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Longitudinal Study of American Youth, 1987-1994, 2007-2011

Version
v5
Resource Type
Dataset : survey data
Creator
  • Miller, Jon D. (University of Michigan)
Other Title
  • Version 5 (Subtitle)
Publication Date
2011-03-31
Funding Reference
  • National Science Foundation
Language
English
Free Keywords
academic achievement; achievement tests; aptitude; engineering industry; high school students; junior high school students; mathematics; parents; postsecondary education; public schools; school age children; school principals; schools; science; science education; secondary education; student attitudes; teacher attitudes; teacher morale; teachers; teaching (occupation)
Description
  • Abstract

    The Longitudinal Study of American Youth (LSAY) is a project that was funded by the National Science Foundation in 1985 and was designed to examine the development of: (1) student attitudes toward and achievement in science, (2) student attitudes toward and achievement in mathematics, and (3) student interest in and plans for a career in science, mathematics, or engineering, during middle school, high school, and the first four years post-high school. The relative influence parents, home, teachers, school, peers, media, and selected informal learning experiences had on these developmental patterns was considered as well. The older LSAY cohort, Cohort One, consisted of a national sample of 2,829 tenth-grade students in public high schools throughout the United States. These students were followed for an initial period of seven years, ending four years after high school in 1994. Cohort Two, consisted of a national sample of 3,116 seventh-grade students in public schools that served as feeder schools to the same high schools in which the older cohort was enrolled. These students were followed for an initial period of seven years, concluding with a telephone interview approximately one year after the end of high school in 1994. Beginning in the fall of 1987, the LSAY collected a wide array of information including: (1) a science achievement test and a mathematics achievement test each fall, (2) an attitudinal and experience questionnaire at the beginning and end of each school year, (3) reports about education and experience from all science and math teachers in each school, (4) reports on classroom practice by each science and math teacher serving a LSAY student, (5) an annual 25-minute telephone interview with one parent of each student, and (6) extensive school-level information from the principal of each study school. In 2006, the NSF funded a proposal to re-contact the original LSAY students (then in their mid-30's) to resume data collection to determine their educational and occupational outcomes. Through an extensive tracking activity which involved: (1) online tracking, (2) newsletter mailing, (3) calls to parents and other relatives, (4) use of alternative online search methods, and (5) questionnaire mailing, more than 95 percent of the original sample of 5,945 LSAY students were located or accounted for. In addition to re-contacting the students, the proposal defined a new eligible sample of approximately 5,000 students and these young adults were asked to complete a survey in 2007. A second survey was conducted in the fall of 2008 that sought to gather updated information about occupational and education outcomes and to measure the civic scientific literacy of these young adults, in which to date more than 3,200 participants have responded. A third survey was conducted in the fall of 2009 that sought to gather updated information about occupational and education outcomes and to measure the participants' use of selected informal science education resources, in which to date more than 3,200 participants have responded. A fourth survey was conducted in the fall of 2010 that sought to gather updated information about occupational and education outcomes, as well as provided questions about the participants' interactions with their children, in which to date more than 3,200 participants have responded. Finally, a fifth survey was conducted in the fall of 2011 that sought to gather updated information about education outcomes and included an expanded occupation battery for all participants, as well as an expanded spousal information battery for all participants. The 2011 questionnaire also included items about the 2011 Fukushima incident in Japan along with attitudinal items about nuclear power and global climate change. To date approximately 3,200 participants responded to the 2011 survey. The public release data files include information collected from the national probability sample students, their parents, and the science and mathematics teachers in the students' schools. The data covers the initial seven years, beginning in the fall of 1987, as well as the data collected in the 2007, 2008, 2009, 2010, and 2011 questionnaires. Part 1: LSAY Merged Cohort (Base File) contains student and parent data from both cohorts of the LSAY from 1987-1994 and student follow-up data from 2007-2011. Additionally, Parts 2 - 5 contain information gathered from two teacher background questionnaires and two principal questionnaires from 1987-1994.
