Intelligence Testing and Minority Students: Foundations, Performance Factors, and Assessment Issues
Publication Year: 2001
Intelligence Testing and Minority Students offers the reader a fresh opportunity to re-learn and re-consider the implications of intelligence testing. Richard R. Valencia and Lisa A. Suzuki discuss the strengths and limitations of IQ testing relative to the factors which may contribute to biased results. They review the history of the adaptation and adoption of intelligence testing; evaluate the heredity-environment debate; discuss the specific performance factors which apply to IQ testing of those in minority ethnic groups. This practical book offers the practitioner a good sense of what can be done to make testing and education serve the needs of all students fairly and validly, whatever their background.
- Front Matter
- Back Matter
- Subject Index
Part I: Foundations
- Chapter 1: Historical Issues
- The Emergence of the Intelligence Testing Movement in Europe
- The Importation and Cultural Appropriation of the Binet-Simon Intelligence Scale by U.S. Psychologists
- Ideology of the Intelligence Testing Movement
- “Race Psychology” Studies of Intellectual Performance
- Actual Use of IQ Tests in Curriculum Differentiation during the 1920s
- Group Intelligence Testing Taking on a Life of its Own
- The Emergence of Contemporary Testing Issues
- Chapter 2: Multicultural Perspectives of Intelligence: Theory and Measurement Issues
- The Search for a Common Definition of Intelligence
- The Split between Theory and the Measurement of Intelligence
- Theoretical Frameworks Incorporating Cultural Aspects of Intelligence
- Emotional, Practical, and Creative Aspects of Intelligence
- Assessment-Based Models of Intelligence
- Group Differences in Intelligence
- Racial/Ethnic Profiles of Intelligence
- Cultural Perspectives of Intelligence
- The Complexities of Multicultural Perspectives of Intelligence
Part II: Performance Factors
- Chapter 3: Socioeconomic Status
- The Meaning and Measurement of SES
- SES and Intelligence: Historical Perspectives
- Research Findings: African Americans
- Research Findings: Mexican Americans
- SES and Intelligence: Contemporary Perspectives
- Chapter 4: Home Environment
- Early Research (1920s)
- Influences of Piaget, Hunt, and Bloom
- Origins of the “Chicago” School of Family Environment Research
- Influence of the Chicago School of Family Environment Research
- Home Environment Research by Caldwell, Bradley, and Associates
- Home Environment Research from Behavioral Genetics
- Home Environment Research: African Americans
- Home Environment Research: Mexican Americans
- Chapter 5: Test Bias
- The Emergence of Claims and Counterclaims of Cultural Bias in Intelligence Tests
- The Concepts of “Cultural Loading” and “Cultural Bias”
- Cultural Bias in Intelligence Tests: A Review of Research Findings
- Appendix A: 62 Cultural Bias Studies Examined and Used in the Review
- Appendix B: Intelligence Tests Examined for Cultural Bias
- Chapter 6: Heredity
- Issues in Human Behavioral Genetics
- Going beyond the Nature-Nurture Debate
- Racial/Ethnic Group Mean Differences in Measured Intelligence: The Role of Genetics
Part III: Assessment Issues
- Chapter 7: Race/Ethnicity, Intelligence, and Special Education
- Historical Issues regarding the Use of Intelligence Tests in Educational Placement
- The Role of Intelligence Tests in the Definitions of Handicapping Conditions
- Defining Mental Retardation and LD
- Racial/Ethnic Incidence Data regarding Students Classified as Mentally Retarded and Learning Disabled
- Future Directions for Special Education
- Chapter 8: Gifted Minority Students
- Historical Sketch of Giftedness in the United States
- Gifted African American Students
- Gifted Latino Students
- Gifted Asian/Pacific Islander Students
- Gifted American Indian Students
- Chapter 9: A Multicultural Review of Cognitive Ability Instruments
- Areas Assessed by Current Cognitive Tests
- Current Test Development Strategies
- Racial and Ethnic Profiles of Abilities and Discrepancies Yielded by Traditional Cognitive Tests
- Current Procedural Modifications of Traditional Cognitive Tests
- Chapter 10: Future Directions and Best-Case Practices: Toward Nondiscriminatory Assessment
- Assessment Competencies
- A Final Note
Racial and Ethnic Minority Psychology Series[Page ii]
Series Editor: Frederick T. L. Leong, The Ohio State University
This series of scholarly books is designed to advance theories, research, and practice in racial and ethnic minority psychology. The volumes published in this new series focus on the major racial and ethnic minority groups in the United States, including African Americans, Hispanic Americans, Asian Americans, and Native American Indians. The series features original materials that address the full spectrum of methodological, substantive, and theoretical areas related to racial and ethnic minority psychology and that are scholarly and grounded in solid research. It comprises volumes on cognitive, developmental, industrial/organizational, health psychology, personality, and social psychology. While the series does not include books covering the treatment and prevention of mental health problems, it does publish volumes devoted to stress, psychological adjustment, and psychopathology among racial and ethnic minority groups. The state-of-the-art volumes in the series will be of interest to both professionals and researchers in psychology. Depending on their specific focus, the books may be of greater interest to either academics or practitioners.
Ann Kathleen Burlew
A. J. Franklin
Please address all correspondence to the Series Editor:
Frederick T. L. Leong
The Ohio State University
Department of Psychology
142 Townshend Hall
1885 Neil Avenue
Columbus, Ohio 43210-1222
Copyright © 2001 by Sage Publications, Inc.
All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.
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Library of Congress Cataloging-in-Publication Data
Valencia, Richard R.
Intelligence testing and minority students: Foundations, performance factors, and assessment issues / by Richard R. Valencia and Lisa A. Suzuki.
p. cm. — (Racial and ethnic minority psychology)
Includes bibliographical references and index.
ISBN 0-7619-1230-4 (cloth: alk. paper)
1. Minorities—Psychological testing—United States. 2. Intelligence levels—United States. 3. Intelligence tests—United States. 4. Intellect—Genetic aspects. 5. Cognition.
I. Suzuki, Lisa A., 1961 – II. Title. III. Racial and ethnic minority psychology series.
BF431.5.U6 V35 2000
04 05 06 07 7 6 5 4 3 2
Acquiring Editor: Jim Brace-Thompson
Editorial Assistant: Anna Howland
Production Editor: Diane S. Foster
Editorial Assistant: Cindy Bear
Typesetter: Tina Hill
Indexer: Juniee Oneida
Cover Designer: Michelle Lee
[Page v]To our wonderful families—Marta, Juan, and Carlos; John and Kaitlyn. You're the greatest. With immense affection, Richard and Lisa.
To all children and youth of color in the United States, we hope that this book encourages society to view your intellectual capabilities as unlimited.
To all clinicians who are faced with the challenges of assessing the intelligence of culturally/linguistically diverse children, we hope that this book will prove valuable in informing your practice, particularly in the very important task of making nondiscriminatory assessments commonplace.[Page vi]
Series Editor's Introduction[Page xi]
During the past two decades, Racial and Ethnic Minority Psychology has become an increasingly active and visible specialty area in psychology. Within the American Psychological Association, the Society for the Psychological Study of Racial and Ethnic Minority Issues was formed as Division 45. The Division has now acquired its own journal devoted to ethnic minority issues in psychology. In APA we also have seen the publication of five bibliographies devoted to racial and ethnic minority groups.
The first one was focused on Black males (Evans & Whitfield, 1988) and the companion volume was focused on Black females (Hall, Evans, & Selice, 1989). In 1990, the APA bibliography on Hispanics in the United States was published (Olmedo & Walker, 1990), followed by one on Asians in the United States (Leong & Whitfield, 1992). The fifth bibliography was focused on American Indians and published in 1995 (Trimble & Bagwell, 1995).
As another indication of the increasing importance of racial and ethnic minority psychology, a brief review of the studies published on racial and ethnic differences in journals cataloged by Psychlit revealed a significant pattern. Between 1974 and 1990 (16 years), Psychlit cataloged 2,445 articles related to racial and ethnic differences. Between 1991 and 1997 (6 years), the number of articles related to racial and ethnic differences was 2,584. As a convenient means of representing the increase in attention paid to racial and ethnic minority issues in psychology, one can easily divide the number of articles published during the time period by the number of years covered by that time period. Doing such a computation will reveal that between 1974 and 1990, an average of 153 articles were published each year on racial and ethnic differences. For 1990 to 1997, that [Page xii]number had jumped to an average of 430 articles published each year on racial and ethnic differences. All indications are that this pattern of growth will continue. There is converging evidence that racial and ethnic minority psychology is becoming an important and central theme in psychology in the United States. In recognition of this development, a new book series on Racial and Ethnic Minority Psychology (REMP) was launched at Sage Publications in 1995.
The REMP series of books is designed to advance our theories, research, and practice related to racial and ethnic minority psychology. It will focus on, but not be limited to, the major racial and ethnic minority groups in the United States (i.e., African Americans, Hispanic Americans, Asian Americans, and American Indians). For example, books concerning Asians and Asian Americans also will be considered. Books on racial and ethnic minorities in other countries will also be considered. The books in the series will contain original materials that address the full spectrum of methodological, substantive, and theoretical areas related to racial and ethnic minority psychology. With the exception of counseling and psychotherapeutic interventions, all aspects of psychology as it relates to racial and ethnic minority groups in the United States will be covered by the series. This would include topics in cognitive, developmental, industrial/organizational, personality, psychopathology, and social psychology. In covering psychology related issues on racial and ethnic minorities, the series will include both books that examine a single racial or ethnic group (e.g., Development of Ethnic Identity Among Asian Americans) as well as books that undertake a comparative approach (e.g., Occupational Stereotyping of Racial and Ethnic Minority Managers). As a series devoted to racial and ethnic minority groups in the United States and other countries, this series will not cover the usual cross-cultural issues and topics such as those covered by the Sage Series on Cross-Cultural Research and Methodology Series (Series Editors: Walter Lonner and John Berry).
Within the field of racial and ethnic minority psychology, one of the recurring controversial themes is concerned with the assessment of intelligence. From the earlier works of Arthur Jensen to that of The Bell Curve by Herrnstein and Murray, the question of the so-called “significant racial differences in intelligence scores” remains a vexing one for psychology. Despite the controversy, the continued use of intelligence tests has very real consequences for racial and ethnic minorities in this country. As scholars, it is important that we continue to use our scientific methodology to examine the meaning and use of intelligence tests so that biases can be corrected and a person's true abilities and talents can be identified and utilized. Given the importance of this topic in racial and ethnic minority psychology, it is not surprising that the first volume in the Sage Series on Racial and Ethnic Minority Psychology was devoted to a practical and constructive approach to the use of intellectual assessment tools with racial and ethnic minority children and adolescents. In the volume entitled Assessing Intelligence: A bio-cultural approach, Armour-Thomas and Gopaul-McNicol provide practitioners with an integrated, four-tier system for the assessment of intelligence with racial and ethnic minority children.
In keeping with this tradition of addressing important and significant issues in racial and ethnic minority psychology, regardless of the controversies, I am proud to [Page xiii]introduce the third volume of the series and the second one devoted to the assessment of intelligence. In this volume entitled, Intelligence Testing and Minority Students: Foundations, Performance Factors, and Assessment Issues, Professor Richard Valencia of The University of Texas and Professor Lisa Suzuki of New York University bring to us a comprehensive and well-reasoned treatment of this important topic. This volume is a fitting companion to that of Armour-Thomas and Gopaul-McNicol. While Armour-Thomas and Gopaul-McNicol's volume serves as a valuable guide to practitioners by presenting an integrated biocultural approach to intelligence assessment with ethnic minority children, Valencia and Suzuki's volume provides a scholarly and scientifically grounded review of the topic. The authors do an admirable job of analyzing and dissecting the complex issues underlying this controversial topic. As leading scholars in this area, they have provided the field of psychology with a much-needed integrated and up-to-date review of the major issues in intelligence testing with ethnic minority students. I am confident that this volume will become “required reading” in graduate programs across the country that offer courses in the assessment of intelligence.—Series Editor,References[Page xiv]1988). Black males in the United States: Abstracts of the psychological and behavioral literature, 1967–1987. (Bibliographies in Psychology, No. 1). Washington, DC: American Psychological Association., & (1989). Black females in the United States: Abstracts of the psychological and behavioral literature, 1967–1987. (Bibliographies in Psychology, No. 3). Washington, DC: American Psychological Association., , & (1986). Counseling and psychotherapy with Asian-Americans: Review of the literature. Journal of Counseling Psvchology, 33, 196–206. http://dx.doi.org/10.1037/0022-022.214.171.124(1992). Asians in the United States: Abstracts of the Psychological and Behavioral Literature, 1967–1991. (Bibliographies in Psychology, No. 11). Washington, DC: American Psychological Association., & (1990). Hispanics in the United States: Abstracts of the psychological and behavioral literature, 1980–1989. (Bibliographies in Psychology, No. 8). Washington, DC: American Psychological Association., & (1982). The mental health of Asian Americans. San Francisco: Jossey-Bass., & (1995). North American Indians and Alaskan Natives: Abstracts of the psychological and behavioral literature, 1967–1994. (Bibliographies in Psychology, No. 15). Washington, DC: American Psychological Association.& (
Intelligence Testing and Minority Students: Foundations, Performance Factors, and Assessment Issues is a psychometric tour de force. Its two authors, Richard R. Valencia and Lisa A. Suzuki, are individually well published in the field of psychometrics, especially in the area of testing of racial/ethnic minority populations, and have successfully collaborated on previous publications as well. In recent memory, their signal work in the American Psychologist (Suzuki & Valencia, 1997) was applauded as a scholarly recognition of the manifold nature of the problem by many of us in the business of improving assessments of minority students in the schools. In addition, it presaged the approach used in this comprehensive tome.
Perusing the first part of the book puts the reader in touch with American psychology's easy adaptation and widespread adoption of the IQ test so that the application of these new instruments to both immigrant and established populations of nondominant ethnic groups can be understood. The potential application of the IQ test for gatekeeping was not lost on politicians, segregationists, and ideologues. The wholesale testing that went on in schools and at Ellis Island supported a selectively exclusionary immigration policy against “undesirable” groups such as Jews, Catholics, and Southern and Eastern Europeans.
The second part of the book reviews the heredity-environment debate and looks at specific performance factors that can bias tests and their use and interpretation. As Valencia and Suzuki point out, even the phrase in common parlance, “racial/ethnic group differences in measured intelligence,” itself contributes to misconceptions and stereotypes among the societal elites in our country given that most educated people do [Page xvi]not have a good grasp of analysis of variance and the ratio of between-group to within-group variabilities. Another negative contribution of measurement psychology to common misunderstanding is the implicit belief that any IQ that can be specified in two—not three—places indicates a lack of ability that will curtail a person's productivity throughout life and, therefore, should exclude that person from certain educational or professional opportunities.
Testing also set into motion the streaming practices in American schools that have persisted into this new millennium, for example, the tracking that ensured the ascendancy not of true merit or ability but rather of privilege or even caste. What is more, all this was done under the guise of objective scientism, the bias that emphasized the “hidden curriculum” and values of the upper middle class. The same items that predict that one will succeed in school are the ones that value the consequences of wealth such as foreign travel, written expression, vocabulary developed from a ready access to books, the manipulation of printed symbols and numbers, and logical analysis.
Meanwhile, other important cognitive abilities go unmeasured in IQ tests. Social intuition, face-to-face communication, tonal or emphatic vocabulary (in speaking or listening), guessing and adaptive strategizing, foresight (e.g., weaving without a sketch, constructing without a blueprint), creativity, leadership, the accurate recounting of events, and estimating risk and successfully beating the odds in real situations all remain, to a great extent, unrecognized in the popular and psychological definitions of intelligence, even though they underlie important life skills and have much to do with vocational choice. It is, then, not surprising that IQ tests administered in school have little predictive validity for success during adulthood. They best predict school-based learning. One is left to ruminate about the possibility that the long-term use of tests of maximal performance for selection and placement over several generations has made even this aspect of their validity a self-fulfilling prophecy as institutions and programs adapt to the types of students they receive.
This book will fascinate psychologists who like honest, if alternative, interpretations of the history of psychological conundrums such as the controversies that attend the use of intelligence tests on minority populations with a view to setting educational policy and affecting educational placement and praxis. The book also examines the notion of a biological substrate and its relevance to the fundamentally unchanging hereditarian explanations of mean ethnic differences in IQ by generations of research psychologists, and it helps to explain the nagging dissatisfaction that practitioners in clinical and school practice face daily in testing culturally and, ever more often, linguistically diverse clients. The third part of the book puts into perspective the explanations that have competed for the attention of psychometricians, who design tests of cognitive ability, and other researchers and psychometrists, who use these tests in their work. Particular tests and assessment techniques are reviewed as well.
Although the book is divided into three parts, the authors manage to never let the reader forget the complex and sometimes beguiling nexus of scientific psychology, political ideology, and societal agendas. Indeed, the degree of cross-referencing in the book seems precisely geared to keep the reader from perceiving only certain parts of the [Page xvii]picture without looking at the broader conceptual and consequential contexts in which psychological testing occurs.
