IBM SPSS Statistics for Survey Data Analysis Training Course

Course overview

Survey datasets often contain hundreds of variables, coded response options, incomplete records and weighting requirements that make simple spreadsheet analysis unreliable. Analysts, researchers and operational teams need to turn raw questionnaire exports into evidence that can withstand scrutiny: clear respondent profiles, statistically valid comparisons, defensible findings and tables that decision-makers can use. This course focuses on the IBM SPSS Statistics workflow required to prepare, analyse and report survey data without losing the meaning of the questionnaire or misrepresenting the sample.

Participants work through the full survey analysis process in IBM SPSS Statistics. They import and inspect survey files, define variable metadata and value labels, recode and compute derived variables, identify missing-data patterns, apply filters and weights, and produce frequency tables and cross-tabulations. The course then develops practical inferential analysis skills using chi-square tests, t-tests, ANOVA, correlation and regression, with attention to assumptions, effect sizes, significance and interpretation. Participants also learn to build scale scores, assess internal consistency with Cronbach's alpha, segment respondents and create presentation-ready tables and charts.

Delivery combines instructor demonstration with guided work in IBM SPSS Statistics using realistic customer, employee or public-opinion survey data. Each day includes practical analysis tasks that require participants to make and document analytical choices rather than merely follow menu steps. By the end of the week, participants produce a structured survey analysis workbook: an SPSS syntax file, cleaned and labelled dataset, analysis plan, statistical output, chart set and concise findings summary suitable for sharing with stakeholders.

The course is designed for professionals who need to analyse questionnaire-based data directly or review survey findings with confidence. It suits both new SPSS users and analysts with basic experience who need a disciplined, repeatable approach to survey preparation, testing and reporting.

Course objectives

By the end of this course, participants will be able to:

• Import survey data into IBM SPSS Statistics and define variable types, labels, value labels and measurement levels
• Audit survey datasets for invalid codes, duplicate records, missing values and routing inconsistencies
• Recode categorical responses and compute composite scores using SPSS Transform and Compute Variable functions
• Apply case selection, split files and weighting procedures to analyse defined respondent populations correctly
• Produce and interpret frequencies, descriptive statistics, custom tables and cross-tabulations for survey questions
• Run chi-square tests, independent-samples t-tests and one-way ANOVA to test survey group differences
• Assess scale reliability with Cronbach's alpha and construct defensible multi-item survey measures
• Create an SPSS syntax-based survey analysis workbook with charts, findings and documented analytical decisions

Benefits of attending

Target competencies

Who should attend

• Market Research Analysts — who need to convert questionnaire results into defensible customer and brand insights
• Business Analysts — who analyse customer, employee or stakeholder feedback to support operational decisions
• Customer Insights Specialists — who need to segment respondents and identify drivers of satisfaction or loyalty
• HR and People Analytics Professionals — who analyse engagement, pulse and employee-experience survey results
• Monitoring and Evaluation Officers — who must assess programme feedback and outcomes across respondent groups
• Policy and Social Research Officers — who need robust descriptive and inferential analysis of survey evidence

Requirements and prerequisites

This is a foundation-to-intermediate course. Participants should be comfortable working with files and tables in a Windows or macOS environment and should understand basic spreadsheet concepts such as rows, columns, filters and numeric versus text values. Familiarity with survey questionnaires, Likert-scale questions or Microsoft Excel is useful but not essential. No prior IBM SPSS Statistics experience, programming knowledge or advanced mathematics is required. Complete beginners should expect to work carefully with variable definitions, data coding and basic statistical terminology. The course explains statistical tests in applied terms, but participants should be prepared to interpret percentages, averages and simple comparisons.

Training methodology

The course uses short instructor-led explanations followed by guided analysis in IBM SPSS Statistics. Participants work with realistic survey extracts containing coded responses, missing values, demographic variables and multi-item scales. Exercises progress from data definition and recoding to weighting, cross-tabulation, significance testing and reporting. The instructor reviews SPSS Viewer output, syntax and common interpretation errors with the group. Case discussions focus on choosing an appropriate test for a business question and explaining findings without overstating the evidence. The final session includes application planning for participants' own survey workflows.

Course outline

Day 1: Survey data structure and SPSS foundations

• Survey research workflow from questionnaire to findings report
• IBM SPSS Statistics Data View and Variable View navigation
• Variable types, measurement levels and response-code definitions
• Value labels, missing-value declarations and user-defined missing codes
• Importing Excel and CSV survey exports into SPSS
• Data validation using Frequencies, Descriptives and Explore
• SPSS syntax fundamentals for reproducible survey preparation

Workshop: Participants import a customer survey file, define its metadata and produce a data-quality log identifying coding and completeness issues.

Day 2: Cleaning, transforming and weighting survey data

• Identifying out-of-range values and inconsistent response patterns
• Handling item non-response and missing-data reporting
• Recode into Different Variables and Recode into Same Variables
• Compute Variable for indices, flags and derived respondent groups
• Conditional transformations using IF and DO IF commands
• Select Cases, Split File and temporary filters
• Weight Cases and the implications of weighted survey estimates

Workshop: Participants clean and transform a survey dataset, create respondent segments and apply a supplied population weight using documented SPSS syntax.

Day 3: Descriptive analysis and survey tabulation

• Frequency distributions and valid-percent interpretation
• Central tendency and dispersion for rating-scale responses
• Multiple-response sets for check-all-that-apply questions
• Crosstabs for demographic and segment comparisons
• Row, column and total percentages for survey tables
• Custom Tables for stakeholder-facing tabulations
• Chart Builder for bar charts, stacked charts and profile displays

Workshop: Participants create a tabulation pack comparing satisfaction and service-use responses across three customer segments.

Day 4: Statistical testing and survey scale analysis

• Selecting tests for categorical, ordinal and continuous survey variables
• Chi-square tests and expected-count requirements
• Independent-samples t-tests for two-group comparisons
• One-way ANOVA and post-hoc comparisons for multiple groups
• Correlation analysis for survey measures and ratings
• Cronbach's alpha for internal consistency of multi-item scales
• Effect sizes, confidence intervals and practical significance

Workshop: Participants test differences in engagement and satisfaction across employee groups and write evidence-based interpretations of the output.

Day 5: Drivers, reporting and reusable analysis workflows

• Linear regression for identifying survey outcome drivers
• Checking regression assumptions and interpreting model output
• Combining item scores into reliable reporting measures
• Exporting SPSS tables and charts to Word, Excel and PowerPoint
• Writing findings statements that distinguish association from causation
• Building a reusable SPSS syntax template for recurring surveys
• Survey analysis governance, disclosure control and documentation

Workshop: Participants complete a capstone survey-analysis workbook containing syntax, key tables, charts, statistical findings and a stakeholder findings summary.

Tools & standards covered

IBM SPSS Statistics, IBM SPSS Statistics Custom Tables, IBM SPSS Missing Values, Microsoft Excel

A typical training day

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

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions