一、中文部分
仇小屏(2004)：論九十三年國中基本學力測驗國文科試題--從辭章學知識與國語文能力之養成切入。國文天地，20(2)，頁86-90。
李昭美(2005)：國中基本學力測驗英語科試題分析(2001年至2004年)。國立高雄師範大學英語研究所碩士論文。
沈育美、陳惠珠、劉佳玲(2001)：九十學年度國中基本學力測驗--社會科中的歷史試題評析。歷史月刊，161 期， 頁108-117。
吳柏林，謝明娟(2001)：從國中基本學力測驗看九年一貫數學科教材教法。教育研究，86期，頁77-89。吳琪玉(2004)：探討我國八年級學生在TIMSS1999與TIMSS2003數學與科學之表現。國立台灣師範大學科學教育研究所碩士論文。
吳毓瑩、林原宏(1996)：潛在類別分析取向的除法概念結構。測驗年刊，43期，頁345-358。林世華(2004)：探究實施九年一貫課程對國民中小學學生語文數理學習結果之研究。台北，教育部。
林世華、盧雪梅、陳伯熹(2005)：國中畢業生能力研究。台北，教育部。
林繼生(2001)：新的開始，新的震撼--國中基本學力測驗第一次測驗國文科試題分析。國文天地，16(12)，頁97-103。林繼生(2005)：輸家與贏家之間--九四年第一次國中基本學力測驗國文科試題分析。國文天地，21(2)，頁80-84。涂伯原(2004)：國中生基本學力測驗自然科試題分析研究。科技化測驗與能力指標評量國際研討會。國立台南教育大學。
孫維民、劉怡薰、邱雯綾(2004)：由國中基本學力測驗數學科試題探討國中生學習情形。科技化測驗與能力指標評量國際研討會。國立台南教育大學。
候亮宇(2002)：判斷句的解讀--以九十一年第一次國中國文科基本學力測驗為例。國文天地，18(5)，頁69-74。
陳竹村(2003)：TIMSS1999台灣名列前茅及可能因素探討。教育研究月刊，108期，頁133-146。
曹博盛(2005)：TIMSS2003臺灣國中二年級學生的數學成就及其相關因素之探討。科學教育，283期，頁2-34，
教育部(2003)：國民中小學九年一貫課程綱要。台北，教育部。游惠玲(2003)：國中基本學力測驗英語科試題分析。雲林科技大學應用外語系研究所碩士論文。
游適宏(2003)：從閱讀評量看九十二年國中基本學力測驗國文科試題。國文天地，19(4)，頁13-18。莊嘉坤(1995)：國小學生科學態度潛在類別的分析研究。屏東師範學報，8期，頁111-136。國立屏東師範學院。詹麗馨(2002)：從91年第一次國中基本學力測驗英語科試題談閱讀策略。敦煌英語教學雜誌，43期，頁38-43。
鄭子韋(2004)：潛在類別模型在分析測驗結果的應用。中原大學應用數學系碩士論文。
鄭圓鈴(2004)：九十三年國中基本學力測驗國文科試題分析。國文天地，20(4)，頁78-86。國民中學學生基本學力測驗推動工作委員會(2002)：九十一學年度國民中學學生基本學力測驗問與答。2002年5月22日，取自http://www.bctest.ntnu.edu.tw/bctest¬_q&a.htm。
盧雪梅(1999)：應用Bootstrap法來評估Mantel-Haenszel DIF統計數的抽樣變異。測驗年刊，46(2)，33-44。譚克平(2005)：從國中基本學力測驗命題的角度看九年一貫數學學習領域的綱要。中等教育，56(1)， 頁104-112。二、西文部分
Agresti, A. (1996). An introduction to categorical data analysis. NY: Wiley.
Baker, F. B. (1992). Item response theory: parameter estimation technique. NY: Marcel.
Collins, L. M., Filder, P. L., Wugalter, S. E., & Long, J. D. (1993). Goodness-of-fit testing for latent class models. Multivariate Behavioral Research, 28(3), 375-389.
Costa, P. T., & McCrae, P. R. (1989). Neo PI/FFI manual supplement. Odessa, Florida: Psychological Assessment Resources.
Cox, M. E., Orme, J. G., & Cuddeback, G. (2003). Willingness to forest children with emotional or behavioral problems: A latent class analysis. Retrieved April 5, 2005, from http://utcmhsrc.csw.utk.edu/caseyproject/ presentations/SSWR%2003%20LCA%20Willingness.pdf
Cressie, N., & Read, T. (1984). Multinominal goodness-of-fit tests. Journal of the Royal Statistical Society, 46, 440-464.
Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. NY: Holt, Rinehart and Winston.Dayton, C. M. (1988). Latent class scaling analysis. Thousand Oaks. CA: Sage.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data via em-algorithm. Journal of the Royal Statistical Society, Serier B, 39, 1-22.
Efron, B., & Tibshirani, R., J. (1993). An introduction to the bootstrap. NY: Chapman & Hall.
Embretson, S. E. (1984). A general latent trait model for response processes. Psychometrika, 49, 175-186.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologist. NJ: Lawrence Erlbaum Associate.
Everitt, B. S. (1993). Cluster analysis. London: Edward Arnold.
Goodman, L. A. (1974). The analysis of systems of qualitative variables when some of the variables are unobservable. Part I: A modified latent structure approach. American Journal of Sociology, 79, 1179-1259.
Gullikson, H. (1987). Theory of mental tests. NJ: Lawrence Erlbaum Associate(Original published in 1950 by New York: John Wiley & Sons).