Valencia and Suzuki's Intelligence Testing and Minority Students will give the reader a truly fresh opportunity to relearn and reconsider the nature of IQ and its strengths and limitations. Better yet, reading this book will give the psychometrician and the practitioner a good sense of what to do in the short run, and the book argues persuasively about what needs to be done on a priority basis if testing and education are ever to serve the needs of all Americans, responding fairly and validly to the growing diversity of this nation's citizens.—[Page xviii]
Our book represents the contributions of a number of individuals. We gratefully thank Fred Leong, editor of the Sage Publications Racial and Ethnic Minority Psychology book series, who gave us support and encouragement throughout this project. Deep appreciation goes to Jim Brace-Thompson, editor at Sage, who provided us with excellent guidance and generous support during the long task of preparing the manuscript. Thanks also go to Anna Howland, editorial assistant, D. J. Peck, copy editor, and all the other fine professionals at Sage who helped to bring this book to fruition. To Ernesto Bernal, who took time out of his busy schedule to write the Preface, mil gracias. Sincere appreciation goes to our superb research assistants—Bruno Villarreal (The University of Texas at Austin) and Ellen Short and Lyndon Aguiar (both of New York University). Very special thanks are extended to Bruno, whose work was outstanding. His bibliographic searches, preparation of tables, and typing of most of the manuscript text were of the highest quality. Your work is deeply appreciated. Thanks also go to Christie Robbins (The University of Texas at Austin, Library and Information Sciences), who helped us track down some hard-to-obtain sources.
I, Richard Valencia, extend my affection and gratitude to my wonderful wife, Marta. Thanks, dear, for your solid and loving support while I worked on the book. To my pride and joy—my twin boys, Juan and Carlos—thanks, fellows, for being so patient while Daddy did his writing. Mijos, you're the best sons a father could have. I wish to thank the Center for Mexican American Studies at the University of Texas at Austin, which provided me with a 1998 research grant to undertake work on this book. Finally, I want to thank Manuel J. Justiz, Dean of the College of Education at The University of Texas at [Page xx]Austin, who awarded me a Dean's Fellowship for the fall semester of 1999. Being free from teaching obligations provided me with valuable time to put this book to rest. Muchísimas gracias, Manny.
I, Lisa Suzuki, would like to express my deepest love and appreciation to my husband, John, for his continual support and understanding during the various phases of the preparation of this book. His editing and challenging of ideas presented in the book were invaluable. To my daughter, Kaitlyn, who was born shortly after the completion of the first draft of this book, you have added immensely to the richness of my life and give added meaning to my work. To the student leaders of my research team—Tamiko Kubo, Alex Pieterse, and Ellen Kim—your growing devotion to this area has inspired my thinking in this area over the years. Finally, I would like to express my appreciation to New York University and the School of Education for awarding me a Goddard Junior Faculty Fellowship. This supported me during the Fall 1999 semester so I too was free from teaching obligations to finish this book.——
Few topics in the behavioral and social sciences have garnered so much interest and generated so much debate in the United States as has intelligence testing and racial/ethnic minority students.1 Research on this topic has spanned nearly 90 years, beginning with the first reported study of “Negro”-White differences in intelligence by Strong (1913),2 who administered the Binet scales to the participants (see Chapter 1 of the present book). Since then, a voluminous amount of literature has accumulated on racial/ethnic differences in “measured intelligence.”3 Although the focal racial/ethnic minority population of these research endeavors primarily has been African Americans, other racial/ethnic minority groups (hereafter referred to as “minorities”) have been participants in these studies—American Indians, Latinos (especially Mexican Americans), and Asian/Pacific Islanders (A/PIs).
Numerous research investigations inform us that racial/ethnic mean differences in measured intelligence are among the most thoroughly documented findings in psychology (see, e.g., Brody, 1992; Garth, 1925; Loehlin, Lindzey, & Spuhler, 1975; Shuey, 1958, 1966; Valencia, 1985a; Vraniak, 1993). This body of research has shown that the mean difference between African Americans and Whites (as groups) on intelligence tests averages about 1 standard deviation (15 points).4 Latinos and American Indians (as groups) tend to have mean scores approximately 0.67 of a standard deviation below the White mean of about 100, and Asian Americans score, on average, slightly above the White mean. One focus of this book is to explore those factors that may best explain these racial/ethnic group differences in average intelligence test scores, with particular emphasis on minority groups that perform below the White mean. The factors on which we [Page xxii]focus are socioeconomic status (SES), home environment, cultural bias in tests, and heredity.5 At this introductory stage in the book, it is important to clear up a common misconception demonstrated by laypeople and scholars alike when referring to “racial/ethnic group differences” in measured intelligence.6 This phrase is misleading and tends to perpetuate stereotypes. What the group differences actually refer to are average intellectual differences in performance of the individuals who are linked, usually through self-identification, to racial/ethnic group membership (e.g., Whites, African Americans, Mexican Americans). Thus, an important question to ask is, “How large are the average differences between groups relative to the range of variation that occurs within groups?” Although “different observers vary considerably in the emphasis they place on average differences between groups compared with variation within groups” (Loehlin et al., 1975, p. 15), the major finding is that “the majority of the variation in… levels of [intellectual] ability lies within U.S. racial/ethnic and socioeconomic groups, not between them” (p. 235). That is, for measured intelligence, average differences in performance between groups tend to be quite modest relative to the range of differences within groups. In any event, the indiscriminant use of the term group differences in referring to patterns in intelligence scores among racial/ethnic populations ignores the reality of overlap of individual scores between groups and perpetuates the myth that nearly everybody of one racial/ethnic group (e.g., Whites) performs higher than practically everybody of another group (e.g., African Americans).7
Although this book focuses on racial/ethnic group differences in intelligence (and with the understanding that within-group differences far exceed between-group differences), it is important to underscore that the field of psychology is primarily interested in individual differences in intelligence. This statement is true of applied psychology but not necessarily for all researchers working in the area of racial/ethnic group differences in intelligence. Many researchers continue to focus on group differences in the absence of a multicultural context. This is a major problem of the literature. In any event, research in the area of individual differences has indicated that “variations in scores on tests of intelligence are attributable primarily to characteristics of persons that are independent of their racial and socioeconomic background” (Brody, 1992, p. 281).8 Furthermore, as Brody (1992) reminded us, one's performance on a measure of intelligence “is never defined or predictable from his or her group identity. Research on intelligence, properly considered, should invariably serve as an antidote to stereotypes” (p. 281). This point of individual differences is central to a second major focus of our book. In Part III, which covers a host of assessment issues (e.g., underrepresentation of gifted minority students, prereferral process), we offer insights as to why such concerns exist as well as suggestions on how assessments can be improved to accommodate individual minority students.
Earlier, we noted that because individual minority students are members of racial/ethnic groups who, on average, perform below the White mean on intelligence tests, there is a likelihood that such students (e.g., Mexican Americans) will be stereotyped as low performers on measures of intelligence. These false perceptions of minority students, particularly those of low SES, often are grounded in “deficit thinking,” which is a mind-set molded by a fusion of ideology and science. Deficit thinking is tantamount to [Page xxiii]“blaming the victim,” a dynamic form of social thought positing that the poor school performance of some minority students is rooted, in part, in students' cognitive deficits (for a comprehensive explication of the deficit thinking paradigm, see Valencia, 1997a). In the present book, particularly in Chapter 1, we proffer that deficit thinking has had a major influence on how some people perceive and interpret the low intellectual performance of some minority students (see also Chapter 6). This book offers, throughout, a number of rival interpretations as to why minority students, on average, tend to perform lower than White students on intelligence tests (e.g., variations in SES, test bias, discriminatory assessments).Purpose and Intended Audience
The existing literature on intelligence testing and minority students is scattered, lacks coherence, and is substantively shallow. This poor state of the art motivated us to prepare a comprehensive, up-to-date book. We also provide, in a number of instances, diverse opinions on controversial issues. We prepared this book for the reader who would want a book that is comprehensive in scope and with updated discussions, data, and references. We believe that this goal has been met. There are more than 1,000 references and more than 300 footnotes. When combined as a whole in our integrative analyses, all these provide a solid state-of-the-art book on intelligence testing and minority students. In our writing of Intelligence Testing and Minority Students, we were guided by the principle that to understand the many testing issues germane to minority students, one has to understand the “big picture.” That is, minority students do not exist in a vacuum. Testing concerns that affect them are part of a wider context of testing issues in the United States. For example, in our chapter on gifted minority students (Chapter 8), the many issues that minority students face are imbedded in the general context of the gifted movement (e.g., how best to identify gifted students, equity vs. excellence). As such, a number of chapters in this book provide a historical, evolving context that sets the framework for understanding the many concerns of intelligence testing vis-à-vis minority students. In taking this general to specific course, it was necessary to be comprehensive in our analyses and syntheses. In sum, this book should meet the needs of scholars, researchers, students in graduate courses, and testing personnel who desire considerable breadth and depth on the subject of intelligence testing and minority students.Organization
This book consists of 10 chapters, organized into three parts. Part I, “Foundations,” provides a necessary substratum for understanding and illuminating the contemporary period regarding the sociopolitical aspects of intelligence testing, theories of intelligence, scholarly search for explaining mean differences in measured intelligence between minority and White students, and assessment issues germane to minorities. Two chapters make up Part I. In Chapter 1, “Historical Issues,” we trace the importation of the first intelligence scales from Europe to the United States. Once culturally and [Page xxiv]psychometrically appropriated by U.S. psychologists, individually administered (and later group-administered) measures of intelligence became tools of numerous “race psychology” investigations during the 1920s in which minority and White children's intellectual performances were compared. The observed lower mean performances of American Indian, African American, and Mexican American students frequently were interpreted to have genetic bases. This chapter also covers the use of intelligence test scores in shaping curriculum differentiation, and hence inequities, in the schooling of minority and White students. We also examine the role of heterodoxy, that is, the early challenges by a number of minority and White scholars to the orthodoxy of hereditarianism of the 1920s. The chapter closes with a discussion of a number of contemporary testing issues that began to emerge during the 1920s and unfolded through the 1990s. Our thesis of Chapter 1 is as follows: To understand the contemporary issues in regard to intelligence testing and minority students, one must have a good sense of the historical context that helped to shape such concerns.
Chapter 2, “Multicultural Perspectives of Intelligence: Theory and Measurement Issues,” highlights the complexities of defining and measuring intelligence. Theoretical frameworks that incorporate cultural aspects of intelligence are noted along with a brief discussion of emotional, practical, and creative facets of this construct. We also provide discussions of assessment-based models of intelligence and specific information regarding racial/ethnic group differences in intelligence. The complexities of understanding intelligence from a theoretical and measurement perspective, within a cultural context, are especially emphasized.
Part II, “Performance Factors,” consists of four chapters. Here, we offer a comprehensive analysis and discussion of four major areas—SES, home environment, test bias, and heredity—that scholars have advanced as possible factors that might help to explain the persistent difference in mean intellectual performance between minority student groups (i.e., African Americans, Latinos, American Indians) and White students. In Chapter 3, “Socioeconomic Status,” we examine the long-standing finding that SES of parents is positively correlated with their children's measured intelligence. This ubiquitous connection between SES and intelligence frequently has been reported with White participants. We examine two central questions here. First, what does the literature inform us about this relation across racial/ethnic groups? The next question has to do with a methodological concern. When White and minority participant groups are “matched” on SES, or SES is controlled, the mean difference in measured intelligence frequently is reduced, but a gap often remains. In these studies, do such findings suggest that attempts to control for SES in White and minority intellectual comparisons cannot truly be made? We proffer the hypothesis that such attempts are difficult, if not futile, because researchers fail to make a distinction between “class” and “caste.” Conventional SES measures might be able to distinguish SES groups, but how these instruments are able to discern various groups based on caste-like societal arrangements is not clear. As such, the unmeasured effects of racism need to be considered in such research given that uncontrolled caste factors are likely to be operative.
In Chapter 4, “Home Environment,” we examine the home intellectual environmental literature as to how it provides insights about minority children's intellectual performance. [Page xxv]Although SES is an important (albeit weak) statistical predictor of children's measured intelligence, it is limited as a construct because of its distal nature. That is, it is a summarizing variable. It does not reveal the complexities and comprehensiveness of what transpires in the home environment regarding the ways in which parents create learning situations that foster children's cognitive development (e.g., parent reading to a young child). As such, researchers in developmental psychology have examined how the home intellectual environment shaped by parents serves as a fairly strong predictor of children's measured intelligence. Most of this research has been conducted using White families. In this chapter, we discuss a representative corpus of home environment research studies in which African American and Mexican American families served as participants. Our review of findings from this research base informs us that what parents do in the home environment is a stronger predictor of their children's measured intelligence than is what they are (i.e., SES). The major implication here has to do with intervention. Although home environment and SES are statistical predictors of children's intellectual performance, the home environment is much more easily modifiable than is SES (an ascriptive marker). That is, structuring the home environment to make it more conducive for stimulating intellectual growth is easier to do than is changing a family's status, for example, from low SES to middle SES. The home environment literature has much to offer in understanding between- and within-racial/ethnic group differences in measured intelligence. In Chapter 4, we also discuss some emerging research findings from behavioral genetics that challenge the major assertion by developmental psychologists that the family is the most important unit of cognitive socialization.
Chapter 5, “Test Bias,” explores one of the most long-standing and contentious claims that has been advanced to explain mean differences in measured intelligence between minority and White groups. That is, intelligence tests, critics assert, are culturally biased against minority children and youths. After introducing this debate, we unpack the notions of “cultural loading” and “cultural bias,” two concepts that are central to an understanding of the test bias controversy. The core of this chapter is a comprehensive review of 62 empirical studies of possible cultural bias regarding four psychometric properties of individually administered intelligence tests: reliability, construct validity, content validity, and predictive validity. Based on our review, we offer several critiques of this literature (e.g., most of what we know about cultural bias is confined to research on the Wechsler scales; many investigations have failed to control SES). Our major conclusion of the 62 test bias studies suggests that the majority of studies weigh in on the “nonbiased” side. We contend, however, that because about 30% of the investigations resulted in findings of “mixed” or “biased” conclusions, the subject of cultural bias in intelligence tests is an open research issue.
In Chapter 6, “Heredity,” we provide comprehensive coverage of a highly controversial hypothesis, that is, that genetic factors are strongly implicated in explaining the mean differences in measured intelligence between minorities (e.g., African Americans) and Whites. In our attempt to illuminate this complex and disputatious subject, we discuss central concepts (and misconceptions) germane to this debate (e.g., heritability). Also, we offer an analysis of the contemporary discourse among the behavioral genetics, environmental, and interactionist perspectives on the nature-nurture debate. The core [Page xxvi]purpose of this chapter is to explore the conjecture that genetics plays a significant role in explaining mean differences in intelligence between African American and White groups. We do so by examining (a) the writing of scholars who have proffered genetic bases to explain such group differences (e.g., Jensen, the team of Herrnstein & Murray) and (b) findings from empirical research investigations (e.g., racial admixture studies, transracial adoption investigations). Our analysis of the literature informs us that the hypothesis of genetic differences between African American and White populations that has been advanced to explain differences in measured intelligence cannot be confirmed.
Part III, “Assessment Issues,” consists of four chapters. The focus is on issues that are pertinent to the intellectual assessment of minority students. Here, we provide discussions of concerns germane to special education, the identification of gifted minority students, multicultural aspects of cognitive ability tests, and best-case practices regarding nondiscriminatory assessment. Chapter 7, “Race/Ethnicity, Intelligence, and Special Education,” begins with a review of the numerous historical factors that have played roles in the development of testing practices in the schools. Our discussion includes Public Law 94–142, the Individuals with Disabilities Education Act amendments of 1997, and court cases focusing on testing of minority students. Current definitions of the special education categories most influenced by intelligence tests are provided, as is a review of the literature surrounding these classifications (i.e., educable mentally retarded [EMR], learning disabled [LD]). A review of the referral and testing process is provided, with specific attention given to the role of intelligence tests. Based on our calculations of data from the most recent survey of the Office for Civil Rights (OCR), disproportionate representation rates of minority students in the EMR and LD classifications are presented along with recommendations for special education programs in the future.
In Chapter 8, “Gifted Minority Students,” we focus on a neglected population of minority students—the gifted. Unfortunately, minority students (i.e., African Americans, Latinos, American Indians) often are stereotyped as performing below the norm on standardized intelligence tests. This chapter is about those minority students who defy this stereotype, that is, minority students who perform substantially above the norm. We begin this topic by providing a historical sketch of giftedness in the United States, with a focus on how minority students have fit in. This is followed by a discussion of a number of issues regarding giftedness that affect minority students (e.g., narrow conceptions of giftedness, charges of elitism). In this discussion, a particularly significant topic is the severe underrepresentation of minority students in gifted programs. To explore this, we calculate national and state underrepresentation rates, using the most recent data of the OCR survey (U.S. Department of Education, OCR, 1997). The results of our analysis are not encouraging. For example, Latino and African American K-12 public school students are underrepresented in gifted programs in 48 of the 50 states. By sharp contrast, White students are overrepresented in 48 of the 50 states. Following this coverage, Chapter 8 contains discussions of select literature on gifted African American, Latino, A/PI, and American Indian students. We pay particular attention to those empirical studies investigating [Page xxvii]innovative conceptions and practices that show promise in increasing the number of gifted minority students.
In Chapter 9, “A Multicultural Review of Cognitive Ability Tests,” we present information regarding many of the instruments currently used to assess intelligence and cognitive abilities more generally. The focus of this review is more broadly defined given that many test developers and writers have moved away from using the term “intelligence” or “IQ” to define their measures. Thus, the phrase “cognitive ability tests” often is used in reference to the instruments reviewed. Specific attention is paid to the areas assessed by cognitive measures, test development strategies currently used (e.g., norming, standardization), racial/ethnic profiles of abilities reported on popular measures, and current procedural modifications in the administration and interpretation of traditional cognitive tests (e.g., biocultural model of assessment, Gf-Gc Cross-Battery Assessment model).
