Lecture 1 Variables

Course Information

• Course Title: Quantitative Research Methods in Political Science
• Lecture Number: 1
• Course Instructor: Michael E. Campbell
• Course Number: PSCI 2702(A)
• Date: 09/05/2024

Course Description

• Purpose: Equip students with knowledge to conduct research using quantitative methods.
• Content: Introduction to quantitative research methods, techniques, and formulas for data analysis.
• Structure: Mix of theoretical lectures and practical tutorials.
• Learning Outcomes:
  • Understanding the purpose and advantages of social science research.
  • Comprehension of foundational concepts and operations associated with empirical data analysis.
  • Effective interpretation and evaluation of data.
  • Application of various statistical techniques for data analysis and hypothesis testing.

Format

• Lectures: Thursdays, 9:35 AM – 11:25 AM
• Tutorials: Before or after lectures, group-dependent

Communication

• Instructor Email: [email protected]
• TA Email (Rohit Samaroo): [email protected]
• TA Email (Kaia Goodhope): [email protected]

Course Materials

• Required Textbook: "Statistics: A Tool for Social Research" by Healey, Donoghue, and Prus (2023, 5th ed.).
• Resources: Textbook is on reserve at the library, additional readings on ARES reserves via Brightspace or Carleton Library Website.
• Downloads: SPSS analytical software, Varieties of Democracy Data (links on Brightspace)

Grading Breakdown

• Tutorial Attendance: 10%
• Assignment #1: 10%; Due 10 October (11:59 PM)
• Midterm Exam: 25%; 17 October (in-class)
• Assignment #2: 20%; Due 5 December (11:59 PM)
• Final Exam: 35%; TBA (during exam period)
• Late Penalty: 5% per day without valid extension; 7-day late assignments will not be graded.

What are Quantitative Research Methods?

• Research: Any systematic and careful process of gathering information to answer questions or test theories.
• Statistics: Mathematical techniques for organizing and analyzing data to answer questions and test theories.
• Quantitative Research: Relies on statistical analysis and predetermined steps. This approach seeks specific information about data in an organized, logical manner.

The Scientific Method

• Steps: Identify the problem, hypothesize, define concepts, gather data, test hypotheses, reflect on theory, publicize results, replicate the analysis.
• Founders: Empirical science can be traced to Aristotle (384 – 322 B.C.).

Natural vs. Social Sciences

• Natural Science: Studies natural phenomena, predictable, deals with facts.
• Social Science: Studies society and behavior, less predictable, deals with facts and values.

Facts vs. Values

• Facts: Observable, measurable, objective aspects of reality (what is).
• Values: Normative, subjective judgments about what ought to be.

Value Judgments

• Value judgment: A choice between beliefs considered right or wrong.
• Quantitative Research: Aims to minimize personal opinions and biases as much as possible.
• Empirical Testing of Opinions: Data should be used to support or refute beliefs.

The Role of Statistics in Social Science

• Theory: Statement about the relationship between phenomena.
• Hypothesis: Statement about the relationship between variables.
• Observations: What is seen during data study.
• Generalizations: Summary of patterns and relationships.

Empirical Research Example

• Example scenario: relationship between informed electorate and voter turnout.
• Theory, hypotheses, and observations relating to the scenario.

The Value of Statistics in Political Science

• Social science is both empirical (factual) and normative (value-based).
• Importance of reliable data collection.
• Methods and methodologies provide structure for research.

Variables and Levels of Measurement

• Variable: Any trait that changes from case to case (e.g., age, gender).

Characteristics of Variables

• Mutually exclusive: Response categories do not overlap.
• Exhaustive: All possible responses are accounted for.
• Homogenous: All responses represent the same underlying concept.

Discrete vs. Continuous Variables

• Discrete: Cannot be divided (whole numbers) (e.g., the number of people in a household).
• Continuous: Can be subdivided infinitely, potentially with decimals (e.g., time, temperature).

Levels of Measurement

• Nominal: Classifies into categories (categorical data).
• Ordinal: Ranked categories (higher/lower).
• Interval-Ratio: Equal intervals, meaningful zero (numerical).

Nominal Level of Measurement

• Categorical data, different categories, cannot rank categories, only count comparisons.

Ordinal Level of Measurement

• Ranked categories, descriptions of differences. Public opinion surveys frequently use ordinal measurement.

Interval-Ratio Level of Measurement

• Highest level; equal intervals, a meaningful zero. Possible to use all mathematical operations.