Training on Research Design, Data Management, and Statistical Analysis using SPSS
Master research design, data management, and statistical analysis using SPSS. Learn to design effective research studies, clean and prepare data, and conduct statistical analyses to draw meaningful conclusions.
Next intake
20 Jul 2026 · Nakuru
Duration
10 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Intermediate
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
In the socio-economic and business context, conducting research, data management and data analysis are imperative for informed decision-making. The availability of several datasets and research techniques opens the gateway to conducting systematic research which will be helpful for consumers, businesses and organizations. A sound knowledge of the methodology of conducting research and use of SPSS as a research, data management and analysis tool is very beneficial for the researchers.
Upon completion of this SPSS short course on research design, data management and statistical Analysis , the participants will develop competence in quantitative techniques through hands-on practices in study design, data collection, and management, as well as the analysis and interpretation of data.
Course Duration
10 Days
Who Should Attend
- Researchers, academics, and students involved in quantitative research.
- Data analysts and statisticians seeking to enhance their skills in SPSS.
- Professionals from various fields such as social sciences, health, education, and business who need to analyze data and interpret statistical results.
- Individuals who are new to SPSS and wish to gain practical experience in using the software for data analysis.
What you'll walk away with
By the end of this course, participants will be able to
- Understand the principles and methodologies of research design.
- Formulate research questions and hypotheses.
- Design and implement surveys and experiments.
- Collect, manage, and clean data using SPSS.
- Perform descriptive and inferential statistical analyses using SPSS.
- Interpret and report the results of statistical analyses.
- Apply appropriate statistical techniques to different types of data.
- Enhance their decision-making and problem-solving skills using statistical evidence.
- Create visual representations of data and statistical results.
- Develop a comprehensive understanding of SPSS functionalities and tools.
What we cover, module by module
Module 1: Introduction to Research
- Introduction to research
- Importance of research in decision-making
- Different types of research
- Formulation of research problem statements
- Formulation of research hypotheses
- Case Study: Poorly defined research problem leading to weak findings
- Practical: Developing a research topic, problem statement, and hypothesis
Module 2: Overview of Evaluation
- Evaluation objectives
- Evaluation criteria
- Evaluation questions
- Difference between research and evaluation
- Results-based evaluation approaches
- Case Study: Program evaluation improving donor-funded project performance
- Practical: Drafting evaluation questions and objectives
Module 3: Research Design & Sampling
- Quantitative research approaches
- Qualitative research approaches
- Mixed-methods research design
- Sampling techniques
- Probability sampling methods
- Non-probability sampling methods
- Sample size determination
- Case Study: Sampling bias affecting survey credibility
- Practical: Selecting an appropriate design and sample size
Module 4: Data Collection Methods & Tools
- Quantitative data collection methods
- Qualitative data collection methods
- Survey questionnaire design
- Focus Group Discussion (FGD) guide design
- Key Informant Interview (KII) guide design
- Creating an evaluation framework
- Case Study: Poor questionnaire design reducing response quality
- Practical: Designing survey and interview tools
Module 5: Developing Research Protocols & Mobile Data Collection (ODK)
- What is a research protocol?
- Structure of a research protocol
- Ethical and operational considerations
- Introduction to mobile data gathering
- Survey form design using ODK Build and XLSForm
- Using ODK Collect and ODK Aggregate
- Working with GPS/spatial data
- Case Study: Real-time field data collection improving survey efficiency
- Practical: Building a mobile survey form in ODK
Module 6: Introduction to SPSS & Data Management
- SPSS interface and features
- Key terminologies used in SPSS
- Variable View, Data View, Syntax Editor
- Data file preparation and entry
- Data cleaning and manipulation
- Merge files, split files, sorting, missing values
- Case Study: Data cleaning errors affecting analysis results
- Practical: Importing and preparing data in SPSS
Module 7: Basic Statistics, Graphs & Reporting in SPSS
- Descriptive statistics for numeric variables
- Frequency tables
- Cross tabulations
- Stub and banner tables
- Introduction to graphs in SPSS
- Types of charts and graphs
- Case Study: Survey summary reporting for management decisions
- Practical: Running descriptive statistics and creating graphs
Module 8: Statistical Tests & Associations in SPSS
- One Sample T Test
- Independent Samples T Test
- Paired Samples T Test
- One-Way ANOVA
- Chi-Square test
- Pearson Correlation
- Spearman Rank Correlation
- Case Study: Testing customer satisfaction differences across branches
- Practical: Performing hypothesis tests in SPSS
Module 9: Predictive Models & Longitudinal Analysis
- Linear Regression
- Multiple Regression
- Logistic Regression
- Ordinal Regression
- Features of longitudinal data
- Exploring longitudinal datasets
- Analysis for continuous outcomes
- Case Study: Predicting employee turnover using regression models
- Practical: Building predictive models in SPSS
Module 10: Qualitative Analysis, Report Writing & Dissemination
- Introduction to NVivo
- NVivo workspace and project setup
- Uploading qualitative data
- Coding and node creation
- Queries and project visualization
- Survey report format and content
- Disseminating survey findings
- Using findings for decision-making
- Case Study: Policy reform informed by mixed-methods research findings
- Practical: Coding interviews and preparing a final research report
Where the change lands
Organizational Impact
-
Enhance research integrity and credibility through standardized best practices.
-
Improve efficiency and accuracy in data analysis, reducing time and costs.
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Foster a data-literate culture for informed, evidence-based decisions.
Personal Impact
-
Gain in-demand skills for a career in research and data analysis.
-
Advance into senior research, PI, or data stewardship roles.
-
Lead and manage complex research projects with confidence.
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
NakuruNext | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kigali | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Accra | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Johannesburg | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Dakar | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
- NakuruNext
20 Jul → 31 Jul·In-Person
Book this intake - Kigali
20 Jul → 31 Jul·In-Person
Book this intake - Accra
20 Jul → 31 Jul·In-Person
Book this intake - Kisumu
27 Jul → 07 Aug·In-Person
Book this intake - Johannesburg
27 Jul → 07 Aug·In-Person
Book this intake - Dakar
27 Jul → 07 Aug·In-Person
Book this intake
Common questions.
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