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"The National Children's Study (NCS) was authorized by the Children's Health Act of 2000 and is being implemented by a dedicated Program Office in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The NCS is planned to be a longitudinal observational birth cohort study to evaluate the effects of chronic and intermittent exposures on child health and development in the U.S.. The NCS would be the first study to collect a broad range of environmental exposure measures for a national probability sample of about 100,000 children, followed from birth or before birth to age 21. Detailed plans for the NCS were developed by 2007 and reviewed by a National Research Council / Institute of Medicine panel. At that time, sample recruitment for the NCS Main Study was scheduled to begin in 2009 and to be completed within about 5 years. However, results from the initial seven pilot locations, which recruited sample cases in 2009-2010, indicated that the proposed household-based recruitment approach would be more costly and time consuming than planned. In response, the Program Office implemented a number of pilot tests in 2011 to evaluate alternative recruitment methods and pilot testing continues to date. At the request of Congress, The National Children's Study 2014 reviews the revised study design and proposed methodologies for the NCS Main Study. This report assesses the study's plan to determine whether it is likely to produce scientifically sound results that are generalizable to the United States population and appropriate subpopulations. The report makes recommendations about the overall study framework, sample design, timing, content and need for scientific expertise and oversight. The National Children's Study has the potential to add immeasurably to scientific knowledge about the impact of environmental exposures, broadly defined, on children's health and development in the United States. The recommendations of this report will help the NCS will achieve its intended objective to examine the effects of environmental influences on the health and development of American children"--Publisher's description

Machine generated contents note: 'Intrinsic Case Study and Generalizability -- 1 The Case Study Method in Social Inquiry -- Robert E. Stake -- 2 The Only Generalization is: There is -- No Generalization -- Yvonna S. Lincoln and Egon G. Guba -- 3 Generalizability and the Single-Case Study -- Robert Donmoyer -- 4 Increasing the Generalizability of -- Qualitative Research -- Janet Ward Schofield -- 5 Case Study and Generalization -- Roger Gomm, Martyn Hammersley and Peter Foster -- Case Study and Theory -- 6 Case Study and Theory in Political Science -- Harry Eckstein -- 7 Case and Situation Analysis -- J. Clyde Mitchell -- 8 The Logical Structure of Analytic Induction -- W.S. Robinson -- 9 The Quest for Universals in Sociological Research -- Ralph H. Turner -- 10 Small N's and Big Conclusions: An -- Examination of the Reasoning in Comparative -- Studies Based on a Small Number of Cases -- Stanley Lieberson -- 11 Cases, Causes, Conjunctures, Stories and Imagery -- Howard S. Becker -- 12 Case Study and Theory -- Martyn Hammersley, Roger Gomm and Peter Foster -- An Annotated Bibliography -- Index