Haberman, S. J. (1979). Analysis of Qualitative Data, Vol 2, New Development. NY: Acamedic Press.
Hagenaars, J. A. (1990). Categorical Longitudinal Data-Loglinear Analysis of Panel Trend and Cohort Data. Newberg Prak: Sage.
Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston: MA: Kluwer-Nijhoff.
Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409-426.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. NY: Wiley.
Kelderman, J., & Rijkes, C. P. M. (1994). Loglinear muitidimensional IRT models for polytomously scores items. Psychometrila, 59, 149-176.
Koehlen, K. J., & Larntz, K. (1980). An empirical inverigation of goodness-of-fit statistics for sprase multinomials. Journal of American Statistics Association, 75, 336-344.
Koeller, O. (1994). Identification of guessing behavior on the basis of the mixed rasch model. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.
Krutetskii, V. A. (1976). The psychology of mathematical abilities in schoolchildren. Chicago: The University of Chicago Press.
Lazarsfeld, P. F. (1950). The logical and mathematical foundation of latemt structure analysis. In S. A. Stouffer, L. Guttman, E. A. Suchman, P. F. Lazardfeld, S. A. Star & J. A. Clausen (Eds.), Studies in social psychology in world war II, Vol. IV: Measurement and prediction (pp. 362-412). Peinceton : Princeton University Press.
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
Li, W., & Nyholt, D. R. (2001). Maker selection by Akaike information criterion and Bayesian information criterion. In Genetic Epidemiology 21(Suppl. 1) (pp. 272-277).
Lin, T. H., & Dayton, C. M. (1997). Model-selection information criteria for non-nested latent class models. Journal of Educational and Behaviroal Statistics, 22(249-364).
Lord, F. M. (1980). Application of item response theory to practical testing problems. NJ: Lawrence Erlbaum Associates.
Lord, F. M., & Novick, M. R. (1968). Statistical theory of mental test scores: Reading, Mass.: Addison-Wesley.McCutcheon, A. L. (1987). Latent class analysis. CA: Sage.
McLanchlan, G. J., & Peel, D. (2000). Finite mixture models. NY: Wiley.
NAEP. (2005). What does the 2005 mathematics assessment measure? Retrieved November 2, 2005, from http://nces.ed.gov/nationsreportcard/mathematics/whatmeasure.asp
NCTM. (2000). Standards for School Mathematics. Retrieved May 15, 2005, from
Owen, R. J. (1971). A Bayesian sequential procedure for quintal response in the context of adaptive mental testing. Journal of American Statistics Association, 70, 351-356.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: The Danish Institute of Educational Research. (Expanded edition, 1980. Chicago: The University of Chicago Press.
Read, T. & Cressie, N. (1988). Goodness-of-Fit Statistics for descrete multivariate data. NY: Springer.
Reckase, M.D.(1979).Unifactor latent trait models applied to multufactor tests results and implications. Journal of Educational Statistics,207-230
Rost, J. (1988). Rating scale analysis with latent class models. Psychometrika, 53(3), 327-348.
Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271-282.
Rost, J. (1997). Logistic mixture models. In W. J. v. d. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 449-463). NY: Springer.
Rost, J., & von. Davier, M. (1995). Mixed distribution Rasch models. In G. Fischer & I. Molenaar (Eds.), Rasch models: Foundations. Recent developments, and applications. NY: Springer.
Rost, J., Carstensen, C., & von Davier, M. (1997). Applying the mixed rasch model to personality quertionnaires. In J. Rost & R. Langeheine (Eds.), Applications of latent traits and latent class models in the social sciences. NY: Waxmann.
Rost, J., Sievers, K., Häußler, P., Hoffmann, L., & Langeheine, R. (1999). Structure and change of intertest in physics among 6th to 10th grade students. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 31(1), 18-31.
Stevens, J. (1992). Applied multivariate statistics for the social sciences. Mahwah, NJ: Erlbaum.
Tatsuoka, K. K. (1985). A probability model for diagnosing misconceptions in the pattern classification approach. Journal of Educational Statistics, 12, 55-73.
TIMSS. (2003). Mathematics Framework. Retrieved April 25, 2005, from http://timss.bc.edu/timss2003i/PDF/t03_AF_math.pdf
Uebersax, J. (2003). Latent Class Analysis. Retrieved March, 3, 2005, from http://ourworld.compuserve.com/homepages/jsuebersax/
Vermunt, J. K. (1979). Log-linear Models for Event Histories. Thousand Oakes: Sage.
Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 89-116). Cambridge, UK: Cambridge University Press.
Vermunt, J. K., & Magidson, J. (2003). Latent Class Analysis. Retrieved March, 8, 2005, from http://www.statisticalinnovations.com/articles/Latclass.pdf
von Davier, M. (1997). Bootstrapping goodness-of-fit statistics for sprase categorial data - Results of a Monte-Carlo study. Princeton: ETS.
von Davier, M. (2001). WINMIRA2001. St, Paul, MN: Assessmemt Systems Corporation.
Weiss, D. J. (1983). New horizons in testing: Latent trait test theory and computerized adaptive testing. NY: Academic Press.Whitely, S. E. (1980). Multicomponent latent trait models for ability tests. Psychometrika, 45, 479-494.Wilson, M. (1985). Measuring stages of growth: A psychometric model of hierarchical development (No. 19). Hawthorn, Victoria: Australian Council for Educational Research.
Yamamoto, K. (1987). A model that combines IRT and latent class models. University of Illinois, Illinois.