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Publications

 

*Dissertations are included in the above categories, but also have their own listing.



Career Choice

Brown, K.G. 1993. The development of student expectations of a career in science, mathematics, or engineering: An analysis of differences by gender and related contextual variables (Doctoral Dissertation, Northern Illinois University).

Hanson, S.L. 1996. Lost Talent: Women in the Sciences. Philadelphia: Temple University Press.

Miller, J.D. & Brown, K.G. 1992. The development of career expectations by American youth. In W. Meeus et. al. (Eds.), Adolescence, careers, and cultures.  Berlin: Walter de Gruyter.

Miller, J.D. & Brown, K.G. 1992. Persistence and Career Choice. In Suter, L. (Ed.), Indicators of Science and Mathematics Education. Washington, DC: National Science Foundation.

Shauman, K.A. 1997. The education of scientists: Gender differences during the early life course (Doctoral Dissertation, University of Michigan).

Wang, J. 1999. A structural model of student career aspiration and science education. Research in the Schools 6(1):53–63.

Wang, J., & Ma, X. 2001. Effects of educational productivity on career aspiration among United States high school students. Alberta Journal of Educational Research 47(1): 75–86.

Wang, J., & Staver, J.R.. 2001. Examining relationships b3etween factors of science education and student career aspiration. The Journal of Educational Research 94(5): 312–319.

Xie, Y. 1995. A demographic approach to studying the process of becoming a scientist/engineer. In National Research Council, Careers: An International Perspective (pp. 43–57). Washington, DC: National Academies Press.

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Citizenship

Miller, J.D. 1995. Scientific Literacy for Effective Citizenship. In Yager, R.E. (Ed.), Science/Technology/ Society as Reform in Science Education.  New York:  State University of New York Press.  Pp. 185–204.

Miller, J.D. 1997. Civic Scientific Literacy in the United States: A Developmental Analysis from Middle–school through Adulthood. In Gräber, W. and Bolte, C. (Eds.), Scientific Literacy. Kiel, Germany:  University of Kiel, Institute for Science Education. Pp. 121–142.

Miller, J.D.  1999.  The Development of Civic Scientific Literacy in the United States. In D.D. Kumar and Chubin, D. (Eds.), Science, Technology, and Society: Citizenship for the New Millennium.  New York: Plenum Press.

Miller, J.D. 2000. The Development of Civic Scientific Literacy in the United States. In Kumar, D.D. & Chubin, D. (Eds.), Science, Technology, and Society: A Sourcebook on Research and Practice. New York: Plenum Press. Pp. 21–47.

Pifer, L.K. 1992. The transmission of issue salience: Setting the issue agenda for American Youth (Doctoral Dissertation, Northern Illinois University).

Pifer, L.K. 1994. Adolescents and animal rights: Stable attitudes or ephemeral opinions. Public Understanding of Science 3: 291–307.

Pifer, L.K. 1996. The development of young adults' attitudes about the risks associated with nuclear power. Public Understanding of Science 5: 135–155.

Pifer, L.K. 1996. Exploring the gender gap in young adults' attitudes about animal research. Society and Animals 4: 37–52.

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Mathematics Achievement and Attitudes

Ai, X. 1999. Gender differences in growth in mathematics achievement: Three–level longitudinal and multilevel analyses of individual, home, and school influences (Doctoral Dissertation, University of California, Los Angeles).

Betebenner, D.W. 2001. Readiness for college–level mathematics (Doctoral Dissertation, University of Colorado at Boulder).

Brookhart, S.M. 1995. Effects of the Classroom Assessment Environment of Achievement in Mathematics and Science. Reports – Evaluative; Speeches/Meeting.

Brookhart, S.M. 1997. Effects of the classroom assessment environment on mathematics and science achievement. The Journal of Educational Research 90:323–30.

Campbell, J.R. & Beaudry, JS. 1998. Gender gap linked to differential socialization for high–achieving senior mathematics students. The Journal of Educational Research 91:140–7.

Cheng, J–YC. 1994. Institutional heterogeneity in public production: The case of secondary math and science education (Doctoral Dissertation, Northern Illinois University).

