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Dramatic advances in statistical modeling, experimental design, and statistical analysis have created unprecedented opportunities for advancing knowledge across a wide range of disciplines. There is an ever-greater demand for scholars who can innovate methodologically, who understand how to use the theory of statistical inference to tackle really hard problems in social, behavioral, and health science.
We look to theory-based models for populations and societies, examining biological, behavioral, and environmental factors and the way they interact. We are primarily interested in a theoretical controversies, questions, and hypotheses that arise in scientific discourse and the formal models that can make these precise, b systematic measurement of key theoretical constructs with known and consistent psychometric properties, c the design of research to test these models, d the specification of assumptions that are required for linking analytic results to theoretical claims, and e the validity of statistical inferences.
The QMSA Concentration This concentration is for students who seek rigorous training and critical exposure to the latest techniques of quantitative social science. Admitted candidates may participate on research teams or conduct independent projects applying quantitative thinking and analysis to important research questions.
Our goal is to prepare students for PhD study in quantitative social science, and for professional positions at research institutions and government or nongovernment agencies.
They must also furnish a statement of purpose outlining their intended research and the two QMSA faculty members they most hope to work with.
If accepted, students will select a minimum of 5 courses in theoretical modeling, research design, causal inference, and statistical analysis, and write their MA thesis with a member of the QMSA faculty.
In addition, students will take Perspectives in Social Science Analysis and up to 3 electives in their social science field. That Workshop invites leading methodologists to present their work, and offers an ideal venue for students to get up to speed with the latest developments in quantitative research.
Sample Electives For a complete list of current courses, please click here. Applied Statistics in Human Development Research.
This course provides an introduction to quantitative methods of inquiry and a foundation for more advanced courses in applied statistics for students in social sciences with a focus on human development research. All statistical concepts and methods will be illustrated with application studies in which we will consider the research questions, study design, analytical choices, validity of inferences, and reports of findings.
The examples include 1 examining the relationship between home environment and child development, 2 evaluating the effectiveness of welfare-to-work programs on maternal and child well-being, and 3 assessing the academic growth of English language learners in comparison with their English-speaking peers.
At the end of the course, students should be able to define and use the descriptive and inferential statistics taught in this course to analyze data and to interpret the analytical results. Students will learn to use the SPSS software. No prior knowledge in statistics is assumed.
Introduction to Causal Inference. This course is designed for graduate students and advanced undergraduate students from the social sciences, education public health science, public policy, social service administration, and statistics who are involved in quantitative research and are interested in studying causality.
The goal of this course is to equip students with basic knowledge of and analytic skills in causal inference. Topics for this course will include the potential outcomes framework for causal inference; experimental and observational studies; identification assumptions for causal parameters; potential pitfalls of using ANCOVA to estimate a causal effect; propensity score based methods including matching, stratification, inverse-probability-of-treatment-weighting IPTW.
Mediation, Moderation and Spillover Effects. This course is designed for graduate students and advanced undergraduate students from social sciences, statistics, public health science, public policy, and social services administration who will be or are currently involved in quantitative research.
Questions about why a treatment works, for whom, under what conditions, and whether on individual's treatment could affect other individuals' outcomes are often key to the advancement of scientific knowledge.
We will clarify the theoretical concepts of mediated effects, moderated effects, and spillover effects under the potential outcomes framework. The course introduces cutting-edge methodological approaches and contrasts them with conventional strategies including multiple regression, path analysis, and structural equation modeling.
The course content is organized around application examples. The textbook "Causality in a Social World: Moderation, Mediation and Spill-Over" Hong, will be supplemented with other reading reflecting latest developments and controversies.
This course develops methods of analyzing Markov specifications of dynamic economic models. Models with stochastic growth are accommodated and their properties analyzed.
Methods for identifying macroeconomic shocks and their transmission mechanisms are developed. Related filtering methods for models with hidden states are studied.
The properties estimation and inference methods based on maximum likelihood and generalized method of moments are derived. These econometric methods are applied to models from macroeconomics and financial economics. The course will review some of the classical methods you were introduced to in previous quarters and give examples of their use in applied microeconomic research.
Our focus will be on exploring and understanding data sets, evaluating predictions of economic models, and identifying and estimating the parameters of economic models. The methods we will build on include regression techniques, maximum likelihood, method of moments estimators, as well as some non-parametric methods.
Lectures and homework assignments will seek to build proficiency in the correct application of these methods to economic research questions.
This is a graduate seminar designed to investigate basic issues in the study of inequality and social mobility.
It is more than a readings course. It is designed to go into topics in depth.psychology differs from other related disciplines (eg, clinical Industrial/organizational psychology and other disciplines of psychology Some of the disciplines within psychology are social psychology, cognitive psychology.
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Jul 08, · Discuss how social psychology differs from other related disciplines (e.g. clinical psychology, general psychology, sociology). Explain the role of research in social plombier-nemours.com: Resolved. A comprehensive review of positive psychology.
Positive psychology. William D. Tillier; Calgary Alberta; Update: Under construction. Agricultural Education. AGRI Interdisciplinary Agricultural Science and Technology. This course is designed to develop competencies of agricultural science teachers to teach essential elements in agricultural business, agricultural mechanization, animal science, and horticulture and crop science.
Research in both areas– psychology and sociology – is important to the future of social sciences. Success in things like developing relationships, growing creative output, and discovering the best jobs for certain personality types are all dependent on both psychology and sociology.