MKB2705 Chap.6 Sampling Design and Total Survey Error
Sampling Design and Total Survey Error
Sampling is a chain from target population to frame, selected sample, contacted cases and respondents. Each transition changes who can speak through the estimate. Define the target population, unit, geography, eligibility and period before choosing a recruitment method. A frame is the operational list or process from which cases can be reached; it may exclude people even when the sample drawn from it is random.
Probability selection gives known inclusion chances and can support design-based inference under its assumptions. Nonprobability recruitment may be practical for exploratory or hard-to-reach work, but a large convenience sample does not become representative through size alone. State the claim boundary created by the design. Sampling precision describes only one part of uncertainty.
A confidence interval for a proportion reflects sampling variation under the stated model and design; it does not include coverage, nonresponse, measurement or processing error. The chapter calculates an independent interval for 248 favourable responses among 400 valid cases, then asks which populations and errors remain outside that arithmetic.
Sample-size planning should start from the primary estimate or contrast, desired precision, design effect and feasible analysis. Inflate invitations for expected eligibility and response, but do not use a guessed response rate as evidence that bias is controlled. Monitor recruitment by relevant groups and channels while fieldwork is active. Nonresponse matters when response propensity relates to the variable of interest.
Compare respondents with frame information or timing patterns where possible, vary contact appropriately and narrow claims when important groups remain weakly represented. Weighting can correct unequal inclusion or align selected margins, but it introduces variance and depends on correct auxiliary information. Record base weights, adjustments, trimming and the population totals used.
Total survey error brings coverage, sampling, nonresponse, measurement, processing and adjustment into one audit. Improving one spoke can worsen another: a longer questionnaire may add variables but increase breakoff; a strict frame may improve eligibility while excluding new members. The best design minimises error that matters to the decision within ethical and practical constraints.
This chapter teaches standard sampling canon aligned to the official sequence. All numbers and exercises are independent and do not reproduce a live dataset, question or rubric.
What this chapter covers
- 01
Decision focus
- 02
Evidence control
- 03
Error control
- 04
Claim boundary
- 05
Decision use
AskSia-authored practice weighting (not an official mark scheme): Original worked model: sampling design and total survey error
- +1State the management or evidence question, population and decision use before naming a method.
- +1Choose and defend the relevant design, measure, sample or analysis, preserving the correct valid base.
- +1Report the result or planned output with magnitude, uncertainty and a precise evidence noun.
- +1State the main limitation and a conditional decision or follow-up that does not exceed the evidence.
Key terms
- sampling frame
- A key concept in Sampling Design and Total Survey Error: define it in the population, context and decision for this study rather than relying on a label alone.
- nonresponse bias
- A control term used to keep sampling design and total survey error technically and conceptually traceable across the project.
- design weight
- A boundary or diagnostic that should appear beside the relevant result, not only in a generic limitations paragraph.
Sampling Design and Total Survey Error FAQ
How does sampling design and total survey error support MKB2705 assessment?
It supplies a specific research decision, evidence control and claim boundary that can support the proposal, class-test reasoning, SPSS report or reflection. Apply it to your own project and current Moodle task rather than copying the worked model.
What is the most common sampling design and total survey error mistake?
The common failure is letting a convenient method, software output or confident phrase replace the decision question and represented evidence. Keep population, construct, valid base and design visible.
Can AI complete this sampling design and total survey error work?
No. Follow the current task-specific rule. Use only permitted support, verify every source and number, author submitted prose yourself and complete the required declaration. Quizzes say AI should not be used.
Assessment move
Draw four nested or overlapping sets for target population, frame, selected sample and respondents. Annotate every gap with the people excluded and the likely direction of bias. For an original proportion, calculate an approximate confidence interval and then list the coverage, nonresponse, measurement and processing errors that the interval cannot see.
Compare one probability and one feasible nonprobability recruitment plan using inclusion logic, cost, access and claim boundary. Build an invitation calculation from desired completes, expected eligibility and response, then create monitoring triggers for underrepresented groups without treating quota completion as proof of representativeness.
If weighting is proposed, document inclusion probabilities, adjustment variables, trimming and the variance consequence. Close by writing the exact population noun permitted by the design and verify any live project requirement on Moodle.
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