Choi, K., & Seltzer, M. 2010. Modeling Heterogeneity in Relationships Between Initial Status and Rates of Change: Treating Latent Variable Regression Coefficients as Random Coefficients in a Three–Level Hierarchical Model. Journal of Educational and Behavioral Statistics 35(1):54–91.

Goff, G.N. 1995. Assessing the impact of tracking on individual growth in mathematics achievement using random coefficient modeling (Doctoral Dissertation, University of California, Los Angeles).

Graham, S.E. 1997. The Exodus from mathematics: When and why? (Doctoral Dissertation, Harvard University).

Graham, S.E., & Singer, J.D. 2006. Using discrete–time survival analysis to study gender differences in leaving mathematics. In S.S. Sawilowsky (Ed.) Real Data Analysis, pp. 325–333. Charlotte, NC: Information Age Publishing.

Hoffer, T.B. 1992. Middle School Ability Grouping and Student Achievement in Science and Mathematics. Educational Evaluation and Policy Analysis 14(3):205–227.

Lai, J–S. 1996. Testing a hypothesis for gender, environment, and mediations in math learning (Doctoral Dissertation, University of Illinois at Chicago).

Ma, L. 2003. Modelling stability of growth between mathematics and science achievement via multilevel designs with latent variables (Doctoral Dissertation, University of Alberta, Canada).

Ma, X. 1997. A national assessment of mathematics participation: A survival analysis model for describing students’ academic careers (Doctoral Dissertation, University of British Columbia, Canada).

Ma, X. 1997. A national assessment of mathematics participation: A survival analysis model for describing students’ academic careers Lewiston, NY: Edwin Mellen.

Ma, X. 1999. Dropping out of advanced mathematics: The effects of parental involvement. Teachers College Record 101(1): 60.

Ma, X. 1999. Gender differences in growth in mathematical skills during secondary grades: A growth model analysis. Alberta Journal of Educational Research 45(4):448–66.

Ma, X. 2000. A longitudinal assessment of antecedent course work in mathematics and subsequent mathematical attainment. The Journal of Educational Research 94(1): 16–28.

Ma, X. 2001. Longitudinal evaluation of mathematics participation in American middle and high schools. In B. Atweh, H. Forgasz, & B. Nebres (Eds.), Sociocultural research on mathematics education: An international perspective (pp. 217–232). Mahwah, NJ: Lawrence Erlbaum.

Ma, X. 2001. Participation in advanced mathematics: do expectation and influence of students, peers, teachers, and parents matter?. Contemporary Educational Psychology 26(1): 132–46.

Ma, X. 2002. Early acceleration of mathematics students and its effect on growth in self–esteem: A longitudinal study. International Review of Education 48(6):443–468.

Ma, X. 2003. Effects of early acceleration of students in mathematics on attitudes toward mathematics and mathematics anxiety. Teachers College Record 105(3): 438–465.

Ma, X. 2005. A longitudinal assessment of early acceleration of students in mathematics on growth in mathematics achievement. Developmental Review 25(1):104–131.

Ma, X. 2005. Early acceleration of students in mathematics: Does it promote growth and stability of growth in achievement across mathematical areas? Contemporary Educational Psychology 30(4): 439.

Ma, X. 2005. Growth in Mathematics Achievement: Analysis with Classification and Regression Trees. The Journal of Educational Research 99(2): 78–86.

Ma, X. 2006. Cognitive and affective changes as determinants for taking advanced mathematics courses in high school. American Journal of Education 113(1): 123.

Ma, X & Ma, L. 2004. Modeling stability of growth between mathematics and science achievement during middle and high school. Evaluation Review 28(2): 104.

Ma, X., & Wilkins, JLM. 2007. Mathematics coursework regulates growth in mathematics achievement. Journal for Research in Mathematics Education 38(3): 230.

Ma, X., & Willms, J.D. 1999. Dropping out of advanced mathematics: How much do students and schools contribute to the problem? Educational Evaluation and Policy Analysis 21(4):365–383.

Ma, X., & Xu, J. 2004. Determining the causal ordering between attitude toward mathematics and achievement in mathematics. American Journal of Education 110(3): 256.

Ma, X., & Xu, J. 2004. The causal ordering of mathematics anxiety and mathematics achievement: A longitudinal panel analysis. Journal of Adolescence 27(2): 165.

