Science and the Scientific Process – Quick Reference Notes

Science is the pursuit and application of knowledge and understanding of the natural and social world following a systematic methodology based on evidence.
It is a systematic and logical approach to understanding phenomena, involving observing, asking questions, and using evidence to build, test, and refine explanations.

Explanation: understand how and why things work
Prediction: anticipate outcomes under certain conditions
Control/Application: improve quality of life via technology, medicine, environment, etc.
Continuous Revision: knowledge is open to further investigation and revision

Foundation of scientific inquiry: conclusions based on logic, evidence, and reproducibility rather than guesswork or bias

Scientists note phenomena, patterns, or problems that spark curiosity
Examples of observation focus: patterns in political discussion during elections, differences between urban and rural communities
Research Spark: question why certain communities engage more in political discussions during elections

Formulate clear, researchable questions based on observations
Example Research Spark: Why do people in some communities engage actively in political discussions during elections, while others do not?
Research Questions (examples):
- 1) What social factors influence the frequency of political discussions among community members during election periods?
- 2) How do community characteristics (urban vs rural, socioeconomics, education) affect political engagement in public spaces during elections?
- 3) To what extent do media exposure and access to information shape participation in political discussions during elections?
- 4) What role do local leaders and social networks play in stimulating political conversations during campaigns?
- 5) How does perceived political efficacy affect willingness to join discussions in various neighborhoods during elections?
- 6) Are there differences in topics or tone of discussions between highly engaged vs. less engaged communities during elections?

Review existing knowledge to inform the investigation and avoid repetition
Key ideas:
- Social capital and community attachments: networks of relationships, trust, and reciprocity influence political participation; stronger social bonds encourage more discussions and engagement.
- Political efficacy and interest: belief in influencing government and genuine interest in politics predict participation; internal (self-efficacy) and external efficacy both matter; interest can be a stronger motivator than efficacy alone.

If higher social capital, then greater engagement in political discussions during elections.
If higher political efficacy, then more active participation in political discussions.
If greater access to political information (media), then higher levels of political discussion and engagement.
If active local leaders/influencers encourage discourse, then higher participation in election-related discussions.

Independent Variables (IVs): factors that influence engagement
- Level of social capital (strength of networks, trust among community members)
- Political efficacy (belief in ability to affect political processes)
- Access to political information (media exposure, internet availability)
- Presence of community leadership encouraging discussions
- Socioeconomic status (education, income)
- Cultural norms regarding political participation
- Urban versus rural setting
Dependent Variable (DV): outcome to measure
- Level of active engagement in political discussions during elections, operationalized as:
- Frequency of participating in political conversations in public or private settings
- Willingness to express political opinions openly
- Participation in election-related discussions on social media or community forums

Organize and interpret data using graphs, tables, and statistics
Determine whether results support or refute the hypotheses
Correlations observed:
- Between political efficacy and frequency of political discussion: $r = +0.62$
- Between social capital and political engagement: $r = +0.55$
- Between media exposure and engagement: $r = +0.48$

Findings: communities with stronger social networks, higher political efficacy, greater access to information, and active community leadership are more likely to engage in political discussions during elections.
Implications: social capital and information accessibility foster civic participation; urban areas tend to show higher engagement, while rural areas may need targeted communication infrastructure and mobilization efforts.

Key components: Theory, Logic, Data, Observation, Data Collection, Data Analysis
Movement: Theory + Data collection leads to Data Analysis; both feed back into Theory

Theoretical Plane:
- Construct A, Construct B, Proposition, Independent Variable, Dependent Variable, Hypothesis
Empirical Plane:
- Data collection, Analysis, Observations, Hypothesis testing

Theoretical Plane vs Empirical Plane: constructs, propositions, hypotheses mapped to data collection and analysis

Principle 1: Pose significant questions that can be investigated empirically
- Nature of questions: science proceeds by posing significant questions with potentially multiple answers
- Manner of posing: questions must be posed so that they lead to testable hypotheses
- Significance: a question’s significance is grounded in prior research, theory, and policy relevance
- Characteristics of significant questions: empirically investigable, relevant/important, focused/clear, feasible
Principle 2: Link research to theory
- Research should be connected to established theories or frameworks relevant to the problem
Principle 3: Use methods that permit direct investigation of the research
- Methods are the design for data collection, measurement, and analysis; the question drives method choice
- Methods should allow direct observation, measurement, or experimentation
- Can include experimental, observational, correlational, or mixed methods
Principle 4: Provide coherent, explicit chain of reasoning
- Build transparent, step-by-step explanations showing how data support conclusions
Principle 5: Replicate and generalize across studies
- Replication increases reliability; generalization tests broader applicability
Principle 6: Disclose research to encourage critique and critique/peer review
- Transparency of methods, data, analyses, and conclusions
- Promotes accountability, replication, and scientific dialogue