Research Methodology: Complete Research Project Blueprint

After completing the course, you will…

• FORMULATE an intriguing but feasible research question.
• DESIGN a criticism-proof study that minimizes alternative interpretations of your results.
• MEASURE using the most suitable techniques to maximize reliability and validity.
• COLLECT DATA while minimizing bias and using the right sample size.
• ANALYZE your data correctly using free and easy-to-use software even if you have zero knowledge of statistics.
• DRAW compelling conclusions that you can feel confident about.

So what will you learn exactly?

• The Three Elements of an Intriguing Research Question.
• How to Formulate an Intriguing Research Question.
• A Simple Template For Your Research Question.
• How to Define The Variables in Your Research Question.
• The Four Elements of Strong Methodology.
• Which Methodology to Use? Qualitative vs Quantitative.
• How Your Research Design Affects the Interpretation of Your Findings.
• Inferring Causation – How to Avoid This Common Mistake!
• The Most Effective Way to Rule out Alternative Explanations.
• Types of Research Designs.
• How to Avoid Unnecessary Complexity in Your Research Design.
• Why You Need to Randomize (And How).
• Experimental And Non-experimental Designs.
• Confounders in Research Designs.
• How to Design Successful Non-Experimental Studies.
• How to Make Your Variables Measurable.
• The Two Elements of an Effective Measurement.
• A Simple Trick To Boost The Reliability of Your Measurements.
• Types of Measurements – Which One is Right for You?
• What to Do If Your Variables Cannot Be Measured Accurately (Simple Hack).
• The Three Elements of Good Data Collection.
• Data Collection Strategies And When To Use Which.
• Sample size – How Many Participants Do You Need?
• The Three Steps of Data Analysis.
• Master Free And Easy-to-use Software for Data Analysis (JASP).
• Data Preparation STEP 1: Import and Format Your Data.
• Data Preparation STEP 2: Deal With Missing Values.
• Data Preparation STEP 3: Handle Outliers (Tricky).
• How to Analyze Data with a Numerical IV and Numerical DV (Step-by-step).
• How to Analyze Data with a Categorical IV and Numerical DV (Step-by-step).
• How to Analyze Data with a Numerical IV and Categorical DV (Step-by-step).
• How to Analyze Data with a Categorical IV and Categorical DV (Step-by-step).
• How to Generalize to a Population.
• The Three Principles Behind Every Statistical Test.
• The One Number You Need to Generalize Your Findings.
• How to Use Inferential Statistics (Step-by-step).
• Generalizing a Correlation.
• Generalizing a Difference Between Groups (ANOVA).
• Generalizing a Difference Between Moments in Time (RM ANOVA).
• Generalizing in a Mixed Design (RM ANOVA).
• Generalizing a Continuous Effect on Probabilities (Logistic Regression).
• Generalizing a Difference Between Probabilities (Chi Square Test).
• Avoid Mistakes! Two Common Mistakes and How to Prevent Them.
• The Two Types of Statistical Errors – And How to Minimize Them.
• How to Select The Right Sample Size (Power Analysis).
• Master Free and Easy-to-use Software For Sample Size Calculation (G*Power).
• Sample Size Calculation For Correlations.
• Sample Size Calculation For ANOVAs.
• Sample Size Calculation For Logistic Regression.
• Sample Size Calculation For Chi Square Tests.
• Three Simple Strategies to Maximize Your Statistical Power.
• How to Test the Reliability of Your Measurements.
• How to Test the Validity of Your Measurements.
• Conducting Your Research: Essential Parts That You Do Not Want to Miss in Any Research Study.
• Recruiting: How To Find Participants.
• Incentives: How To Motivate People to Participate.
• The Four Steps of Interpreting Findings.
• How to Interpret Results in an Experimental Design.
• How to Interpret Results in a Quasi-Experiment.
• How to Interpret Results in a Non-Experimental Design (Categorical IV).
• How to Interpret Results in a Non-Experimental Design (Numerical IV).
• How to Interpret Results in a Non-Experimental Design (Categorical IV and DV).
• How to Interpret a Null-result.
• How to Fix Methodological Problems Even After Your Study is Already Conducted.
• And more!

Who this course is for:

• Everybody who wants to master scientific research.
• Bachelor, Master, and PhD students (e.g., in Social Science, Medical Science, Business, or related fields).
• Companies that need to run tests or answer business questions using data-driven approaches.

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