Reynolds, A.J. 1991. The middle schooling process: Influences on science and mathematics achievement from the Longitudinal Study of American Youth. Adolescence 26.

Reynolds, A.J, & Walberg, H.J. 1992. A process model of mathematics achievement and attitude. Journal for Research in Mathematics Education 23:306–28.

Reynolds, A.J., & Walberg, H.J. 1992. A structural model of high school mathematics outcomes: An extension. Journal of Educational Research, July, 1992.

Rice, J.A.K. 1995. The effects of systemic transitions from middle to high school levels of education on student performance in mathematics and science: A longitudinal education production function analysis (Doctoral Dissertation, Cornell University).

Rice, J.K. 2001. Explaining the negative impact of the transition from middle to high school on student performance in mathematics and science. Educational Administration Quarterly 37(3): 372–401.

Scott, L.A. 2000. A matter of confidence? A new (old) perspective on sex differences in mathematics achievement (Doctoral Dissertation, Loyola University of Chicago).

Shim, M.K. 1995. A longitudinal model for the study of equity issues in mathematics education (Doctoral Dissertation, University of Illinois at Urbana–Champaign).

Wang, H. 2006. Using propensity score methodology to study the effects of ability grouping on mathematics achievement: A hierarchical modeling approach (Doctoral Dissertation, University of California, Los Angeles).

Wang, J., Oliver, J.S. & Lumpe, A.T. 1996. The relationship of student attitudes toward science, mathematics, English and social studies in U.S. secondary schools. Research in the Schools 3(1): 13–21.

Wang, J., & Wildman, L. 1994. The effects of family commitment in education on student achievement in seventh grade mathematics. Education 115(2): 317.

Wang, J., Wildman, L. and Calhoun, G. 1996. The relationship between parental influences and student achievement in seventh grade mathematics. School Science and Mathematics 96(8):395–400.

Wilkins, J.L., & Ma, X. 2002. Predicting student growth in mathematical content knowledge. The Journal of Educational Research 95(5): 288–298.

Wilkins, J.L., & Ma, X. 2003. Modeling change in student attitude toward and beliefs about mathematics. The Journal of Educational Research 97(1): 52–63.

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Methodology

Ai, X. 1999. Gender differences in growth in mathematics achievement: Three–level longitudinal and multilevel analyses of individual, home, and school influences (Doctoral Dissertation, University of California, Los Angeles).

Browne, M.W., & Arminger, G. 1994. Specification and estimation of mean– and covariance–structure models. In G. Arminger, C. Clogg, & M. Sobel (Eds.) Handbook of Statistical Modeling for the Social and Behavioral Sciences. NY: Springer. Pp. 185–250.

Choi, K. 2002. Latent variable regression in a three–level hierarchical modeling framework: A fully Bayesian approach (Doctoral Dissertation, University of California, Los Angeles).

Choi, K., & Seltzer, M. 2005. Modeling heterogeneity in relationships between initial status and rates of change: Latent variable regression in a three–level hierarchical model. CSE Report 647. Los Angeles, CA: National Center for Research on Education.

Choi, K., & Seltzer, M. 2010. Modeling Heterogeneity in Relationships Between Initial Status and Rates of Change: Treating Latent Variable Regression Coefficients as Random Coefficients in a Three–Level Hierarchical Model. Journal of Educational and Behavioral Statistics 35(1):54–91.

Kaplan, D. 2002. Modeling sustained educational change with panel data: The case for dynamic multiplier analysis. Journal of Educational and Behavioral Statistics 27(2): 85–103.

Kaplan, D. 2005. Finite mixture dynamic regression modeling of panel data with implications for dynamic response analysis. Journal of Educational and Behavioral Statistics 30(2): 169–187.

Kaplan, D. 2008. Structural Equation Modeling: Foundations and Extensions. Thousand Oaks, CA: Sage Publications.

Kaplan, D., & George, R. 1998. Evaluating latent variable growth models through ex post simulation. Journal of Educational and Behavioral Statistics 23(3): 216–235.