  • Abstract

    The LSAY sample design consisted of a sample from high schools and a sample of middle or junior high schools that sent students to the participating high schools. Selection of the latter set of schools was accomplished by obtaining information from high school officials on feeder patterns to their schools. Many of the sampled high schools were served by only one feeder school, and nine selections included the middle school grade levels included in the participating high school. A number of the high schools however received students from two or more feeder schools, and in these cases one feeder school had to be selected. The selection procedure involved calculating the proportion of students in the high school who came from each feeder school and then randomly selecting one feeder school, where the probability of selection was proportional to the feeders' contributions to the high school's enrollment. In the event that a school or district declined to participate in the LSAY, a school of similar size and zip code indicating proximity to the original selection was chosen. Once a school's cooperation was secured, the LSAY obtained a complete student roster for the seventh and tenth grade cohorts. To provide a sufficient number of students in each school to compute school effects in subsequent analyses, a sample of 60 students was selected from each school. Students were selected randomly from the lists and asked to participate until the target response size was achieved. In some schools with fewer than 60 students in their seventh or tenth grade classes, all students were selected for participation. When a student refused to participate, the school research coordinator was directed to draw a replacement from an additional list of students, starting at the beginning of the alternate list and proceeding sequentially until a participant was secured. The alternate list was selected randomly, using the same procedures outlined above in constructing the original sample. The LSAY fielded over 40 instruments for Cohort Two and 26 for Cohort One from October 1987 through June 1994. Resumption of LSAY tracking activities began in April, 2006 and re-entry questionnaires were administered in 2007, 2008, 2009, 2010, and 2011. For more information on Study Design, please refer to the Original P.I. Documentation in the ICPSR User Guide.
  • Methods

    The data are not weighted. There are many weights present in Part 1: LSAY Merged Cohort (Base File). Weight variables have been calculated in order to adjust for the unequal erosion from the original sample over the period of the longitudinal study. For example, if 10 percent of students from School A drop out of or are lost to the study and 20 percent of students from School B drop out or are lost to the study, the unweighted use of the dataset would produce estimates that overestimated the contribution of students from School A and underestimated the contribution of students from School B. Correct estimates of national distributions can only be obtained by using the appropriate weight variable for the analysis at hand. A new longitudinal weight was created for the merged file containing both cohorts, WGT12A, and should be used for all longitudinal analyses containing both cohorts for the high school years. In addition, please refer to the Original P.I. Documentation in the ICPSR User Guide for a description of all weights that are present in the data collection.
  • Methods

    ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Checked for undocumented or out-of-range codes..
  • Methods

    Presence of Common Scales: For more information on Scales, please refer to the Original P.I. Documentation in the ICPSR User Guide.
  • Methods

    Response Rates: For more information on Response Rates, please refer to the Original P.I. Documentation in the ICPSR User Guide.
  • Table of Contents

    Datasets:

    • DS0: Study-Level Files
    • DS1: LSAY Merged Cohort File (Base File)
    • DS2: Teacher Background Questionnaires Years 1-5
    • DS3: Teacher Background Questionnaires Year 6
    • DS4: Principal Questionnaire Fall 89
    • DS5: Principal Questionnaire Fall 93
Temporal Coverage
  • 1987 / 1994
    Time period: 1987--1994
  • 2007 / 2011
    Time period: 2007--2011
  • 1987 / 1994
    Collection date: 1987--1994
  • 2007 / 2011
    Collection date: 2007--2011
Geographic Coverage
  • United States
Sampled Universe
7th and 10th grade students in public schools in the United States in 1987 as well as those same students that could be recontacted again in 2007, 2008, 2009, 2010, and 2011 with a follow-up questionnaire. Smallest Geographic Unit: region
Sampling
The sampling scheme for the base year of the LSAY was a two-stage stratified probability sample. The United States was stratified by four geographic regions and by three levels of urban development (central city, suburban, and nonmetropolitan) to produce a total of 12 strata. Stage I involved the selection of schools to participate in the study. Stage II was the random selection of 60 students within each school selected in Stage I. Resumption of LSAY tracking activities began in April, 2006 and re-entry questionnaires were administered in 2007, 2008, 2009, 2010, and 2011. For more information on Sampling, please refer to the Original P.I. Documentation in the ICPSR User Guide.
Collection Mode
  • cognitive assessment test, mail questionnaire, mixed mode, on-site questionnaire, telephone interview, web-based survey

    The original two-cohort, two-file data structure reflected the initial period of data collection, however it was awkward for users that wanted to compare the two cohorts or to combine them for various analyses. The merged data file includes a variable to indicate the original cohort, allowing a user to repeat or extend any analysis conducted with the previous LSAY release file, however the naming of the variables in the merged file has been revised to correct dual or conflicting variable names and indicators. The new merged file structure will facilitate the annual release of new cycles of data collection through the addition of variables to the base system.

