Format: Live instructor-led online training via Zoom / Microsoft Teams

Advanced Statistical Modeling Training Course

Course Introduction

Advanced Statistical Modeling is a critical discipline for modern research, evidence-based decision-making, predictive analytics, policy evaluation, and organizational performance management. The increasing complexity of data generated across healthcare, development programs, social sciences, business management, economics, agriculture, and public policy requires advanced analytical techniques that go beyond descriptive and basic inferential statistics. Advanced statistical models enable researchers and professionals to identify relationships among variables, explain complex phenomena, predict future outcomes, and generate robust evidence for strategic planning and decision-making.

This Advanced Statistical Modeling Training Course provides participants with comprehensive knowledge and practical skills in advanced statistical methods, predictive modeling, multivariate analysis, generalized linear models, longitudinal data analysis, structural equation modeling, survival analysis, time series forecasting, and machine learning applications. The course covers model development, data preparation, model assumptions, diagnostics, validation techniques, interpretation of outputs, and communication of statistical findings using modern analytical software applications. Participants will acquire practical competencies required to build, evaluate, and apply sophisticated statistical models to solve real-world research and organizational problems.

The training emphasizes practical applications of statistical modeling techniques using real-world datasets from monitoring and evaluation systems, public health research, development programs, financial management, education studies, market research, and policy evaluations. Participants will learn how to select appropriate analytical techniques, manage complex datasets, validate model assumptions, assess predictive performance, and communicate analytical insights effectively to technical and non-technical audiences.

Through hands-on exercises, simulations, collaborative group activities, software-based practical sessions, and real-life case studies, participants will develop advanced competencies in statistical modeling and quantitative analytics. Upon successful completion of the course, participants will possess the skills necessary to design and implement advanced analytical models, generate high-quality evidence, improve decision-making processes, and contribute to research excellence and organizational innovation.

Course Objectives

Upon completion of this course, participants will be able to:

1.     Understand the principles and applications of advanced statistical modeling.

2.     Prepare and manage datasets for advanced statistical analyses.

3.     Apply multivariate statistical techniques and predictive models.

4.     Develop and interpret regression and generalized linear models.

5.     Conduct longitudinal and repeated measures analyses.

6.     Apply structural equation modeling and path analysis techniques.

7.     Perform survival analysis and time series forecasting.

8.     Validate model assumptions and assess predictive performance.

9.     Utilize statistical software applications for advanced modeling.

10.  Communicate statistical findings effectively for decision-making and policy development.

Organizational Benefits

1.     Enhanced evidence-based decision-making capabilities.

2.     Improved predictive analytics and strategic planning processes.

3.     Strengthened organizational research and analytical capacities.

4.     Improved program monitoring, evaluation, and impact assessments.

5.     Enhanced capacity to analyze complex organizational data.

6.     Improved forecasting and risk management capabilities.

7.     Strengthened policy analysis and performance measurement systems.

8.     Enhanced staff competencies in advanced quantitative analysis.

9.     Improved quality of organizational research outputs and reports.

10.  Increased institutional competitiveness and innovation capabilities.

Target Participants

This course is designed for Researchers, Statisticians, Monitoring and Evaluation Specialists, Data Scientists, Data Analysts, Economists, Public Health Professionals, University Lecturers, Graduate Students, Doctoral Candidates, Development Practitioners, Policy Analysts, Business Intelligence Professionals, Research Coordinators, Project Managers, Government Officers, Consultants, Financial Analysts, Planning Officers, and professionals involved in advanced quantitative research, predictive analytics, and evidence-based decision-making.

