About This Course
In an era where data-driven decision-making is no longer optional, the ability to design credible research, manage complex datasets, and interpret statistical outputs is a critical professional competency across sectors. Whether you are evaluating a development programme, conducting academic research, generating evidence for policy reform, or analysing organisational performance data, a solid grounding in research methodology and statistical software is indispensable.
This 10-day TrainingComplex programme — Research Design, Data Management and Statistical Analysis using SPSS — provides a comprehensive, practitioner-oriented journey through the entire research cycle. Participants begin with the fundamentals of scientific inquiry and research design, progress through questionnaire development and sampling strategies, and then move into hands-on data entry, coding, and cleaning within SPSS. The second half of the course focuses intensively on descriptive and inferential statistics, regression modelling, and the clear communication of findings to both technical and non-technical audiences.
SPSS (Statistical Package for the Social Sciences) remains one of the most widely used analytical tools in social science research, health, education, monitoring and evaluation, and public policy. This course demystifies the software through guided, step-by-step exercises using realistic datasets drawn from development, public health, and organisational management contexts. Participants leave not only understanding the outputs SPSS generates, but knowing how to interpret, validate, and present them with confidence.
TrainingComplex's approach combines conceptual lectures with intensive computer-lab sessions, peer-reviewed data exercises, and a structured mini-research project that participants develop progressively throughout the course. By Day 10, every participant will have completed a full research cycle — from design to written report — giving them a concrete portfolio artefact and immediate transferable skills for their workplace.
Who Should Attend
- Monitoring, Evaluation, Accountability and Learning (MEAL) officers and managers
- Research officers and programme analysts in NGOs, government agencies, and international organisations
- Public health professionals involved in surveys, assessments, or epidemiological studies
- Policy analysts and planning officers who need to generate or interpret quantitative evidence
- Academic staff, postgraduate students, and institutional researchers
- Human resource and organisational development professionals conducting workforce surveys
- Project managers responsible for results measurement and reporting
- Statisticians and data officers seeking to formalise or deepen their SPSS proficiency
Learning Objectives
- Articulate the principles of scientific research and select appropriate research designs for different study questions
- Formulate clear research questions, objectives, and hypotheses aligned with study purpose
- Design valid and reliable data collection instruments including structured questionnaires and survey scales
- Apply probability and non-probability sampling techniques and calculate appropriate sample sizes
- Enter, code, label, and structure datasets correctly within the SPSS environment
- Perform systematic data cleaning, handle missing values, and create derived variables in SPSS
- Produce and interpret descriptive statistics, frequency distributions, and cross-tabulations using SPSS
- Conduct and interpret key inferential tests including chi-square, t-tests, ANOVA, correlation, and regression
- Select the correct statistical test for a given research question and data type
- Interpret SPSS output tables and translate findings into plain-language narratives
- Produce publication-quality charts and tables from SPSS output
- Write a structured research report that communicates methodology, findings, and recommendations clearly
Course Modules
Module 1: Foundations of Scientific Research and the Research Process
- The nature of scientific inquiry: ontology, epistemology, and paradigms
- Quantitative, qualitative, and mixed-methods approaches — choosing what fits
- The research cycle: from problem identification to dissemination
- Ethical principles in research: informed consent, confidentiality, and research integrity
- Theory and its role in research: inductive versus deductive reasoning and theoretical frameworks
- Reviewing and synthesising literature: purpose, scope, and positioning your study within existing knowledge
Module 2: Formulating Research Problems, Objectives, and Hypotheses
- Identifying and narrowing a researchable problem from organisational or programmatic needs
- Writing SMART research objectives and corresponding research questions
- Formulating null and alternative hypotheses for quantitative studies
- Conceptual and operational definitions: translating concepts into measurable variables
- Mapping the logical flow from problem statement through objectives to hypotheses
- Distinguishing directional from non-directional hypotheses and selecting the appropriate form for your study
Module 3: Research Design and Study Types
- Experimental, quasi-experimental, and non-experimental designs
- Cross-sectional, longitudinal, case-control, and cohort study structures
- Descriptive versus analytical research: purpose and design implications
- Selecting and justifying an appropriate design for your research question
- Internal and external validity: threats to validity and how design choices mitigate them
- Triangulation in mixed-methods designs: integrating quantitative and qualitative components purposefully
Module 4: Measurement, Questionnaire Design, and Scale Development
- Levels of measurement: nominal, ordinal, interval, and ratio — and their analytical implications
- Designing structured and semi-structured questionnaires: item writing, sequencing, and formatting
- Using validated scales: Likert, Likert-type, and semantic differential instruments
- Pilot testing, reliability (Cronbach's Alpha), and validity assessment of instruments
- Content, construct, and criterion validity: distinguishing the types and applying appropriate validation strategies
- Managing response bias: social desirability, acquiescence, and design strategies to reduce their effect
Module 5: Sampling Theory and Sample Size Determination