Kimmel, L.G., & Miller, J.D. 2008. The Longitudinal Study of American Youth: Notes on the first 20 years of tracking and data collection. Survey Practice, December 2008. [Available online at http://surveypractice.org/]

Klein, A.G., & Muthen, B.O. 2006. Modeling heterogeneity of latent growth depending on initial status. Journal of Educational and Behavioral Statistics 31(4): 357–375.

Ma, L., & Ma, X. 2005. Estimating correlates of growth between mathematics and science achievement via a multivariate multilevel design with latent variables. Studies in Educational Evaluation 31(1):79–98.

Muthen, B. 1997. Latent variable modeling of longitudinal and multilevel data. Sociological Methodology 27: 453–480.

Muthen, B.O. 2004. Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D.E. Kaplan (Ed.) The Sage Handbook of Quantitative Methodology for the Social Sciences (pp. 345–370). Thousand Oaks, CA: Sage Publications.

Peugh, J.L., & Enders, C.K. 2004. Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research 74(4): 525–556.

Reynolds, A.J., & Lee, J.S. 1991. Factor analyses of measures of home environment. Educational and Psychological Measurement 51(1): 181.

Seltzer, M., Choi, K, & Thum, Y.M. 2003. Examining relationships between where students start and how rapidly they progress: Using new developments in growth modeling to gain insight into the distribution of achievement within schools. Educational Evaluation and Policy Analysis 25(3): 263–286.

Wang, H. 2006. Using propensity score methodology to study the effects of ability grouping on mathematics achievement: A hierarchical modeling approach (Doctoral Dissertation, University of California, Los Angeles).

Wang, J. 1998. An illustration of the least median squares (LMS) regression using progress. Education 118(4): 515–521.

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Parent, Teacher, School, and Extra–Curricular Factors

Betts, J.R. 1998. The two–legged stool: The neglected role of educational standards in improving America’s public schools. Economic Policy Review – Federal Reserve Bank of New York 4(1): 97–127.

Betts, J.R., & Shkolnik, J.L. 1999. The behavioral effects of variations in class size: The case of math teachers. Educational Evaluation and Policy Analysis 21(2): 193–213.

Betts, J.R., & Shkolnik, J.L. 2000. The effects of ability grouping on student achievement and resource allocation in secondary schools. Economics of Education Review 19(1):1–15.

Bidwell, C.E., Frank, K.A., & Quiroz, P.A. 1997. Teacher types, workplace controls, and the organization of schools. Sociology of Education 70(4): 285–307.

Brookhart, S.M. 1998. Determinants of student effort on schoolwork and school–based achievement. The Journal of Educational Research 91: 201–208.

Carlson, W.S., & Monk, D.H. 1992. Differences between rural and non–rural secondary science teaching: Evidence from the longitudinal study of American Youth. Journal of Research in Rural Education 8(2): 1–10.

Cheng, J–YC. 1994. Institutional heterogeneity in public production: The case of secondary math and science education (Doctoral Dissertation, Northern Illinois University).

Gahng, T–J. 1993. A further search for school effects on achievement and intervening schooling experiences: An analysis of the longitudinal study of American youth data (Doctoral Dissertation, The University of Wisconsin – Madison).

Gamoran, A. 2002, Beyond Curriculum Wars: Content and Understanding in Mathematics. In T. Loveless (Ed.) The Great Curriculum Debate: How Should We Teach Reading and Math? Washington, DC: Brookings Institution Press. Pp. 134–162.

Gutierrez, R. 2000. Advancing African–american, urban youth in mathematics: Unpacking the success of one math department. American Journal of Education 109(1):63–111.

Littman, C.B., & Stodolsky, S.S. 1998. The professional reading of high school academic teachers. The Journal of Educational Research 92(2):75–84.

Ma, X. 1999. Dropping out of advanced mathematics: The effects of parental involvement. Teachers College Record 101(1): 60.

Madigan, T.J. 1992. Cultural capital and educational achievement: Does participation in high–status cultural activities affect achievement in school? (Doctoral Dissertation, The Pennsylvania State University).

Monk, D.H. 1994. Subject area preparation of secondary mathematics and science teachers and student achievement. Economics of Education Review 13(2): 125–145.

Monk, D.H, & Kin, J.A. 1994. Multilevel teacher resource effects in pupil performance in secondary mathematics and science: The case of teacher subject matter preparation. In RG Ehrenberg (ED), Choices and Consequences: Contemporary Policy Issues in Education (pp. 29–58). Ithaca, NY: ILR Press.

