About

Dr. Lia Scott is an Assistant Professor of Epidemiology in the Division of Epidemiology at the University of California, Berkeley School of Public Health. Her research aims to understand how social and structural factors impact breast cancer etiology, with a specific focus on breast cancer in Black women. Her current research is focused on the role of varying structural racism measures as a predictor of breast cancer subtype at diagnosis from a multi-level perspective. She also uses her expertise in spatial epidemiologic methods to support health equity and health disparities research in other health outcomes. Dr. Scott completed her post-doctoral fellowship as a Steven M. Teutsch Prevention Effectiveness Fellow at the Centers for Disease Control and Prevention, Division of Cancer Prevention and Control, and received her PhD from Georgia State University School of Public Health where she was an NIH Ruth L. Kirschstein National Research Service Award fellow.

Research topics

• Mathematics education
• Computer science
• Psychology
• Library science
• Pedagogy

Selected publications

• Concrete Instruction in Elementary School Mathematics: Pictorial vs. Manipulative

  School Science and Mathematics · 1976-01-01 · 8 citations

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• New Designs for Elementary Curriculum and Instruction

  Medical Entomology and Zoology · 1975-06-01 · 9 citations

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• An investigation of structure in elementary school mathematics: Isomorphism

  Educational Studies in Mathematics · 1970-09-01 · 6 citations

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• Reply to Rudolph's Critique

  Psychological Reports · 1969-02-01

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  Emerson said, hate quotations. Tell me what you know (1). A principal argument against the use of covariance analysis for pre-existing groups is presented by Lord (2, p. 305) and an out-of-context quotation by Rudolph (3) eliminates covariance analysis as a respectable tool in educational research. sympathy with Emerson, the quotation merits a more comprehensive examination. Lord bases his discussion on a hypothetical example in which groups to be compared are distinguished by pre-existing uncontrolled significant differences. The example and the quotation are not relevant to the investigation in question (4). this investigation, the pre-existing differences in IQ berween experimental and control groups are nonsignificant. this case, a safe assumption is that there are no pre-existing uncontrolled differences-in direct contrast to the example cited by Lord. Furthermore, it is interesting to note that the use of IQ as covariate does not change the results in the investigation in question. Had there been a logical reason for anticipating some other uncontrolled, systematic variable, an attempt to control for such variable would have been introduced. Had a significant difference berween groups existed with respect to this variable on initial testing, we would have a case like that cited by Lord. Of course, Rudolph's argument that the research would have been strengthened by a complete randomization is a valid one. However, in srudies of school children in classrooms complete randomization is an utopian non-reality. Children are available in pre-formed groups of classroom size, and in larger school district groups with the important characteristic of availability to the researcher. Lord notes (p. 304, if I may overuse the privilege of quoting out of context), In behavioral research and in many other areas, such random assignment is usually difficult or impossible. . the same vein, utilizing scores from available contemporarily used tests may be questioned. A more perfect experiment would seek out the perfect measures, particularly if individual prediction is involved. However, in practice, nominally reliable and valid instruments (the latter often being most critical) are deemed sufficient for group description. The investigation in question is not concerned with individual prediction and abides the reasonable lack of perfection. However, it may be noted that the conclusion brought into question by Rudolph, is appropriately conditional as follows: Certainly,

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• New designs for the elementary school curriculum

  1967-01-01 · 1 citations

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• Trends in Elementary School Mathematics.

  American Mathematical Monthly · 1967-08-01 · 4 citations

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• A Study of the Case for Measurement in Elementary School Mathematics

  School Science and Mathematics · 1966-11-01 · 2 citations

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• Children's perception of mathematical inconsistencies

  The Arithmetic Teacher · 1965-12-01 · 2 citations

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  Existing projects for school mathematics curriculum reform have been occupied principally with the tremendous task of preparing instructional materials. In general, these projects have had neither the time nor the resources to objectively research their contributions. Most of the experimentation which they have undertaken has been confined to the practical consideration of adapting content to the relevant, known characteristics of operating schools. Despite implications to the contrary there is, obviously, no substantial evidence as to the long-term effect of any particular experimental curriculum. These observations notwithstanding, a movement toward the widespread adoption of “experimental” materials for usc in school classrooms is underway, and the trend is not likely to subside in the foreseeable future. In fact, there are such compelling logical and intuitive arguments supporting a modernization of school mathematics that adoption of almost any new program appears reasonable. Though the lack of objective information clearly increases the risk when an experimental curriculum is selected, apparently such risk is regarded as the lesser of evils. Although this position may be realistic, it is clear that an increasing body of information about the new curicula would be welcome grist for the decision-making process. While awaiting the emergence of comprehensive and longitudinal data on the value of tbe various experimental programs, school leaders should enthusiastically endorse any activity designed to produce a bit of information about the new curricula.

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• UCESSP: An experiment in diversity

  Journal of Research in Science Teaching · 1964-12-01

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• A Study of Teaching Division through the Use of Two Algorisms

  School Science and Mathematics · 1963-12-01 · 3 citations

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Frequent coauthors

• William E. Lamon

  4 shared

• John U. Michaelis

  2 shared

• Ruth H. Grossman

  University of Toronto

  2 shared

• Herman Neufeld

  University of California, Berkeley

  1 shared

• L. C. Feldman

  Rutgers, The State University of New Jersey

  1 shared

Awards & honors

• Steven M. Teutsch Prevention Effectiveness Fellow at the Cen…
• NIH Ruth L. Kirschstein National Research Service Award fell…