    For further information about LSAY see the Longitudinal Study of American Youth Web site.

Note
2016-03-24 This is an update to LSAY documentation. Text recognition has been performed on the data collection instrument to facilitate searching. The study has been moved to the CivicLEADS archive.2014-08-26 This is an update to LSAY data (ICPSR 30263). Part 1: LSAY Merged Cohort File (Base File) has been updated and includes new and revised variables. All other variables and parts in the collection remain the same. In addition, a new LSAY User's Manual has been provided.2014-06-26 This is an update to LSAY data (ICPSR 30263). Part 1: LSAY Merged Cohort File (Base File) includes the following: (1) data collected in the 2010 and 2011 questionnaires (this data was not available in the previous release), (2) all data collected in the 2007, 2008, and 2009 questionnaires which include additional cases not available in the earlier release, as well as corrections and clarifications to some cases, and (3) all constructed student and parent variables from 1987-1994. The 2007, 2008, and 2009 data and constructed variables that were previously included in ICPSR (30263) were replaced with Part 1: LSAY Merged Cohort (Base File). Parts 2 - 5 include data collected from 1987-1994. Question text has been added to Part 1: LSAY Merged Cohort (Base File) for the variables that were present in the previous update. Newly added variables do not contain question text. The data files are identical to the previously released files.2014-04-24 This is an update to LSAY data (ICPSR 30263). Part 1: LSAY Merged Cohort File (Base File) includes the following: (1) data collected in the 2008 and 2009 questionnaires (this data was not available in the previous release), (2) all data collected in the 2007 questionnaire which includes additional cases not available in the earlier release, as well as corrections and clarifications to some cases, and (3) all constructed student and parent variables from 1987-1994. The 2007 data and constructed variables that were previously included in ICPSR (30263) were replaced with Part 1: LSAY Merged Cohort (Base File). Parts 2 - 5 include data collected from 1987-1994. The data files are identical to the previously released files. In addition, R data files have been added for Parts 2 - 5.2011-04-01 PI information corrected. Funding insitution(s): National Science Foundation (REC-0337487, MDR-8550085, REC96-27669, DUE-0856695, DRL-0917535, RED-9909569, DUE-0525357).
Availability
Delivery
This version of the study is no longer available on the web. If you need to acquire this version of the data, you have to contact ICPSR User Support (help@icpsr.umich.edu).
Alternative Identifiers
  • 30263 (Type: ICPSR Study Number)
Relations
  • Is previous version of
    DOI: 10.3886/ICPSR30263.v6
  • Is new version of
    DOI: 10.3886/ICPSR30263.v4
Publications
  • Fuchsa, Bruce A., Miller, Jon D.. Pathways to careers in medicine and health. Peabody Journal of Education.87, (1), 62-76.2012.
    • ID: 10.1080/0161956X.2012.642271 (DOI)
  • Kaplan, David. Structural Equation Modeling: Foundations and Extensions. Thousand Oaks, CA: Sage Publications. 2008.
  • Kimmel, L.G., Miller, J.D.. The Longitudinal Study of American Youth: Notes on the first 20 years of tracking and data collection. Survey Practice.2008.
  • Ma, X., Wilkins, J.L.M.. Mathematics coursework regulates growth in mathematics achievement. Journal for Research in Mathematics Education.38, (3), 2302007.
  • Byrne, Barbara M.. Structural Equation Modeling With Eqs: Basic Concepts, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum. 2006.
  • George, Rani. 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), 571-589.2006.
    • ID: 10.1080/09500690500338755 (DOI)
  • Graham, Suzanne E., Singer, Judith D.. Using discrete-time survival analysis to study gender differences in leaving mathematics. Real Data Analysis.Charlotte, NC: Information Age Publishing. 2006.
  • Klein, A.G., Muthen, B.O.. Modeling heterogeneity of latent growth depending on initial status. Journal of Educational and Behavioral Statistics.31, (4), 357-375.2006.
    • ID: 10.3102/10769986031004357 (DOI)
  • Ma, X.. Cognitive and affective changes as determinants for taking advanced mathematics courses in high school. American Journal of Education.113, (1), 1232006.
    • ID: 10.1086/506496 (DOI)
  • Wang, H.. Using Propensity Score Methodology to Study the Effects of Ability Grouping on Mathematics Achievement: A Hierarchical Modeling Approach. Dissertation, University of California-Los Angeles. 2006.