- Probability sampling: simple random, systematic, stratified, and cluster sampling
- Non-probability sampling: purposive, snowball, convenience, and quota sampling
- Sample size calculation principles: confidence levels, margin of error, and population size
- Practical sampling decisions: dealing with non-response and stratification in the field
- Using G*Power and formula-based approaches to determine sample size for specific statistical tests
- Sampling frame construction: sources, limitations, and implications for representativeness and generalisability
Module 6: Introduction to SPSS: Interface, Data Entry, and Variable Setup
- Navigating the SPSS environment: Data View, Variable View, Output Viewer, and Syntax Editor
- Defining variables: name, type, width, decimals, labels, value labels, and missing values
- Data entry methods: manual entry, importing from Excel and CSV, and using SPSS syntax
- Data file management: saving, merging, appending, and restructuring datasets
- Using SPSS syntax for reproducibility: writing, saving, and running command scripts
- Setting measurement levels correctly in Variable View and their downstream effect on available procedures
Module 7: Data Cleaning and Management in SPSS
- Identifying and handling missing data: listwise deletion, pairwise deletion, and mean imputation
- Detecting outliers and inconsistencies using frequency tables and descriptive checks
- Recoding variables, computing new variables, and creating composite scores in SPSS
- Data transformation: normalising skewed variables and preparing data for analysis
- Using the SPSS Missing Value Analysis module to assess patterns of missingness (MCAR, MAR, MNAR)
- Documenting a data cleaning log: tracking decisions, transformations, and version control for auditability
Module 8: Descriptive Statistics and Data Exploration in SPSS
- Frequencies, percentages, and cumulative distributions for categorical variables
- Measures of central tendency (mean, median, mode) and dispersion (SD, range, IQR) for continuous variables
- Cross-tabulations and contingency tables: reading and interpreting row and column percentages
- Visualising data in SPSS: bar charts, histograms, box plots, and pie charts for reporting
- Exploring data by subgroups using the Split File function and comparing descriptive profiles across groups
- Identifying distributional shape: assessing skewness, kurtosis, and normality visually before inferential testing
Module 9: Inferential Statistics I — Testing Differences Between Groups
- Logic of hypothesis testing: p-values, significance levels, Type I and Type II errors, and statistical power
- Independent samples t-test and paired samples t-test: assumptions, execution, and output interpretation
- One-way ANOVA and post-hoc tests (Tukey, Bonferroni) for comparing multiple group means
- Chi-square test of independence: when to use it, how to run it, and how to report results
- Effect size measures: Cohen's d, eta-squared, and Cramér's V — calculating and interpreting practical significance
- Checking and reporting test assumptions: Levene's test for equality of variances and expected cell frequency requirements
Module 10: Inferential Statistics II — Relationships and Association
- Pearson correlation: assumptions, coefficient interpretation, and scatterplot examination
- Spearman rank-order correlation for ordinal or non-normally distributed data
- Simple linear regression: model building, coefficient interpretation, and R-squared
- Partial correlation and controlling for confounding variables in SPSS
- Point-biserial and phi coefficients: appropriate correlation measures when one or both variables are dichotomous
- Interpreting a correlation matrix: identifying multicollinearity risks and meaningful patterns across multiple variables
Module 11: Multiple Regression and Multivariate Analysis
- Multiple linear regression: entering predictors, reading the model summary, and interpreting beta coefficients
- Assumptions of regression: linearity, homoscedasticity, multicollinearity (VIF), and normality of residuals
- Binary logistic regression for categorical outcomes: odds ratios, significance, and model fit
- Model selection strategies: enter, stepwise, forward, and backward methods in SPSS
- Hierarchical regression: building blocks of predictor entry to test incremental variance explained (ΔR²)
- Reporting regression results in publication-ready format: coefficient tables, model fit statistics, and assumption diagnostics
Module 12: Selecting the Right Statistical Test and Checking Assumptions
- A decision framework for matching research questions, variable types, and appropriate tests
- Normality testing: Shapiro-Wilk, Kolmogorov-Smirnov, Q-Q plots, and skewness/kurtosis values
- Non-parametric alternatives: Mann-Whitney U, Kruskal-Wallis, and Wilcoxon signed-rank tests
- Common analytical errors: over-generalising findings, ignoring assumptions, and misreading p-values
- Distinguishing statistical significance from practical significance: when a significant result is not a meaningful one
- Handling multiple comparisons: the problem of alpha inflation and when to apply Bonferroni or FDR corrections
Module 13: Reporting, Presenting, and Disseminating Research Findings
- Structuring a quantitative research report: abstract, introduction, methodology, results, discussion, and recommendations
- Producing and formatting APA-style tables and figures from SPSS output
- Translating statistical findings into clear narrative for non-technical audiences and policy briefs
- Mini-project final presentations: communicating research design, analysis, and findings to peers
- Writing the discussion section: linking results back to objectives, interpreting unexpected findings, and acknowledging limitations
- Dissemination strategies beyond the written report: conference abstracts, executive summaries, and data visualisation for stakeholder engagement
Requirements & Prerequisites
Participants should have a working familiarity with basic computer applications and be comfortable using Windows-based software. A foundational understanding of research or data concepts — such as variables, surveys, or programme indicators — is helpful but not required, as the course begins from first principles. No prior experience with SPSS or advanced statistics is necessary; the programme is designed to build these skills progressively from the ground up.
