).
Crawford, A. V., Green, S. B., Levy, R., Lo, W.-J., Scott, L., Svetina, D., & Thompson, M. S. (2010). Evaluation of parallel analysis methods for determining the number of factors. Educational and Psychological Measurement, 70(6), 885-901. doi: 10.1177/0013164410379332
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. doi: 10.1007/BF02310555
Dalton, B., & Rose, D. (2008). Scaffolding digital comprehension. In C. C. Block & S. R. Parris (Eds.), Comprehension instruction: Research-based best practices. Solving problems in the teaching of literacy (2nd ed., pp. 347-361). New York, NY: Guilford Press.
DeVellis, R. F. (2003). Scale development : Theory and applications (2nd ed.). Thousand Oaks, CA: Sage.
Dillon, W. R., Kumar, A., & Mulani, N. (1987). Offending estimates in covariance structure analysis: Comments on the causes of and solutions to heywood cases. Psychological Bulletin, 101(1), 126.
Edmonds, M. S., Vaughn, S., Wexler, J., Reutebuch, C., Cable, A., Tackett, K. K., & Schnakenberg, J. W. (2009). A synthesis of reading interventions and effects on reading comprehension outcomes for older struggling readers. Review of Educational Research, 79(1), 262-300.
Elliott, A. C., Hynan, L. S., Reisch, J. S., & Smith, J. P. (2006). Preparing data for analysis using Microsoft Excel. Journal of Investigative Medicine, 54(6), 334-341 310.2310/6650.2006.05038.
Eshet-Alkalai, Y., & Geri, N. (2010). Does the medium affect the message? The effect of congruent versus incongruent display on critical reading. Human Systems Management, 29(4), 243-251. doi: 10.3233/HSM-2010-0730
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299. doi: 10.1037/1082-989X.4.3.272
Feinberg, J. (2010). Wordle. In J. Steele & I. Noah (Eds.), Beautiful visualization : Looking at data through the eyes of experts. Sebastopol, CA: O'Reilly.
Fletcher, T. D. (2013). Package ‘psychometric’. Retrieved from http://cran.r-project.org/web/packages/psychometric/psychometric.pdf
Foulkes, L., Viding, E., McCrory, E. J., & Neumann, C. S. (2014). Social Reward Questionnaire (SRQ): Development and validation. Frontiers in Psychology, 5. doi: 10.3389/fpsyg.2014.00201
Frederiksen, J. R., Warren, B. M., & Rosebery, A. S. (1985). A componential approach to training reading skills: Part 1. Perceptual units training. Cognition and Instruction, 2(2), 91-100. doi: 10.1207/s1532690xci0202_1
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of velicer's minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 0013164410389489.
Gerbing, D. W., & Hamilton, J. G. (1996). Viability of exploratory factor analysis as a precursor to confirmatory factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 3(1), 62-72. doi: 10.1080/10705519609540030
Goodman, K. (1967). Reading: A psycholinguistic guessing game. Journal of the Reading Specialist, 6, 126-135.
Goodwin, A. P., & Ahn, S. (2010). A meta-analysis of morphological interventions: Effects on literacy achievement of children with literacy difficulties. Annals of Dyslexia, 60(2), 183-208. doi: 10.1007/s11881-010-0041-
Gray, L., Thomas, N., & Lewis, L. (2010). Teachers’ use of educational technology in U.S. Public schools: 2009. (NCES 2010-040). Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
Guay, F., Morin, A. J. S., Litalien, D., Valois, P., & Vallerand, R. J. (2014). Application of exploratory structural equation modeling to evaluate the academic motivation scale. The Journal of Experimental Education, 1-32. doi: 10.1080/00220973.2013.876231
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). NJ: Prentice Hall.
Happell, B., & Gaskin, C. J. (2014). On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use. International Journal of Nursing Studies, 51(3), 511–521. doi: 10.1016/j.ijnurstu.2013.10.005
Hartman, D. K., Morsink, P. M., & Zheng, J. (2010). From print to pixels: The evolution of cognitive conceptions of reading comprehension. In E. A. Baker (Ed.), The new literacies: Multiple perspectives on research and practice (pp. 131‐164). New York, NY: Guilford Press.