  • Choi, Kilchan, Seltzer, Michael. 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. 2005.
    • ID: http://www.cse.ucla.edu/products/reports/r647.pdf (URL)
  • Kaplan, D.. Finite mixture dynamic regression modeling of panel data with implications for dynamic response analysis. Journal of Educational and Behavioral Statistics.30, (2), 169-187.2005.
    • ID: 10.3102/10769986030002169 (DOI)
  • Ma, L., Ma, X.. Estimating correlates of growth between mathematics and science achievement via a multivariate multilevel design with latent variables. Studies in Educational Evaluation.31, (1), 79-98.2005.
    • ID: 10.1016/j.stueduc.2005.02.003 (DOI)
  • Ma, X.. A longitudinal assessment of early acceleration of students in mathematics on growth in mathematics achievement. Developmental Review.25, (1), 104-131.2005.
    • ID: 10.1016/j.dr.2004.08.010 (DOI)
  • Ma, X.. Early acceleration of students in mathematics: Does it promote growth and stability of growth in achievement across mathematical areas?. Contemporary Educational Psychology.30, (4), 4392005.
    • ID: 10.1016/j.cedpsych.2005.02.001 (DOI)
  • Ma, X.. Growth in mathematics achievement: Analysis with classification and regression trees. Journal of Educational Research.99, (2), 78-86.2005.
    • ID: 10.3200/JOER.99.2.78-86 (DOI)
  • Restivo, Sal. Science, Technology, and Society: An Encyclopedia. Oxford, England: Oxford University Press. 2005.
  • Ma, X., Ma, L.. Modeling stability of growth between mathematics and science achievement during middle and high school. Evaluation Review.28, (2), 1042004.
    • ID: 10.1177/0193841X03261025 (DOI)
  • Ma, X., Xu, J.. Determining the causal ordering between attitude toward mathematics and achievement in mathematics. American Journal of Education.110, (3), 2562004.
    • ID: 10.1086/383074 (DOI)
  • Ma, X., Xu, J.. The causal ordering of mathematics anxiety and mathematics achievement: A longitudinal panel analysis. Journal of Adolescence.27, (2), 1652004.
    • ID: 10.1016/j.adolescence.2003.11.003 (DOI)
  • Muthén, Bengt O.. Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. The Sage Handbook of Quantitative Methodology for the Social Sciences.Thousand Oaks, CA: Sage Publications. 2004.
  • Wu, C-C.. The Educational Aspirations and High School Students' Academic Growth: A Hierarchical Linear Growth Model. Dissertation, University of California-Santa Barbara. 2004.
  • Choi, Kilchan, Seltzer, Michael. 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 614.Los Angeles, CA: UNiversity of California-Los Angeles, National Center for Research on Evaluation, Standards, and Student Testing. 2003.
    • ID: http://www.cse.ucla.edu/products/reports/r614.pdf (URL)
  • George, Rani. 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, (4), 439-448.2003.
    • ID: 10.1023/B:JOST.0000006303.63545.0f (DOI)
  • Ma, X.. Effects of early acceleration of students in mathematics on attitudes toward mathematics and mathematics anxiety. Teachers College Record.105, (3), 438-465.2003.
    • ID: 10.1111/1467-9620.00246 (DOI)
  • Seltzer, Michael, Choi, Kilchan, Thum, Yeow Meng. 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), 263-286.2003.
    • ID: 10.3102/01623737025003263 (DOI)
  • Sloane, F.C.. An Assessment of Sorensen's Model of School Differentiation: A Multilevel Model of Tracking in Middle and High School Mathematics. Dissertation, University of Chicago. 2003.
  • Wilkins, Jesse L.M., Ma, Xin. Modeling change in student attitude toward and beliefs about mathematics. Journal of Educational Research.97, (1), 52-63.2003.
    • ID: 10.1080/00220670309596628 (DOI)
  • Choi, K.. Latent Variable Regression in a Three-level Hierarchical Modeling Framework: A Fully Bayesian Approach. Dissertation, University of California-Los Angeles. 2002.
  • Gamoran, Adam. Beyond curriculum wars: Content and understanding in mathematics. The Great Curriculum Debate: How Should We Teach Reading and Math? .Washington, DC: Brookings Institution Press. 2002.
  • Kaplan, D.. Modeling sustained educational change with panel data: The case for dynamic multiplier analysis. Journal of Educational and Behavioral Statistics.27, (2), 85-103.2002.
