Nguồn ngoài

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 Statistics Statistics

Tác giả: Jenkins-Smith et al.
Nhà xuất bản: University of Oklahoma
Nguồn: LibreTexts
Giấy phép: CC BY
Ngôn ngữ: EN
Danh mục: Khoa học cơ bản

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Mục lục· 86 mục

01: Theories and Social Science
1.01: The Scientific Method
1.02: Theory and Empirical Research
1.03: Theory and Functions
1.04: 1.4 Theory in Social Science
1.05: Outline of the Book
02: Research Design
2.01: Overview of the Research Process
2.02: Internal and External Validity
2.03: Major Classes of Designs
2.04: Threats to Validity
2.05: Some Common Designs
2.06: Plan Meets Reality
03: Exploring and Visualizing Data
3.01: Characterizing Data
3.02: Section 2-
3.03: Section 3-
3.04: Section 4-
3.05: Section 5-
04: Probability
4.01: Finding Probabilities
4.02: Finding Probabilities with the Normal Curve
4.03: Summary
05: Interference
5.01: Populations and Samples
5.02: The Normal Distribution
5.03: Inferences to the Population from the Sample
5.04: Differences Between Groups
06: Association of Variables
6.01: Cross-Tabulation
6.02: Covariance
6.03: Correlation
6.04: Scatterplots
07: The Logic of Ordinary Least Squares Estimation
7.01: heoretical Models
7.02: Estimating Linear Models
7.03: An Example of Simple Regression
08: Linear Estimation and Minimizing Error
8.01: Minimizing Error using Derivatives
8.02: 8.2 Deriving OLS Estimators
8.03: Summary
09: Bi-Variate Hypothesis Testing and Model Fit
9.01: Hypothesis Tests for Regression Coefficients
9.02: Measuring Goodness of Fit
9.03: Summary
10: OLS Assumptions and Simple Regression Diagnostics
10.01: A Recap of Modeling Assumptions
10.02: When Things Go Bad with Residuals
10.03: Application of Residual Diagnostics
10.04: So Now What Implications of Residual Analysis
10.05: Summary
11: Introduction to Multiple Regression
11.01: Matrix Algebra and Multiple Regression
11.02: The Basics of Matrix Algebra
11.03: OLS Regression in Matrix Form
11.04: Summary
12: The Logic of Multiple Regression
12.01: Theoretical Specification
12.02: Partial Effects
12.03: Multiple Regression Example
12.04: Summary
13: Multiple Regression and Model Building
13.01: Model Building
13.02: Evils of Stepwise Regression
13.03: Summary
14: Topics in Multiple Regression
14.01: Dummy Variables
14.02: Interaction Effects
14.03: Standardized Regression Coefficients
14.04: Summary
15: The Art of Regression Diagnostics
15.01: OLS Error Assumptions Revisited
15.02: OLS Diagnostic Techniques
15.03: Summary
16: Logit Regression
16.01: Generalized Linear Models
16.02: Logit Estimation
16.03: Summary
17: Appendix- Basic R
17.01: Introduction to R
17.02: Downloading R and RStudio
17.03: Introduction to Programming
17.04: Uploading Reading Data
17.05: Data Manipulation in R
17.06: Saving Writing Data
17.07: The Tidyverse