Chapter 10, “Future Directions and Best-Case Practices: Toward Nondiscriminatory Assessment,” highlights future directions based on our understanding of the field of intelligence measures and multicultural issues. Despite concerns from the field regarding issues of test bias and test unfairness, the use of standardized instruments continues to increase at accelerated rates given school reform measures and an emphasis on accountability. Our assessment practices have not been able to keep up with the diversity of our growing minority populations. As such, evaluators are in dire need of instruments designed to assess the abilities of language-minority students as well as individuals from cultures different from the U.S. majority. We assert that testing practices must be located within a multicultural context where theoretical knowledge, performance factors, and psychometric testing practices are integrated and merged. Training areas are outlined in view of necessary assessment practices.[Page xxviii]
1. Our population of focus is kindergarten through Grade 12 (K-12) students. At times, however, the literature we examine has various age levels as its focal groups (e.g., infants, toddlers, preschoolers, adults). Regarding the term “racial/ethnic minority,” as well as the referent “White,” we use the descriptions provided in the Office for Civil Rights biennial surveys of elementary and secondary schools. These racial/ethnic descriptions, as listed in Chinn and Hughes (1987), are as follows:
American Indian. American Indian or Alaskan Native—A person having origins in any of the original peoples of North America and who maintains cultural identification through tribal affiliation or community recognition.
Asian. Asian or Pacific Islander—A person having origins in any of the original peoples of the Far East, South Asia, the Pacific Islands, or the Indian subcontinent. This area includes, for example, China, India, Japan, Korea, the Philippine Islands, and Samoa.
Hispanic. A person of Mexican, Puerto Rican, Cuban, Central or South American, or other Hispanic culture or origin regardless of race.
Black. Black not of Hispanic origin—A person having origins in any of the black racial groups of Africa.
White. White not of Hispanic origin—A person having origins in any of the original peoples of Europe, North Africa, or the Middle East. (p. 42)
There is one other point about the concept of race that needs to be mentioned. In the study of racial (or ethnic) differences in intelligence, how one conceptualizes race is a thorny issue. Loehlin, Lindzey, and Spuhler (1975) commented, “In the biological sciences, races are customarily considered as subspecies, defined by gene frequencies” (p. 13). By contrast, in the social sciences (especially cultural anthropology), race typically is conceptualized as a social concept (i.e., a social construction) [Page 294]in which people assign meaning and value to “racial characteristics” (see, e.g., Omi & Winant, 1994). Still, there is another perspective. Population geneticists Cavalli-Sforza, Menozzi, and Piazza (1994) noted, “The classification into races has proved to be a futile exercise. … From a scientific point of view, the concept of race has failed to obtain any consensus” (p. 19). So, what does one make of all this? To us, the case that Cavalli-Sforza et al. forge about the meaninglessness of race as a biological concept makes much sense. Yet, given the race-conscious nature and racial stratification of U.S. society, the social concept of race also makes sense. Thus, in the present book, we prefer to view race as a social construction. In any event, the reader should be aware of the growing criticism of research on racial differences in intelligence in which the social concept of race is implied, yet inferences sometimes are made in a biological sense when conclusions are drawn about interpreting such group differences in intelligence (for more on this and related issues, see Helms, 1992, 1997).
2. We place quotation marks around the term “Negro” here because that was the nomenclature (along with “colored”) typically used during this period. When reviewing the historical research on racial/ethnic differences in intelligence, we often report these racial/ethnic designations to allow the reader to get a better sense of race relations during those times.
3. Throughout this book, we frequently modify the term “intelligence” with the adjective “measured.” We do so to underscore that obtained scores on intelligence tests are samples of behavior. That is, intelligence tests measure developed abilities, not innate ones.
4. There is strong evidence, however, that the IQ scores of American racial/ethnic groups are rising. Although the 15-point IQ gap between African American and White groups still remains, there are findings that the mean intelligence test performance of African Americans in 1990 was approximately equivalent to the mean IQ of Whites, as a group, in 1940 (Neisser, 1998a). On this, Neisser (1998a) noted that although the 1 standard deviation mean intellectual difference between African Americans and Whites continues, “it is now clear that a gap of this size can easily result from environmental influences, specifically, from the differences between the general American environments of 1940 and 1990” (p. 18). For further discussion on the “rising curve,” see Neisser, 1998b).
5. This is not to say that these are the only factors involved that might help to account for the mean difference in measured intelligence (and, in some cases, achievement test scores) across racial/ethnic groups. A host of other variables have been discussed in the literature. These factors include, for example, intrauterine difficulties (as evidenced by premature birth [Storfer, 1990]), nutrition (Loehlin et al., 1975; Samuda, 1998; Sigman & Whaley, 1998), lead exposure (Brody, 1992), motivation (Cook & Ludwig, 1998; Fordham & Ogbu, 1986; Samuda, 1998), and test anxiety (Samuda, 1998; Steele & Aronson, 1998). Another factor that can be advanced to help explain the average difference in measured intelligence between minority group students (e.g., African Americans, Latinos, American Indians), as a whole, and their White peers is inequalities in schooling opportunities. Although we do not offer a separate chapter on schooling in Part II of this book, we do integrate the schooling inequalities factor in several chapters (e.g., Chapters 1 and 3). Scholars of minority education, such as for Mexican Americans (see, e.g., San Miguel & Valencia, 1998; Valencia, 1991), assert that Mexican American students have experienced, historically and contemporarily, a number of schooling conditions (e.g., segregation, tracking, school financing inequities) that have led to diminished achievement (e.g., low achievement test scores, high dropout rates) among these students. Given the moderate correlation (typically .50) between intelligence and scholastic achievement test scores (Jensen, 1980), it is not surprising that the mean intellectual performance of Mexican American students as a group is lower than the mean score of their White peer group. As such, there is strong evidence that inequalities in educational opportunities play some role in explaining the intellectual mean differences between Mexican American and White student groups. The same argument holds for African American-White and American Indian-White comparisons in mean intelligence test scores.
6. This paragraph is excerpted, with very slight modifications, from Suzuki and Valencia (1997, p. 1103).
[Page 295]7. To some extent, this assertion about stereotypical thinking regarding racial/ethnic group differences in intelligence is supported by empirical research. Results from a 1990 survey conducted by the National Opinion Research Center at the University of Chicago found that a majority of White respondents believed that Blacks and Hispanics were less intelligent than Whites (cited in Duke, 1991).
8. See Jensen (1980) for an empirical study clearly demonstrating that the sources of variance in intelligence largely reside within families.
1. The launching of the psychological testing movement also is attributed, in part, to American psychologist James McKeen Cattell (Anastasi, 1988). Cattell studied under Wilhelm Wundt in Leipzig, Germany, during the late 1880s. Like Gal ton, Cattell believed that intellectual functions could be measured by sensorimotor techniques. “On his return to America, Cattell was active both in the establishment of laboratories for experimental psychology and in the spread of the testing movement” (p. 9). Cattell, in an 1890 article titled “Mental Tests and Measurements,” used the term “mental tests” for the first time in the psychological literature (Cohen et al., 1992).
2. Although Terman often is given credit for developing the first widely used American version of the Binet-Simon scale, Goddard (1928) and Kuhlmann (1912) were the first to produce highly useful versions (Freeman, 1926). It is believed that Terman's success in having his Binet-Simon scale version more widely used and known was due to the assertiveness of the test's publisher (Houghton Mifflin) and Terman's reputation as a scholar and author of many books, articles, and tests (French & Hale, 1990).
3. William Stern was the developer of the construct of “mental quotient,” that is, a child's mental age divided by his or her chronological age (Stern, 1914). For example, a 12-year-old child whose mental age was estimated to be 14 years would have a mental quotient of 1.17. Terman, in his development of the Stanford-Binet intelligence test, extended the construct of mental quotient by multiplying it by 100. In the preceding example, the IQ would be 117.
4. It should be noted, however, that Terman did test foreign-born children (and, we suspect, some minority children), but he did not incorporate their test results into the norming process. On this, Terman (1916) commented, “To avoid accidental selection, all the children within two months of a birthday were tested in whatever grade enrolled. Tests of foreign-born children, however, were eliminated in the treatment of results” (p. 53).
5. The most current version, the fourth edition of the Stanford-Binet (Thorndike, Hagen, & Sattler, 1986a, 1986b), contains a standardization sample in which race/ethnicity is one of the stratified variables (based on the 1980 U.S. census data).
6. To be sure, the development of the 1916 Stanford-Binet intelligence test led to a long domination in the field of individually administered intelligence tests. In 1949, however, David Wechsler published the Wechsler Intelligence Scale for Children (Wechsler, 1949). In this edition, the standardization sample also was exclusively White children. It was not until the 1974 revision (Wechsler, 1974) that minority children were represented in the norm group. Likewise, the most current edition, the Wechsler Intelligence Scale for Children: Third Edition (Wechsler, 1991), contains minority representation in the standardization sample.
7. Our assertion is not that all psychologists were ethnocentric when it came to test development and administration. Terman (1917), for example, appeared to be aware of the problems associated with social class, cultural, and language variables as possible confounds in intellectual assessment of children. Nevertheless, such awareness did not stop Terman from drawing racist conclusions about observed racial/ethnic differences in measured intelligence (see Terman  quote in next section).
8. Terman (1916) defined borderline cases as “those which fall near the boundary between that grade of mental deficiency [70 to 80 IQ] which will be generally recognized as such and the [Page 296]higher group usually classified as normal but dull. They are doubtful cases, the ones we are always trying (rarely with success) to restore to normality” (p. 87).
9. Cattell coined the term “individual differences” in a 1916 address commemorating the 25th anniversary of the American Psychological Association (Cattell, 1947, cited in Marks, 1981).
10. The prime mover of the development of the Army mental tests was Robert M. Yerkes, a Harvard University professor and president of the American Psychological Association. Yerkes's test development team consisted of the top hereditarians of the time including, for example, Goddard and Terman.
11. In addition to Terman and Yerkes, there were three other codevelopers of the NIT: M. E. Haggerty, E. L. Thorndike, and G. M. Whipple. It is interesting to note that the NIT team did not always see eye to eye on the test's development. For example, regarding the development of standard norms, Thorndike wanted “foreign” populations and rural districts represented in the standardization sample. Haggerty desired separate age norms for “colored” children. On the latter, “Yerkes replied, ‘No, we don't want to use them. The political situation here will prevent us from going into colored schools’ ” (quoted in Chapman, 1988, p. 81). As Chapman (1988) noted, due to the pressure of time, the schools finally selected for the norming of the NIT were in the cities of Cincinnati, Pittsburgh, Kansas City, and New York. For a test that used “national” in its name, the final locations chosen for norming certainly were not representative of American culture. On this issue, Chapman's point is well taken: “The point to be drawn from this discussion is that the examination board, like Terman in his work on the Stanford-Binet, made rather broad assumptions about the nature of American culture and what constituted the ‘normal’ American experience. These standards were then used to judge the intelligence of many Americans whose cultural background was not adequately reflected in either the tests or the norms. These decisions would have important consequences, too, for in little more than a year, the NIT would be in wide use around the country” (p. 81).
12. The new corpus of group intelligence tests included the familiar “general” instrument (i.e., similar to the NIT) as well as nonverbal and performance tests. Some of these tests (with their respective publication dates) are the Terman Group Tests of Mental Ability (1920), Haggerty Delta I Test (1920), Detroit First Grade Intelligence Test (1921), Otis SA Test of Mental Ability (1922), Otis Classification Test (1923), Detroit Primary Intelligence Test (1924), Cole-Vincent Group Test (1924), and Goodenough Draw-a-Man Test (1926) (see Valencia, 1985a).
Hildreth (1933), in A Bibliography of Mental Tests and Rating Scales (cited in Chapman, 1988), reported that from 1920 to 1929, 70 “mental tests” were developed. From 1900 to 1932, 133 were available. Unfortunately, Hildreth used the term “mental test” in an omnibus fashion, including achievement and aptitude tests and tests measuring various types of ability. Thus, her analysis does not inform us of the number of intelligence tests. In any event, the mental tests she did identify were used, according to Hildreth, for large-scale classification of schoolchildren's abilities.
13. The second major area of race psychology interest stemmed from the massive database of the mental test scores of the Army Alpha and Beta testing of World War I recruits (Chapman, 1988; Gould, 1981; Valencia, 1997b). In his book, A Study of American Intelligence, Princeton University professor Carl C. Brigham provided a reanalysis of the Army mental test data (Brigham, 1923). Hard-hitting themes of his treatise were (a) the decline of American intelligence (imputed to racial admixture), (b) the racial (intellectual) inferiority of Negroes, (c) the racial superiority of the Nordic stock over men from Southern and Eastern Europe, and (d) the need to restrict immigration from Southern and Eastern Europe. Yerkes wrote a laudable foreword to Brigham's book, commenting that Brigham's treatment was fact-filled and not opinionated. Some scholars have noted, however, that Brigham's conclusions did not differ much from those of Madison Grant, the leading American theorist of racism of the period, who wrote The Passing of the Great Race (Grant, 1916).
14. Garth, as well as a number of other race psychologists of this period, used “race” in a biological sense. We know now that race is best viewed as a social concept. Also, Garth and others confused race with national origin and ethnicity.
[Page 297]15. The term “Mexican” typically was used during the first four decades of the 20th century to refer to Mexican-origin children living in the United States. A substantial proportion of these children were citizens of the United States.
16. Not all of these nearly 37,000 participants served in investigations of intellectual performance. A small percentage of studies involved psychological measurements, for example, of color preference, academic achievement, and personality self-ratings.
17. Garretson (1928) reported that the monthly mean incomes for the parents of the White and Mexican children were $250 and $110, respectively.
18. Garretson (1928) commented, “With few exceptions, Spanish is the language spoken in the homes and on the streets of the Mexican part of town. It is unusual for a Mexican child to be able to speak English when he enters the kindergarten or first grade” (pp. 32–33). Even after admitting this and presenting data to show that the IQ differences in Grade 1, favoring the White sample, on the verbal Pintner-Cunningham test and the NIT were substantial, and after commenting that the Mexican children's language difficulty operated to their disadvantage, Garretson still suggested a genetic explanation for group differences in intellectual performance.
19. Furthermore, on several other grade levels, the IQ differences, although favoring Whites, were negligible and, it appears, statistically nonsignificant. For example, on the Pantomime test in Grade 5, the mean IQs were 92.0 and 91.5 for the White and Mexican American groups, respectively.
20. In Goodenough's (1926b) study, there were 79 American Indian participants (Hoopla Valley). Their mean IQ was 85.6.
21. The other two management-of-instruction functions that Resnick (1979) discusses are (a) “monitoring” (i.e., when tests are administered during the course of the instructional process to provide feedback on students' strengths and weaknesses and to make curricular adjustments to improve teaching/learning) and (b) “grading” (i.e., when tests are given at the end of the instructional process to evaluate students' performance).
22. Today, curriculum differentiation is commonly referred to as “ability grouping” at the elementary school level and as “tracking” at the middle and high school levels (Valencia, 1997c). Another framework to describe curriculum differentiation was presented by Slavin (1997). “Tracking” refers to curricular assignments in high school (e.g., college preparatory, general). “Between-class ability grouping” is in reference to student groupings by ability in separate classes in junior high and middle schools. “Within-class ability grouping” refers to student groupings (e.g., mathematics) of similar levels in elementary school classes.
23. Chapman (1988) commented that Terman's use of “homogeneous class groups” was one of the first uses of the notion, which subsequently became a common term in education.
24. It is interesting to note that Terman's five percentage breakdowns coincide with how measured intelligence on later intelligence tests was distributed (by design). The breakdowns are strikingly similar to the areas (i.e., cases) under the normal curve. For example, about 2.5% of the area is contained in 2 standard deviations above the mean (IQ of 130 or Terman's “gifted” group), about 15.0% of the area is contained between the 1st and 2nd standard deviations above the mean (IQs between 115 and 130 or Terman's “bright” group), and so on. The point here is that Terman's a priori groupings were manufactured given his knowledge of the normal distribution. Such groupings are reflections of probability theory through a human-made construction, which subsequently became a social construction of reality.
25. It appears that Terman had considerable influence on some of his students' thinking about race and intelligence. For example, Kimball Young, a former student of Terman's, wrote the following in his autobiography: “I was pretty well fed up with Terman's doses of intelligence being inherited as a biological [racial] trait. But he made a tremendous impression on the educational world. So I played it cool and didn't say much about this in my dissertation, though I wanted to. I had to get that union card, as we all know” (quoted in Lindstrom, Hardert, & Johnson, 1995, p. 16). This admission by Young might explain why he made the racist conclusions in his dissertation (published as Young, 1922) about the intellectual performance of Mexican-origin children. Later [Page 298]publications of Young demonstrated that environmental factors were more important than genetics in influencing intelligence (Lindstrom et al., 1995). In any event, Young went on to have an esteemed career in sociology. He taught at a number of universities (e.g., University of Oregon, Clark University, University of Wisconsin, Northwestern University). In 1945, Young was elected president of the American Sociological Association.
26. In the preface to the book, Terman praised the book's applied implications for actual classroom practice and school organization. He also lauded the author: “Dr. Dickson's experience in the educational use of test results has probably been more extensive than that of any other living educator” (Dickson, 1923, p. xiii). In Mental Tests and the Classroom Teacher, which was written in nontechnical prose and geared for teachers, principals, and administrators, Dickson's views on the nature of intelligence, individual differences, equality of opportunity, educability of children, curriculum differentiation, and social engineering are clone-like in Termanian thought (for a discussion, see Valencia, 1997b).
