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Daphna Harel

Associate Professor of Applied Statistics; Director of A3SR MS Program

Applied Statistics, Social Science, and Humanities

Daphna Harel (she/her) is an applied statistician who studies issues of measurement and modeling in the applied health sciences. Her research focuses on modeling challenges for data arising from self-reported questionnaires and other surveys, as well as other modern forms of data collection. Her methodological work focuses on the creation of theoretically justified guidelines for statistical analysis and issues of model misspecification in polytomous Item Response Theory, shortening patient reported outcome measures, and measures of effect size for differential item functioning. Her applied work centers around the use of advanced statistical methods to answer critical questions in the social and health sciences. She collaborates with researchers across numerous fields and publishes in scientific journals in both statistics and in the applications of her work. 

Her most recent work is in improving statistical practice as it pertains to LGBTQIA+ individuals and their data, as well as improving societal outcomes for members of this community. She currently PI's the NYU QUEER data lab.

Daphna is on the steering committee for the DEPRESSD project and works closely with the Scleroderma Patient-centered Intervention Network to provide statistical support.

Harel received her PhD from the Department of Mathematics and Statistics at McGill University.

Programs

Applied Statistics for Social Science Research

Learn advanced quantitative research techniques and apply them to critical policy issues across social, behavioral, and health sciences.

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Courses

Frequentist Inference

This is a course in the intermediate and advanced foundations of statistical inference in the context of applied research. Assuming some prior exposure to probability and statistics, this course will first cover topics such as the principles of estimation and hypothesis testing, and the general and generalized linear models, including scientific computation. This course thoroughly explores the frequentist approach to inference. The student will be expected to understand the mathematical theory, implement related statistical algorithms in statistical programming language such as R, and interpret models and parameters in the context of applied statistical analysis of real data.
Course #
APSTA-GE 2122
Credits
2
Department
Applied Statistics, Social Science, and Humanities

Practicum in Applied Statistics: Applied Probability

This course will first cover Kolgomorov's axioms of probabilities, basics of set theory, discrete combinatorial probability, Bayes' theorem, probability distributions and their properties and assumptions of dependence and independence, followed by the foundational topics of statistics: sampling distributions, the law of large numbers and the central limit theorem. This course will mix theoretical approaches with simulation-based illustrations of these main topics. The student will solve via analytical and simulation based approaches in statistical programming language R.
Course #
APSTA-GE 2351
Credits
3
Department
Applied Statistics, Social Science, and Humanities

Statistical Mysteries and How to Solve Them

An introductory quantitative and statistical reasoning course designed to help students acquire statistical literacy and competency to survive in a data-rich world. The course introduces students to basic concepts in probability, research design, descriptive statistics, and simple predictive models to help them to become more savvy consumers of the information they will routinely be exposed to in their personal, academic and professional lives. Course material will be conveyed through video clips, case studies, puzzle solving, predictive competitions, and group discussions.

Liberal Arts Core/CORE Equivalent - satisfies the requirement for Quantitative Reasoning for certain programs; students should check with their Academic Advisor for confirmation.
Course #
APSTA-UE 10
Credits
4
Department
Applied Statistics, Social Science, and Humanities
Liberal Arts Core
Quantitative Reasoning

Survey Research Methods

The survey is the leading mechanism for collecting information on a wide array of topics in our data-driven world. This course is designed to introduce students to the fundamental aspects of the survey & ways for evaluating this form of data collection. Principal topics include: survey design; coverage, sampling, & non-response; modes of data collection; questionnaire construction & evaluation. Throughout this course, students will be given opportunities to engage in actual survey research activities.
Course #
APSTA-GE 2139
Credits
3
Department
Applied Statistics, Social Science, and Humanities