Applied statistics for the social and health sciences /

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Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with the basic skills that they need to estimate, interpret, present, and publish statistical models using contemporary standards. The book targets the social and health science branche...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Gordon, Rachel A.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Routledge : Taylor & Francis, 2012.
Temas:
Social sciences > Statistical methods.
Public health > Statistical methods.
Biometry.
Public health.
Social sciences.
Statistics as Topic > methods
Biometry
Public Health
Social Sciences
Sciences sociales > Méthodes statistiques.
Santé publique > Méthodes statistiques.
Biométrie.
Santé publique.
Sciences sociales.
biometrics.
public health.
social sciences.
MATHEMATICS > Probability & Statistics > General.
Social sciences
Public health
Public health > Statistical methods
Social sciences > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1 GETTING STARTED
  • ch. 1 Examples of Quantitative Research in the Social and Health Sciences
  • 1.1. What is Regression Analysis?
  • 1.2. Literature Excerpt 1.1
  • 1.3. Literature Excerpt 1 2
  • 1.4. Literature Excerpt 1.3
  • 1.5. Literature Excerpt 1.4
  • 1.6. Summary
  • ch. 2 Planning a Quantitative Research Project With Existing Data
  • 2.1. Sources of Existing Data
  • 2.2. Thinking Forward
  • 2.3. Example Research Questions
  • 2.4. Example of Locating Studies in ICPSR
  • 2.5. Summary
  • ch. 3 Basic Features of Statistical Packages and Data Documentation
  • 3.1. How are our Data Stored in the Computer?
  • 3.2. Why Learn Both SAS and STATA?
  • 3.3. Getting Started with a Quantitative Research Project
  • 3.4. Summary
  • ch. 4 Basics of Writing Batch Programs with Statistical Packages
  • 4.1. Getting Started with SAS and Stata
  • 4.2. Writing a Simple Batch Program
  • 4.3. Expanding the Batch Program to Create New Variables.
  • 4.4. Expanding the Batch Program to Keep a Subset of Cases
  • 4.5.Complex Sampling Designs
  • 4.6. Some Finishing Touches
  • 4.7. Summary
  • pt. 2 BASIC DESCRIPTIVE AND INFERENTIAL STATISTICS
  • ch. 5 Basic Descriptive Statistics
  • 5.1. Types of Variables
  • 5.2. Literature Excerpts 5.1 and 5.2
  • 5.3. Nominal Variables
  • 5.4. Ordinal Variables
  • 5.5. Interval Variables
  • 5.6. Weighted Statistics
  • 5.7. Creating a Descriptive Table
  • 5.8. Summary
  • ch. 6 Sample, Population and Sampling Distributions
  • 6.1. Statistical Inference
  • 6.2. Population and Sample Distributions
  • 6.3. The Sampling Distribution
  • 6.4. General Concepts for Statistical Inference
  • 6.5. Other Common Theoretical Distributions
  • 6.6. Summary
  • ch. 7 Bivariate Inferential Statistics
  • 7.1. Literature Excerpts
  • 7.2. One Categorical and One Interval Variable
  • 7.3. Two Categorical Variables
  • 7.4. Two Interval Variables
  • 7.5. Weighted Statistics
  • 7.6. Summary.
  • pt. 3 ORDINARY LEAST SQUARES REGRESSION
  • ch. 8 Basic Concepts of Bivariate Regression
  • 8.1. Algebraic and Geometric Representations of Bivariate Regression
  • 8.2. The Population Regression Line
  • 8.3. The Sample Regression Line
  • 8.4. Ordinary Least Squares Estimators
  • 8.5.Complex Sampling Designs
  • 8.6. Summary
  • ch. 9 Basic Concepts of Multiple Regression
  • 9.1. Algebraic and Geometric Representations of Multiple Regression
  • 9.2. OLS Estimation of the Multiple Regression Model
  • 9.3. Conducting Multiple Hypothesis Tests
  • 9.4. General Linear F-Test
  • 9.5.R-Squared
  • 9.6. Information Criteria
  • 9.7. Literature Excerpt 9.1
  • 9.8. Summary
  • ch. 10 Dummy Variables
  • 10.1. Why is a Different Approach Needed for Nominal and Ordinal Predictor Variables?
  • 10.2. How Do We Define Dummy Variables?
  • 10.3. Interpreting Dummy Variable Regression Models
  • 10.4. Putting It All Together
  • 10.5.Complex Sampling Designs
  • 10.6. Summary.
  • Ch. 11 Interactions
  • 11.1. Literature Excerpt 11.1
  • 11.2. Interactions Between Two Dummy Variables
  • 11.3. Interaction Between a Dummy and an Interval Variable
  • 11.4. Chow Test
  • 11.5. Interaction Between Two Interval Variables
  • 11.6. Literature Excerpt 11.2
  • 11.7. Summary
  • ch. 12 Nonlinear Relationships
  • 12.1. Nonlinear Relationships
  • 12.2. Summary
  • ch. 13 Indirect Effects and Omitted Variable Bias
  • 13.1. Literature Excerpt 13.1
  • 13.2. Defining Confounders, Mediators, and Supressor Variables
  • 13.3. Omitted Variable Bias
  • 13.4. Summary
  • ch. 14 Outliers, Heteroskedasticity, and Multicollinearity
  • 14.1. Outliers and Influential Observations
  • 14.2. Heteroskedasticity
  • 14.3. Multicollinearity
  • 14.4.Complex Sampling Designs
  • 14.5. Summary
  • pt. 4 THE GENERALIZED LINEAR MODEL
  • ch. 15 Introduction to the Generalized Linear Model with a Continuous Outcome
  • 15.1. Literature Excerpt 15.1
  • 15.2. Maximum Likelihood Estimation.
  • 15.3. Hypothesis Testing with Maximum Likelihood Estimation
  • 15.4. The Generalized Linear Model
  • 15.5. Summary
  • ch. 16 Dichotomous Outcomes
  • 16.1. Literature Excerpt 16.1
  • 16.2. Linear Probability Model
  • 16.3. Generalized Linear Model
  • 16.4. Goodness of Fit
  • 16.5. Interpretation
  • 16.6. Summary
  • ch. 17 Multi-Category Outcomes
  • 17.1. Multinomial Logit
  • 17.2. Ordered Logit
  • 17.3. Putting It All Together
  • 17.4.Complex Sampling Designs
  • 17.5. Summary
  • pt. 5 WRAPPING UP
  • ch. 18 Roadmap to Advanced Topics
  • 18.1. Revisiting Literature Excerpts from Chapter 1
  • 18.2.A Roadmap to Statistical Methods
  • 18.3.A Roadmap to Locating Courses and Resources
  • 18.4. Summary
  • APPENDICES
  • Appendix A Summary of SAS and Stata Commands
  • Appendix B Examples of Data Coding, and of the SAS and Stata Interface, Commands, and Results, Based on the National Survey of Families and Households
  • Appendix C Screenshots of Data Set Documentation.
  • Appendix D Accessing the National Survey of Families and Households Raw Data File
  • Appendix E Accessing the NHIS Data
  • Appendix F Using SAS and Stata's Online Documentation
  • Appendix G Example of Hand-Calculating the Intercept, Slope, and Conditional Standard Deviation using Stylized Sample
  • Appendix H Using Excel to Calculate and Graph Predicted Values
  • Appendix I Using Hayes-Cai SAS Macro for Heteroskedasticity-Consistent Standard Errors.

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