Arise With Social Studies Book 3 Pdf Download
Janet Stenstrom
CS&SS 221 Statistical Concepts and Methods for the Social Sciences (5) NSc, RSN
Develops statistical literacy. Examines objectives & pitfalls of statistical studies; study designs, data analysis, inference; graphical & numerical summaries of numerical &categorical data; correlation and regression; estimation, confidence intervals, & significance tests. Emphasizes social science examples and cases. May only receive credit for one of STAT 220, STAT 221/CS&SS 221/SOC 221, or STAT 290. Offered: jointly with SOC 221/STAT 221; AWSp.
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CS&SS 320 Evaluating Social Science Evidence (5) SSc, RSN
A critical introduction to the methods used to collect data in social science: surveys, archival research, experiments, and participant observation. Evaluates "facts and findings" by understanding the strengths and weaknesses of the methods that produce them. Case based. Offered: jointly with SOC 320/STAT 320.
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CS&SS 321 Data Science and Statistics for Social Sciences I (5) SSc, RSN
Introduction to applied data analysis for social scientists. Focuses on using programming to prepare, explore, analyze, and present data that arise in social science research. Data science topics include loading, cleaning, and exploring data, basic visualization, reproducible research practices. Statistical topics include measurement, probability, modeling, assessment of statistical evidence. Lectures intermixed with programming and lab sessions. Offered: jointly with SOC 321/STAT 321; W.
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CS&SS 490 Data Science Community Seminar (1)
How data science integrates with various domains, especially the arts, humanities, and social sciences. Reflects on the opportunities of data science and its potential negative effects on society. Covers various subject areas, allowing students to see data science skills and studies in a variety of disciplinary settings. Credit/no-credit only.
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CS&SS 505 Review of Mathematics for Social Scientists (1)
Reviews basic mathematical skills needed for a meaningful understanding of elementary statistics, data analysis, and social science methodology. Overview of core knowledge required for graduate courses in quantitative methods in social sciences. Topics include discrete mathematics, differential and integral calculus, review of matrix algebra, and basic probabilistic and statistical concepts. Credit/no-credit only. Offered: jointly with SOC 512.
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CS&SS 506 Computer Environments for the Social Sciences (1)
Familiarizes graduate students in the social sciences with modern environments for statistical computing. Provides an overview of available resources and a description of fundamental tools used in quantitative courses and doctoral research. Topics include interfaces to web-based resources, UNIX-based computing, and major statistical packages (R, SPLUS, and SAS).
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CS&SS 508 Introduction to R for Social Scientists (1)
Familiarizes students with the R environment for statistical computing ( -project.org). R is a freely available, multi-platform, and powerful program for analysis and graphics similar to S-PLUS. Covers the basics of organizing, managing, and manipulating social science data; basic applications; introduction to programming; links to other major statistical packages. Credit/no-credit only.
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CS&SS 510 Maximum Likelihood Methods for the Social Sciences (5)
Introduces maximum likelihood, a more general method for modeling social phenomena than linear regression. Topics include discrete, time series, and spatial data, model interpretation, and fitting. Prerequisite: POL S 501/CS&SS 501; POL S 503/CS&SS 503. Offered: jointly with POL S 510.
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CS&SS 512 Time Series and Panel Data for the Social Sciences (5)
Extends the linear model to account for temporal dynamics and cross-sectional variation. Focuses on model selection and real-world interpretation of model results. Topics include autoregressive processes, trends, seasonality, stationarity, lagged dependent variables, ARIMA models, fixed effects, random effects, cointegration and error correction models, panel heteroskedasticity, missing data in panel models, causal inference with panel data. Recommended: Graduate level coursework in linear regression and social science research design. Basic familiarity with or willingness to learn the R statistical language. Offered: jointly with POL S 512.
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CS&SS 523 Social Networks and Health: Biocultural Perspectives (5)
Examines the many ways that social interactions positively and negatively influence our health, and vice versa. Considers why such influences are important to understand, how one measures them, what recent research has shown, and explores how they relate to other health determinants, both biological and cultural Offered: jointly with BIO A 523.