27. González (1974b) drew this median IQ data from McAnulty (1929), whose study in turn drew on a compilation of IQ test data surveys in Los Angeles from 1926, 1927, and 1928. The observed median IQ of 91.2 of Mexican American children in Los Angeles during the 1920s was about two thirds of 1 standard deviation below the typical White median (and mean) of 100. This difference of two thirds of 1 standard deviation frequently was found in other race psychology studies of the time. Furthermore, Valencia (1985a), in a comprehensive review of research on intelligence testing and Mexican American school-age children, estimated an aggregated mean IQ of 87.3. His analysis was based on 10,739 Mexican American children in 78 studies spanning six decades. Valencia noted, however, that this aggregated mean IQ of 87.3 is entirely misleading and greatly confounded by the failure of researchers to control critical variables.
28. For further discussion of González's research on the role of intelligence testing in Los Angeles during the 1920s, see Valencia (1997b).
29. The failure of race psychologists to assess (prior to intelligence testing) the language status of children and youths who were most likely to be limited English proficient was a frequent occurrence during the hereditarian period. For example, in Valencia's analysis of intelligence testing research of Mexican American children during the 1920s (Valencia, 1985a), he found that of the eight identified studies, none of the investigators attempted to assess the children's language for dominance and fluency. In addition, in the same review of the literature, Valencia found that in the 124 intelligence testing instances identified in 106 studies (spanning six decades), the language status of the Mexican American children was not even reported in 63 (50.8%) instances. Furthermore, in those studies that did report language status of the participants, about two thirds assessed language (predominantly through indirect means and not through the more precise direct procedures), but one third of the studies still failed to assess language. Thus, one can conclude that taking into account all pertinent studies of Mexican American intellectual performance from 1922 to 1984, the vast majority of investigators failed to assess language status.
30. Klineberg's finding that the various groups studied scored higher on performance tests than on linguistic tests certainly was the case. Support for this is seen in Valencia (1985a), where he employed the same procedure as Klineberg (1935b) except that Valencia used the more appropriate weighted means analysis. In 22 investigations (spanning decades) involving 2,380 Mexican American children, their mean IQs on verbal and performance tests were 85.6 and 94.7, respectively, a difference of 9.1 points (very close to Klineberg's observed difference of 10 points in a much smaller number of studies involving Mexican American children).
31. In his review of pertinent research by African American scholars who criticized early mental testing investigations, Valencia (1997b) identified a study by an African American woman, Martha MacLear (MacLear, 1922).
32. For other discussions of these African American scholars' contributions, see Guthrie (1976).
33. As Chapman (1988) noted, group achievement tests were about as widely used as their sister tests, group intelligence instruments. Achievement tests were used in an accountability role to [Page 299]evaluate the performance of local schools as well as for homogeneous grouping. Achievement tests were produced in far greater numbers than were intelligence tests. Hildreth (1933) reported that between 1920 and 1929, 741 achievement tests were developed (cited in Chapman, 1988). From 1900 to 1932, 1,298 such tests were produced.
34. It should be noted that the administrative practice of ability grouping in public schools did not follow one uniform organizational plan. Chapman (1988), commenting on a 1929 survey by Otto (1931) of 395 superintendents in cities ranging in size from 2,500 to 25,000, wrote that “he [Otto] discovered 20 different plans to classify students within classrooms and 122 different promotion schemes” (p. 159).
35. Regarding intelligence testing research in which minority students served as participants, a similar trend noted by Haney (1981) has been reported. Valencia (1985a), in a literature review of Mexican Americans and intelligence testing, identified 18 journal articles published between 1920 and 1939. Only 1 such article was identified for the period from 1940 to 1949.
36. This paragraph is excerpted, with minor modifications, from Valencia (1999, pp. 125–126).
37. There are some critics, however, who argue that these cases (especially Diana and Larry P.) missed the point regarding the major reason for overrepresentation of African Americans and Mexican Americans in EMR classes. For example, MacMillan and Meyers (1980) contended that “the Court was led into an erroneous conclusion that IQ was the principal determiner of EMR status. … The single critical and unexceptional variable that leads to EMR placement is academic failure” (p. 146).
1. For further discussion of these issues of reification of intelligence tests and hereditarianism, see Chapter 1 of the present book.
2. The 1921 symposium (Terman et al., 1921) was attended by 17 top psychologists who were invited to share their views on how they conceptualize intelligence. This symposium sparked controversy (e.g., over the constancy of IQ) and set the climate for further controversy during the 1920s (see, e.g., Chapman, 1988; Valencia, 1997b).
3. This is a major issue that we also discuss in Chapter 8 of this book.
1. It must be kept in mind, however, that in terms of absolute numbers, there are more Whites (compared to people of color) who live in poverty. Based on 1990 census data, about 65% of all children who lived in poverty (defined as an urban family of four with an income of less than $13,359) were White (Macionis, 1994).
2. It should be noted that reporting SES data for only the Latino aggregate can be misleading because subgroups vary. For example, based on 1990 census data (cited in Chapa & Valencia, 1993), the following Latino child poverty rates were reported (in descending order): Puerto Rican (48.4%), Mexican origin (37.1%), other Latino (28.4%), Central and South American (26.1%), and Cuban (23.8%). The Latino aggregate was 36.2%.
3. Regarding the opposite end of the continuum, only 2% of African American families and 2% of Latino families had incomes of more than $75,000. By contrast, 11% of White families had incomes of more than $75,000. Interestingly, Asian American families were close behind at 10% (Chapa & Valencia, 1993).
4. The reality that minority groups (particularly African Americans, Mexican Americans, Puerto Ricans, and American Indians) face economic disadvantagement and are positively skewed on SES measures is long-standing. Numerous studies in the past have confirmed this (e.g., Garth, 1928; Grebler, Moore, & Guzman, 1970; Klineberg, 1935b; Pettigrew, 1964b; Reynolds, 1933).
[Page 300]5. In Chapter 4 of the present book, we argue that home atmosphere variables as reported in White (1982) are conceptually different from conventional SES variables and, therefore, are poor choices of SES indicators in research studies.
6. Unfortunately, Binet did not have an opportunity to pursue, with vigor, the question of potential class bias in the Binet-Simon scale. He died at an early age in 1911 (Jensen, 1980).
7. Furthermore, these 12 studies frequently were cited in the reviews of the time. For example, in Neff's (1938) literature review, he examined “a list of the more important studies” (p. 729). Of our own 12 studies selected for analysis, 9 are on Neff's list.
8. In some studies (e.g., Bridges & Coler, 1917; Chapman & Wiggins, 1925; Stroud, 1928), the authors mentioned that their participants were White; in other studies, they did not. In any event, we are confident that all participants were White. When minority (e.g., African American) children did serve in “race psychology” studies during this historical era, it was explicitly communicated in the titles of the journal articles or in the texts of the studies.
9. Actually, Goodenough (1928) administered the Kuhlman-Binet scale twice to the participants. The data that we present are from the second testing. As Goodenough commented, “The second examination is the more valid of the two measurements” (p. 288).
10. The favored school is described as being located in a very good residential area near the university, having a modern building, and having well-qualified teachers. By sharp contrast, the unfavored school is located near the railroad in a poor factory district of Columbus, is old and without up-to-date equipment, and has young teachers who have not taught for very long.
11. Regarding children who scored above the norm (third quartile), 44.2% of the students who attended the favored school did so; only 8.1% of the students from the unfavored school scored above the norm.
12. These eight studies, in alphabetical order, are Bridges and Coler (1917), Chapman and Wiggins (1925), Chauncey (1929), Dexter (1923), Goodenough (1928), Haggerty and Nash (1924), Pressey and Ralston (1919), and Stroud (1928). Of these eight studies, only two (Goodenough, 1928; Pressey & Ralston, 1919) interpreted their results as genetic as we have categorized; the other six investigations had environmental, equivocal, or “none” interpretations.
13. Although Pintner (1931) proffered a hereditarian perspective, he did discuss the subject of overlap: “We must remember, of course, that the occupational status of an individual is by no means a sure guide to his mentality. It is only in a general sense that occupational status correlates with mental ability. The distribution of children in all occupational groups runs from very low to very high intelligence. Since it is the duty of education to make the most of all the mental ability of all the pupils, educational classification according to social status is not justified. The brighter children in the lower social groups should be given just as much opportunity as they can profit by. The road through high school and college should be open to all who have the intelligence and interest to travel along it” (pp. 518–519). Furthermore, Pintner did appear to show some understanding of the problems that non-English-speaking persons have when being tested on English-language intelligence tests. Earlier in his career, Pintner (1919) commented, “The best known group tests at the present time depend largely, if not entirely, upon the knowledge and use of language. They presuppose the ability to read and write the English language. … It is very desirable to have tests which do not involve any language” (p. 199). To remedy this, he developed the Pintner Non-Language Group Test. Subsequently, research using his test demonstrated its efficacy. For example, Garth, Elson, and Morton (1936) administered the Otis Classification Test (a verbal intelligence test) and the Pintner Non-Language Group Test to 455 Mexican (i.e., Mexican American) students in Grades 4 to 8. Garth et al. concluded, “Age for age and grade for grade, the Mexican children are inferior to American whites in verbal test results. But in the non-language test results, the Mexicans are practically equal in performance to the American whites” (p. 58). Nonetheless, we find it curious that Pintner, on the one hand, would show sensitivity to the mental testing of non-English-speaking individuals, but on the other, would assert that “mental ability is inherited” (Pintner, 1931, p. 465) and observe, “The general picture [mental testing results of Mexicans and Mexican Americans] is one of low mentality. And this seems to be true for verbal, non-verbal, non-language, [Page 301]and performance tests. Undoubtedly, the recent Mexican immigration into this country is bringing in individuals of poor mental caliber” (p. 456).
14. For reviews of within- and between-racial/ethnic group studies of the relation between SES and intellectual performance among African American students, see Dreger and Miller (1960, 1968), Klineberg (1935b), Osborne and McGurk (1982b), Pettigrew (1964b), and Shuey (1958, 1966). Shuey (1966), who reviewed 42 studies, is the most comprehensive.
15. It is not clear why Beckham (1935) did not use the more recent Taussig (1920) classification.
16. The respective mean IQs for the four groups, in descending order, were New York (104.7), Washington (97.3), Baltimore (95.7), and delinquents (86.0). The higher mean IQ of the New York group might have been due to “educational selection” (Beckham, 1935, p. 89). “In Washington and Baltimore, the schools for white and colored children are separate” (p. 71). This suggests that the New York participants attended schools that were racially/ethnically mixed, probably indicating better schooling.
17. The mean IQ differences that Beckham (1935) described in this quote are slightly in error given that he used the unweighted means (weighted means analysis is called for here). Nonetheless, the patterns still hold.
18. Shuey (1966) criticized Beckham's (1935) study, arguing that the Washington sample was not randomly selected. Shuey (1966) asserted that all of the Washington participants were brought to Howard University for testing by Howard students as part of a psychology course requirement (actually, the Baltimore participants also were brought to Howard for testing by psychology students or parents or were referred by teachers). Thus, Shuey apparently contended that the grand mean IQ (95.2) was likely spurious (inflated) because “the subjects were heavily biased in favor of siblings, neighbors, and friends of the Howard students” (p. 35). Shuey failed to mention, however, that Beckham had 100 delinquents (mean IQ = 86.0) in his total sample. Valencia (1997b) deleted the mean of the delinquent group, recalculated the grand mean for the three metropolitan groups, and observed a new grand mean of 97.2 (2.0 points higher than Beckham's grand mean of 95.2 for the four groups). Valencia contended that the grand mean of 95.2 was spurious, that is, pulled down by the delinquents' mean, a mean of an unrepresentative African American adolescent group. In short, Shuey's (1966) criticism and Valencia's (1997b) recalculation could suggest that there is a canceling out effect and that, therefore, Beckham's initial grand mean is valid.
19. For a more precise comparison, we calculated the mean IQ for the adolescent Negro sample in Kennedy et al. (1963) and compared it to Beckham's (1935) mean IQ (95.2). The mean Stanford-Binet IQ for the Kennedy et al. (1963) adolescent sample (ages 12 to 16 years [same age interval reported in Beckham, 1935], n = 150) was 72.6, a difference (22.6 points) even larger than the mean difference of 14.5 IQ points between Beckham's grand mean and the Kennedy et al. grand mean.
20. The discussion of Arlitt's (1921) study is excerpted, with minor modifications, from Valencia (1997b, pp. 84–85, 103).
21. Arlitt, a psychology professor at Bryn Mawr in Pennsylvania, collected the data with the assistance of four graduate students. Thus, it is very likely that the location of the study was in the Bryn Mawr area.
22. Arlitt (1921) did not mention which edition of the Taussig classification she used. We believe, however, that it was Taussig (1920).
23. It appears that Arlitt (1921) relabeled the occupational groupings because of the home environments of the children. “The home conditions followed closely the divisions by occupation” (p. 180).
24. According to Arlitt (1921), “All of the Italians spoke English without difficulty” (p. 180).
25. Degler (1991), in his own analysis of Arlitt's (1921) study, asserted that she captured a central issue in the field of race psychology, but the implications of controlling for SES in cross-racial/ethnic investigations were not recognized quickly by researchers: “An indication of how slowly social scientists recognized the implications of findings such as those published by Arlitt is provided by some research Arlitt  herself reported the following year. In this study, Arlitt found that black children of ages five and six [years] scored above white children of the same age and [Page 302]social class but that, as the students grew older, the whites surpassed the blacks. That shift Arlitt attributed “to a genuine race difference.” On the basis of her article the previous year, one would have thought she would have asked how it came about that black children fell below whites as they grew older. Instead, she proceeded to explain why the superior scores of the black children at an earlier age may have been influenced by factors other than intelligence. In short, she fell back to the traditionalist position of assuming that race was controlling even when scores clearly called it into question” (p. 173).
26. Bird et al. (1952) did not mention the name of the intelligence test. We do suspect, however, that it was a verbally loaded group-administered test (see Shuey's [1966, pp. 182–183] listing; furthermore, the test data were provided to the researchers by school administrators, who typically administered verbal group IQ tests).
27. In their study, Bird et al. (1952) also compared the intellectual performance of White and Negro children (3rd- to 5th-graders) from another district, one of lower-SES background. Thus, between-SES group differences among Negro children are of interest here. As expected, the Negro children of higher SES background had a higher mean IQ (103.6) than did their lower-SES peers (92.3).
28. For example, in the Boston sample (high-SES group), the White and Negro mothers had completed an average of about 12.0 years of schooling. By contrast, in the Baltimore and Philadelphia sample (low-SES group), the average maternal schooling attainment was just 10.0 years for the Negro mothers and 9.5 years for the White mothers.
29. We also calculated the aggregated grand mean WISC IQ for Whites (mean IQ = 103.3) and for Negroes (mean IQ = 90.3). The mean IQ difference was 13 points, identical to what we found for the Stanford-Binet comparison.
30. The high-SES Negro group showed a slight departure from this pattern. The mean IQs were 100.7, 100.3, and 103.1 for the lowest, intermediate, and highest-SES intervals, respectively.
31. Although González (1932) offered no explanation, it appears that she selected Mexican American participants from a broad range of neighborhoods so as to avoid the range restriction problem that Ellis (1932) experienced in her similar study in San Antonio.
32. González (1932) suggested that the higher mean IQ on the Non-Language Mental Test might have been due to (a) practice effect (the Pantomime was administered first) and (b) inequivalent norms on the two tests.
33. González (1932) also reported a related analysis in which she compared the IQs of the highest-SES group (n = 33) and the lowest-SES group (n = 34). Although she did not report the r values for the groups, González found that the mean IQ for the highest-SES group was 98.6, more than 5 points higher than the mean IQ (93.2) for the lowest-SES group. The author also noted that there was considerable overlapping of IQs in the two groups.
34. González (1932) commented that when she visited the participants' homes to gather the SES data, the subject of lack of employment frequently was raised by the parents. “One of the most pitiful cases observed was that of an elderly man, who for seven years had been working as a tailor in one of the largest department stores in San Antonio and who without any warning was dismissed. They had been paying on a nice comfortable home, but after several months of non-payment, they were forced to give it up. [Incidentally], his daughter, who was included in this research, made an IQ of 120” (p. 30).
35. We assume that the term “Spanish American” refers to Mexican American. The term Spanish American is commonly used in New Mexico to refer to people of Mexican descent. Furthermore, the term “Anglo-American,” which is used by Christiansen and Livermore (1970), is a common referent for Whites in New Mexico and other southwestern states.
36. Children of white-collar workers (e.g., professional, managerial) were considered middle class. Children of blue-collar workers (e.g., semiskilled, laborers) were deemed lower class.
37. As can be seen in Table 3.6 and in the preceding paragraph, SES was particularly powerful in differentiating participants on Verbal Scale IQ. Regarding the lower Verbal Scale IQ scores of the Mexican American participants, Christiansen and Livermore (1970) commented that the [Page 303]depressed scores, “regardless of social class, were probably due to the bilingual nature of the typical Spanish American home which makes it more difficult for these children to acquire many of the verbal skills needed in an Anglo culture” (p. 12). It is not clear from this conjecture whether the authors were suggesting that bilingual homes thwart English-language development (a contentious point, by the way) or whether there simply were limited opportunities to learn English in these homes. Nonetheless, the authors should have assessed all the Mexican American participants' fluency, proficiency, and dominance in English before undertaking this investigation.
38. Oakland (1978) did not report the n's for the six participant groups. However, based on some data about sample sizes he did report (p. 575, Table 1), we surmise that the n's for the six groups range somewhere between the high 20s and high 50s.
