Technical Diploma in Research Methods and Analysis

This course offers a foundational understanding of epidemiology, the cornerstone of public health, and its application in addressing population health challenges. It is designed to equip participants with essential knowledge and skills to describe, measure, and analyze health and disease patterns in populations.

Participants will explore core epidemiological concepts, including measures of disease burden, association, and impact, and delve into various study designs and biostatistical methods. Practical components, such as public health surveillance, outbreak investigation, and data quality assessment, will provide hands-on experience in applying epidemiological principles to real-world situations.

Learning Outcomes

By the end of this module, the participant will be able to:

1. Define epidemiology and explain its importance in public health practice.
2. Use descriptive epidemiology to characterize health-related events by time, place, and person.
3. Calculate and interpret morbidity measures such as incidence and prevalence rates, and disability metrics.
4. Understand and compute mortality-based measures and composite indicators, including life expectancy metrics.
5. Explain and apply measures of association and measures of impact to assess relationships between exposures and outcomes.
6. Compare different study designs and understand their strengths, limitations, and applications in epidemiological research.
7. Demonstrate basic biostatistical methods for summarizing and analyzing health data.
8. Understand the principles and processes of public health surveillance.
9. Execute outbreak investigations and write comprehensive outbreak reports.
10. Analyze and visually present epidemiological data effectively.
11. Recognize the importance of data quality and apply methods to ensure accuracy and reliability in epidemiological studies.

Module Outline

1. Introduction to Epidemiology
2. Introduction to Disease Burden
3. Morbidity-based measures
4. Mortality-based measures
5. Composite Measures of Disease Burden
6. Understanding Life Expectancy Measures Life Expectancy at Birth and Healthy Life
7. Understanding Economic Impact Measures
8. Measures of Association and Impact
9. Overview of Epidemiologic Study Designs
10. Introduction to Biostatistics
11. Public Health Surveillance
12. Outbreak Investigation Part 1
13. Outbreak Investigation Part 2
14. Data Quality

This course provides a comprehensive foundation in research methodologies, equipping participants with the knowledge and skills to design, conduct, and communicate scientific research effectively. The curriculum covers the entire research process, from formulating research questions and hypotheses to writing and publishing manuscripts.

Participants will explore various study designs, including descriptive, analytical, and experimental approaches, and gain practical insights into selecting appropriate methods for specific research objectives. Emphasis is placed on critical elements such as literature review, sampling techniques, data collection tools, and basic statistical analysis.

The course also focuses on developing strong scientific writing skills, enabling participants to produce high-quality manuscripts suitable for publication in peer-reviewed journals. Ethical considerations and critical appraisal of research are integrated throughout, ensuring participants are well-prepared to conduct rigorous and responsible research.

Whether you are new to research or looking to enhance your methodological expertise, this course offers a structured and interactive learning experience to strengthen your ability to address complex scientific questions and contribute meaningfully to your field.

Learning Outcomes

By the end of this module, the participant will be able to:

1. Explain the fundamentals of research methodologies and scientific methods.
2. Develop research questions, hypotheses, and study designs suitable for specific objectives.
3. Differentiate between various study designs and their appropriate applications in research.
4. Define study populations, develop sampling strategies, and use effective data collection tools.
5. Perform basic data analysis and present results in a clear and meaningful way.
6. Compose well-structured research manuscripts and communicate findings effectively in academic formats.
7. Identify and address ethical issues in research and critically evaluate the quality and validity of studies.

Module Outline

1. Basics of Research and Scientific Methods
2. Types and Applications of Research
3. Research Plan
4. Literature Review
5. Research Question and Hypothesis
6. Descriptive Studies, Case Reports, and Case Series
7. Ecological Studies
8. Cross-sectional Studies
9. Cohort Studies
10. Case-Control Study
11. Clinical Trials
12. Quasi-Experimental Designs
13. Basics of Sampling Theory, Methods, and Application
14. Sample Size Calculations
15. Study Variables, and Data Collection Tools and Techniques
16. Questionnaire Design
17. Data Analysis and Presentation
18. Ethical Issues in Public Health Research - I
19. Ethical Issues in Public Health Research - II
20. Research Process and Argument Matrix
21. Writing Title Page, Abstract, and Introduction
22. Writing The Research Methods
23. Writing the Research Results
24. Writing Discussion, Conclusions, and References
25. Good Manuscript Writing

This course addresses the methodological issues crucial to the wide range of epidemiologic applications in public health and medicine. It covers a broad range of concepts and methods, including modern study designs, epidemiologic measures of association and impact, causal inference, methods of handling confounding, methods of identifying effect modification, measurement error and information bias, and validity and reliability. The main objective of the course is to enhance a student’s ability to design and conduct unbiased and efficient health research studies.

