Legal Research Guide

CREAC Method (For Academic Legal Writing)

C - Conclusion (state your thesis)
R - Rule (present the legal rule with authority)
E - Explanation (analyze how courts have interpreted the rule)
A - Application (apply the rule to your specific scenario)
C - Conclusion (restate and refine conclusion)

Legal Research Databases

Primary Sources

Secondary Sources

Citation Systems

Bluebook (US Standard)

# Case citation
Marbury v. Madison, 5 U.S. (1 Cranch) 137 (1803).
Brown v. Board of Education, 347 U.S. 483, 495 (1954).

# Statute citation
42 U.S.C. Section 1983 (2018).
Cal. Civ. Code Section 1798.100 (West 2020).  # California statute

# Law review article
Jane Smith, The Future of AI Regulation, 120 Harv. L. Rev. 456 (2024).

# Book
Richard Posner, Economic Analysis of Law 25 (9th ed. 2014).

# Short form citations (after first full citation)
Brown, 347 U.S. at 495.
Smith, supra note 12, at 460.
Id. at 462.  # Same source as immediately preceding citation

OSCOLA (UK/Oxford Standard)

# Case citation
Donoghue v Stevenson [1932] AC 562 (HL).
R v Brown [1994] 1 AC 212, 237 (HL).

# Statute citation
Human Rights Act 1998, s 3.
Data Protection Act 2018, s 170(1).

# Journal article
Jane Smith, 'The Future of AI Regulation' (2024) 120 Modern Law Review 456.

# Book
Richard Posner, Economic Analysis of Law (9th edn, Aspen 2014) 25.

Computational Legal Research

Case Law Analysis with Python

import requests
import json

# Using the CourtListener API (free, open-source)
BASE_URL = "https://www.courtlistener.com/api/rest/v3"

def search_opinions(query, court="scotus", page_size=20):
    """Search case opinions via CourtListener API."""
    response = requests.get(
        f"{BASE_URL}/search/",
        params={
            "q": query,
            "type": "o",  # opinions
            "court": court,
            "page_size": page_size,
            "order_by": "score desc"
        },
        headers={"Authorization": "Token YOUR_API_TOKEN"}
    )
    results = response.json()
    for case in results.get("results", []):
        print(f"[{case.get('dateFiled', 'N/A')}] {case.get('caseName', 'N/A')}")
        print(f"  Court: {case.get('court', 'N/A')}")
        print(f"  Citation: {case.get('citation', ['N/A'])[0] if case.get('citation') else 'N/A'}")
        print(f"  URL: https://www.courtlistener.com{case.get('absolute_url', '')}")
    return results

# Search for AI-related Supreme Court cases
results = search_opinions("artificial intelligence", court="scotus")

Citation Network Analysis

import networkx as nx

def build_citation_network(seed_case_ids, depth=2):
    """Build a citation network starting from seed cases."""
    G = nx.DiGraph()
    visited = set()
    queue = [(cid, 0) for cid in seed_case_ids]

    while queue:
        case_id, level = queue.pop(0)
        if case_id in visited or level > depth:
            continue
        visited.add(case_id)

        # Get case metadata and citations
        resp = requests.get(f"{BASE_URL}/opinions/{case_id}/",
                           headers={"Authorization": "Token YOUR_API_TOKEN"})
        if resp.status_code != 200:
            continue

        case = resp.json()
        case_name = case.get("case_name", f"Case {case_id}")
        G.add_node(case_id, name=case_name, date=case.get("date_filed"))

        # Get citing opinions (who cites this case)
        for cited_id in case.get("opinions_cited", []):
            G.add_edge(case_id, cited_id)
            if level < depth:
                queue.append((cited_id, level + 1))

    return G

# Analyze: which cases are most cited (highest in-degree)?
# These are the most authoritative precedents

Statutory Text Analysis

# Analyzing legislative text complexity
import re
from textstat import textstat

def analyze_statute(text):
    """Compute readability metrics for statutory text."""
    return {
        "flesch_reading_ease": textstat.flesch_reading_ease(text),
        "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
        "gunning_fog": textstat.gunning_fog(text),
        "word_count": textstat.lexicon_count(text),
        "sentence_count": textstat.sentence_count(text),
        "avg_sentence_length": textstat.avg_sentence_length(text),
        "defined_terms": len(re.findall(r'"[A-Z][^"]*"', text)),
        "cross_references": len(re.findall(r'[Ss]ection \d+', text))
    }

# Example: Analyze a section of the GDPR
gdpr_article_5 = """
Personal data shall be processed lawfully, fairly and in a transparent
manner in relation to the data subject; collected for specified, explicit
and legitimate purposes and not further processed in a manner that is
incompatible with those purposes; adequate, relevant and limited to what
is necessary in relation to the purposes for which they are processed.
"""
print(analyze_statute(gdpr_article_5))

Research Areas in Law

Practical Research Workflow

1. Frame the legal question using IRAC or CREAC structure
2. Search secondary sources first (treatises, law reviews) for background
3. Identify governing law (federal vs. state, statutory vs. common law)
4. Find controlling authority (binding precedent in your jurisdiction)
5. Shepardize / KeyCite every case to ensure it is still good law
6. Analyze and synthesize cases by extracting rules, holdings, and reasoning
7. Consider policy arguments drawing on law and economics, empirical legal studies, or comparative law perspectives
8. Update regularly as law changes frequently (set up alerts on Westlaw/Lexis)

Top Academic Venues