39. The administration of the Slosson Intelligence Test was just one aspect of Oakland's (1978) study. His main purpose was to investigate the predictive validity of six tests of academic readiness across the three racial/ethnic groups.
40. The investigation by Valencia et al. (1995) did have SES as a control variable of concern. The major purpose, however, was to investigate content bias on the K-ABC (Kaufman & Kaufman, 1983a). This study is discussed in Chapter 5 of the present book.
41. Because the population of White 5th-grade students was smaller than that of the Mexican American population, it was necessary to select 24 White 6th-graders who met the SES criterion.
42. The Mexican American participants (50 boys, 50 girls) attended public schools in a highly segregated school district in central California. Of the 100 White children, 37 also attended the high-enrollment Mexican American district, and 63 attended schools in a contiguous, fairly segregated White district in the same community. For the total White group, there were 45 and 55 boys and girls, respectively. The mean ages of the two racial/ethnic groups were quite similar—11.3 and 11.1 years for the White and Mexican American groups, respectively.
43. The K-ABC also contains a totally separate Achievement Scale that focuses on children's knowledge of acquired facts and skills learned in the school and home environments. Given that our focus in this chapter is on intellectual performance and not academic achievement, the results of the participants' performance on the Achievement Scale in Valencia et al. (1995) are not of interest here. Such results are of importance, however, in our coverage of test bias in Chapter 5 of this book.
44. It needs to be noted that Jensen (1973a) was referring to intellectual differences between social classes but within racial groups. Later, however, Jensen suggested that such differences also are found between racial groups (see his Chapters 7 and 11).
45. Shuey (1966) also noted, “Twenty five of the 41 studies were located in the North, and in at least fourteen of the [research studies,] the colored and white children were not only attending the same schools but were living in the same district or neighborhood. The combined mean difference in IQ between the 2,760 colored subjects' tests in the North and the whites of comparable socioeconomic status or occupation was 7.6. Nearly all of these Ss in the eighteen studies were of school age, [with] the whites and Negroes attending the same school and living in the same areas, many with large Negro populations (p. 519).
46. These investigations are Coleman et al. (1966), Scarr-Salapatek (1971), and Wilson (1967).
47. This discussion of Herrnstein and Murray (1994) is excerpted, with minor modifications, from Valencia and Solórzano (1997, pp. 174,201).
48. Herrnstein and Murray (1994), based on the normal distribution of IQ scores, broke the continuum into five “cognitive classes.” The respective classes (with names given, IQ intervals, and percentages of cases) are as follows: Class I (very bright, 125 and above, 5%), Class II (bright, 110 to 124, 20%), Class III (normal, 90 to 109, 50%), Class IV (dull, 75 to 89, 20%), and Class V (very dull, below 75, 5%). These classifications by IQ are very similar to the interval breaks that testing specialists have used since the time of Lewis Terman. Herrnstein and Murray noted, however, that they substituted more “neutral” terms (e.g., “very dull”) for “less damning terms” (e.g., “retarded”).
49. The issue of caste in U.S. society has a long scholarly history. Feagin (1984) pointed out that the “caste school” of race relations developed during the late 1940s under the leadership of [Page 304]W. Lloyd Warner (Warner, 1941, 1949; Warner & Srole, 1945). The caste school of race relations focused on the post-Civil War era during which structured patterns of discrimination replaced the now defunct system of slavery in the South. The new social system, based on caste, led to severe economic and social inequality (Feagin, 1984). Another early analyst of the caste school of race relations was Oliver C. Cox (1948).
50. Ogbu's scholarship is not without its critics. See, for example, Foley (1991) and Trueba (1991).
51. Blau (1981) operationalized “middle-class exposure score” as “the proportion of close neighbors in white-collar occupations, of college-educated neighbors, and of neighbors with a child who has gone on to college … [as well as] the mean education of mother's three closest friends” (p. 18).
52. Valencia, Rankin, and Henderson (1986), in a path-analytic investigation of White (n = 632), African American (n = 488), and Mexican American (n = 558) schoolchildren (ages 5 to 12 years), found weaker status consistency (intercorrelations of parental occupational status and educational attainment) among the two minority groups, compared to that among the White group.
53. Parents' “demographic origins” was a composite score based on four measures (e.g., location of community where parent grew up, size of home community).
54. For discussions of how this is supported mathematically, see Knapp (1977) and Robinson (1950).
55. Although White (1982), in this particular analysis, collapsed studies across dependent measures (achievement and intelligence), the observed r of .35 can be fairly generalizable to studies that examined only the relation between SES and intelligence. That is, achievement and intelligence tests share much in common. This assertion is based on the observation that achievement and intelligence tests show a typical range of correlations of .60 to .70 for elementary school students and of .50 to .60 for high school students (Jensen, 1980).
56. See, for example, Boocock (1972), Charters (1963), and Coleman et al. (1966).
1. For reviews of this literature and reports of empirical studies, see, for example, Gottfried (1984), Henderson (1981), Laosa and Sigel (1982), Marjoribanks (1979), and Walberg and Marjoribanks (1976).
2. The Whittier Scale evaluated five aspects of the home environment: (a) necessities, (b) neatness, (c) size, (d) parental conditions, and (e) parental supervision.
3. Correlations between the various predictor variables and children's IQ for the control group were of higher magnitudes. In descending order, the correlations (corrected for attenuation) were mother's mental age (.57), father's mental age (.55), father's vocabulary (.52), cultural index (.49), mother's vocabulary and Whittier Scale (both .48), and family income (.26).
4. As noted by Wachs and Gruen (1982), Van Alstyne also reported that the home environment variables were correlated with maternal IQ. Wachs and Gruen suggested that this relation might reflect the action of “Type 1 organism-environment covariance,” which refers to “parents transmitting not only genes but also rearing environments to their offspring” (p. 43).
5. The construct of a press variable stems from Murray's (1938) theory of personality. He proposed that an environment can be classified by the ways in which an individual is harmed or benefited (Marjoribanks, 1979). According to Murray (1938), if the environment has an effect that is potentially harmful, then the individual will avoid the environment or defend himself or herself. On the other hand, if the environment has an effect that is potentially beneficial, then the individual typically will select to interact with it. This directional tendency of avoidance/approach is conceived as the “press of the environment.” In his theoretical framework, Murray distinguished between the “alpha press” (i.e., the environment as seen by observers) and the “beta press” (i.e., the individual's perception of the environment). Whereas Murray focused on analyzing the beta [Page 305]press of the family environment, Bloom (1964) and his doctoral students concentrated on the alpha press of family environments (Marjoribanks, 1979).
6. In his dissertation investigation, Davé's (1963) home environment measure consisted of six press variables and 21 process characteristics (for the listing, see Marjoribanks, 1979, pp. 33–34). The criterion variable was the Metropolitan Achievement Tests (MAT), a measure of academic achievement. When the home environment ratings were correlated with MAT scores, Davé (1963) reported multiple correlations ranging from .56 to .79 on the various MAT subtests. The overall index of the home environment rating scale correlated .80 with the total score on the MAT (Marjoribanks, 1979, Table 2.3).
7. Weiss (1969), in his dissertation, examined the relation between the home environment and measures of achievement motivation and self-esteem for 53 boys and girls (M age = 11 years) attending public schools in an Illinois community. Weiss formulated three press variables and 15 process characteristics to define the subenvironment for achievement motivation as well as three press variables and 16 process characteristics to define the subenvironment for self-esteem (for the listing, see Marjoribanks, 1979, pp. 46–47). Weiss found multiple correlations ranging from .27 to .79 when the home environment rating scales were correlated with the criterion measures (Marjoribanks, 1979, Table 2.4).
8. Marjoribanks (1979) also spoke of the “British” school of family environment research. He commented, “As the research of the ‘Chicago’ school was undertaken, another set of studies, which may be loosely grouped together as the ‘British’ school, was being generated. Although these latter studies did not conspicuously adopt Murray's  concept of the alpha press [or] generally use Bloom's  model of sub-environments of press variables, they may be interpreted in relation to these constructs” (p. 31). The interested reader might want to inspect Marjoribanks (1979, Figure 2.2), who provided an informative flowchart of the origins and development of the Chicago and British schools of family environment research.
9. In their review article, Bradley and Caldwell (1978) referred to the impact of Barker and Wright (1954); Escalona and Corman (1971); Honzik (1967); Schaefer, Bell, and Bayley (1959); White and Carew (1973); Whiteman, Brown, and Deutsch (1967); and Yarrow, Rubenstein, and Pederson (1975).
10. For the complete set of 12 propositions, see Henderson (1981).
11. For a list of the 45 items, see Bradley and Caldwell (1984).
12. For a list of the eight subscales, see Gottfried and Gottfried (1984).
13. See, for example, Barnard, Bee, and Hammond (1984); Bradley and Caldwell (1976a, 1976b, 1977, 1980, 1981, 1984); Bradley, Caldwell, and Elardo (1977, 1979); Bradley et al. (1989); Elardo, Bradley, and Caldwell (1975, 1977); Gottfried and Gottfried (1984); Hawk et al. (1986); Johnson, Breckenridge, and McGowan (1984); Johnson et al. (1993); Ramey, MacPhee, and Yeates (1982); Ramey, Mills, Campbell, and O'Brien (1975); Siegel (1984); Wachs, Uzgiris, and Hunt (1971); and Wulbert, Inglis, Kriegsman, and Mills (1975).
14. This set of studies consists of Bradley and Caldwell (1976a, 1976b, 1977) and Elardo et al. (1975, 1977).
15. For a comprehensive and very readable discussion of nonshared and shared environments, see Plomin et al. (1997, Chapter 14).
16. Plomin et al. (1997) asserted that phenotypic variance for cognition is “caused” by environmental variance as well as genetic variance (about 50%). The environmental variance is overwhelmingly of the nonshared variety. According to Plomin et al., environmental factors (both nonshared and shared) are not restricted to the home. Peer groups and educational experiences, for example, also constitute one's environment.
17. Stoolmiller (1999) noted that until more definitive data are made available, his 50% estimate should be viewed as an upper limit.
18. Two notions central to this finding are “genotype-environment correlation” and “genotype-environment interaction.” For explication, see Plomin et al. (1997, Chapter 14).
19. For a discussion of “passive genotype-environment correlation,” see Plomin et al. (1997).
[Page 306]20. These six studies are Bracken, Howell, and Crain (1993); Bradley and Caldwell (1976a, 1976b, 1977, 1980); and Elardo et al. (1975).
21. In two studies (Bradley & Caldwell, 1981; Trotman, 1977), an academic achievement test was used as a criterion measure. Although our focus is on the relation between home environment and measured intelligence, we listed those studies that used academic achievement as the criterion variable given the small number of total investigations that used African American participants.
22. The study by Trotman (listed in Table 4.1) also was published in 1977. That study's African American and White participants were in the 9th grade.
23. The ISC places families in social classes according to a total of weighted ratings based on occupation, source of income, house type, and dwelling area.
24. Trotman's (1977) investigation has been severely criticized on substantive and methodological lines. Longstreth (1978) called into question the reliability and validity of the Wolf (1964) home environment measure, which Trotman used. In our view, it appears that Longstreth did not think highly of maternal interview studies in general. Longstreth (1978) also criticized Trotman for not considering the role of “child-to-parent effects” on shaping the home environment. This criticism has merit, and we briefly discuss this issue later in this chapter. Wolff (1978) commented that Trotman's (1977) study is fatally flawed. First, Wolff (1978) noted that Trotman might be guilty of researcher bias (i.e., her “strong environmentalist leanings” [p. 473]). Second, Wolff commented that Trotman's observed correlations were anomalous (i.e., not showing greater attenuation, compared to existing research). For responses to her critics, see Trotman (1978).
25. These six sites are Seattle, Washington; Hamilton, Ontario; Chapel Hill, North Carolina; Houston, Texas; Fullerton, California; and Little Rock, Arkansas.
26. These investigations are Barnard et al. (1984); Bradley and Caldwell (1984); Caldwell, Elardo, and Elardo (1972); Gottfried and Gottfried (1984); Johnson et al. (1984); Ramey et al. (1982); and Siegel (1984).
27. Because of a confound between race/ethnicity and SES, White, African American, and Mexican American matched samples were constituted. Triads were matched on their 12-month HOME total scores. The result, in effect, was to eliminate a substantial proportion of the middle-class White participants. A total of 112 exact matches were made (±1 point) from the original total sample of more than 900 children.
28. For further discussion of this point, see Chapter 3 of the present book
29. The sample of 65 White children included 6 children “with Spanish surnames” (whom we assume to be Mexican American) and 1 Asian American child. Given the very small percentage (11%) of minority students in the White sample, it does not appear that there is confoundment of data.
30. We also located an unpublished investigation (doctoral dissertation) in which Mexican Americans served as participants (Wickwire, 1971).
31. These seven sociological factors contain 32 characteristics. For a listing, see Henderson and Merritt (1968, Table 1).
32. Henderson and Merritt (1968) is based on Henderson's (1966) doctoral dissertation. For further discussion of the Spanish vocabulary test, see Henderson (1966).
33. For a listing of the items used in the development and validation of the HELPS, see Henderson et al. (1972, pp. 191–192, Table 1).
34. The SEAT is an academic achievement test, and the BTBC is a school readiness test. Although neither instrument is considered a measure of intelligence (the criterion measure of focus in this chapter), we decided to include the Henderson et al. (1972) study for our review given the small number of investigations on the relation between home environment and Mexican American children's measured intelligence.
35. In Valencia et al. (1985), the authors made some changes in the HELPS on the basis of pilot test data suggesting that “(a) some of the items were culturally sensitive; (b) some items were [Page 307]redundant with information from the family background information questionnaire; and (c) a few items were judged to be age inappropriate for this particular sample” (p. 326).
36. The criterion score of the MSCA used in Valencia et al. (1985) was the GCI, which is a global index of cognitive functioning.
37. The Spanish version of the MSCA was developed by Valencia and Cruz (1981). In Valencia et al. (1985), it was used for research purposes, and it is not recommended for use in the psychoeducational assessment of Mexican American children.
38. For a study that sought to investigate child (→) parent effects, see Bradley et al. (1979). For general readings on child effects on adults, see, for example, Bell (1974) and Bell and Harper (1977).
39. Ceci (1990) raised a very important point about the relation between schooling attainment and measured intelligence vis-à-vis African Americans, Latinos, and American Indians. Implicit in his conjecture is that limited access to schooling and subsequent diminishment in schooling attainment for minorities are associated with the persistence of lower IQs, compared to their White peers. Drawing from Sarason and Doris's (1979) review of literature on early immigrants' IQs, Ceci (1990) commented, “What Sarason and Doris  show so nicely in their review is that as the levels of school completion increased among Italian-American children during the first five decades of this century, so did their IQs. I mention this review because nearly every social commentator and IQ researcher during the early part of this century was convinced that the low IQs of Italian-American children were due to nonenvironmental causes. As a matter of fact, no one (except Sarason and Doris) has bothered to ask what happened to the second- and third-generation offspring of these Italian families and what this development implies about the persistence of low IQs among blacks, Hispanics, and American Indians. Today, Italian-American students' IQs are not considered remarkably low (they are, in fact, slightly above average).” (p. 80)
40. For a comprehensive discussion of the construct of deficit thinking, see Valencia (1997a).
1. For more discussion of these cases, see Chapter 1 of the present book.
2. Test validity investigations involving a single group sometimes are referred to as single population validity studies. For example, Valencia (1984), in a concurrent validity study, administered the Kaufman Assessment Battery for Children (K-ABC; Kaufman & Kaufman, 1983a) and the Wechsler Preschool and Primary Scale of Intelligence (WPPSI; Wechsler, 1967) to 42 English-speaking Mexican American preschool children. Statistically significant correlations between K-ABC Global Scale Standard scores and WPPSI IQ Scale scores were observed. In addition to validity research, single population studies can be undertaken on intelligence test reliability (Valencia & Rankin, 1983) as well as on stability (Valencia, 1985b). Although single population studies are useful in understanding a test's psychometric integrity with minority groups, such investigations are limited for assessing cultural bias given the absence of the White comparison group.
3. As we shall see later, in the case of content validity (i.e., item bias) studies, bias also can be detected against the major group.
4. After we had sent our book manuscript to Sage Publications for the production phase, we identified two test bias investigations pertaining to the WISC-III (Weiss & Prifitera, 1995; Weiss, Prifitera, & Roid, 1993). Due to this timing, we were not able to incorporate these studies into our comprehensive analysis of test bias findings provided in this chapter. These two studies (predictive validity bias) found that the WISC-III predicted achievement scores about equally well for African American, Hispanic, and White students. Weiss et al. (1993) did note, however, that using the common regression line overpredicted minority achievement. Thus, if one uses the common regression line (not the minority regression line), then minority students are likely to be overidentified as learning disabled. This contention also supports the findings of Olivárez, Palmer, and Guillemard (1992, No. 34) and Palmer, Olivárez, Wilson, and Fordyce (1989, No. 35), investigations that we discuss later in this chapter.
[Page 308]5. This N of nearly 46,000 participants (as well as the n's for total major and minor group participants) is inflated. In some studies (e.g., Valencia & Rankin, 1986, Nos. 57 and 58), the researchers examined more than one psychometric property of one instrument. Therefore, we counted this as two separate studies, and the sample sizes were counted twice.
6. Some investigations used standard SES measures (e.g., Valencia, 1984, No. 55, used the Warner Revised Occupational Rating Scale [Warner, Meeker, & Eells, 1960]). Other investigations used indirect SES measures. For example, Valencia, Rankin, and Livingston (1995, No. 60) used participants' status in the school district's free or reduced-price lunch program (enrolled/not enrolled).
