Data Cleaning and Validation Using SPSS Training Course

Participants can profile a dataset and identify priority data-quality risks before cleaning.

• Frequencies expose invalid categories, unexpected blanks, and sparse response patterns
• Descriptives flag implausible ranges, extreme values, and distributional anomalies
• Explore procedure compares distributions by site, wave, or data source
• Custom Tables reveal cross-variable inconsistencies and structural missingness
• Chart Builder visualises spikes, gaps, heaping, and coding errors
• Baseline quality metrics quantify completeness, validity, uniqueness, consistency, and timeliness

Participants can distinguish types of missingness and document an appropriate treatment approach.

• User-missing values distinguish nonresponse codes from valid analytical values
• Missing Values Analysis profiles patterns, correlations, and monotonicity
• MCAR, MAR, and MNAR assumptions guide defensible treatment decisions
• Little's MCAR test evaluates evidence for completely random missingness
• Missing-data decisions record exclusions, recodes, imputations, and residual risks
• Multiple-response items require separate treatment of absent and negative answers

Participants can import common source formats and verify that SPSS has interpreted fields correctly.

• GET DATA imports Excel, CSV, text, and database extracts reliably
• Text Import Wizard controls delimiters, encodings, headers, and date recognition
• Unicode settings preserve multilingual names, labels, and non-Latin characters
• Date and time formats prevent silent conversion and sorting errors
• Input validation compares record counts, field counts, and key distributions
• Original extracts remain read-only beside documented working-data copies

Participants can create readable SPSS Syntax that records and reruns cleaning decisions.

• SPSS Syntax records reproducible imports, transformations, checks, and outputs
• Comment blocks identify purpose, inputs, assumptions, author, and revision date
• Variable macros reduce repeated commands across similarly structured fields
• DO IF and ELSE IF implement transparent conditional recoding rules
• OUTPUT MODIFY standardises tables, titles, footnotes, and warning capture
• Syntax logs provide an auditable record of data preparation decisions

Participants can identify duplicate records and apply documented resolution rules using reliable keys.

• Sort Cases and Match Files prepare keys for record-level checks
• Duplicate detection distinguishes repeated identifiers from legitimate repeat observations
• Composite keys combine site, respondent, date, and transaction identifiers
• FREQUENCIES and AGGREGATE quantify duplicate groups and affected records
• Survivorship rules retain authoritative records using stated source priorities
• Duplicate-resolution logs document removals, merges, exceptions, and approvals

Participants can build validation rules that identify invalid, impossible and inconsistent records.

• Range checks compare observed values against codebook limits and business rules
• Allowed-value checks identify undocumented categories and invalid response codes
• Logical edits test dependencies between age, employment, income, and status
• Cross-field rules detect impossible dates, totals, and mutually exclusive selections
• Boolean flag variables preserve failed checks without overwriting source values
• Exception reports separate correctable errors from legitimate unusual records

Participants can identify outliers, investigate their cause and justify their treatment.

• Histograms, boxplots, and stem-and-leaf displays locate candidate outliers
• Z scores identify unusual values under approximately normal distributions
• IQR fences flag observations beyond quartile-based distribution limits
• Standardised residuals identify influential records in preliminary regression models
• Outlier investigation distinguishes entry error, measurement error, and valid rarity
• Treatment rules specify correction, winsorisation, exclusion, or retention rationale

Participants can transform raw variables into documented analytical fields without losing source evidence.

• RECODE converts inconsistent categories into controlled analytical classifications
• COMPUTE derives ratios, totals, flags, and standardised indicators
• Automatic Recode creates numeric codes while retaining original text labels
• Rank Cases produces percentile groups and relative position measures
• Visual Binning applies documented cut-points to continuous variables
• Transformation specifications retain source variables and reversal options

Participants can standardise text, date and code fields for matching, validation and analysis.

• String functions standardise whitespace, case, punctuation, and repeated delimiters
• CHAR LENGTH and INDEX detect malformed identifiers and embedded characters
• SUBSTR extracts consistent components from structured codes and account references
• Date functions calculate age, duration, reporting period, and elapsed days
• Numeric conversion checks prevent invalid text from becoming system-missing values
• Regular coding conventions control leading zeros, prefixes, and suffixes

Participants can select, apply and report missing-data treatments appropriate to the analysis purpose.