Henry, L. A. (2006). Searching for an answer: The critical role of new literacies while reading on the Internet. Reading Teacher, 59(7), 614-627. doi: 10.1598/rt.59.7.1
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393-416. doi: 10.1177/0013164405282485
Hogarty, K. Y., Hines, C. V., Kromrey, J. D., Ferron, J. M., & Mumford, K. R. (2005). The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination. Educational and Psychological Measurement, 65(2), 202-226. doi: 10.1177/0013164404267287
Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. New York, NY: Guilford Press.
Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158-176). Thousand Oaks, CA: Sage.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. doi: 10.1080/10705519909540118
Indrisano, R., & Chall, J. S. (1995). Literacy development. Journal of Education, 177(1), 63.
International ICT Literacy Panel. (2002). Digital transformation: A framework for ICT literacy. http://www.ets.org/Media/Tests/Information_and_Communication_Technology_Literacy/ictreport.pdf
International Reading Association (IRA) (2002). Integrating Literacy and Technology in the
Curriculum: A Position Statement. Newark, Delaware. http://www.reading.org/downloads/positions/ps1048_technology.pdf
Izzo, M. V. (2009). Supported eText: Effects of text-to-speech on access and achievement for high school students with disabilities. Journal of Special Education Technology, 24(3), 9-20.
Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N: q hypothesis. Structural Equation Modeling: A Multidisciplinary Journal, 10(1), 128-141. doi: 10.1207/S15328007SEM1001_6
Johnson, L., Adams, S., & Haywood, K. (2011). The NMC horizon report: 2011 k-12 edition. Austin, Texas: The New Media Consortium.
Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). The 2011 horizon report. Austin, Texas: The New Media Consortium.
Jonassen, D. H. (1996). Computers in the Classroom: Mindtools for Critical Thinking. Englewood Cliffs, NJ: Prentice-Hall.
Jonassen, D.H. (2006). Modeling with technology: Mindtools for conceptual change. Columbus, OH: Merill.
Jonassen, D. H., & Carr, C. (2000). Mindtools: Affording multiple knowledge representations for learning. In S. P. Lajoie (Ed.), Computer as cognitive tools, Volume 2: No more walls (pp. 165-195). Mahwah, NJ: Lawrence Erlbaum.
Jonassen, D. H., Carr, C., & Yueh, H. P. (1998). Computers as mindtools: For engaging learners in critical thinking. TechTrends, 43(2), 24-32
Jonassen, D. H., Peck, K. L., & Wilson, B. G. (1999). Learning with technology: A constructivist perspective. Upper Saddle River, NJ: Merrill.
Jonassen, D. H., & Reeves, T. C. (1996). Learning with technology: Using computers as cognitive tools. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 693-719). NY: Macmillan.
Kaplan, D. (2009). Statistical assumptions underlying structural equation modeling Structural equation modeling: Foundations and extensions (2nd ed.). Thousand Oaks, CA: Sage.
Kirk, D. (1973). On the numerical approximation of the bivariate normal (tetrachoric) correlation coefficient. Psychometrika, 38(2), 259-268. doi: 10.1007/BF02291118
Kline, R. B. (2011). Principles and practice of structural equation modeling. New York, NY: Guilford Press.
Kline, T. (2005). Classical test theory assumptions, equations, limitations, and item analyses. Psychological testing: A practical approach to design and evaluation (pp. 91-106). Thousand Oaks, CA: Sage.
Klinke, S., Mihoci, A., & Härdle, W. (2010). Exploratory factor analysis in Mplus, R and SPSS. Paper presented at the Eighth International Conference on Teaching Statistics, Ljubljana, Slovenia. Retrieved from https://www.stat.auckland.ac.nz/~iase/publications/icots8/ICOTS8_4F4_KLINKE.pdf
Kretzschmar, F., Pleimling, D., Hosemann, J., Füssel, S., Bornkessel-Schlesewsky, I., & Schlesewsky, M. (2013). Subjective impressions do not mirror online reading effort: Concurrent eeg-eyetracking evidence from the reading of books and digital media. PLoS ONE, 8(2), 1-11. doi: 10.1371/journal.pone.0056178
Ledesma, R. D., & Valero-Mora, P. (2007). Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research & Evaluation, 12(2), 1-11.