7. Carlson and Jensen (1981, No. 3) also computed reliability estimates using Hoyt's formula (analogous to the Kuder-Richardson Formula 20) and split-half correlations corrected for attenuation.
8. Valencia (1984, No. 55) and Valencia and Rankin (1986–2, No. 58) used the procedure developed by Feldt (1969), which results in a test statistic distributed as F with N1 – 1df (v1) and N2 – 1df (v2).
9. Investigators frequently use the coefficient of congruence (Harman, 1976; Levine, 1977) to determine the degree of similarity of the various factors across the major and minor groups. The coefficient of congruence is an index of factorial similarity that has interpretive aspects similar to the Pearson r.
10. Bias in content validity also can be investigated at the subtest level (see, e.g., Study Nos. 56 and 60). Our focus here, however, is on bias at the item level.
11. In two such studies we reviewed, the empirically defined and testable definition of content bias suggested by Reynolds (1982b) served as a guide. See Valencia and Rankin (1985, No. 56) and Valencia et al. (1995, No. 60).
12. The PASE (1980) case, which resulted in a landmark legal decision involving the issue of cultural bias in intelligence tests, was initiated by African American students in Chicago. Plaintiffs argued that they were misdiagnosed as mentally retarded based on intelligence tests that were racially biased. Although Grady concluded that the WISC contained some biased items, he eventually upheld that the WISC (as well as the WISC-R and Stanford-Binet) were valid. His method of bias detection and his decision have been severely criticized by some members of the measurement community. For example, Bersoff (1984) commented, “[Grady's analysis] can best be described as naive. At worst, it was unintelligent and completely devoid of empirical content. … At bottom, what it represented was a single person's subjective and personal judgment cloaked in the apparent authority of judicial robes. If submitted as a study to one of psychology's more respected refereed journals, rather than masquerading as a legal opinion, it would have been summarily rejected as an experiment whose sample size and lack of objectivity stamped it as unworthy of publication” (p. 100).
13. The seven items include three from the Information subtest and four from the Comprehension subtest.
14. The 15 biased items were distributed as follows: Vocabulary subtest (n = 6), Information subtest (n = 5), and Similarities subtest (n = 4).
15. This statistical method calls for the correlation between the item and group membership with the contribution of the general ability true score partialled out. The particular item under examination is not included in the true score. The test for bias was the significance of the item-partial correlation, with subgroup membership (English- and Spanish-speaking) controlling for the total score (MSCA General Cognitive Ability, an IQ analog), age, and sex.
16. The subtests are Word Knowledge II, Verbal Memory I, Draw-a-Child, Draw-a-Design, and Opposite Analogies.
17. There is a considerable amount of empirical research on the word length effect. As the length of the stimuli words to be recalled increases (as measured by syllable count), short-term [Page 309]memory becomes taxed (see, e.g., Baddeley, Thomson, & Buchanan, 1975; Eriksen, Pollack, & Montague, 1970). Regarding the acoustic similarity effect, research has found that systematic confusion occurs when words are very similar in phonemic structure. Such confusion arises in short-term memory for item and order information; thus, limited recall occurs (see, e.g., Conrad, 1964; Baddeley, 1976).
18. The subtests in which biased items were detected are Hand Movements, Gestalt Closure, Number Recall, Triangles, Spatial Memory, and Photo Series.
19. The numbers and percentages of biased items in the respective Achievement Scale subtests are as follows: Faces and Places (15 of 26, 57.7%), Arithmetic (10 of 16, 62.5%), Riddles (16 of 18, 88.9%), Reading/Decoding (9 of 16, 56.3%), and Reading/Understanding (8 of 16, 50%).
20. Valencia et al. (1995, No. 60) noted, “An interesting question we asked about possible ethnic differences in achievement was, ‘How did the 37 White children fare who attended Mexican American segregated schools?’ To address this question, we ran a one-way ANOVA. The groups examined were Mexican Americans in segregated schools (Group 1, n = 100), Whites in Mexican American segregated schools (Group 2, n = 37), and Whites in White segregated schools (Group 3, n = 63). The criterion variable was the K-ABC Achievement Scale standard score. As expected, the observed F was significant, F(2, 197) = 21.51, p = .00001, MSe = 107.00. Scheffé tests revealed that the mean K-ABC Achievement score for Group 1 (M = 91.33) was significantly lower than the mean K-ABC Achievement scores for Group 2 (M = 98.41) and Group 3 (M = 101.92). There was no significant difference between Groups 2 and 3. These findings suggest that the White students and the Mexican American students attending the Mexican American segregated schools were different populations. It is unknown, however, whether learning opportunities differed for the two ethnic groups in the Mexican American segregated schools” (pp. 166–167).
21. White (1982), in a review of literature that examined the relation between SES and academic achievement (and measured intelligence), reported a mean correlation of .33 between SES and IQ.
22. Halpern (1997) also noted that males, compared to females, are overrepresented at the low-ability end of distributions in mental retardation, dyslexia, stuttering, attention disorders, and delayed speech.
1. It is beyond the scope of this chapter to present comprehensive discussions of the history, assumptions, models, and findings germane to the field of BGIA. We refer the reader to the following examples of literature in this area: Chipeur, Rovine, and Plomin (1990); Loehlin (1989); McArdle and Goldsmith (1990); McArdle and Prescott (1997); Plomin, DeFries, McClearn, and Rutter (1997); Plomin and Neiderhiser (1991); Plomin and Rende (1990); Scarr (1992, 1997); and Scarr and Carter-Saltzman (1982). Of these publications, McArdle and Prescott (1997) offer one of the best discussions (e.g., comprehensiveness, readability, balance) of the BGIA field.
2. Some examples of publications that voice diverse views pertaining to this debate include Baumrind (1993); Block and Dworkin (1976); Bouchard (1997); Brody (1992); Brody and Brody (1976); Ceci (1990); Ehrman, Omenn, and Caspari (1972); Fancher (1985); Jackson (1993); Kelves (1985); Lewontin, Rose, and Kamin (1984); Neisser (1998); Pearson (1991); Scarr (1997); Steen (1996); Storfer (1990); and Taylor (1980). One book that we particularly recommend is Intelligence, Heredity, and Environment (Sternberg & Grigorenko, 1997). This book offers a comprehensive, fairly balanced, and updated survey on the nature-nurture debate in relation to measured intelligence. We do, however, have some concerns about balance at the book's end (see Note 21 of the present chapter).
3. An example of advances in gene location can be seen in “gene mapping.” McArdle and Prescott (1997) noted, “Rather than inferring genetic influence by studying resemblance among relatives, these methods attempt to locate specific locations in the genome that code for specific [Page 310]functions. Some researchers have begun to apply this approach to intelligence by initiating a molecular genetic search for a small number of single genes of relatively large effect using a methodology termed ‘quantitative trait loci’ (QTL)” (pp. 426–427).
4. Genotype refers to the “sum total of the genetic material in a particular individual, i.e., the total genetic constitution, either at one specific locus or more generally” (Kaplan, 1976, p. 434). Phenotype refers to “the appearance of an individual that results from genotype and environment” (Plomin et al., 1997, p. 315).
5. A related, but distinct, term is “range of reaction” (or “reaction range”). Miller (1997) referred to reaction range “as the distribution of observed phenotypic reactions associated with a given genotype” (p. 293). For a discussion of the conceptual differences between norm of reaction and range of reaction, see Wahlsten and Gottlieb (1997).
6. Hirsch (1976) commented that the norm of reaction is of fundamental significance to understand “because it saves us from being taken in by glib and misleading textbook clichés such as ‘heredity sets the limits but environment determines the extent of development within those limits’ ” (p. 163).
7. More recently, Wahlsten and Gottlieb (1997) made a similar assertion: “The reaction norm is something to be discovered through experimentation, and it allows us no prediction of what will happen when an individual is placed beyond a rearing environment that has already been observed in scientific studies” (p. 172).
8. There remains, however, differences in opinion about the empirical role of the norm of reaction in human development (Baumrind, 1993; Ceci, Rosenblum, de Bruyn, & Lee, 1997; Miller, 1997; Scarr, 1993). Miller (1997), arguing from a cultural psychology perspective, noted that the behavioral genetic perspective (Scarr, 1993) appears to downplay the notion of reaction norm and, by contrast, emphasizes genetically determined reaction range. Miller (1997) commented, “From this perspective [behavioral genetics], claims that development should be understood as an open process [e.g., cultural psychology] are rejected as purely hypothetical in nature since there appears to be no empirical evidence of environmental variation in nonimpaired intellectual outcomes. The indeterminate view of development implied by the concept of reaction norm is also rejected as incompatible with the predictive goals of science” (p. 294).
Critics of the behavioral genetics perspective that the reaction range is genetically determined assert that this view does not account for heredity by environment interaction. For example, Baumrind (1993) contended, “The norm of reaction for a particular individual or group can be ascertained only by attempting to expand it, and prevention or intervention efforts are limited by extant scientific knowledge. Change an individual's experience and, within indeterminate limits, the individual can be changed” (p. 1302).
9. For a discussion of the distinctions between broad- and narrow-sense heritability, see Plomin et al. (1997). It is beyond the scope of this chapter to describe the mathematical models and procedures for estimating the heritability of intelligence. The interested reader might want to refer to the following examples of sources for such explications: Bouchard (1997); Cancro (1971); Falconer (1990); Jensen (1969); Loehlin, Lindzey, and Spuhler (1975); McArdle and Prescott (1997); Plomin et al. (1997); Plomin and Loehlin (1989); Scarr and Carter-Saltzman (1982); and Schönemann (1994). For critiques of how the heritability of intelligence has been estimated and conclusions have been drawn from such work, see Farber (1981), Kamin (1974), Lewontin et al. (1984), and Taylor (1980). Furthermore, there are discussions that assert that calculations of heritability estimates of intelligence frequently violate critical assumptions of the statistical models used and use statistical techniques that are insensitive to identifying heredity-environment interaction (see Wahlsten, 1990, and those who concur with his position: Bullock, 1990; Chiszar & Gollin, 1990; and Schönemann, 1994). Finally, Lewontin (1975) commented that to have a proper study of heritability, there are six requirements that must be met (e.g., sample sizes need to be large [at least several hundred families], the various relationship pairs must be representative of the [Page 311]population to which the heritability estimate is applied such as representativeness in schooling attainment or SES). Lewontin concluded, “In fact, no study of the genetics of IQ has even come close to fulfilling the six requirements, and most studies fail all tests in a significant way” (p. 394).
10. A similar point was made by Kamin (1974): “They [MZ twins] presumably have identical genes. They are necessarily of the same sex, and their physical similarities are typically very striking” (p. 33). Kamin continued, “Evidence [from studies of separated MZ twins] seems especially powerful because it is based on fewer—and simpler—arbitrary assumptions that must be made to interpret other forms of data. … MZ twin pairs seem uniquely equipped to serve as subjects in experiments concerned with heredity” (p. 33).
11. Critics of the research on MZ twins separated and reared apart are not without their countercritics. For critical examinations of Farber (1981), see Bouchard (1982a), Brody (1992), Locurto (1991), and Loehlin (1981), all cited in Bouchard (1997). For criticisms of Kamin (1974), see Bouchard (1982b), Fulker (1975), and D. N. Jackson (1975), all cited in Bouchard (1997).
12. Taylor (1980) pointed out that there actually is a fourth condition: Prenatal (intrauterine) environmental effects on each twin pair are equal and negligible in magnitude.
13. In addition to his reanalysis of the three investigations, Taylor's (1980) conclusion was informed by additional analyses throughout his book.
14. A polygenetic theory of inheritance of intelligence is “the higher the proportion of genes two family members have in common, the higher the average correlation between their IQ's” (Bouchard & McGue, 1981, p. 1055).
15. McArdle and Prescott (1997) commented that the earlier studies commonly sampled individuals from a restricted range of SES backgrounds. Thus, this sampling procedure likely resulted in environmental variance that was limited and probably resulted in a greater percentage of the observed variance being ascribed to genetic sources.
16. For these possible explanations of why newer research, in comparison to older research, has resulted in conclusions of lower estimates of genetic influence on measured intelligence, McArdle and Prescott (1997) referred to Chipeur et al. (1990); Loehlin (1989); Loehlin, Horn, and Willerman (1989); and Plomin and DeFries (1980).
17. It needs to be pointed out that there are many scholars (including a number of leading geneticists) who assert that the heritability construct itself is a misguided notion (for a discussion, see Tucker, 1994, pp. 224–225). For example, Otto Kempthorne, an expert in biostatistics, contended that “heritability does not even exist in the human context” (Kempthorne, 1978, p. 19).
18. The authors of Not in Our Genes (Lewontin et al., 1984) make an interesting team regarding their academic fields. Lewontin is an evolutionary geneticist, Rose is a neurobiologist, and Kamin is a psychologist.
19. For further discussion and critiques of biological determinism, see The Dialectical Biologist (Levins & Lewontin, 1985).
20. Suffice it to say, Scarr's (1992) theorizing did not escape challenges. Baumrind (1993), for example, argued that considerable evidence has accrued from child development and socialization literature to support the position that “what normal parents do or fail to do crucially affects their children's development” (p. 1299). Jackson (1993), who also disagreed with Scarr's (1992) position, focused on rival hypotheses that could explain findings from empirical research on human behavioral genetics, thus leading to environment (→) phenotype interpretations. For her reply to Baumrind (1993) and Jackson (1993), see Scarr (1993).
21. The subject of balance is, in our opinion, a bit uneven at the book's end. We found it curious that Sternberg and Grigorenko (1997) did not participate in the “Integration and Conclusions” section. Rather, they allowed two authors who did not contribute to the core chapters to provide closing chapters. The chapter by Hunt (1997), who appeared to place himself as the referee in the nature-nurture “Super Bowl,” called it a “lopsided victory…. Nature wins 48–6” (p. 531). If this metaphor is not graphic enough, Hunt also likened the nature-nurture debate to “a stomping match between Godzilla and Bambi. For those who did not see the movie, Godzilla wins. Yet we all [Page 312]love Bambi. Why do people keep looking for cultural explanations of intelligence?” (p. 546). It is this latter point about “cultural explanations” that signals Hunt's theoretical bias. To Hunt, the various “culturalist” (his term) arguments in the book by Sternberg and Grigorenko (1997) are mere “worldviews” of “humanistic arguments,” whereas the “nativist side” advances “scientific arguments.” To us, this manufactured distinction between the genetic and cultural/environmental perspectives is harsh and inaccurate because it sets up the latter as a straw person. That is, by relegating the cultural/environmental perspective to a location outside the realm of scientific inquiry, Hunt (1997) created a weak opposition whose arguments are easily refuted. Although Hunt did call for scholars to “go beyond statistics to investigate the mechanisms of genetic action, including the interaction between genetic predisposition and the environment” (p. 549), most of what he presented in his chapter is biased in favor of the behavioral genetics field. At least, Sternberg and Grigorenko (1997) should have allowed rejoinders from the so-called “culturalists” (e.g., Ceci et al., 1997; Gardner et al., 1997; Miller, 1997). This would have provided the reader with a more stimulating and balanced conclusion.
22. The symbol systems approach described by Gardner et al. (1997), which we introduced in Chapter 2 of this book, “holds that the human experience—the thought and behavior of our species—is most felicitously analyzed in terms of the symbol and systems of symbols [e.g., language, mathematics] in which human beings traffic from early in life” (p. 246). The authors asserted that their approach can be seen as a “third perspective” that integrates the nature-nurture debate.
23. The notion of “proximal processes,” which Ceci et al. (1997) defined as “reciprocal interactions between the developing child and other persons, objects, and symbols in [his or her] immediate environment” (p. 311), is somewhat similar to what we referred to as “proximal variable” in Chapter 4 of the present book. For a brief introduction to the bioecological model of intellectual development, see Chapter 2 of the present book.
24. Miller (1997) noted that cultural psychology is an emerging tradition that can be characterized as having a variety of shared assumptions and goals. Its various perspectives include, for example, psychological anthropology, cultural-social psychology, and sociolinguistics. Theory and research from cultural psychology, Miller asserted, challenges a number of assumptions underlying research in behavioral genetics.
25. For a similar point of view on the invalidity of the separation of nature and nurture effects, see Wahlsten and Gottlieb (1997).
26. For examples of other scholars whose writings are part of this growing alternative framework, see Bronfenbrenner and Ceci (1993), Gottlieb (1970, 1992), Lerner (1980, 1991), and Oyama (1985), all cited in Bidell and Fischer (1997).
27. The following sections on Shuey, Garrett, Jensen, Dunn, and the team of Herrnstein and Murray are excerpted, with some revisions (deletions and expansions) from Valencia and Solórzano (1997, pp. 160–183).
28. These criticisms are in reference to Shuey's (1958) first edition of The Testing of Negro Intelligence. Her genetic hypothesis of racial differences in intelligence was presented in both the 1958 and 1966 editions.
29. Estimated ω2 is a measure of the association between the independent and dependent variables, calculated after a statistical test of significance is run. Estimated ω2 has the element of “practical significance.” That is, it estimates the proportion of variance in the dependent variable that can be accounted for by specifying the independent variable. Estimated ω2 is more meaningful than statistical significance because it can be used to inform decisions about practical matters.
30. For a discussion on how hereditarianism was debunked during the 1920s, see Valencia (1997b) and Chapter 1 of the present book. For coverage of what led to the continuing demise of hereditarianism during the 1930s and 1940s, see Foley (1997). For a slightly different interpretation of the vicissitudes of hereditarian thought during these decades, see Provine (1986).
