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Educational policy analysis archives.
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Mathematics achievement by immigrant children : a comparison of five English-speaking countires / Gary G. Huang.
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1 of 16 Education Policy Analysis Archives Volume 8 Number 25May 30, 2000ISSN 1068-2341 A peer-reviewed scholarly electronic journal Editor: Gene V Glass, College of Education Arizona State University Copyright 2000, the EDUCATION POLICY ANALYSIS ARCHIVES. Permission is hereby granted to copy any article if EPAA is credited and copies are not sold. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education Mathematics Achievement by Immigrant Children: A Comparison of Five English-speaking Countries Gary G. Huang Synectics for Management Decisions, Inc. Arlington VA (U.S.A.)Abstract In this study, I examined academic achievem ent of immigrant children in the United States, Canada, England, Aus tralia, and New Zealand. Analyzing data from the Third Internationa l Mathematics and Science Study (TIMSS), I gauged the performance gap s relating to the generation of immigration and the home language bac kground. I found immigrant children's math and science achievement t o be lower than the others only in England, the U.S., and Canada. Non-E nglish language background was found in each country to relate to p oor math and science learning and this disadvantage was stronger among n ative-born children—presumably children of indigenous groups—t han among immigrant children. I also examined the school vari ation in math performance gaps, using hierarchical linear modelin g (HLM) to each country's data. The patterns in which languageand generation-related math achievement gaps varied between schools are di fferent in the five countries.

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2 of 16 The public school system as an institution plays a critical role educating immigrant children and facilitating their participation in th e larger society. This system in the U.S., succeeded in integrating European immigrants, is no w facing a serious challenge as newcomers of non-European heritage have become the primary source of immigration over the decades. This shift in origins of the immi grants is a most striking development in U.S. immigration history (Fix, Passel, Enchauteg ui, & Zimmermann, 1994). Asians and Hispanics are the fastest growing groups among foreign-born population in the U.S., rising from 1.5 percent each in the early 1990s to 25 percent and 43 percent, respectively, in 1990 (Bureau of Census, 1993). Asi an and Hispanic children, respectively, represent 3.5 percent and 14 percent of the U.S. elementary and secondary student enrollment in 1992, more than doubled from the 1.2 and 6.4 percents in 1976 (NCES, 1995). Many developed nations share this challenge The trend of globalization has brought rising waves of foreign labors, refugees, a nd immigrants into affluent countries. Today, the U.S., Canada, Australia, New Zealand, Fr ance, Germany, Britain, and other European countries are receiving newcomers from dif ferent regions of the world. The public schools in these countries confront the daun ting task to educate children of immigrants. Given the gravity of the issue, ironically, educators know little about the schooling of immigrant children. Little research has systemat ically dealt with the issue. It is unclear as to how the new generations of immigrants do in the school system and what their great diversity has to do with their schoolin g. It is even more uncertain about how schools are acting to help immigrant children learn math and science, subjects that are critical for competing in today's technology-orient ed labor market. No baseline comparison is available regarding education of this group in the U.S. and other nations. The lack of knowledge about immigrant child ren's education and general well being concerns educators and policymakers. The Federal In teragency Forum on Child and Family Statistics has published annual reports on c hildren (Federal Interagency Forum on Child and Family Statistics, 1998). But the repo rts contain little information specifically about children of immigrant background A recent study of immigrant children released by the National Research Council and the National Institute of Medicine points out that there is virtually no publ ic dissemination of information on even the most basic indicators of the conditions of children in immigrant families (Hernandez & Carney, 1998). In a policy study repor t, the National Commission on Immigration Reform also calls for increased attenti on to and resources for immigrant children's schooling (see Schnaiberg, 1997). My stu dy was intended to remedy this shortage of knowledge by comparing math and science performance of immigrant children in five English-speaking countries.Literature Review and Research Questions The available research on immigrant childr en's school performance is inconclusive even regarding the basic conditions of performance. Some studies suggest that the children of immigrants do better in school than the rest of American children; their performance is above averages (Rumbaut, 1996; also see Viadero, 1998, Lapin, 1998). In social adaptation, physical and mental health, f oreign-born immigrant children were also seen to fare at least equally well as other ch ildren in the U.S. (Hernandez & Charney, 1998). On the other hand, there is evidenc e that immigrant children, especially Hispanics and others with impoverished background, suffer poor academic achievement