Preface Introduction Indicators for Transit Oriented Development 1.1 Why Start With Indicators? 1.1.1 Mapping & scale bias in areal aggregate data 1.2 Setup 1.2.1 Downloading & wrangling Census data 1.2.2 Wrangling transit open data 1.2.3 Relating tracts & subway stops in space 1.3 Developing TOD Indicators 1.3.1 TOD indicator maps 1.3.2 TOD indicator tables 1.3.3 TOD indicator plots 1.4 Capturing three submarkets of interest 1.5 Conclusion: Are Philadelphians willing to pay for TOD? 1.6 Assignment -- Study TOD in your city Expanding the Urban Growth Boundary2.1 Introduction -- Lancaster development2.1.1 The bid-rent model2.1.2 Setup Lancaster data 2.2 Identifying areas inside & outside of the Urban Growth Area 2.2.1 Associate each inside/outside buffer with its respective town2.2.2 Building density by town & by inside/outside the UGA 2.2.3 Visualize buildings inside & outside the UGA2.3 Return to Lancaster's Bid Rent 2.4 Conclusion -- On boundaries 2.5 Assignment -- Boundaries in your community Intro to geospatial machine learning, Part 1 3.1 Machine learning as a Planning tool 3.1.1 Accuracy & generalizability 3.1.2 The machine learning process 3.1.3 The hedonic model 3.2 Data wrangling -- Home price & crime data 3.2.1 Feature Engineering -- Measuring exposure to crime 3.2.2 Exploratory analysis: Correlation3.3 Introduction to Ordinary Least Squares Regression 3.3.1 Our first regression model3.3.2 More feature engineering & colinearity 3.4 Cross-validation & return to goodness of fit3.4.1 Accuracy -- Mean Absolute Error 3.4.2 Generalizability -- Cross-validation 3.5 Conclusion -- Our first model 3.6 Assignment -- Predict house prices Intro to geospatial machine learning, Part 24.1 On the spatial process of home prices 4.1.1 Setup & Data Wrangling 4.2 Do prices & errors cluster? The Spatial Lag4.2.1 Do model errors cluster? -- Moran's I4.3 Accounting for neighborhood 4.3.1 Accuracy of the neighborhood model 4.3.2 Spatial autocorrelation in the neighborhood model 4.3.3 Generalizability of the neighborhood model4.4 Conclusion -- Features at multiple scalesGeospatial risk modeling -- Predictive Policing 5.1 New predictive policing tools 5.1.1 Generalizability in geospatial risk models 5.1.2 From Broken Windows Theory to Broken Windows Policing 5.1.3 Setup 5.2 Data wrangling: Creating the fishnet5.2.1 Data wrangling: Joining burglaries to the fishnet 5.2.2 Wrangling risk factors 5.3 Feature engineering -- Count of risk factors by grid cell 5.3.1 Feature engineering -- Nearest neighbor features 5.3.2 Feature Engineering -- Measure distance to one point 5.3.3 Feature Engineering -- Create the final-net 5.4 Exploring the spatial process of burglary 5.4.1 Correlation tests 5.5 Poisson Regression 5.5.1 Cross-validated Poisson Regression 5.5.2 Accuracy & Generalzability 5.5.3 Generalizability by neighborhood context5.5.4 Does this model allocate better than traditional crime hotspots? 5.6 Conclusion -- Bias but useful? 5.7 Assignment -- Predict risk People-based ML models6.1 Bounce to work6.2 Exploratory analysis 6.3 Logistic regression6.3.1 Training/Testing sets 6.3.2 Estimate a churn model 6.4 Goodness of Fit 6.4.1 Roc Curves 6.5 Cross-validation 6.6 Generating costs and benefits 6.6.1 Optimizing the cost/benefit relationship 6.7 Conclusion -- churn 6.8 Assignment -- Target a subsidy People-Based ML Models: Algorithmic Fairness7.1 Introduction 7.1.1 The spectre of disparate impact 7.1.2 Modeling judicial outcomes 7.1.3 Accuracy and generalizability in recidivism algorithms 7.2 Data and exploratory analysis 7.3 Estimate two recidivism models 7.3.1 Accuracy & Generalizability 7.4 What about the threshold?7.5 Optimizing 'equitable' thresholds 7.6 Assignment -- Memo to the Mayor Predicting rideshare demand8.1 Introduction -- ride share 8.2 Data Wrangling -- ride share 8.2.1 Lubridate8.2.2 Weather data 8.2.3 Subset a study area using neighborhoods 8.2.4 Create the final space/time panel 8.2.5 Split training and test8.2.6 What about distance features? 8.3 Exploratory Analysis -- ride share 8.3.1 Trip-Count serial autocorrelation 8.3.2 Trip-Count spatial autocorrelation 8.3.3 Space/time correlation? 8.3.4 Weather8.4 Modeling and validation using purrr::map8.4.1 A short primer on nested tibbles 8.4.2 Estimate a ride share forecast 8.4.3 Validate test set by time 8.4.4 Validate test set by space 8.5 Conclusion -- Dispatch8.6 Assignment -- Predict bike share tripsConclusion -- Algorithmic Governance Index

The Need to Do Research Differently -- Researching the Fear of Crime -- Producing Data with Defended Subjects -- Analyzing Data with Defended Subjects -- The Ethics of Researching Psychosocial Subjects -- Biography, Demography and Generalizability -- A Psychosocial Case Study -- Original Afterword -- New Developments since 2000

Introduction -- 1. Democracy: Theoretical and conceptional challenges -- 2. Stateness and democracy -- 3. Tracing the process -- 4. Former Yugoslav republics: Diverging trajectories -- 5. The group of less successful cases -- 6. The group of more successful cases -- 7. Generalizability of the theoretical framework -- Conclusion.

The Value of Studying Subjective Evaluations of Probability -- The True Subjective Probability Problem -- Subjective Probability: A Judgment of Representativeness -- The Psychological Concept of Subjective Probability: A Measurement-Theoretic View -- Are Subjective Probabilities Probabilities? -- On the Generalizability of Experimental Results -- Statistical Analysis: Theory Versus Practice -- A Selected Bibliography -- Author Index.

