🧠 Sampling and Validity in Quantitative Research
📌 Key terms
| Term | Definition |
|---|---|
| Population | The complete group of individuals that a researcher aims to study or generalize findings to. |
| Sample | A smaller, representative group selected from the population for the study. |
| Random Sampling | Every member of the population has an equal chance of being selected. |
| Stratified Sampling | The population is divided into subgroups (strata), and participants are randomly selected from each. |
| Opportunity Sampling | Participants are selected based on availability and willingness at the time of the study. |
| Self-Selected Sampling | Participants volunteer to be part of the study, often through advertisement. |
| Purposive Sampling | Participants are chosen based on specific characteristics relevant to the research question. |
| Snowball Sampling | Existing participants recruit future subjects among their acquaintances. |
| Internal Validity | The extent to which the study accurately measures the effect of the independent variable on the dependent variable. |
| External Validity | The extent to which results can be generalized to other settings, populations, or times. |
| Population Validity | Whether the sample accurately represents the larger population. |
| Ecological Validity | Whether findings apply to real-life situations beyond the lab. |
| Construct Validity | How well a test or tool measures the concept it intends to measure. |
| Reliability | The consistency and replicability of research results over time or across samples. |
📌 Notes
Sampling is a critical step in designing quantitative research — it determines how representative, generalizable, and valid a study’s conclusions are.
1. Sampling Techniques:
- Random Sampling: Ensures unbiased selection and increases generalizability.
Example: Used in large-scale surveys like health psychology research.
Strength: Reduces researcher bias.
Limitation: Often impractical due to accessibility constraints. - Stratified Sampling: Population divided by key characteristics (e.g., gender, age, culture).
Strength: Improves representativeness.
Limitation: Requires detailed population data. - Opportunity Sampling: Using whoever is available.
Example: Students recruited from a psychology class.
Strength: Quick and convenient.
Limitation: Prone to bias and limits population validity. - Self-Selected Sampling: Participants volunteer, often responding to an advertisement.
Strength: Motivated participants.
Limitation: May lead to unrepresentative sample (volunteer bias). - Purposive and Snowball Sampling:
Often used in clinical or social research to reach specific or hidden populations (e.g., trauma survivors, minority groups).
Strength: Useful for hard-to-reach groups.
Limitation: Low generalizability.
2. Validity in Quantitative Research
- Internal Validity:
Controlled experiments aim to isolate the independent variable to ensure causation. Threats include:- Confounding variables
- Experimenter bias
- Demand characteristics
- Testing and maturation effects
- External Validity:
Determined by population representativeness and ecological relevance.- Population Validity — can findings be applied beyond the sample?
- Ecological Validity — do lab-based results hold true in real-world contexts?
- Construct Validity:
Ensures operational definitions measure what they claim to (e.g., a depression inventory truly measures depression, not general sadness).
3. Generalizability and Reliability
Reliability requires replication — consistent findings across samples and time strengthen confidence.
Example: Loftus & Palmer’s (1974) study on eyewitness testimony has been replicated globally, supporting its reliability.
Generalizability depends on sampling quality, cultural diversity, and ecological realism.
🔍Tok link
Quantitative validity raises epistemological questions about objectivity and truth in data:
- Can psychological constructs like “stress” or “happiness” truly be measured numerically?
- To what extent does measurement itself shape reality?
TOK Prompt: “Does the precision of numbers always increase the accuracy of knowledge?”
🌐 Real-World Connection
In applied fields such as clinical psychology, education, and public policy, sampling and validity determine whether research findings can guide treatment, policy, or global health recommendations.
- For instance, culturally biased samples in depression scales may misrepresent symptoms in non-Western populations.
❤️ CAS Link
- Students could conduct a mini-survey or experiment to explore school stress levels or study habits, reflecting on sampling limitations and ethical consent in peer-based research — combining creativity (design), activity (data collection), and service (raising awareness).
🧠 IA Guidance
- IB Psychology IAs must identify and justify sampling technique and population.
- Clearly state:
- How participants were selected
- Sample size and demographics
- Implications for generalizability
- Discuss limitations (e.g., small sample, school bias).
- Reference validity concerns directly in the IA’s evaluation section.
🧠 Examiner Tips
- Always distinguish between internal, external, and construct validity.
- Link validity to research design and sampling methods.
- When evaluating a study, mention whether the findings are generalizable and replicable.
- Avoid vague statements like “the study is reliable” — explain why (e.g., standardized procedure)