    • ID: 10.3102/10769986027002085 (DOI)
  • Ma, X.. Early acceleration of mathematics students and its effect on growth in self-esteem: A longitudinal study. International Review of Education.48, (6), 443-468.2002.
    • ID: 10.1023/A:1021334707732 (DOI)
  • Ma, X., Wilkins, J.L.M.. The development of science achievement in middle and high schools. Evaluation Review.26, (4), 395-418.2002.
    • ID: 10.1177/0193841X02026004003 (DOI)
  • Martinez, A.. Student Achievement in Science: A Longitudinal Look at Individual and School Differences. Dissertation, Harvard University. 2002.
  • Wilkins, Jesse L.M., Ma, Xin. Predicting student growth in mathematical content knowledge. Journal of Educational Research.95, (5), 288-298.2002.
    • ID: 10.1080/00220670209596602 (DOI)
  • Betebenner, D.W.. Readiness for College-level Mathematics. Dissertation, University of Colorado-Boulder. 2001.
  • Betts, J.R., Costrell, R.M., Walberg, H.J., Phillips, M., Chin, T.. Incentives and equity under standards-based reform. Brookings Papers on Education Policy.4, 9-74.2001.
  • Ma, X.. Participation in advanced mathematics: Do expectation and influence of students, peers, teachers, and parents matter?. Contemporary Educational Psychology.26, (1), 132-146.2001.
    • ID: 10.1006/ceps.2000.1050 (DOI)
  • Ma, Xin. Longitudinal evaluation of mathematics participation in American middle and high schools. Sociocultural Research on Mathematics Education: An International Perspective.Mahwah, NJ: Lawrence Erlbaum. 2001.
  • Rice, Jennifer King. Explaining the negative impact of the transition from middle to high school on student performance in mathematics and science. Educational Administration Quarterly.37, (3), 372-400.2001.
    • ID: 10.1177/00131610121969352 (DOI)
  • Shumow, Lee, Miller, Jon D.. Parents' at-home and at-school academic involvement with young adolescents. Journal of Early Adolescence.21, (1), 68-91.2001.
    • ID: 10.1177/0272431601021001004 (DOI)
  • Yasumoto, Jeffrey Y., Uekawa, Kazuaki, Bidwell, Charles E.. The collegial focus and high school students' achievement. Sociology of Education.74, (3), 181-209.2001.
    • ID: http://www.jstor.org/stable/2673274 (URL)
  • Betts, J.R., Shkolnik, J.L.. The effects of ability grouping on student achievement and resource allocation in secondary schools. Economics of Education Review.19, (1), 1-15.2000.
    • ID: 10.1016/S0272-7757(98)00044-2 (DOI)
  • George, R.. Measuring change in students' attitudes toward science over time: An application of latent variable growth modeling. Journal of Science Education and Technology.9, (3), 213-225.2000.
    • ID: 10.1023/A:1009491500456 (DOI)
  • Gutierrez, R.. Advancing African-American, urban youth in mathematics: Unpacking the success of one math department. American Journal of Education.109, (1), 63-111.2000.
    • ID: 10.1086/444259 (DOI)
  • Ma, X.. A longitudinal assessment of antecedent course work in mathematics and subsequent mathematical attainment. Journal of Educational Research.94, (1), 16-28.2000.
    • ID: 10.1080/00220670009598739 (DOI)
  • Miller, Jon D.. The development of civic scientific literacy in the United States. Science, Technology, and Society: A Sourcebook on Research and Practice.New York : Kluwer Academic/Plenum. 2000.
  • Scott, L.A.. A Matter of Confidence? A New (Old) Perspective on Sex Differences in Mathematics Achievement. Dissertation, Loyola University-Chicago. 2000.
  • Ai, X.. Gender Differences in Growth in Mathematics Achievement: Three-level Longitudinal and Multilevel Analyses of Individual, Home, and School Influences. Dissertation, University of California-Los Angeles. 1999.
  • Betts, J.R., Shkolnik, J.L.. The behavioral effects of variations in class size: The case of math teachers. Educational Evaluation and Policy Analysis.21, (2), 193-213.1999.
  • Gambro, J., Switzky, H.N.. Variables associated with American high school students' knowledge of environmental issues related to energy and pollution. Journal of Environmental Education.30, (2), 15-22.1999.
    • ID: 10.1080/00958969909601866 (DOI)
  • Ma, X.. Dropping out of advanced mathematics: The effects of parental involvement. Teachers College Record.101, (1), 601999.