Lee, T., Cai, L., & MacCallum, R. C. (2012). Power analysis for tests of structural equation models. In R. H. Hoyle (Ed.). Handbook of structural equation modeling (pp. 181-194). New York, NY: Guilford Press.
Leong, C. K., & Ho, M. K. (2008). The role of lexical knowledge and related linguistic components in typical and poor language comprehenders of Chinese. Reading and Writing, 21(5), 559-586. doi: 10.1007/s11145-007-9113-7
Leu, D. J. (2000). Literacy and technology: Deictic consequences for literacy education in an information age. In M. L. Kamil, P. Mosenthal, P. D. Pearson & R. Barr (Eds.), Handbook of reading research (Vol. 3). NJ: Erlbaum.
Leu, D. J. (2007). Expanding the reading literacy framework of PISA 2009 to include online reading comprehension. A working paper commissioned by the PISA 2009 Reading Expert Group. Princeton, NJ: Educational Testing Services.
Leu, D. J., Castek, J., Hartman, D., Coiro, J., Henry, L., Kulikowich, J., & Lyver, S. (2005). Evaluating the development of scientific knowledge and new forms of reading comprehension during online learning. Final report presented to the North Central Regional Educational Laboratory/Learning Point Associates. Retrieved from http://www.newliteracies.uconn.edu/ncrel.html
Leu, D. J., Coiro, J., Castek, J., Hartman, D. K., Henry, L. A., & Reinking, D. (2008). Research on instruction and assessment in the new literacies of online reading comprehension. In C. C. Block & S. R. Parris (Eds.), Comprehension instruction: Research-based best practices. Solving problems in the teaching of literacy (2nd., pp. 321-346). New York, NY: Guilford Press.
Leu, D. J., Kinzer, C. K., Coiro, J. L., & Cammack, D. W. (2004). Toward a theory of new literacies emerging from the internet and other information and communication technologies. In R. B. Ruddell & N. Unrau (Eds.), Theoretical models and processes of reading (5th ed.). Newark, DE: International Reading Association. Retrieved from http://www.readingonline.org/newliteracies/lit_index.asp?HREF=/newliteracies/leu.
Leu, D. J., O'Byrne, W., Zawilinski, L., McVerry, J., & Everett-Cacopardo, H.(2009). Expanding the New Literacies Conversation. Educational Researcher, 38(4), 264-269.
Leu, D. J., Zawilinski, L., Castek, J., Banerjee, M., Housand, B., Liu, Y., & O’Neil, M. (2006). What is new about the new literacies of online reading comprehension? In L. S. Rush, A. J. Eakle & A. Berger (Eds.), Secondary school reading and writing: What research reveals for classroom practices (pp. 37-68). Chicago: NCTE/NCRLL.
Li, T., McBride-Chang, C., Wong, A., & Shu, H. (2012). Longitudinal predictors of spelling and reading comprehension in Chinese as an L1 and English as an L2 in Hong Kong Chinese children. Journal of Educational Psychology, 104(2), 286-301.
Lin, W. U., Valcke, M., & van Keer, H. (2012). Validation of a Chinese version of metacognitive awareness of reading strategies inventory. Eurasian Journal of Educational Research(48), 117.
Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9(2), 151-173.
MacArthur, C. A., & Haynes, J. B. (1995). Student assistant for learning from text (SALT): A hypermedia reading aid. Journal of Learning Disabilities, 28(3), 150-159. doi: 10.1177/002221949502800304
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51(1), 201.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological methods, 1(2), 130.
MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111(3), 490-504.
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84-99. doi: 10.1037/1082-989X.4.1.84
MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36(4), 611-637.
Maïano, C., Morin, A. S., Lanfranchi, M.-C., & Therme, P. (2013). The eating attitudes test-26 revisited using exploratory structural equation modeling. Journal of Abnormal Child Psychology, 41(5), 775-788. doi: 10.1007/s10802-013-9718-z
Mallette, Marla H. & Duke, Nell K. (Eds.). (2011). Literacy Research Methodologies (2nd ed.). New York, NY: Guilford Press.
Malone, P. S., & Lubansky, J. B. (2012). Preparing data for structural equation modeling. In R. H. Hoyle (Ed.). Handbook of structural equation modeling (pp. 263-276). New York, NY: Guilford Press.