Course Outline

Module 1: Foundations of Advanced Statistical Modeling

1.     Concepts and principles of advanced statistical modeling

2.     Applications of statistical modeling in research and decision-making

3.     Types of statistical models and analytical frameworks

4.     Model development processes and analytical workflows

5.     Statistical software and analytical environments

6.     Case Study: Applications of advanced modeling in development and research programs

Module 2: Data Preparation and Exploratory Analysis

1.     Data management and preparation techniques

2.     Data transformation and variable creation

3.     Handling missing values and outlier detection

4.     Exploratory data analysis procedures

5.     Assessing data quality and model readiness

6.     Case Study: Preparing complex datasets for statistical modeling

Module 3: Multiple Regression Modeling

1.     Principles of multiple linear regression

2.     Model specification and variable selection

3.     Regression assumptions and diagnostics

4.     Multicollinearity assessment techniques

5.     Interpretation and presentation of regression outputs

6.     Case Study: Predicting organizational performance using regression models

Module 4: Generalized Linear Models

1.     Concepts of generalized linear modeling

2.     Logistic regression techniques

3.     Poisson and count data regression models

4.     Probit and multinomial regression models

5.     Model comparison and goodness-of-fit assessment

6.     Case Study: Modeling categorical and count outcomes

Module 5: Multivariate Statistical Analysis

1.     Principles of multivariate analysis

2.     Factor analysis and principal component analysis

3.     Cluster analysis techniques

4.     Discriminant analysis procedures

5.     Interpretation of multivariate analytical outputs

6.     Case Study: Multivariate analysis of organizational datasets

Module 6: Longitudinal and Repeated Measures Analysis

1.     Introduction to longitudinal data structures

2.     Repeated measures analysis techniques

3.     Mixed effects and random effects models

4.     Growth curve modeling approaches

5.     Interpretation of longitudinal analytical outputs

6.     Case Study: Evaluating program performance over time

Module 7: Structural Equation Modeling and Path Analysis

1.     Concepts and principles of structural equation modeling

2.     Measurement and structural models

3.     Confirmatory factor analysis techniques

4.     Path analysis and causal modeling

5.     Model fit assessment and validation

6.     Case Study: Modeling complex relationships among variables

Module 8: Survival Analysis Techniques

1.     Introduction to survival and event history analysis

2.     Kaplan-Meier survival estimation methods

3.     Cox proportional hazards models

4.     Hazard ratios and risk analysis techniques

5.     Interpretation of survival analysis outputs

6.     Case Study: Survival analysis applications in public health and development

Module 9: Time Series Analysis and Forecasting

1.     Principles of time series analysis

2.     Trend and seasonal decomposition techniques

3.     Autoregressive and moving average models

4.     Forecasting methods and prediction intervals

5.     Model evaluation and forecasting accuracy

6.     Case Study: Forecasting development and organizational indicators

Module 10: Predictive Modeling and Machine Learning Applications

1.     Principles of predictive analytics and machine learning

2.     Classification and regression tree techniques

3.     Ensemble modeling approaches

4.     Model training and validation procedures

5.     Performance evaluation and predictive accuracy assessment

6.     Case Study: Predictive modeling for program outcomes and decision-making

Module 11: Model Validation and Diagnostics

1.     Assessing model assumptions and robustness

2.     Cross-validation and resampling techniques

3.     Goodness-of-fit assessment methods

4.     Sensitivity and uncertainty analyses

5.     Communicating limitations and analytical findings

6.     Case Study: Model validation and performance assessment

Module 12: Reporting and Communication of Statistical Findings

1.     Developing analytical reports and presentations

2.     Visualization of complex statistical results

3.     Translating analytical findings into actionable insights

4.     Developing recommendations based on model outputs

5.     Communicating findings to technical and non-technical audiences

6.     Case Study: Preparing comprehensive statistical modeling reports

General Information

1.     Customized Training: All our courses can be tailored to meet the specific needs of participants.

2.     Language Proficiency: Participants should have a good command of the English language.

3.     Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.

4.     Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).

5.     Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.

6.     Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.

7.     Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.

8.     Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.

9.     Equipment: Tablets and laptops can be provided to participants at an additional cost.

10.  Post-Training Support: We offer one year of free consultation and coaching after the course.

11.  Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.

12.  Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.

13.  Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.

14.  Website: Visit our website at www.fdc-k.org for more information.