    • ID: 10.1111/0161-4681.00029 (DOI)
  • Ma, X.. Gender differences in growth in mathematical skills during secondary grades: A growth model analysis. Alberta Journal of Educational Research.45, (4), 448-466.1999.
  • Ma, X., Willms, J.D.. Dropping out of advanced mathematics: How much do students and schools contribute to the problem?. Educational Evaluation and Policy Analysis.21, (4), 365-383.1999.
  • Betts, J.R.. 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), 97-127.1998.
  • Brookhart, Susan M.. Determinants of student effort on schoolwork and school-based achievement. Journal of Educational Research.91, 201-208.1998.
    • ID: 10.1080/00220679809597544 (DOI)
  • Brookhart, Susan M.. Effects of the classroom assessment environment on mathematics and science achievement. Journal of Educational Research.90, 323-330.1998.
  • Campbell, J.R., Beaudry, J.S.. Gender gap linked to differential socialization for high-achieving senior mathematics students. Journal of Educational Research.91, 140-147.1998.
    • ID: 10.1080/00220679809597534 (DOI)
  • Kaplan, D., George, R.. Evaluating latent variable growth models through ex post simulation. Journal of Educational and Behavioral Statistics.23, (3), 216-235.1998.
  • Littman, C.B., Stodolsky, S.S.. The professional reading of high school academic teachers. Journal of Educational Research.92, (2), 75-84.1998.
    • ID: 10.1080/00220679809597579 (DOI)
  • Shimizu, K.. The Effect of Inquiry Science Activity in Educational Productivity. Dissertation, University of Illinois-Chicago. 1998.
  • Wang, Jianjun. An illustration of the least median squares (LMS) regression using progress. Education.118, (4), 515-521.1998.
  • Bidwell, C.E., Frank, K.A., .Quiroz, P.A.. Teacher types, workplace controls, and the organization of schools. Sociology of Education.70, (4), 285-307.1997.
    • ID: http://www.jstor.org/stable/2673268 (URL)
  • George, Rani. Multivariate Latent Variable Growth Modeling of Attitudes Toward Science: An Analysis of the Longitudinal Study of American Youth. Dissertation, University of Delaware. 1997.
  • Graham, S.E.. The Exodus From Mathematics: When and Why?. Dissertation, Harvard University. 1997.
  • Ma, X.. A National Assessment of Mathematics Participation: A Survival Analysis Model for Describing Students' Academic Careers. Dissertation, University of British Columbia. 1997.
  • Ma, Xin. A National Assessment of Mathematics Participation: A Survival Analysis Model for Describing Students' Academic Careers. Lewiston, NY: Edwin Mellen. 1997.
  • Miller, Jon D.. Civic scientific literacy in the United States: A developmental analysis from middle-school through adulthood. Scientific Literacy.Kiel, Germany: University of Kiel, Institute for Science Education. 1997.
  • Monk, D., Rice, J.K.. The distribution of mathematics and science teachers across and within secondary schools. Educational Policy.11, (4), 479-498.1997.
  • Muthen, B.. Latent variable modeling of longitudinal and multilevel data. Sociological Methodology.27, 453-480.1997.
    • ID: 10.1111/1467-9531.271034 (DOI)
  • Shauman, Kimberlee Akin. The Education of Scientists: Gender Differences During the Early Life Course. Dissertation, University of Michigan. 1997.
  • Shkolnik, J.L.. School Resource Allocation and the Production of Education. Dissertation, University of California-San Diego. 1997.
  • Wallace, S.R.. Structural Equation Model of the Relationships Among Inquiry-based Instruction, Attitudes Toward Science, Achievement in Science, and Gender. Dissertation, Northern Illinois University. 1997.
  • Zuiker, M.A.. Four Structural Models of the Effects of Selected Teacher Background Variables on Mathematics Attitude and Achievement. Dissertation, Ohio State University. 1997.
  • Gambro, J.. A national survey of high school students' environmental knowledge. Journal of Environmental Education.27, 28-33.1996.
    • ID: 10.1080/00958964.1996.9941464 (DOI)
  • Hanson, Sandra L.. Lost Talent: Women in the Sciences. Philadelphia: Temple University Press. 1996.
  • Lai, J-S.. Testing a Hypothesis for Gender, Environment, and Mediations in Math Learning . Dissertation, University of Illinois-Chicago. 1996.
