Special Call Prof. Jay L. Garfield Commemorative Volume · Submissions Open
Announcements Newsletters About Us Contact
Search Donate
20 min read
Chapter 8 • PORE Master Curriculum

Sampling Design & Population Representation

Probability vs. Non-Probability Sampling, Stratification & Error Control

Sampling Theory Probability Sampling Stratified Sampling Sample Size
Executive Overview & Learning Objectives

Learn how to construct representative sample frames, calculate statistical sample sizes, avoid selection biases, and deploy advanced multi-stage and cluster sampling techniques.

1. Foundational Concepts & Core Principles

1

Census versus sample surveys: implications for cost, time, and precision

Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.

2

Steps in sampling design: Universe definition, Sampling unit, Source list, Sample size

Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.

3

Probability sampling methods: Simple random, Systematic, Stratified, Cluster sampling

Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.

4

Non-probability sampling: Purposive/Judgmental, Quota, Snowball, Convenience sampling

Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.

5

Sampling errors (standard error, variance) vs. non-sampling errors

Rigorous academic inquiry requires precise delimitation of this principle to ensure internal consistency, falsifiability, and methodological replicability.

2. Methodological Comparison & Trade-offs

Simple Random

Every unit has equal probability; ideal for homogeneous populations.

Stratified Random

Population split into sub-strata; ensures subgroup representation.

Purposive/Snowball

Expert-guided or network-referred; essential for hidden populations or niche philology.

3. Practical Guidelines & Common Methodological Pitfalls

Best Practices (Do This)
  • Explicitly state operational definitions before measurement.
  • Acknowledge potential confounders and data limitations in the methodology section.
  • Pre-register hypothesis and statistical tests when conducting quantitative trials.
  • Provide full provenance for historical, epigraphical, and archival texts.
Critical Pitfalls (Avoid This)
  • Confusing statistical correlation with causal determination.
  • Using non-probability convenience samples to claim population generalizations.
  • Failing to report negative results or unexpected anomalies.
  • Relying on unverified secondary citations without reading the primary source.
← Chapter 7 Apply in Research Toolkits Chapter 9 →