Social science Study Guide

📖 Core Concepts Social Science – Systematic study of societies & relationships; blends positivist (natural‑science‑like) and interpretivist (symbolic, critique) approaches. Positivism vs. Interpretivism – Positivists seek predictive generalizations; interpretivists focus on meaning & context. Disciplinary Scope – Anthropology, Economics, Sociology, Psychology, Political Science, etc., each with distinct objects (e.g., culture, wealth, behavior, power). Methodology – Quantitative (measurable, statistical) vs. Qualitative (contextual, observational) vs. Mixed‑Methods (combined). Mathematical Modeling – Uses equations, dynamical systems, statistical or game‑theoretic structures to capture essential system features. Program Evaluation – Systematic assessment of a program’s intended effects, cost‑effectiveness, and unintended outcomes. Social Theories – Frameworks (e.g., Rational Choice, Structural Functionalism, Critical Theory) that explain patterns of social life. --- 📌 Must Remember Robbins’ definition of Economics: study of human behavior as a relationship between ends and scarce means with alternative uses. Durkheim = Positivist founder of Sociology; Marx = Historical materialist; Weber = Verstehen (interpretive) founder. Four research designs: Quantitative, Qualitative, Mixed‑Methods, Mathematical Modeling. Four marketing mix elements: Product, Price, Promotion, Distribution. Three core functions of leadership/administration: Planning, Executing, Controlling. Key data‑collection tools: Questionnaires, Field observation, Archival databases, Laboratory experiments. Types of mathematical models: Dynamical systems, Statistical models, Differential equations, Game‑theoretic models. --- 🔄 Key Processes Social Research Cycle Define problem → Choose design (quantitative/qualitative/mixed) → Collect data (questionnaire, observation, archive, lab) → Analyze (statistical or thematic) → Interpret → Report. Mathematical Modeling Workflow Identify system → Abstract essential variables → Formulate equations/rules → Validate with data → Refine model. Program Evaluation Steps Set evaluation questions → Develop indicators (outcome, cost, unintended effects) → Collect evidence → Analyze → Recommend improvements. Mixed‑Methods Integration Parallel (collect both types simultaneously) or Sequential (one informs the other) → Merge results → Draw comprehensive conclusions. --- 🔍 Key Comparisons Quantitative vs. Qualitative Quantitative: numbers, large N, statistical inference, generalizable. Qualitative: words/observations, small N, contextual depth, interpretive. Microeconomics vs. Macroeconomics Micro: individual agents (households, firms). Macro: whole‑economy aggregates (GDP, inflation). Positivist vs. Interpretivist Positivist: seeks falsifiable laws, uses measurement. Interpretivist: seeks meaning, uses symbolic analysis. Statistical vs. Game‑Theoretic Models Statistical: estimates relationships from data. Game‑theoretic: models strategic interaction, often with no empirical data required. --- ⚠️ Common Misunderstandings “All social science is qualitative.” – Wrong; many fields (e.g., economics, sociology) heavily use quantitative methods. “Program evaluation proves a program works.” – It assesses effectiveness; results can be inconclusive or show mixed impact. “Mathematical models give exact predictions.” – Models are simplifications; they provide insights, not perfect forecasts. “Critical Theory = only criticism.” – It also offers alternative visions for social change, not merely negative appraisal. --- 🧠 Mental Models / Intuition “Lens of Scale”: Think of a research question first: Is the answer about a single case (qualitative) or a pattern across many cases (quantitative)? “Model as Map”: A model is a map, not the territory; it highlights relevant features while omitting others. “Theory as Lens”: Choose a social theory (e.g., Rational Choice) to focus attention on particular mechanisms (cost‑benefit calculations). --- 🚩 Exceptions & Edge Cases Mixed‑Methods may be required when a purely quantitative study misses cultural nuance, or a purely qualitative study lacks generalizability. Interpretivist approaches can still incorporate systematic data collection (e.g., discourse analysis) that resembles quantitative rigor. Game‑theoretic models sometimes apply to non‑economic settings (e.g., political negotiation) despite their origin in economics. --- 📍 When to Use Which Choose Quantitative when you need statistical generalization, large samples, or hypothesis testing. Choose Qualitative when exploring meaning, context, or generating new hypotheses. Use Mixed‑Methods when both breadth (general patterns) and depth (context) are essential. Apply Statistical Model if you have reliable data and want to estimate variable relationships. Apply Game‑Theoretic Model when the problem involves strategic decision‑making among interdependent actors. --- 👀 Patterns to Recognize “Triad of Methods” – Most social‑science questions can be mapped to one of: survey → statistical analysis, interview → thematic coding, or simulation → mathematical model. “Theory → Variable → Test” – Identify the underlying theory, translate it into measurable variables, then select the appropriate design. “Evaluation → Impact → Cost” – Good program evaluations always report both effect size and cost per participant. --- 🗂️ Exam Traps Distractor: “All social sciences are positivist.” – Many fields (e.g., anthropology, interpretivist sociology) explicitly use interpretive methods. Distractor: “Program evaluation guarantees policy change.” – Evaluation provides evidence; implementation decisions rest elsewhere. Distractor: “Differential equations are only for physical sciences.” – They also model continuous change in social systems (e.g., population dynamics). Distractor: “Micro‑ and macro‑economics use the same models.” – Micro focuses on individual optimization; macro aggregates behavior and often requires different assumptions (e.g., aggregate demand). ---