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3 of 16and lower educational attainment (e.g., McPartland, 1998; Vernez & Abrahamse, 1996). A foremost concern for research is to provide clear description of this population's schooling with solid baseline indicator of performa nce. Aggregated comparisons may mask crucial var iation within the immigrant population. For example, while Hispanic adolescents of all generations have grade point averages and math test scores that are lower than t hose of white adolescents in U.S.-born families (NCES, 1998a), academic achievement of imm igrant students appears to decline by generations (Hernandez & Charney, 1998). The social, economic, and cultural factors that either protect or disadvantag e immigrant children are not well understood. Thus, baseline indicators should also s ummarize performance differences by important subcategories of the immigrant children, such as generation of immigration, sex, native language, and socioeconomic status. A small number of recent studies of immigra nt children's academic achievement provide some insights for understanding the variati on among immigrant children's academic achievement. For example, Hao and Bonstead -Bruns (1998) used the concept social capital to explain immigrant children's acad emic performance. This concept, though useful in understanding the behavioral and c ultural attributes of immigrant groups affecting academic learning, is less relevan t to study of the functioning of institutions, such as public schools. It is not cle ar from such research as to how schools could reduce the detriment caused by meager social capital for an immigrant child. Theories and research are needed to sort out instit utional factors that account for the wide variation and the changing pattern of this pop ulation's academic performance. As a preliminary study intended to address some of these concerns, I examine the following issues in the analysis.Generation Difference The generation of immigration distinguishes a number of demographic characteristics among children from immigrant famil ies. Compared with children in U.S.-born families, first-generation immigrant chil dren (the foreign-born) are more likely to experience high poverty; to have a large family with both parents; and their parents are more likely to have attained little edu cation yet to participate in labor force (Hernandez & Charney, 1988). Second-generation chil dren (those born in the U.S. to at least one foreignborn parent) tend to experience substantially less risk than do first-generation children, but are likely to lose p sychological resilience that the first generation often demonstrates. Such cross-generatio n distinctions imply different risks and strength for immigrant children's schooling. The analysis first addresses the question a bout the performance gap relating to the generation of immigration in different countries. T he second question is to what extent this gap differs across schools in each country. To answer this question, the analysis explores the variation of the generation gap across schools in each country. With the international test results available from the data, it should be particularly interesting to see how the schoollevel variation of the gap diff ers across countries. The resulting baseline indicator may reveal the extent to which t he overall school setting relates to the variation of the gap—in contrast to the extent to w hich individual factors account for the variation. Future study may elucidate school roles in reducing the generation gap by examining specific school factors relating to the v ariation of the performance gap. Language Barrier

