Publications

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

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

Careers and Career Choice

Ahmed, W. (2018). Developmental trajectories of math anxiety during adolescence: associations with STEM career choice. Journal of Adolescence, 67, 158-166.

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.

Ing, M., & Nylund-Gibson, K. (2013). Linking early science and mathematics attitudes to long-term science, technology, engineering, and mathematics career attainment: Latent class analysis with proximal and distal outcomes. Educational Research and Evaluation: An International Journal on Theory and Practice, 19, 510-524.

Ing, M. (2014). Gender differences in the influence of early perceived parental support on student mathematics and science achievement and stem career attainment. International Journal of Science & Mathematics Education, 12(5), 1221-1239.

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.

Lee, S.W., Min, S., Mamerow, G.P. (2015). Pygmalion in the classroom and the home: Expectation's role in the pipeline to STEMM. Teachers College Record. 117(9), 1-40.

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.

McCue, P.L. (2017). The pre-collegiate pipeline to diversify the nursing workforce (Doctoral Dissertation). University of Rhode Island, Kingston, RI.

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.

National Science Foundation. Science and Engineering Indicators - (1993). NSB-93-1, Arlington, VA: National Science Foundation, Division of Sciences Resources Statistics.

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.

Wang, M., Degol, J., & Ye, F. (2015). Math achievement is important, but task values are critical, too: examining the intellectual and motivational factors leading to gender disparities in STEM careers. Frontiers in Psychology, 6(36), 1-9.

Wang, M., Ye, F., & Degol, J.L. (2017). Who chooses STEM careers? Using a relative cognitive strength and interest model to predict careers in science, technology, engineering, and mathematics. Journal of Youth and Adolescence, 46(8), 1805-1820.

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.

Top of Page

Citizenship

Harmon, M.D., Fontenot, M., Geidner, N., & Mazumdar, A. (2019). Affluenza revisited: Casting doubt on cultivation effects. Journal of Broadcasting and Electronic Media. 63(2), 268-284.

Kuziemko, I., Pan, J., Shen, J., & Washington, E. (2020). The Mommy Effect: Do Women Anticipate the Employment Effects of Motherhood?. NBER Working Paper No. 24740. Cambridge, MA: National Bureau of Economic Research.

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. & Cepuran, C. (2019). The impact of the Great Recession on generation X. Longitudinal and Life Course Studiesi>, 10(2), 201-216.

Miller, J.D., Kalmbach, J., Woods, L.T., & Cepuran (2020). The accuracy and value of voter validation in national surveys: Insights from longitudinal and cross-sectional studies. Political Research Quarterly, 1-16.

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.

Ryan, L.H. (2019). The impact of the Great Recession on educational pursuits in adulthood in the US. Longitudinal and Life Course Studies, 10(2), 241-257.

Wray-Lake, L., Rote, W.M., Benavides, C.M., & Victorino, C. (2014). Examining developmental transitions in civic engagement across adolescence: Evidence from a national U.S. sample. International Journal of Developmental Science. 8,(3), 95-104.

Wray-Lake, L., & Shubert, J. (2019). Understanding stability and change in civic engagement across adolescence: A typology approach. Developmental Psychology, 55(10), 2169-2180.

Top of Page

Mathematics Achievement and Attitudes

Ahmed, W. (2018). Developmental trajectories of math anxiety during adolescence: associations with STEM career choice. Journal of Adolescence, 67, 158-166.

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).

Betts, J., Costrell, R., Walberg, H., Phillips, M., & Chin, T. (2001). Incentives and Equity under Standards-Based Reform. Brookings Papers on Education Policy, (4), 9-74.

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.

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.

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

Hoffer, T. B. (1995). High school curriculum differentiation and postsecondary outcomes. In P. W. Cookson & B. Schneider (Authors), Transforming schools. New York: Garland Pub.

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.

Liu, X. (2018). The relationship between students’ mathematics performance and social influence: parental involvement, teacher support, and peer influence (Doctoral dissertation). Retrieved from ProQuest. (ProQuest Number 10827088)

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).

Shoraka, M., Arnold, R., Kim, E., Salinitri, G., & Kromrey, J. (2015). Parental Characteristics and the Achievement Gap in Mathematics: Hierarchical Linear Modeling Analysis of Longitudinal Study of American Youth (LSAY). Alberta Journal of Educational Research, 61(3), 280-293.

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., & 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.

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, Z., Oh, W., Malanchini, M., Borriello, G.A. (2020). The developmental trajectories of mathematics anxiety: Cognitive, personality and environmental correlates. Contemporary Educational Psychology, 61, 2-4.

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

Top of Page

Methodology

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.

Byrne, B.M. (2006). Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum.

Byrne, B. M. (2010). Structural Equation Modeling With AMOS : Basic Concepts, Applications, and Programming, Second Edition. New York: Routledge.

Cao, C., Kim, E.S., Chen, Y., Ferron, J., & Stark, S. (2018). Exploring the test of covariate moderation effects in multilevel MIMIC models. Educational and Psychological Measurement, Educational and Psychological Measurement, 76, 43-63. doi:10.1177/0013164418793490.

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. (2003). Addressing Questions Concerning Equity in Longitudinal Studies of School Effectiveness and Accountability: Modeling Heterogeneity in Relationships Between Initial Status and Rates of Change. CSE Report 624. Los Angeles, CA: University of California - Los Angeles, National Center for Research on Evaluation, Standards, and Student Testing.

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.

