SOC613 Chap.3 Data Management and Reproducible Research
Data Management and Reproducible Research
A reproducible analysis preserves the received data and records every transformation in code. This chapter starts with rows, columns, cells, units and codebooks, then moves through recoding, constructed variables and missing-value handling. Each new variable is checked by tracing selected cases and examining frequencies or summaries.
Import, cleaning, analysis and reporting remain separable so a surprising result can be located. The final script uses project-relative paths, explicit ordering and an auditable analytic sample another person can recreate. The data matrix is read substantively before it is processed.
Each row must have a stated unit, each column must have a label and valid coding scheme, and nonresponse values must be distinguished from high numerical values. Raw frequency distributions serve as checks against the documentation and reveal rare or impossible categories. Transformations are then written as decisions rather than shortcuts.
Collapsing categories changes which observations count as equivalent; rescaling changes a unit; constructing an indicator places a boundary; and reversing a scale changes direction. Each operation therefore receives a named output and an explanation. Missingness is handled explicitly because functions, filters and models can otherwise use different samples without drawing attention to the change.
Reporting valid N for every result protects coefficient comparisons from this hidden sample drift. The workflow also separates code that imports data, code that cleans it and code that estimates results. That separation makes an unexpected value traceable to a file version, a transformation or a model choice.
Reproducibility is achieved when another analyst can begin with the same received files, run an ordered script and obtain the cleaned data, tables and reported values without manual intervention.
What this chapter covers
- 01
Rows, columns and units of observation
- 02
Codebooks and raw frequencies
- 03
Recoding without overwriting
- 04
Constructed variables and scale direction
- 05
Missing values and analytic samples
- 06
Scripts, projects and verification checks
Worked example · free
Repair the order of a recode
- 1Identify that nonresponse codes satisfy the faulty condition.
- 1Convert documented missing codes first.
- 1Create a new indicator from valid values only.
- 1Cross-tabulate original and new variables.
- 1Report the valid analytic count.
Key terms
- Data Frame
- A rectangular object with observations in rows and variables in columns.
- Codebook
- Documentation for variable names, meanings, valid codes and nonresponse values.
- Recoding
- Changing a variable's values according to an explicit substantive rule.
- Complete Case
- An observation with nonmissing values for every variable used in a particular analysis.
- Reproducible Script
- Ordered code that recreates transformations, analysis and reported output from source data.
Data Management and Reproducible Research FAQ
Why should raw data remain unchanged?
An unchanged received file provides the fixed starting point for verification. Every cleaning decision can then be rerun, inspected and reversed through code. Manual edits erase the audit trail and make disagreements impossible to locate reliably.
How should I verify a transformed variable?
Trace several known source values through the rule by hand, then print a cross-tabulation, frequency table or numerical summary. Check valid counts, impossible categories, scale direction and missing values before the transformed field enters any model.
Why can model samples differ without warning?
Complete-case deletion removes any row missing a variable used in that model. Adding a predictor can therefore change the sample as well as the specification. Report N for each result and compare coefficients cautiously when the underlying observations differ.
What should a useful code comment explain?
Explain the reason for a transformation, exclusion or ordering decision rather than translating obvious syntax. A collaborator needs to know why the analysis changed the data and what substantive rule the code implements.
How can I test whether a pipeline is reproducible?
Restart the statistical session, begin with the unchanged received files and run the documented entry script. It should recreate cleaned data, tables and reported results without manual edits, hidden objects or machine-specific working-directory commands.
Exam move
Organise each exercise as import, inspect, transform, verify, analyse and report. At import, compare dimensions and variable types with the documentation. During inspection, print raw frequencies and identify missing codes. During transformation, retain originals and use descriptive names. Immediately follow every transformation with a check that would fail if the rule were wrong.
Keep paths relative to the project and avoid hidden interactive steps. At the end, restart the session and run the entry script from the unchanged data. Record package versions and the analytic sample. For revision, practise diagnosing deliberately flawed pipelines: missing codes handled late, overwritten variables, inconsistent samples and undocumented manual corrections.
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