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Daphna Harel

Associate Professor of Applied Statistics; Co-Director of A3SR MS Program

Applied Statistics, Social Science, and Humanities

Daphna Harel 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 crowdsourcing, and requiring the use of multilevel models. Her methodological work focuses on the creation of theoretically justified guidelines for statistical analysis and issues of model misspecification in polytomous Item Response Theory, and the shortening of patient reported outcome measures. Harel received her PhD from the Department of Mathematics and Statistics at McGill University.

Selected Publications


Applied Statistics for Social Science Research

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


Applied Statistical Modeling and Inference: Frequentist

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.
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Practicum in Applied Statistics: Applied Probability

This is a course in the foundations of statistical inference techniques. Assuming some prior exposure to foundational & intermediate statistical methods, this course will first cover topics such as Kolgomorov’s axioms of probabilities, basics of set theory, discrete combinatorial probability, Bayes’ theorem, probability distributions & their properties & assumptions of dependence & independence. These topics are followed by the foundational topics of statistics: sampling distributions, the law of large numbers & the central limit theorem. This course will mix theoretical approaches with simulation-based illustrations of these main topics. The student will be expected to understand the mathematical theory & apply the topics covered to problem solving via analytical & simulation based methods in statistical programming language such as R.
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Statistical Mysteries and How to Solve Them

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

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.
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