Political Science Research Methods: Statistics in Service introduces research methodology and quantitative analysis for political and social science. Taking a broad and applied approach to the subject, the book covers social scientific inquiry and the basic statistical tools used to study politics. The book not only provides the quantitative foundations required in political science but also teaches the skills to become truly proficient applied researchers. Readers will be skilled collecters and analyzers of data, and be able draw links between political theory and the real world. The book counters the perception that quantitative methods are a form of mathematics to be dreaded, and instead presents methods with specific application for the study of political science. CONTENTS: Introduction---Putting Statistics into Service for You- The -Why- of Statistics in Political Science Your Role as Researcher The Usefulness of Qualitative Data The Variable Basic Research Concepts Gathering Data Goals for You and this Book--The Research Opportunity Chapter Two---The Research Design: Investigating Causality in Political Science- Grounding Your Causal Story--Theory Constructing Your Theory--The Literature Review Testing the -How---The Research Hypothesis (and X and Y) Try It--Constructing a Political Science Research Design for Yourself Chapter Three-- Chapter Three---Understanding the Xa Y Relationship: Qualitative and Quantitative Foundations- Controls Interactions Mediating, Moderating, and Additive Relationships Let's Operationalize Putting it Together: Research Hypotheses Redux Chapter Four---Basic Measures of Description, Central Tendency, and Statistical Inference- You and the Math: What You Already Know Descriptive Measures of Your Variables Central Tendency Let's Start to Infer--Statistical Significance, T Tests, and P Values Significant Associations--Correlation and ANOVA Significance and Your Research Hypotheses Chapter Five---All about Regression- Estimating Causality--Regression Analysis The Gauss-Markov Theorem Bivariate Regression--X and Y Controls and Multiple Regression Path Analysis Moderating and Interaction Variables Multicollinearity--Causes and Remedies Dealing with Error Variance Violations--Weighted Least Squares Dealing with Error Correlation Violations--Two Stage Least Squares Chapter Six---In the Real World--Statistical Sampling and Weights- Understanding the Population Drawing Your Sample Data Collection Options (CATI emphasis) Comparing the Sample to Reality: The Case for Weights Sample Balancing Chapter Seven---Beyond Observational Research--Causality through Experimental Design- The Logic of Experimentation The Treatment Effect Random Assignment Experiments in Surveys, the Lab, and the Field Chapter Eight---When OLS is Out: Binary, Ordered, and Multinomial Logit- Non-Continuous Dependent Variables Binary Measures Ordered and Scaled Variables Categorical Dependent Outcomes Interaction Terms and Logistic Regression Gauging Predictor Magnitude Chapter Nine---Counts: Poisson and Binomial Regression- The Case of Counts The Poisson Distribution Overdispersion and Negative Binomial Regression Bounded Counts and the Binomial Distribution Chapter Ten---Categorical Difference and Hierarchical Linear Models- When Categories Abound in Data Nested and Hierarchical Linear Models Chapter Eleven---Dynamic Data--Time Series Analysis- When Time is On Your Side Dynamic Causality Autocorrelation and its Remedies Chapter Twelve---Putting it Together Using Your Findings for the Client Presenting Your Research for General Audiences How Technical is Technical? The Anatomy of a Professional Research Report Chapter Thirteen---Building a Researcher's Resume- You've Come a Long Way What You Know, and Why Potential Employers Should Know It Describing Your Statistical Skills on a Resume What You Can Honestly Say in Job Interviews Where Can You Go from Here?