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This course is designed to introduce the fundamentals of data analysis in policy research. The approach accentuates understanding basic concepts and techniques, with emphasis on problem solving and computer applications. The course is designed for students, researchers, data analyst is intended to provide an in-depth knowledge on issues in quantitative analysis, including use of statistical software.

We begin with methodological issues, including concept formation, measurement of concepts and variables, validity and reliability, explanation and hypothesis formation, and causal inferences. We follow with basic statistics and an introduction to inference. Detailed topics include distributions of single variables, measures of central tendency, and measures of association. We conclude with regression analysis, covering ordinary least squares and logistic regression techniques.

Learning Outcomes

The course offers a number of learning outcomes for participants:

  • Clarify the relationship between concepts and measurement
  • Familiarity with techniques for describing and summarizing data
  • Develop skills in formulating explanations, hypothesis testing, and making controlled comparisons.
  • Understand the relationship between random sampling and underlying populations
  • Learn to assess relationships between variables using measures of association
  • Develop knowledge of and skills for using regression techniques
  • Understand use of logistic regression for categorical variables
  • Gain skills and comfort using IBM SPSS for data analysis.
Session One Introduction

Definition & Measurement of Concepts

Session TWO Introduction to IBM SPSS

Measuring & Describing Variables

Session THREE Transforming Variables

Descriptive Statistics

Session FOUR Proposing Explanations, Framing Hypotheses, Making Comparisons

Making Comparisons

Foundations of Statistical Inference

Session SIX Tests of Significance & Measures of Association

Inferences about Sample Means

Chi-Square & Measures of Association

Session SEVEN Correlation & Linear Regression

Dummy Variables & Interaction Effects

Session EIGHT Logistic Regression
Session NINE Sharing Results