SPSS Statistical Analysis Training Course

Duration 10 days 18 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Data Analyst, Research Analyst and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-DA-059

Before you join: Participants should be comfortable working with tables of data and basic spreadsheet functions such as sorting, filtering and simple formulas.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Organisations collect operational, customer, employee, programme and financial data, but decisions are often based on spreadsheet summaries that conceal relationships, uncertainty and data quality issues. Analysts need to move from raw records to defensible findings: testing whether differences are meaningful, estimating drivers of outcomes, segmenting populations and presenting results that non-specialists can act upon. IBM SPSS Statistics provides a practical environment for this work when analysts understand both the statistical method and the discipline required to prepare, validate and report data.

This course develops the applied statistical analysis skills needed to work confidently in SPSS. Participants prepare datasets, define variables, manage missing values, produce descriptive statistics and charts, and select appropriate statistical tests. The course covers hypothesis testing, confidence intervals, correlation, linear and logistic regression, analysis of variance, non-parametric tests, factor analysis, cluster analysis and reliability analysis. Participants learn to check assumptions, interpret SPSS output, write reproducible syntax and distinguish statistical significance from operational importance.

Teaching combines focused instructor briefings with guided analysis of realistic datasets from public services, banking, customer research, HR and programme monitoring. Participants work through the full analytical process, from a business question and data audit to tested findings and a decision-focused report. They leave with a completed SPSS analysis pack containing prepared data, saved syntax, output tables, visualisations, an assumptions checklist and a concise findings presentation.

The course suits professionals who already work with structured data and need to produce or review statistical analysis. It is particularly relevant to analysts, researchers, monitoring and evaluation staff, risk teams, HR specialists and managers who commission analytical work.

What you will be able to do

  • Prepare SPSS datasets using variable definitions, recoding rules and missing-data treatments
  • Produce descriptive statistics and visualisations matched to variable type and decision question
  • Select and conduct hypothesis tests using assumptions, effect sizes and confidence intervals
  • Build and interpret linear and logistic regression models in SPSS
  • Apply ANOVA, chi-square and non-parametric methods to comparative analysis questions
  • Develop factor, cluster and reliability analyses for survey and segmentation data
  • Write reusable SPSS syntax for auditable data preparation and analysis
  • Present a complete statistical analysis with findings, limitations and recommendations

Benefits of attending

For you

  • Ability to turn raw SPSS data into defensible statistical findings
  • Confidence selecting tests rather than relying on default software output
  • Reusable SPSS syntax for repeatable monthly, survey or programme analysis
  • Stronger interpretation of regression, significance and effect-size results
  • A portfolio-ready analysis pack for demonstrating applied analytical capability

For your organisation

  • More consistent statistical analysis across surveys, operational reporting and evaluations
  • Reduced risk of incorrect conclusions from unsuitable tests or unchecked assumptions
  • Faster repeat analysis through documented SPSS syntax and saved output procedures
  • Clearer evidence for customer, workforce, risk and programme decisions
  • Improved auditability of analytical methods, data transformations and reported findings

Course modules

18 modules over 10 days

Participants can frame an analytical question and organise an SPSS project for traceable work.

  • Decision questions translated into measurable outcomes, populations and comparison groups
  • IBM SPSS Statistics interface, Data View, Variable View and Output Viewer
  • Measurement levels: nominal, ordinal, scale and their analytical consequences
  • Analytical workflow from business question through reporting and decision use
  • Project folders, output naming conventions and reproducible analysis records
  • Data provenance, confidentiality and disclosure risks in analytical reporting

Participants can import and document structured datasets accurately in SPSS.

  • Import Excel, CSV and text files using controlled variable settings
  • Variable labels, value labels and user-defined missing-value codes
  • Date, string and numeric formats specified for valid analysis
  • Codebooks built from source definitions, field rules and response categories
  • Restructure wide and long files for repeated-measures data designs
  • Merge files using unique identifiers, matched fields and case checks

Participants can identify data quality problems and create analysis-ready variables.

  • Frequency tables expose invalid codes, duplicates and unexpected category values
  • Explore distributions using histograms, boxplots and descriptive statistics
  • Missing-data patterns examined before deletion, imputation or category recoding
  • Recode categorical values with documented old-to-new transformation rules
  • Compute indices, rates and composite scores from source variables
  • Outliers investigated using z-scores, boxplots and casewise diagnostics

Participants can write and run SPSS syntax for repeatable data preparation and analysis.

