• Identify the challenges of various health settings. 
• Assess the appropriateness of existing disease control strategies. 
• Develop new interventions and strategies by working with communities and stakeholders. 
• Specify IR questions. 
• Design rigorous research projects. This includes how to identify IR outcomes, evaluate effectiveness, and make plans to scale-up implementation in real-life settings. 

• Submission of protocol and the ethical review form within three weeks of completing module 1
• Submission of the data documentation sheets within two weeks of completing module 2
• Submission of proof of study completion and data collection about six weeks before module 3
• Submission of a paper to a peer-review journal within four weeks of end of module 3

Writing a research protocol

Flexible schedule

Offered in two parts-each part taking 30-40 min to compete.

Free

Shareable certification on scoring at least 80% in the final quiz section

This course is offered on the Global Health Training Center in collaboration with REDe (Research Capacity Network), ALERRT and Global Research Nurses.  

This course is designed to guide researchers through the basic stages and concepts surrounding the creation of a protocol. Upon completion of the course, the participants will have gained an understanding of:

1. What a protocol is and why it is necessary
2. The basic concepts of designing a protocol
3. The sections that should be included in a protocol
4. Points to remember when creating a protocol
5. Common errors that should be avoided when developing a protocol

Writing a scientific manuscript

Free

Flexible schedule

Videos and online materials 

Developed by Research Capacity Network, as a week  long virtual workshop. After going through the videos, you will…

1. the basic structure of a scientific article to be published in a peer reviewed journal.
2. To develop the principal elements to be highlighted in the different components of a scientific publication.    
3. To develop the capacity to establish the "main point" or global messages from the most important data of the results.
4. To learn the essential components of the “Tree of Arguments” and define the three main messages and arguments of the results
5. To write a draft of the complete article based on the" tree of arguments” sketch
6. To write the main conclusions and recommendations derived from your data

• The basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R.
• R programming examples in a way that will help make the connection between concepts and implementation

• Calculate summary statistics from public health and biomedical data
• Interpret written and visual presentations of statistical data
• Evaluate and interpret results of various regression methods
• Choose the most appropriate statistical method to answer your research question  

• Calculate continuous data measures
• Interpret data visualizations
• Analyze binary data
• Analyze time to event data   

• Use statistical methods to analyze sampling distribution
• Estimate and interpret 95% confidence intervals for single samples
• Estimate and interpret 95% confidence intervals for two populations
• Estimate and interpret p values for hypothesis testing   

• Practice simple regression methods to determine relationships between an outcome and a predictor
• Recognize confounding in statistical analysis
• Perform estimate adjustments

• Practice multiple regression methods to determine relationships between an outcome and multiple predictors
• Use the Spline approach for non-linear relationships with continuous predictors
• Perform calculations with multiple predictor variables   

• understand general principles of research design;
• know why and how research is undertaken;
• be able to identify the overall process of designing a research study from its inception to its report;
• be able to identify a research problem stated in a study;
• be familiar with conducting a literature review for a research study;
• know the primary characteristics of quantitative research, qualitative research, and mixed methods research;
• know the steps involved in conducting research;
• be able to distinguish between the writing structure used for a quantitative study and one used for a qualitative study. 

• Compare and contrast different epidemiological study designs in order to describe their strengths and weaknesses

• How to make sense of statistical results presented in the published literature and research.
• An overview of widely used statistical analysis techniques and how to interpret their results. 

• Describe the steps in conducting a systematic review
• Develop an answerable question using the “Participants Interventions Comparisons Outcomes” (PICO) framework
• Describe the process used to collect and extract data from reports of clinical trials
• Describe methods to critically assess the risk of bias of clinical trials
• Describe and interpret the results of meta-analyses

• Different research methodologies
• Different sampling approaches
• Primary and secondary data collection approaches  

• Different research methodologies
• Different sampling approaches
• Primary and secondary data collection approaches  

• how to identify which data sources likely matches your research question,
• how to turn your research question into measurable pieces, and how to think about an analysis plan.
• to identify errors associated with different data sources. 

•  A range of survey data collection methods that are both interview-based (face-to-face and telephone) and self-administered (paper questionnaires that are mailed and those that are implemented online, i.e. as web surveys).
• Mixed mode designs as well as several hybrid modes for collecting sensitive information e.g., self-administering the sensitive questions in what is otherwise a face-to-face interview.    

• the basic elements of designing and evaluating questionnaires.
• practical techniques for evaluating questions, mode specific questionnaire characteristics,
• review methods of standardized and conversational interviewing.

• simple random sampling that can be used for sampling persons or records,
• cluster sampling that can be used to sample groups of persons or records or networks,
• stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. 
• how to estimate and summarize the uncertainty of randomized sampling.

• the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration.
• adjustments using estimated response propensities, poststratification, raking, and general regression estimation.
• Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation,
• the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®