Learning Outcomes

By the end of this module, the participant will be able to:

1. Understand the strengths, limitations, and principles of different modern study designs.
2. Identify and interpret effect modification.
3. Identify potential sources of selection and information bias and understand how to control bias by appropriate study design.
4. Identify potential sources of confounding and understand how to address confounding in the design and analysis of epidemiological studies.
5. Explain commonly used considerations for causal inference and models of causality.
6. Understand the concepts of validity and precision, random and systematic measurement error, differential and non-differential misclassification, and the use of validation and reproducibility studies for epidemiological research.

Module Outline

1. Stratified Analysis
2. Epidemiologic Bias and Causation
3. Error and Bias
4. Interaction
5. Matching
6. Concept of Confounding
7. Implementation Research

This course provides a comprehensive introduction to biostatistics, emphasizing its application in analyzing and interpreting health data. It equips participants with the statistical tools and techniques necessary to conduct robust data analysis in public health, clinical research, and other health-related fields.

Participants will learn foundational concepts in descriptive and inferential statistics, hypothesis testing, and various statistical methods. The course introduces software-based analysis using SPSS, covering tests for comparison (e.g., t-tests, ANOVA, chi-square) and regression techniques.

By integrating theoretical knowledge with practical applications, this course prepares participants to critically analyze data, draw meaningful conclusions, and communicate findings effectively. It is ideal for public health professionals, researchers, and students aiming to enhance their statistical skills.

Learning Outcomes

By the end of this module, the participant will be able to:

1. Define biostatistics and its role in health sciences research and decision-making.
2. Differentiate between descriptive and inferential statistics.
3. Use descriptive statistics to summarize and present health data effectively.
4. Construct interval estimates and test hypotheses for various research scenarios.
5. Perform statistical tests such as t-tests, chi-square tests, and non-parametric tests to compare groups.
6. Conduct analysis of variance (ANOVA) and repeated measures analysis to evaluate multiple factors.
7. Use simple and multiple linear regression to identify and quantify relationships between variables.
8. Apply binary logistic regression for categorical outcome variables in health data.
9. Interpret and report statistical results in a clear and meaningful manner.
10. Assess the appropriateness of statistical methods used in health research.

Module Outline

1. Review of Descriptive Statistics
2. Interval Estimation and Hypothesis Testing
3. Independent t Test
4. The Chi-Square Test
5. ANOVA Test
6. Repeated Measures Analysis
7. Simple Linear Regression
8. Multiple Linear Regression
9. Binary Logistic Regression

In this course, participants will learn the main features of the software, how to set up a new data file in IBM SPSS ready for analysis, as well as techniques for data management and advanced statistical procedures available in SPSS. The course provides intensive training in the latest version of the Statistical Package for the Social Sciences (SPSS), now known as IBM SPSS Statistics.

Training combines lectures and hands-on sessions involving analysis of real datasets. Participants should have basic knowledge of statistics and computer use; prior SPSS experience is not required.

Learning Outcomes

By the end of this module, the participant will be able to:

1. Understand the main features of the software.
2. Manage data efficiently in SPSS.
3. Apply SPSS statistical techniques to summarize and describe data.
4. Apply advanced SPSS statistical procedures to analyze data.
5. Conduct independent statistical analysis based on study design and data type.
6. Interpret results and present findings clearly.

Module Outline

1. Intro to SPSS
2. Descriptive
3. Transform
4. Missing values
5. Chi-square
6. t-test
7. One - way ANOVA
8. Two-way ANOVA
9. Repeated Measures ANOVA
10. Linear Regression
11. Binary logistic regression
12. Multinomial Logistic Regression
13. Factor Analysis
14. ROC
15. A. Non - Parametric 2 groups
16. B. Non - Parametric 3 groups
17. Kaplan Meier