31. After Shuey's death, and in her memory, Osborne and McGurk (1982b) published an edited book, The Testing of Negro Intelligence: Volume 2. The book covered new material on Black-White studies of intellectual performance, spanning 1966 to 1979. Osborne and McGurk (1982a), [Page 313]echoing (word by word) Shuey's earlier conclusion, commented that Black-White differences in intelligence “inevitably point to the presence of native differences between Negroes and Whites as determined by intelligence tests” (p. 297).
32. For interesting discussions of the events that led to the publication of Jensen's (1969) monograph and accounts of what transpired shortly after publication, see Fancher (1985), Jensen (1972), and Pearson (1991). These accounts attest to the highly politicized climate surrounding the publication of Jensen's (1969) treatise.
33. It appears that Jensen's current understanding of the magnitude of heritability of intelligence has changed from years past. “The broad heritability of IQ is about .40 to .50 when measured in children, [is] about .60 to .70 in adolescents and young adults, and approaches .80 in later maturity” (Jensen, 1998, p. 169).
34. Jensen (1998) noted that, based on citation data from the Institute for Scientific Information, his 1969 HER article soon became a “citation classic”—a publication that has an unusually high frequency of being cited in the scientific literature. Based on a list of the 100 most frequently cited articles in the social science literature (1969–1978) compiled by Tucker (1994), Jensen's (1969) article ranked number 6. Tucker (1994) did comment, however, that the other articles tended to be “seminal works” in their fields, whereas Jensen's (1969) article was frequently cited because it was a subject of controversy.
35. Whether Jensen wished to embrace this accolade is, perhaps, another question.
36. For examples of publications in response to Jensen (1969) and writings about the debate that ensued, see Block and Dworkin (1976), Eysenck (1971), Fancher (1985), Flynn (1980), Herrnstein (1973), Jensen (1972), Kamin (1974), Modgil and Modgil (1987), Pearson (1991), Snyderman and Rothman (1988), and Tucker (1994). See also “Bibliography of Articles About ‘How Much Can We Boost IQ and Scholastic Achievement? by Arthur R. Jensen” (in Jensen, 1972).
37. Although Jensen (1969) did not cite the Westinghouse-Ohio study, it appears that he was referring to the preliminary report by Cicirelli et al. (1969).
38. During recent years, however, the case of fraud against Burt appears to have weakened. Fletcher (1991) and Joynson (1990) presented unabashed defenses of Burt's work and reputation. Part of these authors' cases are criticisms of the biography by Hearnshaw (1979), who wrote, in part, a comprehensive exposé of Burt's alleged fraudulent publications on MZA twins. Fletcher (1991) and Joynson (1990) claimed that Hearnshaw (1979) made numerous errors, questionable assumptions, and false conclusions. For book reviews (pro, con, and neutral) of Fletcher's (1991) and Joynson's (1990) works, see Apple (1992a, 1992b), Beloff (1990), Blinkton (1989), Eysenck (1989), Fancher (1991), Fletcher (1990), Skrabanek (1989), and Zenderland (1990). What the “Burt affair” truly means in the final analysis is not clear. Drawing from Mackintosh's (1995) book, Cyril Burt: Fraud or Framed?, Plomin et al. (1997) observed, “Although the jury is still out on some of the charges …, it appears that some of Burt's data are dubious” (p. 137).
39. There is one study that presents strong evidence questioning the assumed invariance of Jensen's (1969) Level I-Level II theory. See the Taylor and Skanes (1976) investigation with Canadian Inuit Indian and Canadian White children.
40. The subtitle of the monograph is Emphasizing Studies Involving the English- and Spanish-Language Versions of the Peabody Picture Vocabulary Test–Revised.
41. Dunn (1988) contended that American Guidance Service (AGS) “did not publish my monograph and did not sell it. They only reproduced and mailed it, upon request, as a free service” (p. 302). It appears that Dunn attempted to clarify matters in that some critics of Dunn's piece advocated the blacklisting of AGS. Although it might be true that Dunn had such an agreement with AGS, the title page of the monograph does read “published by AGS.”
42. Notwithstanding the consensus that the PPVT series does not represent general measures of intelligence, the PPVT-R still is listed under “assessment of intelligence” in texts on testing (Anastasi, 1988; Cohen et al., 1992; Salvia & Ysseldyke, 1988). Valencia and Solórzano (1997) opined that for authors to do such listings not only is misleading but also helps to perpetuate the [Page 314]misconception that the PPVT-R is an appropriate measure of intelligence. We believe that the test best fits under the “assessment of language” category.
43. Dunn (1988), in his rejoinder to Berliner (1988), asserted, “I did not say that the PPVT/TVIP [Spanish version of the Peabody test] should be used as a measure of verbal intelligence/scholastic aptitude for immigrant Hispanic youth. … For such children, the [tests] are only achievement tests of vocabulary for single words spoken in isolation” (p. 315). Yet, in his monograph, Dunn (1987) relied on data from two sources to draw his conclusions of heritability of intelligence. The first source included studies with “instruments other than the Peabody tests” (e.g., WISC-R), and the second cluster included research involving the original PPVT (which used an IQ index) and the PPVT-R. Thus, Dunn's (1988) response to Berliner (1988) made little sense, based on the overall approach he took in reviewing studies germane to his partial genetic interpretation.
44. These reform ideas are not really new. For the most part, these ideas stem from discourse beginning during the early 1980s and are viewed, by critics, as agenda items of the current conservative school reform movement.
45. Herrnstein and Murray (1994) argued that the funding priority for the gifted and the economically disadvantaged “turned 180 degrees” with the passage of the Elementary and Secondary Education Act of 1965, a federal program designed to serve the educational needs of students in low-income areas.
46. For an earlier discussion of the argument for the custodial state, see Murray (1988).
47. Of these five books, two (Fraser, 1995; Kinchloe et al., 1996) consist of collections of short reviews, all of which are unfavorable to The Bell Curve. The book by Jacoby and Glauberman (1995) also contains brief reviews and commentaries, but of diverse views. The books by Dickens et al. (1996) and Fischer et al. (1996), by contrast, are based in large part on reanalysis of the National Longitudinal Survey of Youth study data used by Herrnstein and Murray (1994) in The Bell Curve. Both books' conclusions about class structure formation are very different from those presented by Herrnstein and Murray.
48. Our search for book reviews of The Bell Curve, which was conducted in August 1999, was confined to the Periodical Abstracts database. As such, there probably are more book reviews and commentaries than the nearly 70 we identified.
49. This range of responses to The Bell Curve also was observed by Murphey (1995), who did a survey of approximately 125 book reviews, articles, columns, and television commentaries that appeared from late 1994 to early 1995.
50. For Murray's response to the critics of The Bell Curve, see Murray (1995).
51. As Gould (1995) noted, a correlation of .40 yields an R2 of only .16. R2, a statistic known as the coefficient of determination, is the square of the observed correlation coefficient and is useful in explaining how much of the variance in the dependent variable (e.g., dropping out of high school) can be accounted for by the independent variable (e.g., IQ). Gould commented that the values of R2 presented in Appendix 4 of The Bell Curve are very low.
52. One example of this type of study is by Mercer and Brown (1973).
53. For more than a decade, J. Phillipe Rushton, a Canadian developmental psychologist at the University of Western Ontario, has been advancing a theory that the three “races” he has studied have evolved to different levels of intellectual development. According to Rushton, “Mongoloids” are most advanced, “Caucasoids” are intermediate, and “Negroids” are least advanced. In that such an ordering of the races stems from an evolutionary basis, the engine must be genetics, he asserted (see, e.g., Rushton, 1995). One of the many variables in his analysis is brain size. Rushton claims that, given that the “Negroid race” has the smallest mean brain size and the lowest mean IQ, such findings are evidence of a genetic hypothesis about racial differences in intelligence. Suffice it to say, Rushton's overall theory (and his work on brain size) has been criticized frequently (Cain & Vanderwolf, 1990; Horowitz, 1995; Lynn, 1989; Zuckerman & Brody, 1988). For discussions of other controversial scholars (and their connections with the Pioneer Fund, a right-wing [Page 315]organization) on whom Herrnstein and Murray (1994) relied, in part, for source material, see Lane (1995), Linklater (1995), Miller (1995), Sedgwick (1995), and Tucker (1994).
54. For a discussion of some of the earlier transracial adoption studies, see Loehlin et al. (1975). Loehlin et al. pointed out that these studies are not that informative because the focus was mainly on the adopting parents.
55. Scarr and Weinberg (1976) also found that Black adopted children who had two natural Black parents, compared to Black adopted children who had one White natural parent and one Black natural parent, tended to have lower IQs. The authors pointed out, however, that this finding should not be interpreted as genetic given that the differences in IQs were associated with maternal schooling attainment and preplacement history. Once these variables were controlled, the mean IQ difference between the two groups was substantially less.
56. Brody (1992) commented, however, that the data from Scarr and Weinberg (1976) should not be construed as definitive evidence in favor of an environmental interpretation because the children were quite young at the time of testing. Research has shown that heritability of intelligence increases from childhood to adulthood (Plomin et al., 1997). See also McCartney, Bernieri, and Harris (1990).
57. For a discussion of research problems associated with racial admixture studies, see Loehlin et al. (1975), Mackenzie (1984), and Scarr et al. (1977).
58. For several other studies on the degree of White ancestry in the African American population, see the references provided in Scarr et al. (1977, p. 70).
59. It must be pointed out, however, that in Race Differences in Intelligence (Loehlin et al., 1975), then considered the definitive work on the topic, the authors concluded that the studies they reviewed “provided no unequivocal answer to the question of whether the differences in ability-test performance among U.S. racial-ethnic populations do or do not have a substantial component reflecting genetic differences among the subpopulations” (p. 133). Provine (1986) observed that, based on the studies reviewed by Loehlin et al. (1975), such a conclusion is considered an objective assessment of the available studies. Provine (1986) did comment, however, that “the social and public-policy implications of the possible genetic differences between racial-ethnic groups in the U.S. were, however, far less objective” (p. 881). Note the following statement by Loehlin et al. (1975): “We consider it quite likely that some genes affecting some aspects of intellectual performance differ appreciably in frequency between U.S. racial-ethnic groups—leaving open the issue of which groups, which aspects, and which direction of difference. Thus, we consider it most unwise to base public policy on the assumption that no such genetic differences exist” (p. 240). Provine (1986) asserted that, based on the evidence reviewed by Loehlin et al. (1975), the first sentence in the preceding quote cannot be supported. Provine (1986) also found that the public policy conclusion presented by the authors was disturbing. He opined that objective scientific ignorance of genetic differences between races cannot be used to guide social policy. In the final analysis, the ambiguous timbre of the conclusions drawn by Loehlin et al. (1975) left a specter of openness to the question of genetic bases in mean intelligence differences between races. Scarr and Carter-Saltzman (1982) commented, “Their [Loehlin et al.'s (1975)] equivocal conclusion led many social scientists to take the position that the possibility of genetic differences between the races was an open issue” (p. 863).
1. Based on OCR survey data presented by Chinn and Hughes (1987) and from the U.S. Department of Education, OCR (1991), we calculated overrepresentation rates of Black students in EMR classes from 1978 to 1988 and juxtaposed these data with the most recent figure (for 1994) provided in this chapter. These disparities are as follows: 1978, +141.8%; 1980, +125.7%; [Page 316]1982, +109.3%; 1984, +97.0%; 1988, +94.4%; 1994, +86.7%. Although the overrepresentation rate for Blacks in the EMR category (national level) has steadily declined over the data points, it still remains high.
2. Using the same sources mentioned in Note 1 (Chinn & Hughes, 1987; U.S. Department of Education, OCR, 1991), we calculated underrepresentation rates of Hispanic students in EMR classes from 1978 to 1988 and compared these data to the most recent figure (for 1994) provided in this chapter. Our calculations are as follows: 1978, –31.7%; 1980, –46.4%; 1982, –56.8%; 1984, –23.5%; 1988, –23.6%; 1994, –35.1%. Notwithstanding fluctuations, the underrepresentation of Hispanics in EMR classes (national level) has been holding steady.
1. Searches were done electronically. ERIC served as the database for CIJE, and PsycINFO served as the database for PA. The time frame for the searches was from 1967 to 1999. We also conducted searches using “gifted” and “talented” as well as “children,” “youth,” and “students.” There were 1,836 and 2,358 citations listed in the CIJE and PA databases, respectively. Of these citations, only a handful pertained to gifted minority children and youths.
2. Ford and Harris (1990) did not describe how they undertook their bibliographic search.
3. Good (1960) noted, “The girls were given domestic education by the mother in the home” (p. 25).
4. See, for example, Cleveland (1920), Gosling (1919), Race (1918), and Specht (1919), all cited in Newland (1976). According to Margolin (1994), the earliest use of the term “gifted child” that he could find was in Van Sickle (1910).
5. As a supplementary test, the Goddard form board also was administered as a basis for entrance.
6. It appears that Hollingsworth (1929) suggested a causal link between the racial origins of successful American adults and distinction in life. Citing Woods (1914), she commented that Anglo-Saxons are 3 to 10 times more likely than other races to achieve positions of distinction nationally. Hollingsworth failed to consider that it might not be racial origin that is the driving force of distinction but rather that socially transmitted privilege, status, and power are keys to mobility and distinction among Anglo-Saxon people.
7. For our discussion here, we refer to Terman's second edition (Terman, 1926).
8. Group II participants were selected by volunteer assistants who administered the Stanford-Binet to students not covered in the main study. Group II cities included, for example, Santa Barbara, Fresno, San Jose, Pasadena, and Burbank.
9. For exact nomination instructions given to teachers, see Terman (1926, pp. 21–22).
10. See Terman (1926, p. 55, Table 8). As we noted in Chapter 1 of the present book (Note 14), many scholars of this era confused race with national origin and ethnicity.
11. The n values provided are our own calculations given that Terman provided only percentages. Our estimates for the various n values are very accurate. We calculated n values for each of the 37 racial stocks. Our total N was 636 cases, very close to Terman's N of 643 cases. The difference between our N and Terman's N is due to rounding error.
12. For the total Jewish group, Terman (1926) also reported nine subgroupings (e.g., Russian Jewish, German Jewish, Polish Jewish).
13. Although four Japanese children surfaced in Terman's main study, they and Chinese students were not canvassed. These children were required, by state law, to attend segregated schools. Terman (1926) acknowledged that he intentionally did not canvass these schools. He did not, however, provide a reason. We suspect that he did not because of the atypical nature of these segregated schools. This rationale, if true, makes little sense given that African American and Mexican American children also attended, for the most part, segregated schools in California (see, e.g., González, 1990).
[Page 317]14. Whether or not the teachers exhibited social class bias in their nominations is unknown. It is clear, however, that the gifted children identified in Terman's (1926) study came from backgrounds of privilege. Based on occupational data from 560 fathers in the main study, Terman reported that 31.4% of children's fathers were classified as professional in occupational status, 50.0% as semiprofessional and business, 11.8% as skilled labor, and 6.8% as semiskilled or slightly skilled labor. This skewed distribution in favor of high-SES fathers was quite different from the distribution in the general population in the target cities. For example, Terman noted that, based on census data, the percentage of professional fathers in the population was 2.5%. Yet, in his study, 31.4% of the fathers were considered part of the professional group (an overrepresentation of 1,156%). “Common laborers” constituted 15.0% of the population, yet fathers who were common laborers in Terman's study constituted 0.1% of the total group (an underrepresentation of 99%). We calculated these disparities based on the data described in Terman (1926, pp. 63–64).
15. For the larger quote from which this quote is taken, see Terman (1916, pp. 91–92).
16. Terman and his associates also followed up on his original sample in one of the most ambitious longitudinal studies of the gifted ever undertaken (see, e.g., Cox, 1926; Terman, Burks, & Jensen, 1930; Terman & Oden, 1947, 1959). One of Terman's major findings was that many of the children identified as gifted in his initial study went on to lives of distinction (e.g., a disproportionate representation in Who's Who in America). However, the argument that “IQ = intelligence = real-world success” (Ceci, 1990, p. 58) has been sharply challenged. For example, drawing from the analysis by Sorokin (1956), Ceci (1990) raised an interesting methodological issue that questions Terman's assertion about the relation between IQ and life success. Ceci commented that if the gifted children from the original study “are compared to children from their same childhood social class instead of to economically unselected children, as Terman had originally done, they do not appear to be nearly as exceptional in their later professional or personal lives. I became aware of this when I came across a relatively obscure paper by the sociologist P. Sorokin (1956), writing in Fads and Foibles in Modern Sociology. Sorokin conducted an informal comparison of Terman's “geniuses” with children from their same social group on everything from high school and college grades to marital dissolution rates and professional accomplishments. He concluded that the instances of superiority among the “geniuses” in Terman's study are within the expectation of superiority irrespective of IQ, in view of their family social background. Notwithstanding the effusive praise that psychologists have heaped on this study as evidence of the predictive power of IQ tests, the IQ scores of these “geniuses” appear to have added little to the prediction of their life outcomes over that gained simply from a consideration of their parental income, education, and occupational status” (p. 59).
17. It appears that little attention was given to the gifted even into the 1950s. The survey by Heck (1953, cited in Tannenbaum, 1983) revealed that of 3,203 cities with populations of 2,500 people or more, only 15 cities (0.5%) had special classes or schools for children with superior measured intelligence.
