social-science-analysis

Social Science Analysis

Research methods for social and behavioral sciences. Venv: source /Users/zhangmingda/clawd/.venv/bin/activate

Survey Design

Question Types & Best Practices

• Closed-ended: Likert scales, multiple choice, ranking
• Open-ended: Free text (harder to analyze, richer data)
• Matrix questions: Multiple items, same scale (efficient but watch for straight-lining)

Likert Scale Design

Strongly Disagree (1) — Disagree (2) — Neutral (3) — Agree (4) — Strongly Agree (5)

• Use 5 or 7 points (odd for neutral option)
• Mix positively and negatively worded items (reverse-code in analysis)
• Avoid double-barreled questions
• Pilot test with 10-20 respondents

Sampling Methods

Method	When	Pros	Cons
Simple random	Known population	Unbiased	Need sampling frame
Stratified	Subgroup comparison	Precise estimates per stratum	Complex
Cluster	Geographic spread	Cost-effective	Higher design effect
Convenience	Exploratory	Easy	Not generalizable
Snowball	Hard-to-reach populations	Access hidden groups	Selection bias
Quota	Ensure representation	Practical	Not truly random

Psychometrics & Scale Development

Reliability

import numpy as np

def cronbachs_alpha(items_df):
    """Calculate Cronbach's alpha for scale reliability"""
    k = items_df.shape[1]
    item_vars = items_df.var(axis=0, ddof=1)
    total_var = items_df.sum(axis=1).var(ddof=1)
    alpha = (k / (k - 1)) * (1 - item_vars.sum() / total_var)
    return alpha

# Interpretation: α > 0.7 acceptable, > 0.8 good, > 0.9 excellent

Exploratory Factor Analysis

from sklearn.decomposition import FactorAnalysis
import numpy as np

# Determine number of factors (parallel analysis or scree plot)
fa = FactorAnalysis(n_components=3, rotation='varimax')
fa.fit(X_scaled)
loadings = pd.DataFrame(fa.components_.T, index=item_names, columns=['F1', 'F2', 'F3'])
print(loadings.round(3))
# Items loading > 0.4 on a factor belong to that construct

Confirmatory Factor Analysis / SEM

For CFA and SEM, recommend using R with lavaan package or Python semopy:

# pip install semopy
import semopy

model_spec = """
    Latent1 =~ item1 + item2 + item3
    Latent2 =~ item4 + item5 + item6
    Latent1 ~ Latent2
"""
model = semopy.Model(model_spec)
model.fit(df)
print(model.inspect())
# Check fit indices: CFI > 0.95, RMSEA < 0.06, SRMR < 0.08

Qualitative Analysis

Thematic Analysis (Braun & Clarke)

1. Familiarization: Read and re-read data
2. Initial coding: Generate codes systematically
3. Theme search: Collate codes into potential themes
4. Theme review: Check themes against coded extracts and full dataset
5. Theme definition: Name and define each theme
6. Report: Select vivid examples, relate to research question

Coding Framework Template

| Code | Definition | Example Quote | Theme |
|------|-----------|---------------|-------|
| ADAPT | Adaptation strategy | "We had to change our approach..." | Resilience |
| BARR | Barrier encountered | "The main obstacle was..." | Challenges |

Grounded Theory

1. Open coding → Axial coding → Selective coding
2. Constant comparison method
3. Theoretical sampling until saturation
4. Memo writing throughout

Content Analysis

# Quantitative content analysis
import pandas as pd
from collections import Counter

def content_analysis(texts, codebook):
    """
    codebook: dict of {category: [keywords]}
    Returns frequency matrix
    """
    results = []
    for text in texts:
        text_lower = text.lower()
        counts = {}
        for category, keywords in codebook.items():
            counts[category] = sum(text_lower.count(kw.lower()) for kw in keywords)
        results.append(counts)
    return pd.DataFrame(results)

# Inter-coder reliability (Cohen's Kappa)
from sklearn.metrics import cohen_kappa_score
kappa = cohen_kappa_score(coder1_labels, coder2_labels)
# κ > 0.8 excellent, 0.6-0.8 substantial, 0.4-0.6 moderate

Social Network Analysis

import networkx as nx
import numpy as np

G = nx.from_pandas_edgelist(df, 'source', 'target')

# Centrality measures
degree = nx.degree_centrality(G)
betweenness = nx.betweenness_centrality(G)
closeness = nx.closeness_centrality(G)
eigenvector = nx.eigenvector_centrality(G)

# Community detection
from networkx.algorithms.community import greedy_modularity_communities
communities = list(greedy_modularity_communities(G))

# Network statistics
print(f"Nodes: {G.number_of_nodes()}, Edges: {G.number_of_edges()}")
print(f"Density: {nx.density(G):.4f}")
print(f"Clustering coefficient: {nx.average_clustering(G):.4f}")

Tips

• Pre-register hypotheses and analysis plans (OSF, AsPredicted)
• Report Cronbach's alpha for all scales
• Use power analysis for sample size determination
• For qualitative research, document your positionality
• Mixed methods: clearly state the integration strategy
• IRB/ethics approval is mandatory for human subjects research