Cui, R., Groot, P. & Heskes, T. (2018). Learning causal structure from mixed data with missing values using Gaussian copula models. Statistics and Computing, 1-23.

Garcia, J.A. (2017). The race project: researching race in the social sciences researchers, measures, and scope of studies. Journal of Race, Ethnicity, and Politics, 2(2), 300-346.

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.

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., & Kurlaender, M. (2011). Using propensity scores in educational research: General principles and practical applications. The Journal of Educational Research, 104, 340-353.

Hedges, L.V., Hedberg, E.C. (1992). Intraclass correlation values for planning group-randomized trials in education. Educational Evaluation and Policy Analysis. 14(3), 205-227.

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

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.

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.

Leroux, A. J. & Beretvas, S.N. (2020). Estimation of a latent variable regression growth curve model for individuals cross-classified by clusters. Multivariate Behavioral Research, 53(2), 231-246.

Leroux, A. J., Cappelli, C.J., Fikis, D.R. (2020). The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model. British Journal of Mathematical and Statistical Psychology.

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).

Miller, J.D. & Laspra, B. (2017). Generation X in mid-life: a summary from the Longitudinal Study of American Life. Generations, 41(3), 27-33.

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.

Rockwood, Nicholas J. (2019). Estimating Multilevel Structural Equation Models with Random Slopes for Latent Covariates (Doctoral Dissertation). Ohio State University

Scott, P. W. (2018). Disaggregating between and within-person effects in autoregressive cross-lab models (Doctoral dissertation). Retrieved from University of Pittsburgh D-Scholarship.

Scott P.W. (2020). Accounting for time-varying inter-individual differences in trajectories when assessing cross-lagged models. Structural Equation Modeling: A Multidisciplinary Journal, 27(5), 1-11.

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

Top of Page

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.

Carlsen, 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).

Degol, J.L., Wang, Ml, Ye, F., & Zhang, C. (2017). Who makes the cut? Parental involvement and math trajectories predicting college enrollment. Journal of Applied Developmental Psychology, 50, 60-70.

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.

Hall, J.A., Kearney, M.W., & Xing, C. (2018). Two tests of social displacement through social media use. Information, Communication & Society. 22(10), 1396-1413.

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

Jung, E., Hwang, W., Zhang, Y., and Zhang, Y. (2018). Do parents' educational expectations in adolescence predict adult life satisfaction?. Family Relations Interdisciplinary Journal of Applied Family Science, 67(4), 552-566.

Lee, S.W. (2018). Pulling back the curtain: revealing the cumulative importance of high-performing, highly qualified teachers on students’ educational outcome. Educational Evaluation and Policy Analysis, 40(3), 359-381.

Lee, S.W., Lee E.A. (2020). Teacher qualification matters: The association between cumulative teacher qualification and students’ educational attainment. International Journal of Educational Development, 77. 1-8.

Lessard, L.M., Watson, R., Puhl, R.M. (2019). Bias-Based Bullying and School Adjustment among Sexual and Gender Minority Adolescents: The Role of Gay-Straight Alliances. Journal of Youth and Adolescence, 49, 1094-1107.

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.

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.

Shoraka, M. (2016). Students’ Perceptions of Their Mathematics Teachers in the Longitudinal Study of American Youth (LSAY): A Factor Analytic Approach. In: van der Ark L., Bolt D., Wang WC., Douglas J., Wiberg M. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 167. Springer, Cham

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.

Sommerfeld, A. K. (2016). Education as a collective accomplishment: how personal, peer, and parent expectations interact to promote degree attainment. Social Psychology of Education, 19(2), 345-365.

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

Suter, L.E. (2014). Visiting science museums during middle and high school: A longitudinal analysis of student performance in science. Science Education. 98(5), 815-839.

Tang, S. & Miller, J.D. (2019). The impact of the Great Recession on the ability of parents to fund their children’s college education. Longitudinal and Life Course Studies, 10(2), 217-240.

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. (1996). An empirical assessment of textbook readability in secondary education. Reading Improvement 33:41–5

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.

Zhang, Y. (2018). The associations between parental involvement and science achievement via children’s perceived academic competence and academic effort (Doctoral Dissertation). Syracuse University, Syracuse, NY.

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).

Top of Page

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.

Hoffer, T. B. (1995). High school curriculum differentiation and postsecondary outcomes. In P. W. Cookson & B. Schneider (Authors), Transforming schools. New York: Garland Pub.

Johnson Bey, C.E. (2019). An exploratory study of engineering identity development in African American youth. Old Dominion University, 17, 40-47

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. (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.

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 achievementi> (Doctoral Dissertation, University of Illinois at Chicago).

Suter, L.E. (2014). Visiting science museums during middle and high school: A longitudinal analysis of student performance in science. Science Education. 98(5), 815-839.

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.

Top of Page

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).

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).

Liu, X. (2018). The relationship between students’ mathematics performance and social influence: parental involvement, teacher support, and peer influence (Doctoral dissertation). Retrieved from ProQuest. (ProQuest Number 10827088)

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).

McCue, P.L. (2017). The pre-collegiate pipeline to diversify the nursing workforce (Doctoral Dissertation). University of Rhode Island, Kingston, RI.

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

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).

Rockwood, N. J. (2019). Estimating Multilevel Structural Equation Models with Random Slopes for Latent Covariates (Doctoral Dissertation). Ohio State 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).

Scott, P. W. (2018). Disaggregating between and within-person effects in autoregressive cross-lab models (Doctoral dissertation). Retrieved from University of Pittsburgh D-Scholarship.

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).

Top of Page