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4 of 16 Limited English proficiency handicaps immig rant children's learning on key subject areas such as mathematics and science. Language bar riers are often more detrimental for children of low socioeconomic background. Living in socially and linguistically isolated communities, poor immigrant children can hardly imp rove their new language skills and the language barriers persist over the school years On the other hand, bilingual proficiency, defined as the mastery of both the mot her tongue and a new language, is found to be a strength for immigrant children's cog nitive growth (e.g., Bumberger & Larson, 1998; Hao & Portes, 1998). I first estimate the size of the math and s cience performance gaps related to non-English language background in each country. I then examine the variation of the gaps between schools in each country. While these b aseline indicators are descriptive, they imply the extent to which the overall school c ontext is associated with the variation of the gap-relative to the individual level varia tion. The analysis may provide a ground for further study of specific school functions in r educing language-caused performance gap for immigrant children.School Variation of Performance Gap Does school has something to do with the pe rformance gaps? It is conceivable that the average performance and the performance gap bet ween immigrant children and the other children may vary across schools. Schools wit h different demographic composition, resources, and curricular and instruct ional programs theoretically could achieve different levels of excellence and equity. Relevant to policymaking, gauging such school-level variation is crucial for further assessing institutional role in achieving educational equity. Understanding the school-level variation in performance gaps and school features relating to such variation can help school improve equity. In this preliminary analysis I only examine the school-leve l variance in math achievement gap relating to the generation of immigration and langu age backgrounds.Data Source TIMSS is the most comprehensive and rigoro us international education comparison ever (NCES, 1998b). I extracted TIMSS Po pulation 1 (students of grades 3-4 or ages 8-9) data of five English-speaking coun tries including the U.S., Canada, England, Australia, and New Zealand, with unweighte d samples size of, respectively, 10,670, 14,639, 5,584, 10,433, and 4,670. Conducted in 1995, TIMSS researchers tested the mathematics and science knowledge of more than half a million students in 41 countries at three grade levels—primary, middle, an d end of secondary school. TIMSS ensured that the participating students in each cou ntry were representative of its population. It generated information on the backgro und and math and science achievement tests for children of the participating countries. While tests on math and science were administered to students, survey data were collected from teachers, schools, as well as students. The resulting informa tion encompasses student demographic background and math learning experience ; teachers' background and instruction; and school facilities, program provisi ons, and demographic attributes. Information for identifying foreign-born children i s available, including the nation of birth for both the parents and the child. The TIMSS nationally representative sample designs generated data for the population of each target age group (or grade level ) in a country. The sample for a given age group in a country was selected in a two-level stratified design. In this design, a

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5 of 16school sample representative to the national popula tion of schools was drawn first, and within each selected school, typically one classroo m at the target grade level was selected for the test and survey. While certain min ority groups were oversampled, sample weights were provided to compensate the bias resulting from the oversampling. Unit nonresponse bias was corrected by sample weigh ts as well. The tests were designed through collaborati on among experts from the participating countries. Recognizing vast differences in social a nd educational context, the tests were meant to measure students' general math and science knowledge and skills at the given age/grade. The results were widely accepted as valu able for cross-national comparison, given the caution of contextual differences among t he participating nations (Forgione, 1998). Four items were used to identify students' i mmigrant background. They presented information about the child's birthplace (foreignor native-born in one of the five countries), the number of years living in the curre nt country, and the foreign-born status of the child's mother and father. I defined a child as a first-generation immigrant if the child was foreignborn regardless of the birthplac e of the parents, and a second-generation immigrant if the child was born i n the current country to one or both foreign-born parent; and the rest were considered a s non-immigrants. With a data item about student home languages, I categorized student s as a non-native language speaker if he or she reported that a language other than the T IMSS test language (English) was “often” or “always” spoken at home.Analytical Methods The analysis included two components. To g enerate baseline indicators of the overall performance patterns, I ran a series of des criptive analysis. To estimate school-level variance of performance gaps, I conduc ted two-level hierarchical linear modeling.Descriptive Analysis Descriptive analysis entailed comparing me ans of the test scores for the groups of interest. As specified earlier, baseline indicators of math and science performance gaps between immigrant and non-immigrant children will b e estimated in a comparison of means with significance tests (all at the p<. 05 le vel if not otherwise specified). All the remaining indicators will be generated by breaking down the test data by two categorical variables, immigrant status and non-English languag e background, with significance tests. I ran the procedure with data for each coun try. The five plausible values for estimating performance on mathematics were used. Th e estimates from the five runs were then averaged as the final estimates in the co mparisons (see TIMSS User's Guide for rationale for this special approach, Internatio nal Study Center, 1988). Student-level sample weight (TOTWGT) was used to correct bias fro m unequal sampling of some student groups and unit nonresponse. I used jackkni fe procedures to correct the design effects caused by the stratified clustering sample design (rather than simple random design). See Chapters 5 and 7 of the User's Guide ( International Study Center, 1998) for rationale of using sample weights and special proce dures for correcting design effects. HLM Procedure