Evidence from public good game experiments holds the promise of instructive and cost-effective insights to inform environmental policy-making, for example on climate change mitigation. To fulfill the promise, such evidence needs to demonstrate generalizability to the specific policy context. This paper examines whether and under which conditions such evidence generalizes to voluntary mitigation decisions. We observe each participant in two different decision tasks: a real giving task in which contributions are used to directly reduce CO2 emissions and a public good game. Through two treatment variations, we explore two potential shifters of generalizability in a within-subjects design: the structural resemblance of contribution incentives between the tasks and the role of the subject pool, students and non-students. Our findings suggest that cooperation in public good games is linked to voluntary mitigation behavior, albeit not in a uniform way. For a standard set of parameters, behavior in both tasks is uncorrelated. Greater structural resemblance of the public goods game leads to sizable correlations, especially for student subjects.

Introduction -- The international politics of assimilation, accommodation, & exclusion -- Why the Balkans? -- Explaining cross-national variation: nation-building in post-World War I Balkans -- Explaining odd cases -- Explaining subnational variation: Greek nation-building in western Macedonia, 1916-1920 -- Explaining temporal variation: Serbian nation-building toward Albanians, 1878-1941; nation-building in the revisionist Kingdom of Serbia; nation-building in the status quo Kingdom of Serbs, Croats and Slovenes -- Generalizability -- Conclusion.

Intro -- Acknowledgments -- Contents -- List of Tables -- 1 Introduction -- Empirical Issue: Direct Democratic Economic Policy (DDEP) Legitimation -- Empirical Puzzles -- Empirical Agenda -- Research Question 1: How Do Partisanship and Self-interest Interact to Shape Partisan Voters' Rationalizations of Their Position on Direct Democratic Economic Policy (DDEP)? -- Research Question 2: What Are the Discourses That Voters Draw Upon to Rationalize Their Direct Democratic Economic Policy (DDEP) Position? -- Theory Building Agenda and Generalizability -- Case Selection -- Research Design -- Research Agenda for the Selection of Each of the Three Ballot Measures -- Chapter Overview -- Bibliography -- Part I -- 2 Theory -- Introduction -- Legitimacy and Legitimation -- Legitimacy -- Legitimation -- Framework for Research Questions -- Widely Held Beliefs -- Values: Most Sacred Values -- Normative Ends: Norms of Self-Interest -- Standards of Governance: Fairness -- Fairness: Moral Economy -- Fairness: Neoliberalism -- Building on Direct Democracy Literature -- Literature in Political Science and Economics on Direct Democracy -- Debate on Direct Versus Representative Democracy -- Bibilography -- 3 Analytical Approach and Broader American Political and Economic Discourses -- Analytical Approach -- Generalizability -- Outline of Code Generation Process -- Coding: Turning Talk into "Rationales" -- Core Theories -- Core Theories: Most Sacred Values -- Normative Ends: Norms of Self-Interest -- Inductively Generated Codes -- Fairness: Moral Economy -- Fairness: Neoliberalism -- Bibliography -- 4 Data and Methods -- Semi-structured Interview Procedures -- Quasi-Experimental Research Design -- Operationalization of Quasi-Independent Variables -- Operationalization of Partisanship as Partisan Affiliation -- Operationalization of Self-Interest as Economic Position.

While most books on missing data focus on applying sophisticated statistical techniques to deal with the problem after it has occurred, this volume provides a methodology for the control and prevention of missing data. In clear, nontechnical language, the authors help the reader understand the different types of missing data and their implications for the reliability, validity, and generalizability of a study's conclusions. They provide practical recommendations for designing studies that decrease the likelihood of missing data, and for addressing this important issue when reporting study resu

This Element offers a way to understand the evolution of authoritarian rule in Southeast Asia. The theoretical framework is based on a set of indicators (judged for their known advantages and mimicry of democratic attributes) as well as a typology (conceptualized as two discreet categories of 'retrograde' and 'sophisticated' authoritarianism). Working with an original dataset, the empirical results reveal vast differences within and across authoritarian regimes in Southeast Asia, but also a discernible shift towards sophisticated authoritarianism over time. The Element concludes with a reflection of its contribution and a statement on its generalizability.