Training Methodology
- Structured conceptual lectures that introduce research and statistical theory before each hands-on application session
- Guided SPSS computer-lab exercises using realistic datasets from health, development, and organisational research contexts
- Small-group dataset critique sessions where participants review and correct intentionally flawed datasets to build data quality instincts
- Progressive mini-research project — each participant develops their own research design, collects or simulates data, and analyses it in SPSS across the 10 days
- Facilitated interpretation workshops where SPSS output tables are projected and groups debate the correct reading of results
- Peer-review rounds in which participants exchange draft questionnaires and analysis plans for structured feedback
- Step-by-step syntax demonstrations showing both point-and-click and command-syntax approaches to SPSS operations
- Daily reflection check-ins linking the day's technical content to participants' real-world research or evaluation responsibilities
- A final presentation session on Day 10 where each participant presents their mini-project findings to the group for feedback
Organisational Benefits
- Strengthened organisational capacity to generate credible, internally produced evidence for decision-making
- Improved quality and rigour of evaluation reports, baseline studies, and programme assessments
- Reduced reliance on external consultants for routine data analysis and reporting tasks
- More consistent and defensible use of statistics in donor reports, policy briefs, and strategy documents
- Faster turnaround on data analysis tasks due to improved SPSS proficiency across the team
- Better alignment between data collection instruments and the analytical questions they are designed to answer
- Enhanced staff confidence in presenting quantitative findings to senior management and external stakeholders
- A culture of evidence-based planning embedded within teams through shared research language and methods
Where Is Research Design, Data Management and Statistical Analysis using SPSS Training Offered?
This course runs in-person across: Zanzibar, Mombasa, Abuja, Nakuru, Addis Ababa, Accra, Pretoria, Dar es Salaam, Kampala, Naivasha, Arusha, Kisumu, Johannesburg, Dubai, Nairobi, Cape Town, Lagos, Kigali, Riyadh, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Zanzibar, Tanzania |
In-Person |
28 Sept 2026 – 9 Oct 2026 |
USD 5,000 |
| Mombasa, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Abuja, Nigeria |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 5,600 |
| Nakuru, Kenya |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
KES 230,000 |
| Addis Ababa, Ethiopia |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 4,800 |
| Accra, Ghana |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,600 |
| Pretoria, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 6,600 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 4,600 |
| Live Virtual |
Online |
19 Oct 2026 – 28 Oct 2026 |
KES 160,000 |
| Kampala, Uganda |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 3,800 |
| Naivasha, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Arusha, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,200 |
| Kisumu, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Johannesburg, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 7,000 |
| Live Virtual |
Online |
24 Oct 2026 – 13 Dec 2026 |
KES 160,000 |
+235 more dates — view live schedule →
Frequently Asked Questions
Do I need prior experience with SPSS or statistics to attend this course?
No prior SPSS experience is required. The course is structured to build skills progressively from the very basics of research design and the SPSS interface through to advanced regression modelling. A basic comfort with computers and familiarity with work-related data concepts is all that is needed.
Will participants receive a certificate upon completion?
Yes. Participants who attend the full 10-day programme and complete the mini-research project and final presentation will receive a TrainingComplex Certificate of Completion in Research Design, Data Management and Statistical Analysis using SPSS.
Is SPSS software provided, or do participants need to bring their own licence?
For in-person sessions, TrainingComplex provides fully licensed SPSS installations on training-lab computers for the duration of the course. Participants attending the Live Virtual track are advised to install a licensed or trial version of IBM SPSS Statistics before the programme begins; guidance notes are sent upon enrolment.
What kind of datasets are used during the practical exercises?
The course uses realistic datasets drawn from public health surveys, NGO programme evaluations, education assessments, and HR workforce studies. These are carefully designed to reflect the data challenges participants encounter in real professional settings, including messy data, missing values, and common coding errors.
Can organisations send a team of staff to attend together?
Absolutely. Group enrolment is encouraged — team attendance accelerates the embedding of shared data management and analysis practices within an organisation. TrainingComplex also offers dedicated in-house delivery of this programme, where the content and datasets can be customised to reflect the organisation's specific sector and data environment.