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6 of 16 To assess school-level variance of the perf ormance gap relating to immigrant status, I used hierarchical linear modeling (HLM) technique (Bryk & Raudenbush, 1992). HLM was appropriate for this part of analysis because i n the TIMSS design students as level-1 units were nested in schools (level 2) and HLM enab led me to separate the variance by two levels and to formally estimate the portion of variance taking place at school level. In an unconditional (one-way ANOVA) with ra ndom effect model, I estimated variance separately at the student and school level s. This model answered the question as to whether schools differed from each other in a verage math performance. It provided basic estimates for making decision if it was neces sary to further model the variance at the two levels. The unconditional models were: At student level (level 1), Y ij = 0j + r ij and at school level (level 2), 0j = 00 + u 0j. As the school level variance was sufficient ly large (10 percent or more of the total variance, measured with the intraclass correlation coefficient) for each country, I specified random coefficient models to estimate sch ool-level variance of the math achievement mean and achievement gaps associated to home language and the firstand second generation immigrant backgrounds (all studen t-level predictor variables were centered around the school mean). At level 1, the e quation had the overall achievement mean, the average achievement differences relating to the non-English language and immigrant status, and the random error, Y ij = 0j + 1j (LANGUAGE) + 2j (FIRST_G) + 3j (SECOND_G) + r ij At level-2, the equations included no schoo l variables but only the school average math score (the intercept) and the estimates of the variance around the average measures of the three gaps (the slope): 0j = 00 + u 0j and qj = q0 + u qj where q=1, 2, 3. In case the gaps did not vary statistically signifi cantly at the school level, the random effect u qj was removed from the equation and the effect was e stimated only as fixed. I used the software package HLM (version 4. 03) for the analysis, running the Plausible Value procedure available from the packag e (Bryk, Raudenbush, & Congdon, 1996). This procedure included the five plausible v alues as the outcome variable and automatically averaged the resulting estimates afte r the runs. Normalized student level weight and school level weight were used in the pro cedures for generating the estimates to the student population in each country.FindingsStudents of Immigrant and Non-English Backgrounds Each of the five countries' elementary stu dent populations contained a substantial

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7 of 16portion of students with immigrant and nonEnglish backgrounds (see Figure 1). Australia and New Zealand had the highest rates of immigrant students of both the first and second generations, followed by Canada and the U.S. Strikingly, the second generation immigrant children comprised almost one third of Australia's population of third and fourth graders. The U.S. had a relatively high proportion of children of non-English background (16.7 percent), though this group was fairly large in Canada and New Zealand as well. Performance Gaps Associated with Immigrant Status The math achievement gaps to the disadvanta ge of immigrant students took place only in England, the U.S., and Canada, not in Austr alia and New Zealand. This pattern is particularly evident in the gap between non-immigra nt and the first generation immigrant children (Figure 2). In England, the gap in math score was 41 point, in the U.S., 60, and in Canada, 44, all statistically sign ificant; whereas in Australia and New Zealand, the gap was not observed. In the U.S. and Canada, the non-immigrant children scored higher than the second-generation immigrant children; but in England, this difference was not statistically significant. The patterns of performance gaps associate d with immigrant status were similar in