  • Ma, L.. Modelling Stability of Growth Between Mathematics and Science Achievement via Multilevel Designs With Latent Variables. Dissertation, University of Illinois-Chicago. 1996.
  • Pifer, L.K.. The development of young adults' attitudes about the risks associated with nuclear power. Public Understanding of Science.5, 135-155.1996.
    • ID: 10.1088/0963-6625/5/2/004 (DOI)
  • Pifer, Linda K.. Exploring the gender gap in young adults' attitudes about animal research. Society and Animals.4, (1), 37-52.1996.
    • ID: 10.1163/156853096X00034 (DOI)
  • Wang, Jianjun. An empirical assessment of textbook readability in secondary education. Reading Improvement.33, 41-50.1996.
  • Wang, Jianjun, Oliver, J. Steve, Lumpe, Andrew T.. The relationship of student attitudes toward science, mathematics, English and social studies in U.S. secondary schools. Research in the Schools.3, (1), 13-21.1996.
  • Young, Deidra J., Reynolds, Arthur J., Walberg, Herbert J.. Science achievement and educational productivity: A hierarchical linear model. Journal of Educational Research.89, (5), 272-278.1996.
    • ID: 10.1080/00220671.1996.9941328 (DOI)
  • Brookhart, Susan M.. Effects of the Classroom Assessment Environment of Achievement in Mathematics and Science. Annual Meeting of the American Educational Research Association.San Francisco, CA. 1995.
  • Browne, Michael W., Arminger, Gerhard. Specification and estimation of mean- and covariance-structure models. Handbook of Statistical Modeling for the Social and Behavioral Sciences.New York: Plenum Press. 1995.
  • Goff, G.N.. Assessing the Impact of Tracking on Individual Growth in Mathematics Achievement Using Random Coefficient Modeling. Dissertation, University of California-Los Angeles. 1995.
  • Hoffer, Thomas B.. High school curriculum differentiation and postsecondary outcomes. Transforming Schools.New York: Routledge. 1995.
  • Keller, D.K.. An Assessment of National Academic Achievement Growth. Dissertation, University of Delaware. 1995.
  • Miller, Jon D.. Scientific literacy for effective citizenship. Science/Technology/ Society as Reform in Science Education.New York: State University of New York Press. 1995.
  • Rice, J.A.K.. 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. Dissertation, Cornell University. 1995.
  • Rocheleau, Bruce. Computer use by school-age children: Trends, patterns and predictors. Journal of Educational Computing Research.12, (1), 1-17.1995.
    • ID: 10.2190/MHUR-4FC9-B187-T8H4 (DOI)
  • Shim, M.K.. A Longitudinal Model for the Study of Equity Issues in Mathematics Education. Dissertation, University of Illinois-Urbana-Champaign. 1995.
  • Spychala, W.P.. Influences of Science Teacher Characteristics on Student Achievement. Dissertation, University of Illinois-Chicago. 1995.
  • Wang, Jianjun, Wildman, Louis. 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, (8), 833-837.1995.
    • ID: 10.1002/tea.3660320801 (DOI)
  • Xie, Y.. A demographic approach to studying the process of becoming a scientist/engineer. National Research Council, Careers: An International Perspective.Washington, DC: National Academies Press. 1995.
  • Zill, Nicholas, Nord, Christine Winquist, Loomis, Laura Spencer. Adolescent Time Use, Risky Behavior, and Outcomes: An Analysis of National Data. Washington, DC: Department of Health and Human Services. 1995.
  • Cheng, J-YC.. Institutional Heterogeneity in Public Production: The Case of Secondary Math and Science Education. Dissertation, Northern Illinois University. 1994.
  • Gallagher, S.A.. Middle school predictors of science achievement. Journal of Research in Science Teaching.31, (7), 721-734.1994.
    • ID: 10.1002/tea.3660310705 (DOI)
  • Kunicki, J.A.. The Effects of Impertinence Upon the Validity of a Process Model of Mathematics Achievement and Attitude. Dissertation, Ohio State University. 1994.
  • Monk, D.H.. Subject area preparation of secondary mathematics and science teachers and student achievement. Economics of Education Review.13, (2), 125-145.1994.
    • ID: 10.1016/0272-7757(94)90003-5 (DOI)
  • Monk, David H., King, Jennifer A.. Multilevel teacher resource effects in pupil performance in secondary mathematics and science: The case of teacher subject-matter preparation. Choices and Consequences: Contemporary Policy Issues in Education.Ithaca, NY: ILR Press. 1994.
