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Quantitative Research Methods for Political Science, Public Policy and Public Administration

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Quantitative Research Methods for Political Science, Public Policy and Public Administration

The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses.

Applied StatisticsStatistics
Author
Jenkins-Smith et al.
Publisher
University of Oklahoma
Source
LibreTexts
License
CC BY
Language
EN
Category
Basic Sciences
Read online Download PDF (open licence)
Contents· 86 sections
  1. 01: Theories and Social Science
  2. 1.01: The Scientific Method
  3. 1.02: Theory and Empirical Research
  4. 1.03: Theory and Functions
  5. 1.04: 1.4 Theory in Social Science
  6. 1.05: Outline of the Book
  7. 02: Research Design
  8. 2.01: Overview of the Research Process
  9. 2.02: Internal and External Validity
  10. 2.03: Major Classes of Designs
  11. 2.04: Threats to Validity
  12. 2.05: Some Common Designs
  13. 2.06: Plan Meets Reality
  14. 03: Exploring and Visualizing Data
  15. 3.01: Characterizing Data
  16. 3.02: Section 2-
  17. 3.03: Section 3-
  18. 3.04: Section 4-
  19. 3.05: Section 5-
  20. 04: Probability
  21. 4.01: Finding Probabilities
  22. 4.02: Finding Probabilities with the Normal Curve
  23. 4.03: Summary
  24. 05: Interference
  25. 5.01: Populations and Samples
  26. 5.02: The Normal Distribution
  27. 5.03: Inferences to the Population from the Sample
  28. 5.04: Differences Between Groups
  29. 06: Association of Variables
  30. 6.01: Cross-Tabulation
  31. 6.02: Covariance
  32. 6.03: Correlation
  33. 6.04: Scatterplots
  34. 07: The Logic of Ordinary Least Squares Estimation
  35. 7.01: heoretical Models
  36. 7.02: Estimating Linear Models
  37. 7.03: An Example of Simple Regression
  38. 08: Linear Estimation and Minimizing Error
  39. 8.01: Minimizing Error using Derivatives
  40. 8.02: 8.2 Deriving OLS Estimators
  41. 8.03: Summary
  42. 09: Bi-Variate Hypothesis Testing and Model Fit
  43. 9.01: Hypothesis Tests for Regression Coefficients
  44. 9.02: Measuring Goodness of Fit
  45. 9.03: Summary
  46. 10: OLS Assumptions and Simple Regression Diagnostics
  47. 10.01: A Recap of Modeling Assumptions
  48. 10.02: When Things Go Bad with Residuals
  49. 10.03: Application of Residual Diagnostics
  50. 10.04: So Now What Implications of Residual Analysis
  51. 10.05: Summary
  52. 11: Introduction to Multiple Regression
  53. 11.01: Matrix Algebra and Multiple Regression
  54. 11.02: The Basics of Matrix Algebra
  55. 11.03: OLS Regression in Matrix Form
  56. 11.04: Summary
  57. 12: The Logic of Multiple Regression
  58. 12.01: Theoretical Specification
  59. 12.02: Partial Effects
  60. 12.03: Multiple Regression Example
  61. 12.04: Summary
  62. 13: Multiple Regression and Model Building
  63. 13.01: Model Building
  64. 13.02: Evils of Stepwise Regression
  65. 13.03: Summary
  66. 14: Topics in Multiple Regression
  67. 14.01: Dummy Variables
  68. 14.02: Interaction Effects
  69. 14.03: Standardized Regression Coefficients
  70. 14.04: Summary
  71. 15: The Art of Regression Diagnostics
  72. 15.01: OLS Error Assumptions Revisited
  73. 15.02: OLS Diagnostic Techniques
  74. 15.03: Summary
  75. 16: Logit Regression
  76. 16.01: Generalized Linear Models
  77. 16.02: Logit Estimation
  78. 16.03: Summary
  79. 17: Appendix- Basic R
  80. 17.01: Introduction to R
  81. 17.02: Downloading R and RStudio
  82. 17.03: Introduction to Programming
  83. 17.04: Uploading Reading Data
  84. 17.05: Data Manipulation in R
  85. 17.06: Saving Writing Data
  86. 17.07: The Tidyverse