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8 of 16 science (Figure 3). In short, immigrant students la gged behind in math and science learning in England, the U.S., and Canada, but they did not in Australia and New Zealand. Immigrant and Language Background Non-English home language is clearly a disa dvantage to students' math and science learning regardless of immigrant status. In each gr oup (non-immigrant and first and second generations of immigrant children), those wh ose home language was not English averaged substantially lower score than the rest of the students in math (Tables 1). Further, the language disadvantage was more acute a mong native-born children than among immigrant children. Consistent in each countr y, the second-generation immigrant children with non-English home languages did better in math than the non-immigrants with non-English home languages. I speculate that t he latter was likely to be the indigenous groups or the groups that experienced pe rsistent social and linguistic isolation, e.g., the American Indians and Hispanics in the U.S. Unfortunately, TIMSS contains no data to allow me confirm this assumptio n. With exception of the U.S. and Canada, this pattern holds between the first genera tion immigrant children and non-immigrants as well, though to a lesser extent. Table 1 Average Math Achievement Scores by Immigrant Status and Home Language: TIMSS Population 1 (grades 3 or 4 and age 9 or 10) in the Five Countries EnglandU.S.A.CanadaAustraliaNew Zealand Non-immigrant English Non-English486.6 489.6 419.2 522.9 527.9 472.2 511.1 514.4 470.5 516.3 518.2 436.2 472.6 479.8 407.7First-generation Immigrant English Non-English446.2 452.4 426.8 462.9 475.5 449.3 466.5 476.2 457.8 517.9 534.8 499.8 474.7 491.9 445.4Second-generation immigrant English Non-English489.3 496.8 498.5 505.4 493.5 499.1 516.5 521.9 471.4 482.0

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9 of 16 464.9486.5477.9489.7427.4 Two-level Analysis School level variance was substantial and s tatistically significant in all the five countries (Table 2). As indicated by the intraclass correlation coefficients, school-level variance proportional to the total variance around the given country's average math achievement ranged from 9 percent (Canada) to 26 pe rcent (New Zealand). This finding suggests that to a considerable extent, students' m ath scores in each of the five countries tended to cluster around their school average score s. The reliability of the achievement measure was around 0.80, with exception of Canada, where the estimate was only 0.49. These baseline statistics justified further two-lev el modeling to examine the performance gaps relating to the language and immigrant status. Table 2 Two-level unconditional models: Baseline estimates from TIMSS Population 1 math ach ievement (plausible values average) in the five nationsParametersEnglandU.S.A.CanadaAustraliaNew ZealandAverage school mean g00 483.51 505.18501.88522.53470.85Reliability of thedependent variable 0.880.820.490.750.86Intraclass correlation 0.190.180.090.160.26School-level variance uoj 1,700.631,755.811,349.941,864.952,102.65Note: The school-level variance for each country wa s significant at p< 0.001 level. Table 3 presents the estimates from the two -level random coefficient models. The first panel shows the fixed effects. The overall me an of each country (the intercept 00 ) provides a reference for interpreting the other est imates. First, nonEnglish home languages were indeed a detrimental factor to child ren's math learning across the five nations. The large and negative coefficients consis tently indicate that children with non-English home language background achieved lower than the overall mean in each country. The language barrier to math learning seem s especially solid to students in England and the U.S. The immigration status was a disadvantage o nly in some countries. Clearly, there was a negative relationship between the first gener ation of immigrants and the math achievement in England, the U.S., and Canada. But t he relationship was reversed in Australia, where the first generation immigrant chi ldren achieved higher than the national average (a positive 13.4 at p<.01 level). There seems no relationship between the generation of immigration and achievement among New Zealanders as the two coefficients were small (2.42 and –5.46) and not st atistically significant. The gap between the second generation of immigrants and the national average in general was narrower than that between their first generation c ounterpart and the national average. The second-generation children in England appeared to do slightly better than the