Monk, D., & Rice, J.K. 1997. The distribution of mathematics and science teachers across and within secondary schools. Educational Policy 11(4): 479–498.

Reynolds, A.J., & Lee, J.S. 1991. Factor analyses of measures of home environment. Educational and Psychological Measurement 51(1): 181.

Rocheleau, B. 1995. Computer use by school–age children: Trends, patterns and predictors. Journal of Educational Computing Research, 12(1):1–17.

Shumow, L., and Miller, J. D. 2001. Parents’ At–Home and At–School Academic Involvement with Young Adolescents. Journal of Early Adolescence, 21(1):68–91.

Spychala, W.P. 1995. Influences of science teacher characteristics on student achievement (Doctoral Dissertation, University of Illinois at Chicago).

Wang, J. 1996. An empirical assessment of textbook readability in secondary education. Reading Improvement 33:41–5

Wang, J., & Wildman, L. 1994. The effects of family commitment in education on student achievement in seventh grade mathematics. Education 115(2): 317.

Wang, J., & Wildman, L. 1995. An empirical examination of the effects of family commitment in education on student achievement in seventh grade science: analysis of data from the Longitudinal Study of American Youth. Journal of Research in Science Teaching 32: 833–7.

Wang, J., & Wildman, L. 1996. The relationship between parental influences and student achievement in seventh grade mathematics. School Science and Mathematics 96(8):395–400.

Yasumoto, J.Y., Uekawa, K., & Bidwell, C.E. 2001. The collegial focus and high school students’ achievement. Sociology of Education 74(3): 181–209.

Zill, N., et al. 1995. Adolescent Time Use, Risky Behavior, and Outcomes: An Analysis of National Data. Westat, Inc., Rockville, MD, for the Department of Health and Human Services, Washington, D.C. HHS–100–92–0005 (ED395052).

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Science Achievement and Attitudes

Brookhart, S.M. 1995. Effects of the Classroom Assessment Environment on Achievement in Mathematics and Science. Reports – Evaluative; Speeches/Meeting.

Brookhart, S.M. 1997. Effects of the classroom assessment environment on mathematics and science achievement. The Journal of Educational Research 90:323–30.

Carlson, W.S., & Monk, D.H. 1992. Differences between rural and non–rural secondary science teaching: Evidence from the longitudinal study of American Youth. Journal of Research in Rural Education 8(2): 1–10.

Cheng, J–YC. 1994. Institutional heterogeneity in public production: The case of secondary math and science education (Doctoral Dissertation, Northern Illinois University).

Gallagher, S.A. 1994. Middle school predictors of science achievement. Journal for Research in Science Teaching 31(7):721–734.

Gambro, J.S. 1991. A survey and structural model of environmental knowledge in high school students (Doctoral Dissertation, Northern Illinois University).

Gambro, J. 1996. A national survey of high school students’ environmental knowledge. The Journal of Environmental Education 27: 28–33.

Gambro, J., & Switzky, HN. 1999. Variables associated with American high school students’ knowledge of environmental issues related to energy and pollution. The Journal of Environmental Education 30(2): 15–22.

George, R. 1997. Multivariate latent variable growth modeling of attitudes toward science: An analysis of the longitudinal study of American youth (Doctoral Dissertation, University of Delaware).

George, R. 2000. Measuring change in students’ attitudes toward science over time: an application of latent variable growth modeling. Journal of Science Education and Technology 9(3): 213–225.

George, R. 2003. Growth in students’ attitudes about the utility of science over the middle and high school years: Evidence from the Longitudinal Study of American Youth. Journal of Science Education and Technology 12.

George, R. 2006. A cross–domain analysis of change in students’ attitudes toward science and attitudes about the utility of science. International Journal of Science Education 28(6):571–589.

Gibson, G.D. 1993. High school science classrooms: Teachers’ teaching and students’ learning (Doctoral Dissertation, University of Illinois at Chicago).

Hoffer, T.B. 1992. Middle School Ability Grouping and Student Achievement in Science and Mathematics. Educational Evaluation and Policy Analysis 14(3):205–227.

