Foundations of Social Science Inquiry

Errors of Personal Inquiry

• Overgeneralization: Drawing conclusions from limited experience.
• Selective Observation: Focusing on confirming evidence and ignoring contradictory data.
• Premature Closure: Stopping inquiry too early without exploring further.
• Halo Effect: Letting one positive or negative trait influence judgments of others.

Scientific Inquiry

• A systematic approach to collecting, analyzing, and drawing conclusions from data.
• Employs empirical methods, unlike personal inquiry which relies on intuition or experience.
• Uses objective methods and logical reasoning.

Foundations of Social Science

• Empirical Observation: Collecting data based on observable phenomena.
• Theory: Developing ideas or models to explain social phenomena.
• Objectivity: Maintaining neutrality to avoid personal biases in research.
• Systematic Study: Using structured approaches to gather and analyze data.
• Interdisciplinary Nature: Drawing on insights from various fields (e.g., sociology, psychology) to understand social behavior.

Variables

• Characteristics or properties that can take on different values.
• Used to examine relationships between factors in research.

Attributes

• Specific characteristics or qualities that describe a variable.
• For instance, "education level" may have attributes like "high school diploma," "bachelor's degree."

Purposes of Research

• Exploration: Investigating new topics and phenomena to gather basic information.
• Description: Providing a detailed account of a situation or group's characteristics.
• Explanation: Understanding the reasons behind observed phenomena.
• Prediction: Forecasting future trends or behaviors based on existing data.

Independent and Dependent Variables

• Independent Variable (IV): A variable that is manipulated or changed to observe its effect on another variable.
• Dependent Variable (DV): A variable affected by the independent variable.

Science vs. Other Systems

• Science uses systematic, evidence-based methods to understand the world, relying on observation and testing.
• Other systems may use tradition, authority, intuition, or personal experience.

Idiographic vs. Nomothetic Methods

• Idiographic: Focuses on a detailed understanding of individual cases (qualitative).
• Nomothetic: Seeks general laws and patterns across large groups (quantitative).

Inductive vs. Deductive Reasoning

• Inductive: Starts with specific observations and builds general theories.
• Deductive: Starts with a general theory and makes specific predictions to be tested.

Theory

• A set of ideas that explains social phenomena and offers a framework for understanding observations.

Paradigms

• Broad frameworks that shape how we interpret the world and conduct research.
• Examples include structural functionalism, social conflict, symbolic interactionism, feminism, and social constructionism.

Functions of Theory

• Explanation: Explaining social phenomena and relationships.
• Prediction: Forecasting future outcomes based on existing patterns.
• Understanding: Developing a deeper understanding of social behavior.
• Guiding Research: Providing a framework for formulating hypotheses and conducting studies.

Traditional Model of Science

• Observation: Collecting data or making observations about the world.
• Hypothesis: Developing a testable prediction.
• Experimentation: Testing the hypothesis using controlled methods.
• Analysis: Analyzing the results to see if they support the hypothesis.
• Conclusion: Drawing conclusions from the analysis and refining the theory.

Voluntary Participation

• Individuals should choose to participate in research without coercion.
• Participants should have the right to withdraw from the study without penalty.

Informed Consent

• Participants must be fully informed of the study's risks and benefits.
• Consent should be voluntary without undue pressure.

Belmont Report Principles

• Respect for Persons: Protecting autonomy and ensuring voluntary participation.
• Beneficence: Maximizing benefits and minimizing harms to participants.
• Justice: Ensuring fairness in participant selection and benefits distribution.

ASA Code of Ethics Principles

• Professional and Scientific Responsibility, Respect for People's Rights, Dignity, and Diversity, Social Responsibility, Conflicts of Interest, and Research Ethics.

Deception in Research

• Risks: Emotional distress, loss of trust, ethical concerns.
• Use cases: When the benefits outweigh risks, and/or is essential for study validity.
• Debriefing is essential after deception.

Challenges of Ethical Codes

• Ambiguity: Gray areas in ethical dilemmas.
• Diversity: Varying cultural and institutional norms.
• Enforcement: Difficulty in monitoring and ensuring adherence.

Anonymity vs. Confidentiality

• Anonymity: Data is collected without identifying information.
• Confidentiality: Identifiers are collected but kept private and secure.