• Listwise deletion conditions require documented impact on sample composition
• Pairwise deletion preserves available observations but changes analysis denominators
• Median and mode imputation suit limited descriptive reporting circumstances
• Multiple Imputation generates pooled estimates under stated MAR assumptions
• Imputation diagnostics compare observed and imputed distributions before analysis
• Missingness flags retain information about nonresponse for downstream modelling

Participants can combine datasets safely and test that joins have produced expected results.

• ADD FILES appends compatible extracts after variable alignment checks
• MATCH FILES joins datasets using verified one-to-one and one-to-many keys
• Key uniqueness tests prevent accidental record multiplication during joins
• Field reconciliation resolves conflicting names, formats, labels, and coding schemes
• Pre-merge counts establish expected matches, nonmatches, and unmatched percentages
• Post-merge diagnostics compare totals, distributions, and key-level exceptions

Participants can validate questionnaire data, scale items, weights and fieldwork patterns.

• Skip-pattern checks verify routing against questionnaire eligibility conditions
• Multiple-response sets require documented coding, labels, and denominator rules
• Reverse-coded items are identified before scale calculation and reliability testing
• Scale scores apply stated minimum-item thresholds and missing-item rules
• Weight variables are inspected for zeros, extremes, and design consistency
• Survey dates expose fieldwork gaps, duplicate submissions, and timing anomalies

Participants can assess whether multi-item measures are coded and scored consistently.

• Corrected item-total correlations identify items weakening multi-item scales
• Cronbach's alpha assesses internal consistency under stated scale assumptions
• Inter-item correlation matrices expose redundant or poorly aligned questions
• Exploratory factor analysis examines dimensionality before composite score construction
• Reverse-key verification prevents direction errors in attitudinal measures
• Scale documentation records item wording, coding, scoring, and exclusions

Participants can compare SPSS data against trusted sources and explain material differences.

• Reconciliation compares SPSS totals with finance, operations, or registry controls
• Sampling checks compare file composition with population and frame distributions
• Benchmark comparisons identify implausible shifts against prior reporting periods
• Control totals verify transactions, balances, headcounts, or beneficiary counts
• Independent review tests selected records against original source documentation
• Variance explanations distinguish operational change from data-quality deterioration

Participants can define ownership, thresholds, remediation actions and controls for critical data elements.

• Data-quality dimensions define accuracy, completeness, consistency, validity, uniqueness, and timeliness
• ISO 8000 concepts inform data specifications, ownership, and quality controls
• Critical data elements receive named owners, rules, thresholds, and review cycles
• Severity ratings prioritise defects by reporting, regulatory, financial, and operational impact
• Issue registers assign root cause, correction action, owner, and due date
• Change control protects approved syntax, codebooks, and release versions

Participants can communicate data-quality findings through clear metrics, evidence and release decisions.

• Quality scorecards report pass rates, exceptions, trends, thresholds, and ownership
• Validation matrices link each field to rule, method, result, and disposition
• Before-and-after profiling quantifies the effect of every cleaning decision
• Exception samples provide reviewers with record-level evidence and source references
• SPSS OMS captures output tables for repeatable quality reporting
• Release sign-off records fitness-for-purpose decisions and unresolved limitations

Participants can package a repeatable cleaning process for peer review, refresh and controlled handover.

• Project folders separate raw data, syntax, outputs, documentation, and releases
• Master syntax sequences imports, checks, transformations, outputs, and save commands
• Parameter files centralise paths, dates, thresholds, and reporting periods
• Version labels distinguish draft, reviewed, approved, and superseded data products
• Peer review tests syntax readability, rerun success, and rule coverage
• Handover packs specify refresh steps, dependencies, controls, and escalation contacts

Participants can plan a risk-based data-cleaning assignment with scope, acceptance criteria and review gates.

• Cleaning plans define scope, critical fields, rules, roles, and acceptance criteria
• Risk-based sequencing addresses high-impact defects before low-priority formatting issues
• Effort estimates separate automated checks from manual source investigation
• Stakeholder questions convert reporting concerns into testable validation rules
• Decision logs record trade-offs affecting comparability, completeness, and analytical fitness
• Release plans schedule review gates, remediation, approval, and data publication

Participants can build, review and present a complete SPSS data-cleaning and validation pack for a business dataset.

• Case requirements translate business questions into data-quality acceptance criteria
• Import syntax establishes controlled access to source extracts and metadata
• Profiling results prioritise missingness, duplicates, invalid values, and outliers
• Cleaning syntax applies documented recodes, transformations, joins, and exception flags
• Validation matrix evidences rule results, residual defects, and disposition decisions
• Quality report presents metrics, limitations, approval recommendation, and refresh instructions