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10 of 16 national average (a higher score of 8.42 at p<.05). The estimates for random effects revealed h ow the above statistics varied at the school level. The language-related achievement gap varied among schools only in England; in other countries, this gap was rather st able across schools. The math achievement gap related to the first generation imm igrant status did not vary across schools in any of the five countries. This finding implies that the problem of this group (or its strength in Australia) in math learning was regular across schools. Finally, the gap associated with the second generation of immigratio n in the U.S. varied substantially across schools, indicating that schools probably mi ght have some thing to do with this group's performance. This gap also varied across sc hools in New Zealand, despite that the fixed estimate for the effect was nil (not stat istically significant). This irony probably hints that the second-generation immigrant children performed quite differently in New Zealand pending on school environment, although the average difference at student level was not observed.Table 3 Two-level random coefficient models: Estimates for TIMSS Population 1 math achievementParameterEnglandU.S.A.CanadaAustralia New Zealand Fixed effects: Student-Level Effects (Level-1 models) Intercept (overall mean achievement)00 482.47***504.95***501.86***522.46***470.85***Non-English language difference 10 -38.26***-39.15***-14.09***-20.68***-30.06***First generation immigrantdifference 20 -21,87***-26.95***-44.75***13.64**2.42Second generation immigrantdifference 30 8.42*-4.25***-12.21***-7.61*-5.46Random effects: School-level variance (Level-2 models) School mean achievement, u0j 1710.05***1783.27***1352.19***1868.44***2087.59***Non-English language difference, u1j 674.44*————First generation immigrant difference, u2j —————

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11 of 16 Second generation immigrantdifference, u2j —1076.18***——421.99**Student level variance(Level-1 random effect), rij 7121.757387.0313760.0010017.035988.68 p<.05; ** p<.01, *** p<.001 The symbol "—" indicates that the random variance w as too small to model and thus the associated variable was specified only as a fixed effect in the model.Note: All student-level predictor variables were ce ntered on school means.Summary This analysis only touched on the surface of the immigrant children's academic learning in the five developed countries. It descri bed the status of the group's math and science performance and help to settle the issue as to whether immigrant children achieve the same level as do nonimmigrant childre n. In a cross-national comparison based on fairly comprehensive and reliable test inf ormation, the analysis indicated that in the U.S., England, and Canada, immigrant childre n--especially those known as the first generation of immigrants--did lag behind in m ath and science achievement. Further, non-English home languages, typically spoken by chi ldren of immigrants and indigenous people, were strongly and negatively related to low er math and science performance. Considerable effort is needed to untangle the complicated issues surrounding the newcomers' schooling. For example, immigrants' soci oeconomic status, family environments, gender role, and health conditions, c ould critically influence these children's math and science learning. Moreover, in academic subjects such as reading, writing, and social studies, where the language is either a pivotal tool of learning or simply the subject of study, we know even less abou t immigrant children's learning experience. Immigrant children's schooling and perf ormance in those subject areas call for extended research. The analysis also hints at the overall pot ential effect that schools might have in reducing the performance gaps associated with the i mmigrant and non-English backgrounds. The first-generation immigrant childre n's disadvantage (in the U.S., England, and Canada) and the strength (in Australia ) in math performance seem consistent across schools in a given country. Does this finding suggest that schools can make little difference regarding immigrant children 's learning? Maybe. However, it may also imply the overwhelming effect of immigrant soc io-cultural conditions on their schooling and, possibly, the public education syste ms' uniform indifference to the group's needs.To an extent differentiated by the countries, perfo rmance gaps associated with the second-generation immigrants and non-English home l anguage varied among schools. This finding implies that schools could possibly ma ke some difference in narrowing the gaps. Learning about specific school factors that m ay work to close the gaps requires further research. School factors such as socio-demo graphic attributes, resource allocation, special programs, staff training, and c urriculum and instruction methods are subject to study if we are to understand the learni ng processes of the increasingly large group of immigrant children in public schools.

