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

by Jenkins-Smith et al.

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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.

Description from LibreTexts.

Publisher
University of Oklahoma
Subjects
Statistics, Applied Statistics
Licence
CC BY 4.0, as LibreTexts states it
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: Theories and Social Science
  7. 1.1: The Scientific Method
  8. 1.2: Theory and Empirical Research
  9. 1.3: Theory and Functions
  10. 1.4: 1.4 Theory in Social Science
  11. 1.5: Outline of the Book
  12. 2: Research Design
  13. 2.1: Overview of the Research Process
  14. 2.2: Internal and External Validity
  15. 2.3: Major Classes of Designs
  16. 2.4: Threats to Validity
  17. 2.5: Some Common Designs
  18. 2.6: Plan Meets Reality
  19. 3: Exploring and Visualizing Data
  20. 3.1: Characterizing Data
  21. 3.2: Section 2-
  22. 3.3: Section 3-
  23. 3.4: Section 4-
  24. 3.5: Section 5-
  25. 4: Probability
  26. 4.1: Finding Probabilities
  27. 4.2: Finding Probabilities with the Normal Curve
  28. 4.3: Summary
  29. 5: Inference
  30. 5.1: Populations and Samples
  31. 5.2: The Normal Distribution
  32. 5.3: Inferences to the Population from the Sample
  33. 5.4: Differences Between Groups
  34. 6: Association of Variables
  35. 6.1: Cross-Tabulation
  36. 6.2: Covariance
  37. 6.3: Correlation
  38. 6.4: Scatterplots
  39. 7: The Logic of Ordinary Least Squares Estimation
  40. 7.1: heoretical Models
  41. 7.2: Estimating Linear Models
  42. 7.3: An Example of Simple Regression
  43. 8: Linear Estimation and Minimizing Error
  44. 8.1: Minimizing Error using Derivatives
  45. 8.2: 8.2 Deriving OLS Estimators
  46. 8.3: Summary
  47. 9: Bi-Variate Hypothesis Testing and Model Fit
  48. 9.1: Hypothesis Tests for Regression Coefficients
  49. 9.2: Measuring Goodness of Fit
  50. 9.3: Summary
  51. 10: OLS Assumptions and Simple Regression Diagnostics
  52. 10.1: A Recap of Modeling Assumptions
  53. 10.2: When Things Go Bad with Residuals
  54. 10.3: Application of Residual Diagnostics
  55. 10.4: So Now What? Implications of Residual Analysis
  56. 10.5: Summary
  57. 11: Introduction to Multiple Regression
  58. 11.1: Matrix Algebra and Multiple Regression
  59. 11.2: The Basics of Matrix Algebra
  60. 11.3: OLS Regression in Matrix Form
  61. 11.4: Summary
  62. 12: The Logic of Multiple Regression
  63. 12.1: Theoretical Specification
  64. 12.2: Partial Effects
  65. 12.3: Multiple Regression Example
  66. 12.4: Summary
  67. 13: Multiple Regression and Model Building
  68. 13.1: Model Building
  69. 13.2: Evils of Stepwise Regression
  70. 13.3: Summary
  71. 14: Topics in Multiple Regression
  72. 14.1: Dummy Variables
  73. 14.2: Interaction Effects
  74. 14.3: Standardized Regression Coefficients
  75. 14.4: Summary
  76. 15: The Art of Regression Diagnostics
  77. 15.1: OLS Error Assumptions Revisited
  78. 15.2: OLS Diagnostic Techniques
  79. 15.3: Summary
  80. 16: Logit Regression
  81. 16.1: Generalized Linear Models
  82. 16.2: Logit Estimation
  83. 16.3: Summary
  84. 17: Appendix- Basic R
  85. 17.1: Introduction to R
  86. 17.2: Downloading R and RStudio
  87. 17.3: Introduction to Programming
  88. 17.4: Uploading/Reading Data
  89. 17.5: Data Manipulation in R
  90. 17.6: Saving/Writing Data
  91. 17.7: The Tidyverse
  92. Back Matter
  93. Index
  94. Glossary
  95. Detailed Licensing