MKB2705 Chap.7 Data Collection, Coding and SPSS Preparation
Data Collection, Coding and SPSS Preparation
Data preparation is a controlled derivation from raw capture to an analysis-ready file. Preserve the received export unchanged, record its date and source, and perform every correction or transformation in a reproducible process. Editing the only copy by hand destroys the evidence trail.
A case ledger should reconcile received rows, duplicates, eligibility, withdrawals, route failures and retained cases before any variable-specific denominator is calculated. The codebook gives each variable a name, meaning, source item, eligible base, type, labels, missing rule, range, derivation and analysis role. Numeric codes must not be confused with measurement scale.
Missing values need distinct treatment for structural skips, refusal, don't know, technical loss and invalid response when those states matter. Range checks find impossible values, while logic checks test relationships across variables, such as an ineligible case answering a routed item or a completion date preceding consent. Neither proves truth; they identify records requiring a documented response.
Derived variables should follow rules written before looking for a preferred result. Reverse scoring, composite construction, category collapse and outlier treatment each need an input, transformation, output and exception count. Keep the original components so a score can be audited.
In SPSS, syntax should import or reference a controlled file, apply labels and missing values, run cleaning, derive variables and produce final tables. A rerun from the same inputs should reproduce the analysis file and output.
The chapter's independent example moves 427 received rows through duplicates, eligibility and route checks, then reports variable-specific valid bases instead of pretending one denominator fits every result. Version names should distinguish raw, working, analysis and release files. Record who changed what, why and when; reconcile key counts after every material update.
Before analysis, inspect frequencies, ranges, missingness and a sample of source-to-file records. Protect personal data by removing unnecessary identifiers, restricting access and retaining only what the approved purpose requires. Standard data-management and SPSS canon in this chapter aligns to the official sequence and assessment briefs. It does not reproduce a private dataset, live task file, 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: data collection, coding and spss preparation
- +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
- codebook
- A key concept in Data Collection, Coding and SPSS Preparation: define it in the population, context and decision for this study rather than relying on a label alone.
- structural missingness
- A control term used to keep data collection, coding and spss preparation technically and conceptually traceable across the project.
- analysis file
- A boundary or diagnostic that should appear beside the relevant result, not only in a generic limitations paragraph.
Data Collection, Coding and SPSS Preparation FAQ
How does data collection, coding and spss preparation 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 data collection, coding and spss preparation 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 data collection, coding and spss preparation 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
Practise on a small invented export while preserving an untouched raw copy. Create a case ledger that reconciles received rows, duplicates, eligibility, withdrawals and retained cases. Build a codebook with source item, labels, missing states, valid range, eligible base and analysis role for every variable.
Write range and cross-variable logic checks, then decide in advance whether each failure is corrected from evidence, recoded missing or retained with a flag. Derive one composite entirely through SPSS syntax and verify it against several hand-calculated cases. Rerun the full sequence from raw input and compare record counts, frequencies and output to prove reproducibility.
Finish with a release log naming file version, owner, date and downstream tables. Use only permitted data and current task instructions; never practise with another student's file.
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