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12 of 16ReferencesBooth, A., Crouter, A. C., & Landale, N. (Eds.). (1 997). Immigration and the Family: Research and Policy on U.S. Immigrants. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Bryk, A.S., & Raudenbush, S.W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. Newbury Park, CA: Sage. Bryk, A. S., Raudenbush, S.W., & Congdon, R. T. (19 96). HLM: Hierarchical linear and Nonlinear Modeling with the HLM/2L and HLM/3L P rograms. Chicago, IL: Scientific Software International.Duran, B. J.; Dugan, T. & Weffer, R. E. (1998). Lan guage minority students in high school: the role of language in learning biology co ncepts. Science Education, 82, 3, 311-41.Federal Interagency Forum on Child and Family Stati stics. (1998). America's Children: Key National Indicators of Well-being. Website: http://childstats.gov/ac1998/ac98.htm. Fix, M., Passel, J. S., Enchautegui, M. E. & Zimmer mann, W. (1994). Immigration and Immigrants: Setting the Record Straight. Washington DC: Urban Institute. Forgione, P.D. (1998). What We've Learned From TIMSS About Science Educati on in the United States Washington, DC: National Center for Education Sta tistics. Website http://nces.ed.gov/Pressrelease/science/index.html.Hao, L. & Bonstead-Bruns, M. (1998). Parent-child d ifferences in educational expectations and the academic achievement of immigr ant and native students. Sociology of Education, 71 3, 175-198. Hao, L. & Portes, A. (1998). E Pluribus Unum: Bilingualism and loss of language in the second generation. Sociology of Education, 71 4, 269-294. Hernandez D. J. & Charney, E. (Eds.). (1998). The Health and Well-being of Children in Immigrant Families Washington, D.C.: National Academy Press Huang, G. G. & Weng, S. (1998). Minority post-secon dary education attendance, high school desegregation and student characteristics. Race, Ethnicity and Education, 1 2, 241-265.International Study Center. (1998). User's Guide for the Third International Mathematics and Science Study (TIMSS) and U.S. Augm ented Data Files Boston, MA: Boston College.Lapin, L. (1994). Study: Immigrants' Kids Learn Bet ter. The Sacramento Bee February 23, 1994, p.1.McPartland, J. (1998). Project #7126: The Adaptation of Immigrant Children in the American Educational System Center for Research on the Education of Disadvantaged Students (CDS). Johns Hopkins University website

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13 of 16http://www.csos.jhu.edu/default1.htm.NCES (1998a). The Condition of Education 1998. Washington, DC: the author. NCES (1998b). Pursuing Excellence: A Study of U.S. Twelfth-Grade Mathematics and Science Achievement in International Context. Washington, DC: the author. NCES (1995). The Condition of Education 1995 / Supplemental Table 40-2 Enrollment in public elementary and secondary schools, by race /ethnicity: 1976, 1984, 1986, 1988, 1990, and 1992 on website: http://nces.ed.gov/pubs/ ce/c9540d02.html. Oakes, J. (1990). Opportunities, Achievement. and C hoice: Women and Minority Students in Science and Mathematics. Review of Research in Education, 16 2, 153-166. Rumbaut, R. G. (1996). The New Californians: Assess ing the Educational Progress of Children of Immigrants. CPS Brief, 8, 3, 1-14. Rumberger, R. W. & Larson, K. A. (1998). Toward exp laining differences in educational achievement among Mexican American lang uage-minority students. Sociology of Education, 71 1, 69-93. Shavarini, M. K. (1996). California's Immigrant Chi ldren, book review. Harvard Educational Review, 66 ,3, 668-674. Schnaiberg, L. (1997). Panel urges greater focus on immigrant children's needs. Education Week 17, 29, p. 18 U.S. Bureau of Census. (1993). We the American…Foreign Born Washington DC: the author.U.S. Congress. (1980). Science and Engineering Equal Opportunities Act Section 32(B), Part B of P.L. 96-516. Washington, DC: the a uthor. Valdles, G. (1998). The World outside and inside sc hools: language and immigrant children. Educational Researcher, 27 6, 4-18. Van, J. (1994). Immigrant Children Get Better Grade s, Study Finds. The Chicago Tribune February 23, 1994, p. 6. Vernez, G. & Abrahamse, A. (1996). How Immigrants Fare in U.S. Education. Santa Monica, CA: RAND Center for Research on Immigration Policy. Viadero, D. (1997). Immigrant children succeed desp ite barriers, report says. Education Week, 17, 29, p. 14, Appendix Wainer, H. & Steinberg, L. S. (1992). Sex Differenc es in Performance on the Mathematics Section of the Scholastic Aptitude Test : A Bidirectional Validity Study. Harvard Educational Review, 62 3, 323-336.About the AuthorG. Huang, Ph.D.