Ma, L. 2003. Modelling stability of growth between mathematics and science achievement via multilevel designs with latent variables (Doctoral Dissertation, University of Alberta, Canada).

Ma, X., & Ma, L. 2004. Modeling stability of growth between mathematics and science achievement during middle and high school. Evaluation Review 28(2): 104.

Ma, X., & Wilkins, J.L.M. 2002. The development of science achievement in middle and high schools. Evaluation Review 26(4): 395–418.

Martinez, A. 2002. Student achievement in science: A longitudinal look at individual and school differences (Doctoral Dissertation, Harvard University).

Miller, J.D. 1989. The Development of Interest in Science. In W. G. Rosen (Ed.). High School Biology Today and Tomorrow. Washington, DC: National Research Council.

Miller, J.D. 1995. Scientific Literacy for Effective Citizenship. In Yager, R.E. (Ed.), Science/Technology/Society as Reform in Science Education.  New York:  State University of New York Press.  Pp. 185–204.

Miller, J.D. 1997. Civic Scientific Literacy in the United States: A Developmental Analysis from Middle–school through Adulthood. In Gräber, W. and Bolte, C. (Eds.), Scientific Literacy. Kiel, Germany:  University of Kiel, Institute for Science Education. Pp. 121–142.

Miller, J.D.  1999.  The Development of Civic Scientific Literacy in the United States. In D.D. Kumar and Chubin, D. (Eds.), Science, Technology, and Society: Citizenship for the New Millennium.  New York: Plenum Press.

Miller, J.D. 2000. The Development of Civic Scientific Literacy in the United States. In Kumar, D.D. & Chubin, D. (Eds.), Science, Technology, and Society: A Sourcebook on Research and Practice. New York: Plenum Press. Pp. 21–47.

Reynolds, A.J. 1991. Note on adolescents' time–use and scientific literacy. Psychological Reports 68:63–70.

Reynolds, A.J. 1991. The middle schooling process: Influences on science and mathematics achievement from the Longitudinal Study of American Youth. Adolescence 26.

Reynolds, A.J., & Walberg, H.J. 1992. A structural model of science achievement. Journal of Educational Psychology. 83(1):97–107.

Reynolds, A.J., & Walberg, H.J. 1992. A structural model of science outcomes: An extension to high school. Journal of Educational Psychology 84:371–82.

Rice, J.A.K. 1995. The effects of systemic transitions from middle to high school levels of education on student performance in mathematics and science: A longitudinal education production function analysis (Doctoral Dissertation, Cornell University).

Rice, J.K. 2001. Explaining the negative impact of the transition from middle to high school on student performance in mathematics and science. Educational Administration Quarterly 37(3): 372–401.

Shimizu, K. 1998. The effect of inquiry science activity in educational productivity (Doctoral Dissertation, University of Illinois at Chicago).

Spychala, W.P. 1995. Influences of science teacher characteristics on student achievement (Doctoral Dissertation, University of Illinois at Chicago).

Wallace, S.R. 1997. Structural equation model of the relationships among inquiry–based instruction, attitudes toward science, achievement in science, and gender (Doctoral Dissertation, Northern Illinois University).

Wang, J., Oliver, J.S., &. Lumpe, A.T. 1996. The relationship of student attitudes toward science, mathematics, English and social studies in U.S. secondary schools. Research in the Schools, 3(1): 13–21.

Young, D., Reynolds, A.J., & Walberg, H.J. 1996. Science achievement and educational productivity: A hierarchical linear model. Journal of Educational Research, 89(5): 272–278.

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Dissertations

Ai, X. 1999. Gender differences in growth in mathematics achievement: Three–level longitudinal and multilevel analyses of individual, home, and school influences (Doctoral Dissertation, University of California, Los Angeles).

Betebenner, D.W. 2001. Readiness for college–level mathematics (Doctoral Dissertation, University of Colorado at Boulder).

Brown, K.G. 1993. The development of student expectations of a career in science, mathematics, or engineering: An analysis of differences by gender and related contextual variables (Doctoral Dissertation, Northern Illinois University).

Cheng, J–YC. 1994. Institutional heterogeneity in public production: The case of secondary math and science education (Doctoral Dissertation, Northern Illinois University).