  • Syntax Editor commands reproduce transformations, procedures and output settings
  • GET DATA, DATASET NAME and SAVE commands manage file workflows
  • COMPUTE, RECODE and IF statements create controlled derived variables
  • Comments and command blocks document analytical intent and change history
  • Paste function converts menu selections into editable SPSS syntax
  • Rerun syntax after source updates to maintain consistent reporting

Participants can summarise distributions and communicate key patterns with appropriate SPSS outputs.

  • Mean, median, mode and trimmed mean matched to distribution shape
  • Standard deviation, variance, range and interquartile range quantify spread
  • Crosstabs compare categorical variables using counts, percentages and residuals
  • Chart Builder produces bar charts, histograms, scatterplots and boxplots
  • Confidence intervals communicate estimation uncertainty around sample statistics
  • Tables formatted with meaningful labels, decimals and denominator information

Participants can interpret samples, confidence intervals and p-values in decision contexts.

  • Population, sample, parameter and statistic distinguished in analytical reporting
  • Random sampling, stratification and selection bias affect inference quality
  • Sampling distributions explain standard errors and confidence interval width
  • Null and alternative hypotheses stated for measurable decision questions
  • P-values interpreted alongside effect sizes, intervals and practical importance
  • Type I and Type II errors considered when setting significance thresholds

Participants can select statistical procedures by question type, variable structure and assumptions.

  • Decision trees match research questions to appropriate statistical procedures
  • Normality assessed using histograms, Q-Q plots and Shapiro-Wilk tests
  • Homogeneity of variance checked with Levene's test and residual plots
  • Independence, linearity and influential cases reviewed before model estimation
  • Parametric and non-parametric choices justified from data characteristics
  • Sample size, statistical power and effect magnitude considered jointly

Participants can test and report differences between two independent or paired groups.

  • Independent-samples t-test compares mean outcomes between separate groups
  • Paired-samples t-test evaluates before-and-after or matched-pair change
  • Mann-Whitney U test compares ordinal or non-normal independent groups
  • Wilcoxon signed-rank test examines paired non-parametric outcome differences
  • Cohen's d quantifies standardised mean differences beyond p-values
  • Results tables report group statistics, test values, intervals and interpretation

Participants can analyse relationships between categorical variables using crosstabs and chi-square tests.

  • Crosstabs display joint distributions using row, column and total percentages
  • Pearson chi-square tests independence between categorical variables
  • Expected cell counts checked before accepting chi-square results
  • Fisher's exact test used when small expected frequencies occur
  • Phi and Cramer's V report categorical association strength
  • Standardised residuals identify cells contributing to significant associations

Participants can compare mean outcomes across multiple groups and explain interaction effects.

  • One-way ANOVA compares means across three or more independent groups
  • Post-hoc comparisons use Tukey, Bonferroni and Games-Howell procedures
  • Eta squared and partial eta squared quantify explained variance
  • Factorial ANOVA estimates main effects and interaction effects
  • Interaction plots show conditional group differences clearly for decision makers
  • Welch ANOVA handles unequal variances across comparison groups

Participants can analyse change over time and adjust group comparisons for relevant covariates.

  • Repeated-measures ANOVA analyses outcomes measured at multiple time points
  • Mauchly's test assesses sphericity for within-subject factor comparisons
  • Greenhouse-Geisser correction adjusts results when sphericity is violated
  • ANCOVA adjusts group comparisons using baseline or control covariates
  • Estimated marginal means support adjusted comparisons and presentation
  • Baseline differences and regression assumptions examined before adjustment

Participants can quantify and interpret associations between two measured variables.

  • Pearson correlation estimates linear association between scale variables
  • Spearman rank correlation handles ordinal data and monotonic relationships
  • Scatterplots reveal nonlinearity, clusters, outliers and restricted ranges
  • Correlation matrices identify candidate predictors and multicollinearity concerns
  • Correlation coefficients interpreted with confidence intervals and sample size
  • Association distinguished from causation when writing analytical conclusions

Participants can build, diagnose and report multiple linear regression models.

  • Regression equations estimate continuous outcomes from multiple predictor variables
  • Enter, hierarchical and stepwise methods compared for model construction
  • Dummy variables represent categorical predictors within regression models
  • Adjusted R-squared, F-tests and coefficients evaluate model performance
  • Residual plots assess linearity, normality and constant error variance
  • Variance inflation factors identify multicollinearity among predictor variables

Participants can model binary outcomes and assess classification performance using logistic regression.

  • Binary logistic regression models yes-no outcomes from predictor variables
  • Odds ratios translate coefficients into interpretable relative likelihoods
  • Classification tables compare predicted outcomes with observed outcomes
  • ROC curves assess discrimination across alternative probability thresholds
  • Hosmer-Lemeshow test examines agreement between observed and predicted outcomes
  • Predicted probabilities support prioritisation, targeting and risk decisions

Participants can assess survey scales and identify underlying dimensions in questionnaire data.