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14 of 16 Senior Research Analyst Synectics for Management Decisions, Inc.North Moore St. Arlington, VA 22209Phone: 703.807.2324 703.528.2857 Email: garyh@smdi.com Gary Huang is a research sociologist working in edu cation and public policy related areas. His research interests include minority chil dren's socialization and schooling, institutional effects on learning, rural education, and cross-cultural research and communication. He works mainly with large-scale sur vey data analyses but has a broad interest in other approaches to research. Supported by a grant from U.S. Department of Education, he is currently working on a cross-datas et synthetic analysis looking at the district-level resource and student-level academic achievement relationships.Copyright 2000 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, glass@asu.edu or reach him at College of Education, Arizona State University, Tempe, AZ 8 5287-0211. (602-965-9644). The Commentary Editor is Casey D. C obb: casey.cobb@unh.edu .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov hmwkhelp@scott.net Thomas F. Green Syracuse University Alison I. Griffith York University Arlen Gullickson Western Michigan University Ernest R. House University of Colorado Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Calgary Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Dewayne Matthews Western Interstate Commission for HigherEducation

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15 of 16 William McInerney Purdue University Mary McKeown-Moak MGT of America (Austin, TX) Les McLean University of Toronto Susan Bobbitt Nolen University of Washington Anne L. Pemberton apembert@pen.k12.va.us Hugh G. Petrie SUNY Buffalo Richard C. Richardson New York University Anthony G. Rud Jr. Purdue University Dennis Sayers Ann Leavenworth Centerfor Accelerated Learning Jay D. Scribner University of Texas at Austin Michael Scriven scriven@aol.com Robert E. Stake University of Illinois—UC Robert Stonehill U.S. Department of Education David D. Williams Brigham Young UniversityEPAA Spanish Language Editorial BoardAssociate Editor for Spanish Language Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico roberto@servidor.unam.mx Adrin Acosta (Mxico) Universidad de Guadalajaraadrianacosta@compuserve.com J. Flix Angulo Rasco (Spain) Universidad de Cdizfelix.angulo@uca.es Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma deMxicocanalesa@servidor.unam.mx Ursula Casanova (U.S.A.) Arizona State Universitycasanova@asu.edu Jos Contreras Domingo Universitat de Barcelona Jose.Contreras@doe.d5.ub.es Erwin Epstein (U.S.A.) Loyola University of ChicagoEepstein@luc.edu Josu Gonzlez (U.S.A.) Arizona State Universityjosue@asu.edu Rollin Kent (Mxico)Departamento de InvestigacinEducativa-DIE/CINVESTAVrkent@gemtel.com.mx kentr@data.net.mx Mara Beatriz Luce (Brazil)Universidad Federal de Rio Grande do Sul-UFRGSlucemb@orion.ufrgs.brJavier Mendoza Rojas (Mxico)Universidad Nacional Autnoma deMxicojaviermr@servidor.unam.mxMarcela Mollis (Argentina)Universidad de Buenos Airesmmollis@filo.uba.ar

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16 of 16 Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma deMxicohumberto@servidor.unam.mxAngel Ignacio Prez Gmez (Spain)Universidad de Mlagaaiperez@uma.es Daniel Schugurensky (Argentina-Canad)OISE/UT, Canadadschugurensky@oise.utoronto.ca Simon Schwartzman (Brazil)Fundao Instituto Brasileiro e Geografiae Estatstica simon@openlink.com.br Jurjo Torres Santom (Spain)Universidad de A Coruajurjo@udc.es Carlos Alberto Torres (U.S.A.)University of California, Los Angelestorres@gseisucla.edu