Choi, K. 2002. Latent variable regression in a three–level hierarchical modeling framework: A fully Bayesian approach (Doctoral Dissertation, University of California, Los Angeles).

Gahng, T–J. 1993. A further search for school effects on achievement and intervening schooling experiences: An analysis of the longitudinal study of American youth data (Doctoral Dissertation, The University of Wisconsin – Madison).

Gambro, J.S. 1991. A survey and structural model of environmental knowledge in high school students (Doctoral Dissertation, Northern Illinois University).

George, R. 1997. Multivariate latent variable growth modeling of attitudes toward science: An analysis of the longitudinal study of American youth (Doctoral Dissertation, University of Delaware).

Gibson, G.D. 1993. High school science classrooms: Teachers’ teaching and students’ learning (Doctoral Dissertation, University of Illinois at Chicago).

Goff, G.N. 1995. Assessing the impact of tracking on individual growth in mathematics achievement using random coefficient modeling (Doctoral Dissertation, University of California, Los Angeles).

Graham, S.E. 1997. The Exodus from mathematics: When and why? (Doctoral Dissertation, Harvard University).

Keller, D.K. 1995. An assessment of national academic achievement growth (Doctoral Dissertation, University of Delaware).

Kunicki, J.A. 1994. The effects of impertinence upon the validity of a process model of mathematics achievement and attitude (Doctoral Dissertation, The Ohio State University).

Lai, J–S. 1996. Testing a hypothesis for gender, environment, and mediations in math learning (Doctoral Dissertation, University of Illinois at Chicago).

Ma, L. 2003. Modelling stability of growth between mathematics and science achievement via multilevel designs with latent variables (Doctoral Dissertation, University of Alberta, Canada).

Ma, X. 1997. A national assessment of mathematics participation: A survival analysis model for describing students’ academic careers (Doctoral Dissertation, University of British Columbia, Canada).

Madigan, T.J. 1992. Cultural capital and educational achievement: Does participation in high–status cultural activities affect achievement in school? (Doctoral Dissertation, The Pennsylvania State University).

Martinez, A. 2002. Student achievement in science: A longitudinal look at individual and school differences (Doctoral Dissertation, Harvard University).

Pifer, L.K. 1992. The transmission of issue salience: Setting the issue agenda for American Youth (Doctoral Dissertation, Northern Illinois University).

Rice, J.A.K. 1995. The effects of systemic transitions from middle to high school levels of education on student performance in mathematics and science: A longitudinal education production function analysis (Doctoral Dissertation, Cornell University).

Scott, L.A. 2000. A matter of confidence? A new (old) perspective on sex differences in mathematics achievement (Doctoral Dissertation, Loyola University of Chicago).

Shauman, K.A. 1997. The education of scientists: Gender differences during the early life course (Doctoral Dissertation, University of Michigan).

Shim, M.K. 1995. A longitudinal model for the study of equity issues in mathematics education (Doctoral Dissertation, University of Illinois at Urbana–Champaign).

Shimizu, K. 1998. The effect of inquiry science activity in educational productivity (Doctoral Dissertation, University of Illinois at Chicago).

Shkolnik, J.L. 1997. School resource allocation and the production of education (Doctoral Dissertation, University of California, San Diego).

Sloane, F.C. 2003. An assessment of Sorensen’s model of school differentiation: A multilevel model of tracking in middle and high school mathematics (Doctoral Dissertation, The University of Chicago).

Spychala, W.P. 1995. Influences of science teacher characteristics on student achievement (Doctoral Dissertation, University of Illinois at Chicago).

Wallace, S.R. 1997. Structural equation model of the relationships among inquiry–based instruction, attitudes toward science, achievement in science, and gender (Doctoral Dissertation, Northern Illinois University).

Wang, H. 2006. Using propensity score methodology to study the effects of ability grouping on mathematics achievement: A hierarchical modeling approach (Doctoral Dissertation, University of California, Los Angeles).

Wu, C–C. 2004. The educational aspirations and high school students’ academic growth: A hierarchical linear growth model (Doctoral Dissertation, University of California, Santa Barbara).

Zuiker, M.A. 1997. Four structural models of the effects of selected teacher background variables on mathematics attitude and achievement (Doctoral Dissertation, The Ohio State University).

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