  • Pifer, L.K.. Adolescents and animal rights: Stable attitudes or ephemeral opinions. Public Understanding of Science.3, 291-307.1994.
    • ID: 10.1088/0963-6625/3/3/004 (DOI)
  • Wang, Jianjun, Wildman, Louis. The effects of family commitment in education on student achievement in seventh grade mathematics. Education.115, (2), 3171994.
  • Brown, K.G.. The Development of Student Expectations of a Career in Science, Mathematics, or Engineering: An Analysis of Differences by Gender and Related Contextual Variables. Dissertation, Northern Illinois University. 1993.
  • Gahng, T-J.. A Further Search for School Effects on Achievement and Intervening Schooling Experiences: An Analysis of the Longitudinal Study of American Youth Data. Dissertation, University of Wisconsin–Madison. 1993.
  • Gibson, G.D.. High School Science Classrooms: Teachers' Teaching and Students' Learning. Dissertation, University of Illinois-Chicago. 1993.
  • National Science Foundation. Science and Engineering Indicators - 1993. NSB-93-1, Arlington, VA: National Science Foundation, Division of Sciences Resources Statistics. 1993.
  • Carlson, W.S., Monk, D.H.. 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), 1-10.1992.
  • Hedges, L.V., Hedberg, E.C.. Intraclass correlation values for planning group-randomized trials in education. Educational Evaluation and Policy Analysis.14, (3), 205-227.1992.
  • Hoffer, T.B.. Middle school ability grouping and student achievement in science and mathematics. Educational Evaluation and Policy Analysis.14, (3), 205-227.1992.
  • Madigan, T.J.. Cultural Capital and Educational Achievement: Does Participation in High-status Cultural Activities Affect Achievement in School?. Dissertation, Pennsylvania State University. 1992.
  • Miller, Jon D., Brown, K.G.. Persistence and career choice. Indicators of Science and Mathematics Education.Washington, DC: National Science Foundation. 1992.
  • Miller, Jon D., Brown, K.G.. The development of career expectations by American youth. Adolescence, Careers, and Cultures.Berlin: Walter de Gruyter. 1992.
  • Pifer, L.K.. The Transmission of Issue Salience: Setting the Issue Agenda for American Youth. Dissertation, Northern Illinois University. 1992.
  • Reynolds, Arthur J., Walberg, Herbert J.. A process model of mathematics achievement and attitude. Journal for Research in Mathematics Education.23, (4), 306-328.1992.
    • ID: http://www.jstor.org/stable/749308 (URL)
  • Reynolds, Arthur J., Walberg, Herbert J.. A structural model of science outcomes: An extension to high school. Journal of Educational Psychology.84, (3), 371-382.1992.
    • ID: 10.1037/0022-0663.84.3.371 (DOI)
  • Gambro, J.S.. A Survey and Structural Model of Environmental Knowledge in High School Students. Dissertation, Northern Illinois University. 1991.
  • Reynolds, A.J.. Note on adolescents' time-use and scientific literacy. Psychological Reports.68, 63-70.1991.
    • ID: 10.2466/pr0.1991.68.1.63 (DOI)
  • Reynolds, A.J.. The middle schooling process: Influences on science and mathematics achievement from the Longitudinal Study of American Youth. Adolescence.26, 1991.
  • Reynolds, A.J., Lee, J.S.. Factor analyses of measures of home environment. Educational and Psychological Measurement.51, (1), 1811991.
    • ID: 10.1177/0013164491511018 (DOI)
  • Reynolds, Arthur J., Walberg, Herbert J.. A structural model of science achievement. Journal of Educational Psychology.83, (1), 97-107.1991.
    • ID: 10.1037/0022-0663.83.1.97 (DOI)
  • Zeltmann, M.L.. Influences of Aptitude and Environmental Factors on the Quality and Quantity of Mathematics Instruction. Dissertation, University of Illinois-Chicago. 1991.
  • Miller, Jon D.. The development of interest in science. High School Biology Today and Tomorrow: Papers Presented at a Conference.Washington, DC: National Academy Press. 1989.

Update Metadata: 2016-03-24 | Issue Number: 7 | Registration Date: 2015-06-16

Miller, Jon D. (2011): Longitudinal Study of American Youth, 1987-1994, 2007-2011. Version 5. Version: v5. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/ICPSR30263.v5