Criteria for Causality

• Correlation: A relationship between variables.
• Time Order: Cause precedes effect.
• Non-Spuriousness: Relationship not explained by another variable.

Ecological Fallacy

• Drawing conclusions about individuals from group-level data.

Individualistic Fallacy

• Drawing conclusions about groups from individual-level data.

Reductionism

• Oversimplifying explanations by focusing on a single cause or level of analysis, neglecting other factors.

Units of Analysis

• The entity being studied, which can be individual, group, etc.

Retrospective Studies

• Examining past events to understand their causes or effects.

Steps of Social Research

• Define the problem. Review the literature. Formulate hypotheses. Choose a method. Collect data. Analyze data. Report findings.

Strengths and Weaknesses of Longitudinal Research

• Strengths: Tracks changes over time, reveals trends, and helps establish causality.
• Weaknesses: Time-consuming, costly, and prone to participant dropout.

Quantitative vs. Qualitative Data

• Quantitative: Numerical data for statistical analysis.
• Qualitative: Descriptive data about experiences and behaviors.

Conceptualization vs. Operationalization

• Conceptualization: Defining the abstract meaning of a concept.
• Operationalization: Specifying how to measure the concept in a study.

Nominal, Ordinal, Interval, Ratio Variables

• Nominal: Categorical data without order.
• Ordinal: Categorical data with an inherent order.
• Interval: Numerical data with equal intervals but no true zero.
• Ratio: Numerical data with equal intervals and a true zero.

Reliability vs. Validity

• Reliability: Consistency of a measure across time and contexts.
• Validity: Extent to which a measure accurately represents the concept it claims to measure.

Indexes vs. Scales

• Indexes: Composite measures combining multiple indicators.
• Scales: Measures intensity or degree of a concept with different item weights.

Typologies

• Classifying data based on multiple dimensions.
• Used to create groups with distinct characteristics.

Sampling Methods

• Purposive Sampling: Selecting participants based on specific characteristics.
• Snowball Sampling: Existing participants recruit future participants.
• Quota Sampling: Ensuring specific subgroups within the population are represented.
• Simple Random Sampling: Each individual has an equal chance of selection.
• Systematic Random Sampling: Selecting every k-th individual.

Sample Size Determination

• Factors like confidence level, margin of error, variability influence sample size.

Unobtrusive Research

• Studying existing data or behavior without impacting participants.

Content Analysis

• Systematic examination of communication content.

Coding

• Organizing qualitative data into themes and categories for easier analysis.

Existing Sources

• Pre-collected data for research analysis.

Comparative and Historical Research

• Analyzing social phenomena across time and cultures.

Manifest vs. Latent Content

• Manifest: Directly observable content.
• Latent: Underlying meanings.

Unit of Analysis vs. Unit of Observation

• Unit of analysis: The entity of interest in research.
• Unit of observation: The source of the data used to study the unit of analysis.

Strengths and Weaknesses of Content Analysis

• Strengths: Systematic, avoids bias, useful for large amounts of data.
• Weaknesses: Can be time-consuming, subjective interpretations, may miss context.

Quantitative Research

• Collects numerical data to examine relationships and patterns.

Frequency Distribution

• How often different values appear in a dataset.

Bivariate Analysis

• Examining the relationship between two variables.

Cross-Tabulation

• Table-based method to analyze the relationship between two categorical variables.

Continuous vs. Discrete Variables

• Continuous: Infinite values within a range.
• Discrete: Distinct countable values.

Univariate vs. Multivariate Analysis

• Univariate: Analysis of a single variable.
• Multivariate: Analysis of multiple variables.

Mean, Median, Mode

• Mean: Average of values.
• Median: Middle value when sorted.
• Mode: Most frequent value.

Dispersion in Sample

• Range: Difference between highest and lowest values.
• Variance: Average squared deviation from the mean.
• Standard Deviation: Square root of variance, measuring spread around the mean.
• IQR: Interquartile range, difference between 75th and 25th percentiles.

This quiz explores the key concepts of scientific inquiry and personal inquiry in social sciences. It covers topics such as overgeneralization, selective observation, and the systematic approach used in empirical studies. Test your understanding of these foundational principles!