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CS&SS 526 Structural Equation Models for the Social Sciences (3)
Structural equation models for the social sciences, including specification, estimation, and testing. Topics include path analysis, confirmatory factor analysis, linear models with latent variables, MIMIC models, non-recursive models, models for nested data. Emphasizes applications to substantive problems in the social sciences. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent. Offered: jointly with SOC 529.
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CS&SS 527 Survey Research Methods (4)
Provides students with skills in questionnaire development and survey methods. Students develop a questionnaire and design a survey research proposal on a health-related or social topic. Prerequisite: either HSERV 511/HSERV 513; BIOST 517/BIOST 518; or EPI 512/EPI 513, which may be taken concurrently, or permission of instructor. Students should have a survey project in mind. Offered: jointly with G H 533/HSERV 527.
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CS&SS 536 Analysis of Categorical and Count Data (3)
Analysis of categorical data in the social sciences. Binary, ordered, and multinomial outcomes, event counts, and contingency tables. Focuses on maximum likelihood estimations and interpretations of results. Prerequisite: either SOC 504, SOC 505, SOC 506/CS&SS 507, STAT 423, or STAT 504/CS&SS 504. Offered: jointly with SOC 536/STAT 536.
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CS&SS 567 Statistical Analysis of Social Networks (4)
Statistical and mathematical descriptions of social networks. Topics include graphical and matrix representations of social networks, sampling methods, statistical analysis of network data, and applications. Prerequisite: SOC 504, SOC 505, SOC 506, or equivalent. Offered: jointly with STAT 567.
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Ancient philosophers explored the mix between natural and humancontributions in the construction of familiar features of the world.They did not, however, theorize much about exactly whatpeople do in order to create the social world. Instead, they wrote ofagreements, compacts, conventions, habits, laws, customs, and so on,without paying particular notice to separating these from one another.In the early modern period, theories of these sources broadenedconsiderably, as did the variety of social phenomena beinginvestigated. Approaches developed in the seventeenth and eighteenthcenturies include:
Other approaches also see social categories as being set up by thedistributed mental states of members of a society. Many of thesetheories are less specific about the particular states thatindividuals must be in. Husserl (1936, 1950) argues that how anindividual represents an object depends, in part, on thepresupposition that others are representing it as well. Individualempathy for the representations of others plays a part in how theindividual herself constructs representations. The representations ofobjects in a society, therefore, are a product of harmonized mentalstates among its members. But these mental states are not required tobe attitudes or dispositions. Other theorists associate differentmental characteristics with structuring social categories. Berger andLuckmann (1966), for instance, regard the identification of people ina society with social roles as central to the characteristics of thoseroles.
As analyses of social functional kinds, both role-kinds andrealizer-kinds have shortcomings. In particular, they miss out on thenormative character of functions. Cummins (1975) analyzes functions interms of the capacities of the components of a system to contribute toa capacity of a larger system. On his account, the function that anentity plays is sensitive to the context of the larger system in whichit is embedded. Social kinds, then, may arise from components ofsocial systems having particular Cummins-functions.
Boyd (1999a) applieshomeostasis to the analysis of kinds, both natural and social.According to Boyd, kinds are clusters of entities that stably havesimilar properties, with these similarities sustained by a causalhomeostatic mechanism. Marriage, for instance, is a kind because thereare many particular entities with similar properties (such as beingformed by ceremonies, involving couples paired up, and so on), andbecause there are mechanisms causing entities to have and keep theseproperties. Like Millikan, Boyd argues that kinds are a product ofactual tokens in the world and the causal processes in which thosetokens are involved. Since his account involves actual causal patternsover time, kinds are historical, but they do not need to involvefunctional roles or evolution.
A different large class of theories hold that practical engagementwith the environment is the basis for the setup of social categories.Many of these theories are influenced by Heidegger 1927, Merleau-Ponty1945, and Wittgenstein 1953. And still other theories are pluralisticand heterogeneous: they hold that social categories are not just setup in one uniform way, but in a variety of distinct ways.
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