Markland, D. (2007). The golden rule is that there are no golden rules: A commentary on paul barrett’s recommendations for reporting model fit in structural equation modelling. Personality and Individual Differences, 42(5), 851-858. doi: 10.1016/j.paid.2006.09.023
Marsh, H. W., Hau, K.-T., Balla, J. R., & Grayson, D. (1998). Is more ever too much? The number of indicators per factor in confirmatory factor analysis. Multivariate Behavioral Research, 33(2), 181-220. doi: 10.1207/s15327906mbr3302_1
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320-341. doi: 10.1207/s15328007sem1103_2
Marsh, H. W., Liem, G. A. D., Martin, A. J., Morin, A. J. S., & Nagengast, B. (2011). Methodological measurement fruitfulness of exploratory structural equation modeling (ESEM): New approaches to key substantive issues in motivation and engagement. Journal of Psychoeducational Assessment, 29(4), 322-346. doi: 10.1177/0734282911406657
Marsh, H. W., Ludtke, O., Muthen, B., Asparouhov, T., Morin, A. J., Trautwein, U., & Nagengast, B. (2010). A new look at the big five factor structure through exploratory structural equation modeling. Psychological Assessment, 22(3), 471-491. doi: 10.1037/a0019227
Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating cfa and efa: Application to students' evaluations of university teaching. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 439-476. doi: 10.1080/10705510903008220
Mayer, R. E. (1996). Learning strategies for making sense out of expository text: The soi model for guiding three cognitive processes in knowledge construction. Educational Psychology Review, 8(4), 357-371. doi: 10.1007/BF01463939
McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64-82. doi: 10.1037/1082-989X.7.1.64
McLuhan, M. (1964). Understanding media: The extensions of man. New York, NY: McGraw-Hill.
Meyer, D., Hornik, K., & Feinerer, I. (2008). Text mining infrastructure in R. Journal of Statistical Software, 25(5), 1-54.
Mokhtari, K., Kymes, A., & Edwards, P. (2008). Assessing the new literacies of online reading comprehension: An informative interview with W. Ian O'Byrne, Lisa Zawilinski, J. Greg Mcverry, and Donald J. Leu at the University of Connecticut. The Reading Teacher 62(4), 354-357. doi: 10.1598/RT.62.4.9
Morin, A. J. S., & Maïano, C. (2011). Cross-validation of the short form of the physical self-inventory (PSI-S) using exploratory structural equation modeling (ESEM). Psychology of Sport and Exercise, 12(5), 540-554. doi: 10.1016/j.psychsport.2011.04.003
Mullis, I. V. S., Martin, M. O., Foy, P., & Drucker, K. T. (Eds.). (2012). The PIRLS 2011 international results in reading. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.
Mullis, I. V. S., Martin, M. O., Kennedy, A. M., Trong, K. L., & Sainsbury, M. (2009). The PIRLS 2011 assessment framework. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College.
Mullis, I. V., Martin, M. O., Foy, P., & Drucker, K. T. (2012). PIRLS 2011 International Results in Reading. International Association for the Evaluation of Educational Achievement. Herengracht 487, Amsterdam, 1017 BT, The Netherlands.
Mundfrom, D. J., Shaw, D. G., & Ke, T. L. (2005). Minimum sample size recommendations for conducting factor analyses. International Journal of Testing, 5(2), 159-168. doi: 10.1207/s15327574ijt0502_4
Muthén, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non‐normal likert variables. British Journal of Mathematical and Statistical Psychology, 38(2), 171-189
Muthén, L.K. & Muthén, B.O. (2012). Mplus User’s Guide. 7th Edition. Los Angeles, CA: Muthén & Muthén.
Myers, N. D. (2013). Coaching competency and (exploratory) structural equation modeling: A substantive-methodological synergy. Psychology of Sport and Exercise, 14(5), 709-718. doi: 10.1016/j.psychsport.2013.04.008
National Governors Association Center for Best Practices, Council of Chief State School Officers. (2010). Common Core State Standards. Washington D.C.: National Governors Association Center for Best Practices, Council of Chief State School Officers.
