The Scientific Study of Politics - Kellstedt and Whitten

Many political science students are drawn to the subject due to interests in political issues rather than methodology.
This chapter introduces the goals of this book and advocates for a scientific approach over a simplistic "just-the-facts" approach.
It emphasizes understanding politics through models where concepts are transformed into variables causally linked by theories.
Importance of establishing the goals and standards of political science research.
Concludes with a brief overview of the book's structure.

Common misconceptions about political science focus on partisanship, while the discipline is characterized as a scientific study of political phenomena.
Importance of conducting research such that personal political views of political scientists cannot be discerned.
The reality is that modern political science heavily incorporates scientific and mathematical methodologies.
Encouragement for students to persevere through challenges in learning scientific methods, emphasizing the utility of these tools in broader contexts: - Goal 1: Enable students to consume academic political science research effectively. - Goal 2: Help students become better consumers of information, particularly in evaluating claims about causal relationships. - Goal 3: Foster skills to produce scientific research on politics.
Highlighting the contrast with a more fact-based learning approach in political science, illustrated by an example from the politics of the European Union (EU).
Explanation of how political contexts and phenomena evolve, citing changes in EU membership and institutional rules.

Core Philosophical Question: "How do we know what we know?" - This reflects the scientific approach, which is characterized by: - Skepticism and willingness to reconsider evidence. - The development and testing of theories, leading to hypotheses.
Definitions: - Theory: A tentative conjecture about causal relationships in political phenomena. - Hypothesis: A theory-based expectation about observed relationships between variables. - Null Hypothesis: The expectation of no effect or relationship, typically the counterpart to the hypothesis. - Variables: Abstract concepts that vary, e.g., voter turnout defined as the percentage of eligible voters who cast votes in an election.

Steps in the scientific process: 1. Develop causal theories. 2. Derive testable hypotheses from theories. 3. Conduct empirical tests of hypotheses. 4. Assess evidence to confirm or reject hypotheses. 5. Re-evaluate the underlying causal theory based on findings.
Theories must withstand rigorous testing to gain confidence, not only in the hypotheses but also in the associated theories.
Unlike lawyers, who build cases to advocate for a position, scientists seek to disprove their theories through tough testing.
Statistical techniques gauge the probability that findings aren't due to chance, with a preference for the null hypothesis due to its conservative nature concerning false claims.

Established theories become foundations for further research following Thomas Kuhn's cycles of knowledge accumulation and revolutions (paradigm shifts).
Example: Changes in the study of public opinion in the U.S. through mass surveys post the 1944 Erie County study that revealed voters' stable preferences instead of campaign-related influences. - Resulted in new theories about long-standing partisan attachments and youth political socialization.

Development of political theories involves framing concepts as variables with causal relationships: - Independent Variable: Variable presumed to influence the outcome. - Dependent Variable: Outcome variable affected by changes in the independent variable.
Causal statement example: If the state of the economy influences election outcomes, then economic performance as the independent variable affects the incumbent vote as the dependent variable.

Using economic voting as a theory: - Hypothesis: Stronger economic performance (independent variable) leads to higher incumbent vote percentages (dependent variable). - Expected relationship: Positive if economic conditions are favorable.
Discussed how different operationalizations (e.g., economic growth vs unemployment rates) would inform the numeric relationships and shape hypotheses accordingly.

The following general principles guide inquiry in political science: - Causality: Ensure theories address causal dynamics, not mere covariation. - Theory Development: Formulate theories before data investigation to maintain theoretical integrity. - Empirical Evidence: Base knowledge on observable phenomena and empirical tests. - Avoid Normative Statements: Political science must remain objective, without conflating personal values and beliefs into scientific discourse. - Generality and Parsimony: Strive for theories that are broadly applicable while maintaining simplicity. - Generality: Broad theories across phenomena versus limited scope. - Parsimony: Simplicity in theories may sometimes require trade-offs for greater broad applicability.

Upcoming chapters will progressively build tools for developing and testing theories, starting from Chapter 2 on theory building to Chapter 12 focusing on regression models.

Causal: Pertaining to establishing cause and effect relationships.
Correlation: Relationship where two variables vary together.
Covariation: Synonymous with correlation pertaining to relational patterns between variables.
Dependent Variable: The outcome variable influenced by independent variables.
Empirical: Based on observations and measurements from the real world.
Hypothesis: Predictive statement derived from theories.
Hypothesis Testing: Method of evaluating the viability of hypotheses against empirical evidence.
Independent Variable: The factor proposed to affect the dependent variable.
Normal Science: Period of research conducted under shared paradigms.
Normative Statements: Value-based statements about what ought to be.
Null Hypothesis: Hypothesis stating no effect or relationship exists.
Operationalize: Define concepts in measurable terms.
Paradigm: Framework of shared assumptions shaping scientific research.
Paradigm Shift: Fundamental changes in scientific thought processes.
Parsimony: The principle of simplicity in theories.
Theoretical Models: Simplified representations of complex phenomena.
Theory: A systematic explanation for a range of phenomena.
Variable: Attributes or characteristics that can change.