GEA1000 Chap.1 Getting Data: Questions, Samples and Studies
Getting Data: Questions, Samples and Studies
The claim begins in the plan
The PPDAC cycle links a quantitative question to a target population, sampling frame, observed sample, analysis and conclusion. Coverage and selection are separate checks: random selection can improve representation within a frame, but it cannot add people who were never reachable through that frame.
The research question also fixes the unit of observation.
A household claim needs households as the independent units, while repeated measurements on one person remain linked to that person. Counting dependent observations as separate people changes both the denominator and the apparent amount of information.
Before analysing, write the population noun, the unit and the characteristic being estimated or compared.
Selection and response are separate filters
A probability sample describes how invitations were selected from a frame. The responding sample can still differ if participation depends on the topic, access or interest.
Examine who was invited, who replied and whether response rates differ across groups visible in the frame. More responses reduce random variation, but they do not automatically correct coverage or nonresponse.
Design controls the final verb
Experiments manipulate an explanatory variable and use comparison groups to support causal reasoning.
Observational studies measure what already occurred and can establish association without, by themselves, ruling out alternative explanations. Random selection supports generalisation; random assignment supports comparability between treatment groups.
Blinding addresses another route.
Participants who know their treatment can change behaviour or reporting, and assessors who know the group can classify outcomes differently. Blinding cannot replace random assignment, and neither feature widens a narrow sampling frame.
A strong conclusion states what each design element achieved rather than calling the entire study random.
Repair the design, not only the sentence
When a claim is too broad, name the change that would support it. That may be a frame covering the target population, a probability selection mechanism, random assignment to conditions, blinded assessment or follow-up of nonrespondents.
The repair shows which limitation matters and keeps criticism connected to the original quantitative question.
What this chapter covers
- 01
PPDAC as a question-to-conclusion cycle
- 02
Target population and unit of observation
- 03
Sampling frames and coverage
- 04
Probability sampling and nonresponse
- 05
Experiments and observational studies
- 06
Random assignment and blinding
Worked example · free
Auditing a residence-hall survey
- 2Separate the target population from the sampling frame.
- 2State a frame and selection route that support the intended claim.
Key terms
- Target Population
- The full group named in the research question and intended conclusion.
- Sampling Frame
- The list or mechanism through which members of a population can be selected.
- Coverage Bias
- Systematic distortion caused when part of the target population is absent from the sampling frame.
- Random Selection
- A chance-based method for choosing units from a sampling frame.
- Random Assignment
- Chance allocation of experimental units to conditions to support comparable groups.
- Blinding
- Withholding group information to reduce belief-driven changes in behaviour or assessment.
Getting Data: Questions, Samples and Studies FAQ
Why can a large sample still be misleading?
Sample size controls chance-to-chance variability after the frame and selection process are settled. A very large sample drawn from a narrow frame can estimate that narrow group precisely while remaining systematically different from the population named in the question.
What is the difference between random selection and random assignment?
Random selection chooses units from a frame and supports generalisation to that frame when the design works. Random assignment allocates experimental units to conditions and supports a causal comparison by balancing other characteristics across treatment groups.
When is causal language justified?
Causal language needs a credible experimental comparison in which the explanatory condition is manipulated and alternative explanations are controlled. An observational association may be strong and precisely estimated while still being compatible with confounding or reverse direction.
Exam move
Practise turning every study description into a four-line audit. Line one names the target population, unit of observation and outcome. Line two names the sampling frame, who is missing and how selected cases became respondents. Line three classifies the design as experimental or observational and identifies whether assignment was random and whether blinding was used.
Line four writes the narrowest verb the design licenses: estimates, is associated with or caused. Use the chapter solution questions to identify the recurring formats, but create fresh settings and populations for your own practice. When a result looks impressive because the sample is large, force yourself to answer the coverage question before discussing precision.
When a study is called random, specify whether the randomness entered selection, assignment or both. Finish each practice answer with one repair that would support a stronger claim, such as a broader frame, a probability draw, a random assignment step or blinded outcome assessment.
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