18. At first observation, this trend of increased interest in gifted children might seem at odds with the earlier quote by Tannenbaum (1983, p. 15), who commented that interest likely hit a low during and after World War II. It appears, however, that Tannenbaum was speaking about the actual practice of “gifted education,” whereas Albert (1969) was focusing on the general research/scholarly publication base.
19. The survey was funded under a grant from the Office of Gifted and Talented in the U.S. Office of Education.
20. See California Administrative Code (1971, cited in Zettel, 1979).
21. It must be pointed out, however, that the use of intelligence tests in more recent times has not been the exclusive means to identify gifted students. In a national survey of elementary and secondary school programs for gifted students in 1,108 school districts, Cox, Daniel, and Boston (1985) found a variety of procedures that were used to identify gifted students. In descending order, the percentages of districts reporting these procedures were teacher nomination (91%), achievement tests (90%), IQ tests (82%), grades (50%), peer nomination (25%), “other” (22%), parent [Page 318]nomination (21%), self-nomination (6%), creativity measure (6%), and “none” (3%). Although not discussed by Cox et al., we assume that the categories were not mutually exclusive.
22. Renzulli (1986) drew a distinction between “motivation” and “task commitment” (a focused form of motivation): “Whereas motivation is usually defined in terms of a general energizing process that triggers responses in organisms, task commitment represents energy brought to bear on a particular problem (task) or specific performance area” (p. 69).
23. See also Renzulli (1978).
24. For a listing of research studies providing evidence of the effectiveness of the Schoolwide Enrichment Model, see Renzulli and Reis (1997). For a discussion of this research, see Renzulli and Reis (1994).
25. For a full discussion of his triarchic theory of intelligence, see Sternberg (1988).
26. As a case in point regarding deficit thinking views held toward minority children, Margolin (1994) referred to a list of “common descriptors affected by cultural diversity, socioeconomic deprivation, and geographical location” described in Educational Psychology of the Gifted by Khatena (1982, pp. 247–249), who in turn drew from Baldwin (1977). Khatena (1982) listed a number of “external and internal deficits” of children that have “possible environmental causality.” For example, one deficit is “inability to attend to task without supervision.” The likely environmental cause is that “tradition dictates strict adherence to directions” and that “discipline does not encourage inner locus of control” (p. 247). Another deficit is “inability to trust or consider ‘beauty’ in life.” A possible cause: “Environment dictates need to survive. Anger and frustration increase animalistic desire to survive” (pp. 247–248). Similar deficit views toward minority children are seen in Clark's (1997) Growing Up Gifted. Although she provided some positive descriptions of minority students, the negative characteristics that she listed can be thought of as being barriers in identifying gifted children. Low-SES African American students, for example, are said to have “unsupportive home environments,” “low expectations from family,” “manipulative behavior,” and “limited experience with varied or extended language patterns” (p. 508). Clark noted that such problems “are typical of the poverty culture” (p. 508; for a critique of the “culture of poverty” model, a deficit thinking perspective, see Foley, 1997). Clark also described other minority group students in negative ways. For example, American Indians have a “thinking style” that is “nonanalytic” (p. 509). Asian Americans have “little experience with independent thinking” (p. 510). Finally, Hispanics hold a “belief in school as primary educator; value of parent as educator is not recognized” (p. 511). It is noteworthy that Clark's negative descriptions of minority students are unchanged from an edition of Growing Up Gifted published 14 years earlier (Clark, 1983).
27. Margolin's (1994) reference to a “pedagogy of the oppressed” is in regard to Freire's (1970) Pedagogy of the Oppressed.
28. The first paragraph of this section is excerpted, with minor modifications, from Reyes and Valencia (1993, pp. 258–259).
29. Goals 2000: The Educate America Act was signed into law on March 31, 1994. At that time, two additional goals had been added: parental involvement and professional development (Gallagher, 1993).
30. The excellence versus equity debate also is active abroad (e.g., in Russia, Taiwan, South Africa, Israel, France, and Sweden; Gallagher, 1984, 1986).
31. For the position that the elimination of grouping practices would be harmful to U.S. education, see Kulik and Kulik (1997).
32. By underrepresentation, we mean that a minority student group (e.g., African Americans) is disproportionately represented (percentage-wise) in a gifted program relative to the group's percentage in the school population.
33. See, for example, Chinn and Hughes (1987); Baldwin (1977, 1987); Fatemi (1991); Hartz, Adkins, and Sherwood (1984); High and Udall (1983); McKenzie (1986); Passow (1989); Richert (1985a, 1987); Riley (1993); U.S. Department of Education (1991b); Van Tassel-Baska (1991); and Van Tassel-Baska, Patton, and Prillaman (1989).
[Page 319]34. See, for example, Clark (1997); Colangelo and Davis (1997); Davis and Rimm (1998); Khatena (1982); Maker and Schiever (1989a); and Pendarvis, Howley, and Howley (1990).
35. Based on the most recent OCR survey (U.S. Department of Education, OCR, 1997), other categories included, for example, “corporal punishment,” “suspension,” and “AP [advanced placement] mathematics.”
36. The “loss” of 502 school districts (5,173 minus 4,671) is a result of school districts being removed from the sample for a number of reasons (e.g, the school district no longer exists, the school district was consolidated with another district or districts, the school district was found to be an administrative school district rather than a district with students). The 502 figure also represents nonresponding school districts (personal communication, Peter McCabe, July 26, 1999).
37. Disparities were calculated by comparing each racial/ethnic group's percentage in total school enrollment to each group's percentage in the gifted and talented category. The OCR data allow users to access projected or reported data. Projected data, which we selected to use for our disparity analysis, represent the total values that would have resulted if every district in the universe had been sampled.
38. The top 10 states in combined minority K-12 enrollments (American Indian, A/PI, Hispanic, and Black) are as follows:
39. Here, a predominantly White school is defined as having an enrollment of 70% or greater White students. A predominantly minority school is defined as having an enrollment of 70% or greater combined minority enrollment.
40. The year-to-year fluctuations probably are due to methodological factors (e.g., different size samples, nonrandom samples, the OCR not checking on the accuracy of the school or district reports; Finn, 1982).
41. See, for example, National/State Leadership Training Institute, Office of Ventura County Superintendent of Schools (1981); Baldwin, Gear, and Lucito (1978); Callahan and McIntire (1994); Kitano and Chinn (1986); and Maker and Schiever (1989a). See also sections/chapters on gifted minorities in major texts on the gifted and their education (citations listed in Note 34 of the present chapter).
42. Bond (1927) reported that his 30 participants included children from “professional middle-class. …, and laboring homes” (p. 259). He did not, however, report how the children were selected, nor did he provide their ages.
43. Bond's (1927) participants were indeed exceptional. Of the 30 children, nearly half (n = 14) scored IQs higher than 125.
[Page 320]44. See Jenkins (1936, p. 178, Table 1). In most of the 15 studies, the focus was not on gifted African American children but rather on racial comparisons in intelligence (e.g., Goodenough, 1926b; Hewitt, 1930). All of the investigations, however, included some African American children with IQs higher than 120.
45. Examples of other publications on gifted African American children and youths during this early period are Bond (1959), Long (1935), and Valien and Horton (1954).
46. Notwithstanding this praise of Jenkins's (1935) dissertation, Wilkerson (1936) also offered some concerns (e.g., representativeness of the seven elementary schools, possible experimenter bias).
47. This extremely bright child mentioned in Guthrie (1976) was discussed in “The Case of ‘B’: A Gifted Negro Girl” (Witty & Jenkins, 1935). This child (9 years, 4 months of age), first identified in Jenkins (1935), had a Stanford-Binet IQ of 200 and was described by Witty and Jenkins (1935) as follows: “Undoubtedly …, [B] is one of the most precocious and promising children in America” (p. 118). B, a student in the Chicago public schools, was described as being quite precocious in language development during her toddler years. As a student, “B's preferred leisure-time activity is reading; she spends about 28 hours weekly in this activity” (p. 123). B's parents were college educated; her mother was a schoolteacher, and her father was an electrical engineer. Regarding B's racial ancestry, “The mother reports B to be of pure Negro stock. There is no record of any white ancestors on either the maternal or paternal side” (p. 123).
48. We urge the reader to examine the article by Ford and Webb (1994) for details germane to these reasons that help to explain the underrepresentation of African American students. For example, regarding the “misunderstanding of and inattention to cultural differences in learning” (p. 363) of African American students, Ford and Webb drew from several works (i.e., Dunn & Griggs, 1990; Hale-Benson, 1986; Hilliard, 1992). Based on these studies, Ford and Webb (1994) asserted, “Several researchers have noted differences in learning styles among African American and White students. … Specifically, African Americans tend to be relational, visual, mobile/kinesthetic, concrete/global tactile learners, while school success in the United States is heavily dependent upon abstract, auditory, less mobile, tactile, and kinesthetic learning” (p. 363).
49. Our assessment of the literature on gifted African American students, as well as on other gifted minorities, is that the predominant proportion of these writings focuses on (a) factors that hinder the identification of gifted minority students and (b) principles and strategies that can lead to increased identification of such students (Baldwin, 1987; Ford, 1995; Ford & Harris, 1990; Ford & Webb, 1994; Frasier, 1987; Gay, 1978; Hamilton, 1993; Harris & Ford, 1991; Patton, 1992; Rhodes, 1992). By sharp contrast, there is a small proportion of empirically based investigations that actually are designed to demonstrate the effectiveness of implementing innovative procedures that lead to increased minority representation in gifted programs. We are not suggesting, however, that gifted African American students and other gifted minority students cannot be identified using traditional standardized intelligence tests. For example, regarding Mexican American students, as a case in point, Ortiz and Volloff (1987) reported that the Wechsler Intelligence Scale for Children–Revised (WISC-R) was quite effective in identifying migrant Mexican American students (Grades 3 to 6) in California who were referred by their teachers for possible placement in a gifted program. Compared to a number of group-based intelligence, achievement, and self-esteem measures, the WISC-R Performance Scale IQ and Full Scale IQ were the most effective in the identification of high-IQ students. If an IQ of 130 or higher was the standard used for identification of gifted students, then 32% and 23% of the students would have been identified based on their Performance Scale IQ and Full Scale IQ, respectively. By stark contrast, 0% of the students would have been identified based on the Otis-Lennon School Ability Test (OLSAT; Otis & Lennon, 1982), a group test. Ortiz and Volloff (1987) contended that because the children in their sample had reading skills only somewhat higher than the average range, these students' “performance on school ability tests like the OLSAT may be more of a function of reading skills … than of school ability” (p. 51). The authors suggested that group-based screening tests emphasizing reading skills might be inappropriate for identifying migrant Mexican American children and that individually [Page 321]administered intelligence tests (e.g., the WISC-R) might be more appropriate instruments. On a final note, given the time required to administer the WISC-R, Ortiz and González (1989) undertook a study in which they provided validity data for a short form of the WISC-R. The abbreviated version was found to be effective as a screening tool in a sample of migrant Mexican American students referred for possible gifted program placement. The authors did comment, however, that the WISC-R short form should not be the only criterion used for the selection of these students.
50. The group tests were the California Achievement Test and the Test of Cognitive Skills. “Students who scored ≥ 85th percentile in a designated number of areas were eligible to participate in the target group” (Woods & Achey, 1990, p. 22).
51. The group meetings with parents were comprehensive in information dissemination (e.g., how children became eligible, evaluation procedures used, invitation to visit the schools).
52. The group-administered achievement test was the Comprehensive Test of Basic Skills. The group-administered intelligence test was the OLSAT. Regarding the individually administered intelligence tests, the WISC-R and the Kaufman Assessment Battery for Children (K-ABC, both the Mental Processing Composite and Achievement Scales) were used.
53. The number of White students increased 12% (n = 653 to n = 732).
54. The second most frequently occurring combination of tests was the WISC-R and the K-ABC Achievement scale.
55. For the non-SOMPA group, the mean IQs of children who were given the WISC-R and Stanford-Binet were 131.6 and 133.4, respectively. For the SOMPA group, the initial mean WISC-R IQ was 119.0. After SOMPA sociocultural adjustment procedures were done, the adjusted mean was 135.7.
56. The Ross Test of Higher Cognitive Processes (no author, date, or publisher provided by Matthew et al., 1992) was normed on gifted students (Grades 4 to 6) in Washington State and is designed to measure a student's ability to understand verbal abstractions (e.g., deductive reasoning, analogies).
57. For criticisms of the SOMPA, see Brown (1979a, 1979b), Clarizio (1979a, 1979b), Goodman (1979a, 1979b), and Oakland (1979a, 1979b). For a response to these criticisms, see Mercer (1979).
58. The SOMPA also has been shown to be quite effective in identifying gifted Mexican American children. See, for example, Mercer (1977).
59. Two explanations can be advanced to explain the historical inattention given to gifted Latino students. First, in sharp contrast to early African American scholars who were interested in mental testing (small in number but very active in research and publications), there was only one Latino scholar, George Sánchez (Valencia, 1997b; see also Chapter 1 of the present book). Second, scholarly work on Latino intellectual performance largely focused on the issue of White-Latino differences in measured intelligence (see also Chapter 1).
60. Examples of other earlier studies in which some Mexican American students attained exceptionally high IQs are, chronologically, Sheldon (1924), Terman (1925), Goodenough (1926b), Randalls (1929), Keston and Jiménez (1954), and Ratliff (1960).
61. By contrast, White students made up 71.0% of the total K-12 population but constituted 87.5% of the gifted population, an overrepresentation of 23.2%.
62. Examples of such publications are Bernal (1979), DeLeon (1983), Gratz and Pulley (1984), Melesky (1985), Perrine (1989), Riojas Clark (1981), and Zappia (1989).
63. For other empirical investigations in which the SRBCSS has been used in the identification of gifted Mexican American students, see Argulewicz, Elliott, and Hall (1982); Argulewicz and Kush (1984); Elliott and Argulewicz (1983); and High and Udall (1983).
64. These studies referred to by High and Udall (1983) are Jensen and Rosenfeld (1974), Leacock (1969), and Yee (1968).
65. For selection to the gifted program, the district used a nonweighted matrix consisting of several indicators—grades, parents' and teachers' nominations, standardized achievement tests, and group- and individually administered intelligence tests.
[Page 322]66. Examples of such publications are Chan and Kitano (1986), Chen (1989), Chen and Goon (1976), Gallagher (1989), Hasegawa (1989), Kaplan (1989), Kitano (1986, 1989), Larson (1989), Plucker (1996), Tanaka (1989), Woliver and Woliver (1991), Wong and Wong (1989), and Woo (1989).
67. Plucker (1996) referred to “Asian Americans.” We believe that his assertions also can generalize to Pacific Islanders.
68. Much of the literature cited in Note 66 discusses this diversity among and within A/PI people.
69. For literature that has attempted to debunk the model minority myth, see Chen (1989), Gallagher (1989), Hartman and Askounis (1989), Hu (1989), Kim and Hurh (1983), Lesser (1985–1986), Ong (1984), and Plucker (1996).
70. For data to support this statement, see Chapa and Valencia (1993, p. 177, Table 10). See also Hu (1989).
71. This is not to suggest, however, that low-SES A/PI students cannot be identified as gifted. See, for example, Chen and Goon's (1976) study of gifted low-SES Asian American children in Manhattan's Lower East Side (greater Chinatown area) in New York City.
72. One must be very cautious in concluding that parents of students who are underrepresented among the gifted (e.g., Latinos) do not value education. The perception that Latinos do not value the importance of education is false and can be rebuked by the available evidence (see, e.g., Valencia & Solórzano, 1997).
73. Examples of other investigations from the 1920s in which some American Indian children and youths performed at relatively high levels on standardized intelligence tests are Fitzgerald and Ludeman (1926); Garth, Schuelke, and Abell (1927); Garth, Serafini, and Dutton (1925); and Hunter and Sommermier (1922).
74. Examples of publications on gifted American Indian students are Bradley (1989), Brittan and Tonemah (1985), Brooks (1989), Callahan and McIntire (1994), Christensen (1991), Daniels (1988), Garrison (1989), George (1989), Hartley (1991), Kirschenbaum (1988, 1989), Maker and Schiever (1989b), Montgomery (1989), Pfeiffer (1989), Robbins (1991), Romero (1994), Scruggs and Cohn (1983), Sisk (1989), Throssel (1989), and Tonemah (1987).
75. A common (and false) perception is that many American Indian children and youths attend separate reservation schools. The vast majority (85%) of American Indian students are enrolled in regular public schools. Another 12% of the students are enrolled in schools funded by the Bureau of Indian Affairs (i.e., bureau-operated, grant, or contract), and 3% attend private or mission schools (Callahan & McIntire, 1994).
76. See Ross (1989), who asserts this “right brain” position. For a refutation of the “right-brained Indian” thesis, see Chrisjohn and Peters (1989).
77. There are three distinct language families among the New Mexico Pueblos: Keresan, Zunian, and Tanoan. Romero's (1994) study focused on the seven Keresan-speaking communities of the Pueblos (e.g., Acoma, Laguna, Zia, Cochiti).
78. It appears that the study by Scott et al. (1992) was conducted in Miami, Florida. As such, it is very likely that the vast majority of the Hispanic students were Cuban American.
1. “Racial/ethnic bias” is what we referred to as “cultural bias” in Chapter 5.
2. Independent investigations by Valencia and associates have provided some confirmations that Hispanic (i.e., Mexican American) children tend to score at normative means on the K-ABC Mental Processing Scale and Nonverbal Scale and below the normative mean on the K-ABC Achievement scale (see, e.g., Valencia, Rankin, & Livingston, 1995).
3. For other criticisms of the SOMPA, see references cited in Note 57 of Chapter 8 of the present book.
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