Research and Data Analysis

Power Analysis and Sample Size Determination for Complex Designs Training Course

Introduction

In today's data-driven world, precision in research design is crucial. Power Analysis and Sample Size Determination for Complex Designs Training Course empowers participants with cutting-edge tools and methodologies to conduct robust statistical analyses. This intensive, hands-on program is designed to help researchers, data analysts, and professionals understand the core principles of power analysis, effect size, and sample size estimation across various complex statistical models, including multilevel, longitudinal, and mixed designs. With growing reliance on data-informed decision-making, mastering these techniques ensures resource-efficient and statistically valid outcomes.

This course is tailored to match current demands in advanced quantitative research by combining theoretical knowledge with practical application. Using real-world case studies and advanced software tools, participants will learn how to optimize study design, increase analytical precision, and minimize errors. Whether you're designing randomized controlled trials or multivariate observational studies, this course provides a comprehensive toolkit to help ensure valid and generalizable results, driving higher impact and credibility in your findings.

Programme Curriculum

Power Analysis and Sample Size Determination for Complex Designs Training Course

Introduction

Course Objectives

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

Understand the fundamentals of power analysis and its role in research.
Define effect size, Type I and II errors, and their implications for statistical inference.
Calculate sample size for t-tests, ANOVA, regression, and chi-square tests.
Apply power analysis techniques in multilevel and hierarchical models.
Conduct power analysis for repeated measures and longitudinal data.
Determine optimal sample sizes for complex and mixed study designs.
Use software tools like G*Power, R, and SAS for sample size estimation.
Analyze the impact of design parameters on statistical power.
Explore Bayesian approaches to power analysis and sample determination.
Interpret power curves and sensitivity analysis in real-world scenarios.
Integrate ethical and practical considerations in sample planning.
Translate statistical results into actionable research decisions.
Evaluate case-based scenarios for methodological rigor and accuracy.

Target Audiences

Biostatisticians
Academic Researchers
Clinical Trial Designers
Social Science Methodologists
Epidemiologists
Market Researchers
Graduate Students in Quantitative Fields
Data Scientists and Analysts

Course Duration: 10 days

Course Modules

Module 1: Introduction to Power Analysis and Sample Size Determination

Understanding key statistical concepts
Importance of power analysis in research
Overview of sample size estimation methods
Ethical implications of under/over-sampling
Introduction to G*Power software
Case Study: Underpowered clinical trial outcomes

Module 2: Effect Size and Its Implications

Types of effect size: Cohen’s d, η², odds ratio
Choosing an appropriate effect size
Impact on power and sample size
Reporting and interpreting effect size
Estimating effect sizes from pilot data
Case Study: Effect size inflation in social science

Module 3: Power Analysis for Parametric Tests

t-tests (independent, paired), ANOVA
Sample size tables vs software computation
Setting alpha and beta levels
Post-hoc vs a priori power analysis
Effect of variance on sample size
Case Study: Educational intervention effectiveness

Module 4: Regression and Correlation Analysis

Power in linear and logistic regression
Detecting small vs large effects
Predictor selection and sample planning
Multicollinearity impact
Using R for regression power analysis
Case Study: Predicting disease risk using lifestyle factors

Module 5: Multilevel and Hierarchical Models

Introduction to nested designs
Sample size at each level
Intraclass correlation and power
Design effect and design efficiency
Using MLwiN or R packages for computation
Case Study: School-based intervention study

Module 6: Longitudinal and Repeated Measures Design

Timepoints vs subjects: planning strategies
Handling attrition and dropout
Mixed model vs ANOVA approaches
Autocorrelation and variance structure
Software tools for longitudinal design
Case Study: Behavioral tracking study in children

Module 7: Mixed and Complex Designs

Combining designs: factorial, crossover, split-plot
Interaction effects and sample size
Balanced vs unbalanced designs
Covariates and confounders
Power analysis for nested factorial designs
Case Study: Agricultural productivity experiment

Module 8: Bayesian Power and Sample Size Approaches

Contrast with frequentist methods
Use of priors in determining power
Posterior predictive checks
Simulating Bayesian power
Bayesian software (e.g., JAGS, Stan) overview
Case Study: Ecological modeling and forecasting

Module 9: Software Applications and Tools

Overview of G*Power, R, PASS, SAS, STATA
Input/output interpretations
Common user errors and solutions
Data visualization of power curves
Hands-on software tutorials
Case Study: Public health intervention planning

Module 10: Sensitivity Analysis and Power Curves

Exploring changes in assumptions
Interpreting and using power curves
Adaptive designs and sample recalculation
Threshold analysis for decision making
Interactive visualization tools
Case Study: Medical device testing redesign

Module 11: Planning and Reporting Guidelines

CONSORT and SAMPL guidelines
Transparent reporting of power analysis
Writing sample size justifications
Journal expectations and templates
Integrating results into research protocols
Case Study: Grant proposal for randomized trial

Module 12: Ethical and Practical Considerations

Risks of under/overestimating sample size
Budget and time constraints
Stakeholder communication strategies
Balancing power with feasibility
Risk mitigation plans
Case Study: Ethical dilemmas in community trials

Module 13: Real-World Applications Across Fields

Medical, behavioral, economic, and engineering applications
Case diversity and design variety
Cross-sectional vs longitudinal sampling
Non-normal distributions and small samples
Lessons learned from published studies
Case Study: Policy research power analysis audit

Module 14: Common Pitfalls and Troubleshooting

Misinterpretation of statistical parameters
Overreliance on defaults in software
Ignoring design complexity
Sample bias and generalizability
Corrective strategies and tools
Case Study: Retrospective audit of flawed study

Module 15: Capstone: Project Simulation and Peer Review

Choose or design a study
Perform full power analysis and sample planning
Peer review and critique
Presentation and reporting session
Instructor feedback and revision
Case Study: Capstone report based on peer-reviewed journal standards

Training Methodology

Instructor-led presentations with live demonstrations
Hands-on software tutorials (G*Power, R, SAS)
Real-world case study analysis
Group activities and collaborative simulations
Peer-reviewed project presentations
Access to downloadable templates and guides

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Power analysis sample size planning experimental design

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000