  • Cronbach's alpha assesses internal consistency of multi-item scales
  • Item-total correlations identify weak items within a measurement scale
  • Exploratory factor analysis identifies latent dimensions among survey items
  • KMO and Bartlett tests assess factorability before extraction
  • Principal axis factoring and varimax rotation support factor interpretation
  • Factor scores create usable dimensions for subsequent statistical analysis

Participants can create and profile meaningful groups from multivariable data.

  • Standardise variables before distance-based segmentation procedures begin
  • Hierarchical clustering explores plausible group structures using dendrograms
  • K-means clustering assigns cases to specified segment solutions
  • Cluster centres and profiles distinguish operationally useful segments
  • Outliers and variable selection assessed before accepting cluster results
  • Segment labels linked to service, customer or programme actions

Participants can communicate statistical findings clearly, accurately and with appropriate limitations.

  • Analytical reports connect questions, methods, findings and recommended actions
  • APA-style statistical notation presents tests, degrees of freedom and p-values
  • Effect sizes and confidence intervals accompany statistical significance statements
  • Tables and charts selected for executive interpretation rather than output volume
  • Limitations address data quality, sampling, assumptions and causal claims
  • Recommendations distinguish evidenced actions from further analysis requirements

Participants can build, review and present a complete SPSS analysis answering a defined decision question.

  • Frame a decision question, hypotheses and measurable analytical outcomes
  • Prepare source data using documented cleaning, coding and transformation rules
  • Select tests and models using variable types and assumption evidence
  • Build reusable SPSS syntax, output tables and decision-focused visualisations
  • Review findings for accuracy, limitations, effect sizes and practical meaning
  • Present recommendations supported by a complete auditable analysis pack

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. SPSS data preparation checklist
  2. Reusable SPSS syntax template
  3. Statistical test selection decision tree
  4. Regression diagnostics checklist
  5. Statistical reporting template
  6. Completed capstone analysis pack

Who should attend

  • Data Analyst — needs defensible statistical findings from operational or customer data
  • Research Analyst — needs to test hypotheses and report survey results accurately
  • Monitoring and Evaluation Officer — needs evidence from programme and beneficiary datasets
  • Risk Analyst — needs to identify predictors, segments and significant risk differences
  • HR Analyst — needs to analyse workforce surveys, retention and performance data
  • Policy or Programme Manager — needs to commission and critically review statistical analysis

Skills and tools covered

SPSS data managementHypothesis testingRegression modellingSurvey analysisStatistical reportingSyntax automation

Tools and standards

IBM SPSS Statistics, APA Style, ROC curve analysis, Cronbach's alpha

How the course runs

Training methodology

The course uses short instructor briefings followed by guided SPSS work on realistic datasets. Participants interpret output, compare alternative methods, diagnose assumption failures and discuss decisions with peers. Cases include customer survey results, service performance records, workforce data and programme outcomes. Group activities focus on translating decision questions into testable hypotheses and reporting findings for managers. Individual application planning helps participants identify a suitable workplace dataset, define analysis steps and prepare a practical post-course action plan.

Requirements and prerequisites

Participants should be comfortable working with tables of data and basic spreadsheet functions such as sorting, filtering and simple formulas. They should understand common business measures, percentages and averages, and be able to interpret a chart or summary table. Prior experience of IBM SPSS Statistics is helpful but not required. The course introduces the statistical concepts needed for the methods taught, although participants should be prepared to work with formulas and numerical output. Programming knowledge, advanced mathematics and prior experience with R, Python or database querying are not required.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should be comfortable with spreadsheets, percentages, averages and reading basic charts. No prior SPSS experience, programming experience or advanced mathematics is required.

Bring a laptop capable of running the course software if attending in person, plus any non-sensitive examples of analytical questions from your role. Training datasets and course materials are provided.

It is designed for analysts, researchers, monitoring and evaluation staff, risk professionals, HR analysts and managers who work with structured data. It is an intermediate course for people who need more than descriptive spreadsheet reporting.

This course focuses on selecting, conducting and interpreting inferential statistical methods, not only using menus or producing charts. It also covers regression, survey scale analysis, segmentation and reusable syntax.

You can use the methods for customer surveys, employee data, service performance, risk indicators, evaluations and operational datasets. The analysis workflow, syntax template and reporting format are designed for reuse with workplace data.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The first week establishes data preparation, descriptive analysis and core tests; the second week develops modelling, survey methods, reporting and the capstone.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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