Course Overview
Data analysis is the application of one or more statistical techniques to a set of data as collected. In designed experiments, some form of treatment is applied to experimental units and responses are observed. This course is designed to transform participants into professional data analysts. It is designed for participants without or with very little experience using statistical software. Some basic knowledge on statistics is required. During the course, the instructors will interchangeably use Stata, Excel, SAS and SPSS to demonstrate relevant techniques in each topic.
Duration
10 Days
Target Audience
- Researchers
- Data Analysts and Statisticians
- Program and Project Managers
- Aspiring Researchers
Organizational Impact
- Improved quality and reliability of research outcomes leading to better decision-making.
- Enhanced research capabilities, leading to innovative solutions and product development.
- Increased ability to meet regulatory and ethical standards in experimental research.
- Strengthened capacity to conduct and analyze experimental studies efficiently, leading to cost savings.
- Enhanced reputation through the production of high-quality, impactful research.
Personal Impact
- Mastery of advanced research design and data analysis techniques.
- Increased confidence in conducting and analyzing experimental studies.
- Enhanced ability to critically evaluate the validity and reliability of research findings.
- Improved problem-solving skills through the application of statistical methods to experimental data.
- Greater career opportunities in research, academia, and industry.
Course Level:
Course Objectives
- Understand Experimental Design and Variable Classification
- Review Probability and Statistical Distributions
- Conduct Exploratory Data Analysis (EDA)
- Utilize Data Analysis Software
- Perform Hypothesis Testing and ANOVA
- Apply Regression Analysis Techniques
- Implement Advanced ANOVA Techniques
- Calculate and Improve Statistical Power
- Explore Contrasts and Mixed Models
- Analyze Categorical Data
Course Outline
Module 1: Introduction to Statistical Analysis
- The importance of careful experimental design
- Variable Classification
- Overview of statistical analysis
- Parametric Versus Nonparametric Analyses
- Case Study: Designing an Experiment to Test a New Drug
Module 2: Probability and Distributions
- Review of Probability
- Common Distributions (Binomial, Poisson, Gaussian, etc.)
- Parameters describing distributions (Central Tendency, Spread, etc.)
- Central Limit Theorem
- Case Study: Analyzing the Probability of Success in a Marketing Campaign
Module 3: Exploratory Data Analysis (EDA)
- Univariate Non-Graphical and Graphical EDA
- Multivariate Non-Graphical and Graphical EDA
- Covariance and Correlation
- Cross-Tabulation
- Case Study: Exploring a Dataset from a Customer Satisfaction Survey
Module 4: Software Tools for Data Analysis
- Overview of SPSS, Stata, Excel, and SAS
- Data Entry and Import
- Creating and Recoding Variables
- Graphical and Non-Graphical EDA in Software
- Case Study: Performing Data Analysis Using SPSS for a Retail Dataset
Module 5: Hypothesis Testing
- t-Test (Independent and Paired)
- One-Way ANOVA
- Assumption Checking and Results Interpretation
- Threats to Experiment Validity
- Case Study: Comparing Customer Retention Rates Between Two Different Marketing Strategies
Module 6: Regression Analysis
- Simple Linear Regression
- Multiple Regression and Interaction
- Analysis of Covariance (ANCOVA)
- Regression Calculations and Interpretation
- Case Study: Analyzing the Impact of Advertising Spend on Sales
Module 7: Advanced ANOVA Techniques
- Two-Way ANOVA
- Interpreting Two-Way ANOVA Results
- Application Areas and Practical Examples
- ANCOVA with Interaction
- Case Study: Evaluating the Effect of Different Training Programs on Employee Performance
Module 8: Statistical Power and Effect Size
- Understanding Statistical Power
- Improving Power and Calculating Effect Sizes
- Power Calculations for ANOVA and Regression
- Practical Considerations for Power Analysis
- Case Study: Planning a Study to Determine the Sample Size Needed for Reliable Results
Module 9: Contrasts and Mixed Models
- Contrasts and Custom Hypotheses
- Within-Subjects Designs and Paired t-Test
- Mixed Models and Their Applications
- Model Selection and Penalized Likelihood Methods
- Case Study: Analyzing a Clinical Trial with Repeated Measures
Module 10: Categorical Data Analysis
- Contingency Tables and Chi-Square Analysis
- Logistic Regression
- Testing Independence and Goodness-of-Fit
- Predictions in Logistic Regression Models
- Case Study: Predicting Customer Churn Using Logistic Regression
Related Courses
Course Administration Details:
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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