National Institute of Child Health and Human Development. (2000). Report of the national reading panel: Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction. Washington, DC: National Institute of Child Health and Human Development.
Naumann, J., Richter, T., Christmann, U., & Groeben, N. (2008). Working memory capacity and reading skill moderate the effectiveness of strategy training in learning from hypertext. Learning and Individual Differences, 18, 197-213.
Norris, M., & Lecavalier, L. (2010). Evaluating the use of exploratory factor analysis in developmental disability psychological research. Journal of Autism & Developmental Disorders, 40(1), 8-20. doi: 10.1007/s10803-009-0816-2
O’Connor, B. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and velicer’s map test. Behavior Research Methods, Instruments, & Computers, 32(3), 396-402. doi: 10.3758/BF03200807
OECD (2011). PISA 2009 assessment framework: Key competencies in reading, mathematics and science.
OECD (2013). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy, OECD Publishing. doi:10.1787/9789264190511-en
Olsson, U. (1979a). On The Robustness Of Factor Analysis Against Crude Classification Of The Observations. Multivariate Behavioral Research, 14(4), 485-500. doi: 10.1207/s15327906mbr1404_7
Olsson, U. (1979b). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44(4), 443-460. doi: 10.1007/BF02296207
Paris, S. G., Carpenter, R. D., Paris, A. H., & Hamilton, E. E. (2005). Spurious and genuine correlates of children's reading comprehension. In S. G. Paris & S. A. Stahl (Eds.), Children's reading comprehension and assessment (pp. 131-160). Mahwah, NJ: Erlbaum.
Perfetti, C. (1999). Comprehending written language: A blueprint of the reader. In C. M. Brown & P. Hagoort (Eds.), The neurocognition of language (pp. 167-208). Oxford: Oxford University Press.
Preacher, K. J., Cai, L., & MacCallum, R. C. (2007). lternatives to traditional model comparison strategies for covariance structure models. In T. D. Little, J. A. Bovaird & N. A. Card (Eds.), Modeling contextual effects in longitudinal studies (pp. 33-62). Mahwah, NJ: Lawrence Erlbaum Associates. Available from http://quantpsy.org/.
Preacher, K. J., & Coffman, D. L. (2006). Computing power and minimum sample size for RMSEA. Available from http://quantpsy.org/.
Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1-6.
Potocki, A., Ecalle, J., & Magnan, A. (2013). Effects of computer-assisted comprehension training in less skilled comprehenders in second grade: A one-year follow-up study. Computers & Education, 63(2013), 131-140. doi: 10.1016/j.compedu.2012.12.011
R Development Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria.
Raîche, G., Walls, T. A., Magis, D., Riopel, M., & Blais, J.-G. (2013). Non-graphical solutions for Cattell’s scree test. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(1), 23.
Raykov, T. (2009). Evaluation of scale reliability for unidimensional measures using latent variable modeling. Measurement and Evaluation in Counseling and Development, 42(3), 223-232.
Reader, W. R., & Payne, S. J. (2007). Allocating time across multiple texts: Sampling and satisficing. Human–Computer Interaction, 22(3), 263-298. doi: 10.1080/07370020701493376
Reed, D. K. (2008). A synthesis of morphology interventions and effects on reading outcomes for students in grades k–12. Learning Disabilities Research & Practice, 23(1), 36-49. doi: 10.1111/j.1540-5826.2007.00261.x
Rigdon, E. E. (1994). Calculating degrees of freedom for a structural equation model. Structural Equation Modeling: A Multidisciplinary Journal, 1(3), 274-278.
Robertson, S. (2006). What's wrong with online readings? Text, hypertext, and the history web. History Teacher, 39(4), 441-454.
Rouet, J.-F., Vörös, Z., & Pléh, C. (2012). Incidental learning of links during navigation: The role of visuo-spatial capacity. Behaviour & Information Technology, 31(1), 71-81. doi: 10.1080/0144929x.2011.604103
Sagan, A., & Pawełek, B. (2013). Single–indicator SEM with measurement error. Case of Klein I model. Paper presented at the Advances in Latent Variables-Methods, Models and Applications.
Salmerón, L., & García, V. (2011). Reading skills and children’s navigation strategies in hypertext. Computers in Human Behavior, 27(3), 1143-1151. doi: 10.1016/j.chb.2010.12.008
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. Journal of Educational Research, 99(6), 323-337.
