# Articles, Books, & Dissertations

The LSAY data has been used by numerous researchers focusing on a wide array of topics including the following:

- Careers and Career Choice
- Citizenship
- Mathematics Achievement and Attitudes
- Methodology
- Parent, Teacher, School, and Extra–Curricular Factors
- Science Achievement and Attitudes
- Dissertations*

If you have published an article or book using the LSAY data, and we have not listed it here, please contact us so that we can add it to our records.

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

## Careers and 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).

Fuchs, B.A., & Miller, J.D. 2012. Pathways to careers in medicine and health. *Peabody Journal of Education* 87(1): 62-76.

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

Kimmel, L.G., Miller, J.D., & Eccles, J.S. 2012. Do the paths to STEMM professions differ by gender? *Peabody Journal of Education* 87(1): 92-113.

Ma, X. & Johnson, W. 2008. Mathematics as the critical filter: Curricular effects on gendered career choices. In, Watt, H. M. G. & Eccles, J. S. (Eds.), *Gender and Occupational Outcomes: Longitudinal Assessments of Individual, Social, and Cultural influences*. Washington, DC: American Psychological Association.

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.

Miller, J.D., & Kimmel, L.G. 2012. Pathways to a STEMM profession. *Peabody Journal of Education* 87(1): 26-45.

Miller, J.D., & Pearson, Jr., W. 2012. Pathways to STEMM professions for students from noncollege homes. *Peabody Journal of Education* 87(1): 114-132.

Miller, J.D., & Solberg, V.S. 2012. The composition of the STEMM workforce: Rationale for differentiating STEMM professional and STEMM support careers. *Peabody Journal of Education* 87(1): 6-15.

Pearson, Jr., W., & Miller, J.D. 2012. Pathways to an engineering career. *Peabody Journal of Education* 87(1): 46-61.

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

Solberg, V.S., Kimmel, L.G., & Miller, J.D. 2012. Pathways to STEMM support occupations. *Peabody Journal of Education* 87(1): 77-91.

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.

Xie, Y., & Shauman, K.A. 2005. *Women in Science: Career Processes and Outcomes*. Cambridge, MA: Harvard University Press.

## 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.

## 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. 2010. Using propensity scores to reduce selection bias in mathematics education research. *Journal for Research in Mathematics Education* 41(2): 147-168.

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.

Hong, S. 2010. The reciprocal relationship between parental involvement and mathematics achievement: Autoregressive cross-lagged modeling. *Journal of Experimental Education* 78(4): 419-439.

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

Lindberg, S.M., Hyde, J.S., & Petersen, J.L. 2010. New trends in gender and mathematics performance: A meta-analysis. *Psychological Bulletin* 136(6): 1123-1135.

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.

McDonald, S.R., Ing, M., & Marcoulides, G.A. 2010. An investigation of early parental motivational strategies on mathematics achievement by ethnicity: A latent curve model approach. *Educational Research and Evaluation* 16(5): 401-419.

Newton, X.A. 2010. End-of-high-school mathematics attainment: How did students get there? *Teachers College Record* 112(4): 1064-1095. https://www.tcrecord.org ID Number: 15660, Date Accessed: 1/17/2012 3:41:31 PM.

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).

Tian, M., Wu, X, Li, Y, & Zhou, P. 2008. An analysis of mathematics and science achievements of American youth with nonparametric quantile regression. *Journal of Data Science* 6: 449-465.

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.

## 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).

Baraldi, A.N. and Enders, C.K. 2010. An introduction to modern missing data analyses. *Journal of School Psychology* 48:5-37.

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.

Graham, S.E., Singer, J.D., & Willett, J.B. 2009. Modeling individual change over time. In R. Milsap & A. Maydeu-Olivares (Eds.), *Handbook of Quantitative Methods in Psychology*. London: Sage.

Hedges, L.V., & Hedberg, E.C. 2007. Intraclass correlation values for planning group-randomized trials in education. *Educational Evaluation and Policy Analysis* 29(1): 60-87.

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 https://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.

McGuire, L. 2010. *Practical Formulations of the Latent Growth Item Response Model*. (Doctoral Dissertation, University of California, Berkeley).

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

Muthén, 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.

Muthén, B. & Asparouhov, T. (2011). Beyond multilevel regression modeling: Multilevel analysis in a general latent variable framework. In J. Hox & J.K. Roberts (eds), *Handbook of Advanced Multilevel Analysis*, pp. 15-40. New York: Taylor and Francis.

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.

Sirin, S.R. 2005. Socioeconomic status and academic achievement: A meta-analytic review of
research. *Review of Educational Research* 75(3): 417–453.

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.

## 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.

Hong, S. 2010. The reciprocal relationship between parental involvement and mathematics achievement: Autoregressive cross-lagged modeling. *Journal of Experimental Education* 78(4): 419-439.

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).

Maozai, T., Xizhi, W., Yuan, L, & Pengpeng, Z. 2008. Longitudinal study of the external pressure effects on children’s mathematics and science achievements using nonparametric quantile regression. *Chinese Journal of Applied Probability and Statistics* 24(3): 327-336.

McDonald, S.R., Ing, M., & Marcoulides, G.A. 2010. An investigation of early parental motivational strategies on mathematics achievement by ethnicity: A latent curve model approach. *Educational Research and Evaluation* 16(5): 401-419.

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.

Schmidt, W.H.. 2012. Measuring content through textbooks: The cumulative effect of middle-school tracking. *Mathematics Teacher Education* 7 (2): 143-160.

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).

## 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.

Miller, J.D. 2010. Adult science learning in the Internet era. *Curator* 53(2):191-208.

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).

Tian, M., Wu, X, Li, Y, & Zhou, P. 2008. An analysis of mathematics and science achievements of American youth with nonparametric quantile regression. *Journal of Data Science* 6: 449-465.

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.

## 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).

*Institutional heterogeneity in public production: The case of secondary math and science education* (Doctoral Dissertation, Northern Illinois University).

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