Schmar-Dobler, E. (2003). Reading on the internet: The link between literacy and technology. Journal of Adolescent & Adult Literacy, 47(1), 80-85.
Schoemann, A. M., Preacher, K. J., & Coffman, D. L. (2010). Plotting power curves for RMSEA. Available from http://quantpsy.org/
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107-120
Sit, P. S., & Cheung, K. C. (2009). An examination of the design and implementation logistics of electronic reading assessment in PISA 2009 study: The Macao experiences. Paper presented at the PISA Research Conference, Kiel, Germany.
Snow, C. (2002). Reading for understanding: Toward an R&D program in reading comprehension. RAND Corporation, Santa Monica, CA.
Snow, C. E., & Sweet, A. P. (2003). Reading for comprehension. In A. P. Sweet & C. E. Snow (Eds.), Rethinking reading comprehension (pp. 1-11). New York, NY: Guilford Press.
Streiner, D. L. (1994). Figuring out factors: The use and misuse of factor analysis. Canada Journal of Psychiatry, 39(3), 135-140.
Srivastava, P., Gray, S., Nippold, M., & Schneider, P. (2012). Computer-based and paper-based reading comprehension in adolescents with typical language development and language-learning disabilities. Language, Speech & Hearing Services in Schools, 43(4), 424-437. doi: 10.1044/0161-1461(2012/10-0108)
Sun, Y. C. (2003). Extensive reading online: An overview and evaluation. Journal of Computer Assisted Learning, 19(4), 438-446. doi: 10.1046/j.0266-4909.2003.00048.x
Sung, Y.-T., Chang, K.-E., & Huang, J.-S. (2008). Improving children’s reading comprehension and use of strategies through computer-based strategy training. Computers in Human Behavior, 24, 1552-1571. doi: 10.1016/j.chb.2007.05.009
Tung, L.-L., Debreceny, R., Chan, Y.-G., Chan, A. T.-L., & Le, S. E.-B. (2003). Interacting with hypertext: An experimental investigation of navigation tools. Electronic Commerce Research and Applications, 2(1), 61-72. doi: 10.1016/S1567-4223(03)00006-1
Velicer, W., Eaton, C., & Fava, J. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment (pp. 41-71). New York, NY: Springer.
Wagner, R. K., Muse, A. E., & Tannenbaum, K. R. (2006). Vocabulary acquisition: Implications for reading comprehension.New York, NY: Guilford Press.
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.). Handbook of structural equation modeling (pp. 209-231). New York, NY: Guilford Press.
Wexler, J., Vaughn, S., Edmonds, M., & Reutebuch, C. K. (2008). A synthesis of fluency interventions for secondary struggling readers. Reading and Writing, 21(4), 317-347. doi: 10.1007/s11145-007-9085-7
Wolfe, M. B. W., Schreiner, M. E., Rehder, B., Laham, D., Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998). Learning from text: Matching readers and texts by latent semantic analysis. Discourse Processes, 25(2-3), 309-336. doi: 10.1080/01638539809545030
Wood, J. M., Tataryn, D. J., & Gorsuch, R. L. (1996). Effects of under- and overextraction on principal axis factor analysis with varimax rotation. Psychological Methods, 1(4), 354-365. doi: 10.1037/1082-989X.1.4.354
Wright, S., Fugett, A., & Caputa, F. (2013). Using e-readers and internet resources to support comprehension. Journal of Educational Technology & Society, 16(1), 367-379.
Zawilinski, L., Carter, A., O'Byme, I., McVerry, G., Nierlich, T., & Leu, D. J. (2007). Toward a taxonomy of online reading comprehension strategies. Paper presented at the 57th Annual National Reading Conference, Austin, TX.
Zenotz, V. (2012). Awareness development for online reading. Language Awareness, 21(1-2), 85-100. doi: 10.1080/09658416.2011.639893
Zumbo, B. D., Gadermann, A. M., & Zeisser, C. (2007). Ordinal versions of coefficients alpha and theta for Likert rating scales. Journal of Modern Applied Statistical Methods, 6(1), 4.
Zwick, W. R., & Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological bulletin, 99(3), 432.