Research Methodology: Complete Research Project Blueprint

Research Methodology: Complete Research Project Blueprint, available at $89.99, has an average rating of 4.37, with 113 lectures, 8 quizzes, based on 1165 reviews, and has 8631 subscribers.

You will learn about 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. This course is ideal for individuals who are Bachelor, Master, and PhD students that do research with human participants (e.g., social science, medical Science, business, or related fields). or Companies that need to run tests or answer business questions using data-driven approaches. or Perfect for people who want to do research on human behavior. It is particularly useful for Bachelor, Master, and PhD students that do research with human participants (e.g., social science, medical Science, business, or related fields). or Companies that need to run tests or answer business questions using data-driven approaches. or Perfect for people who want to do research on human behavior.

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Summary

Title: Research Methodology: Complete Research Project Blueprint

Price: $89.99

Average Rating: 4.37

Number of Lectures: 113

Number of Quizzes: 8

Number of Published Lectures: 113

Number of Published Quizzes: 8

Number of Curriculum Items: 122

Number of Published Curriculum Objects: 122

Original Price: €219.99

Quality Status: approved

Status: Live

What You Will Learn

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.

Who Should Attend

Bachelor, Master, and PhD students that do research with human participants (e.g., social science, medical Science, business, or related fields).
Companies that need to run tests or answer business questions using data-driven approaches.
Perfect for people who want to do research on human behavior.

Target Audiences

Bachelor, Master, and PhD students that do research with human participants (e.g., social science, medical Science, business, or related fields).
Companies that need to run tests or answer business questions using data-driven approaches.
Perfect for people who want to do research on human behavior.

There is a lot to think about as a researcher.

What design? How to Measure? How to Analyze?

This course will guide you through your entire research project: from formulating an intriguing research question all the way to drawing compelling conclusions.

What will you be able to DO after this course?

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 and that you can defend against criticism.

What TOOLS will you receive?

FULL RESEARCH CHECKLIST to ensure that your research is complete and criticism-proof.
CONFOUNDER CHECKLIST to address all holes in your research design.
DATA ANALYSIS DECISION CHART to easily select the correct data analysis technique.
Links to free and easy-to-use SOFTWARE for data analysis and sample size calculation.
QUIZZES to solidify and deepen your understanding of each section.

In short, you will get everything you need to complete your research and make it successful.

Many people feel lost when they think about research methodology for the first time.

I was no exception: I felt like I was blindly stumbling through a forest during my first project.

However, I have learned that research does not need to be complicated.

In fact, research can be very simple if you know what steps to follow and how to avoid unnecessary complexity.

Even high-impact research often uses simple methods that can be mastered by anyone.

So, in this course, I want to give you a complete blueprint that guides you through your entire research project.

You will learn what to do, how to do it, and how to keep your research simple yet effective.

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!

In short, you will learn specifically why, what, when, where, and how to perform research.

Let me add that I want you to be truly happy with this course.

I used to be in your shoes and I want to give you the best possible course to help you succeed.

If this course does not deliver on every promise (and then some) you can get a full refund.

So go ahead and give this course a test drive.

You can take the whole course, enjoy all of its benefits, and still get your money back if you are not satisfied.

After you click on the red button, the lectures will guide you through the research process.

You will also receive tools such as checklists, decision charts, and free software to make your work easier.

In addition, you can ask me questions anytime and can have my full support on every step of the way.

Course Curriculum

Chapter 1: The Building Blocks of a Successful Research Project

Lecture 1: What You Will Learn In This Course

Lecture 2: The First Step

Lecture 3: How to Make Your Research Project Successful

Lecture 4: The Three Elements of an Intriguing Research Question

Lecture 5: How to Formulate an Intriguing Research Question

Lecture 6: The Four Elements of Compelling Research Methodology

Lecture 7: Which Methodology to Use? Qualitative vs Quantitative

Chapter 2: Formulate a Clear and Intriguing Research Question

Lecture 1: A Simple Template For Your Research Question

Lecture 2: Define The Variables in Your Research Question

Lecture 3: PRACTICE: Specifying Your Research Question and Variables

Chapter 3: Create a Solid Research Design

Lecture 1: How Your Research Design Affects the Interpretation of Your Findings

Lecture 2: Inferring Causation – Avoid This Common Mistake!

Lecture 3: Sneaky mistakes that happen when inferring causation

Lecture 4: The Most Effective Way to Rule out Alternative Explanations

Lecture 5: Types of Research Designs

Lecture 6: How to Avoid Unnecessary Complexity in Your Research Design

Lecture 7: Why You Need to Randomize And How to Do It Correctly

Lecture 8: Experimental and Non-Experimental Designs – Which One Should You Choose?

Lecture 9: PRACTICE: Experimental And Non-experimental Designs

Lecture 10: Practice Materials: Experimental And Non-experimental Designs

Lecture 11: Fill the Holes: Confounders in Research Designs

Lecture 12: Typical Confounders – Check These to Ensure That Your Design is Criticism-proof

Lecture 13: Download the CONFOUNDER CHECKLIST

Lecture 14: How to Design Successful NON-Experimental Studies

Lecture 15: Putting it together – How to Choose The Right Design?

Lecture 16: SUMMARY: Everything You Need to Know about Research Designs

Chapter 4: Measure Your Variables Accurately

Lecture 1: How to Make Your Variables Measurable

Lecture 2: The Two Aspects of an Effective Measurement

Lecture 3: A Simple Trick To Boost The Reliability of Your Measurements

Lecture 4: Self-report: What is it and How Should you Use it?

Lecture 5: Other Measurement Types And Their Advantages/ Distadvantages

Lecture 6: Which Measurement Type Should you Pick?

Lecture 7: What to Do If Your Variables Cannot Be Measured Accurately (Simple Hack)

Lecture 8: SUMMARY: Everything You Need to Know About Measuring

Chapter 5: Draw a Representative Sample

Lecture 1: The Three Elements of Good Data Collection

Lecture 2: Data Collection Strategies – And When To Use Them!

Lecture 3: Sample size – How Many Participants Do You Need?

Chapter 6: Data Analysis 1: Which Patterns Exist in Your Sample?

Lecture 1: The Three Steps in Data Analysis

Lecture 2: A Link to Free And Easy-to-use Software for Data Analysis

Lecture 3: Download The Practice Data Sets For This Course

Lecture 4: How to Spot Relationships Between Numerical Variables

Lecture 5: How to Spot Relationships Between Numerical Variables (Part 2)

Lecture 6: TUTORIAL: Spot Relationships Between Numerical Variables (Correlations)

Lecture 7: How to Spot Relationships Between a Categorical IV and Numerical DV (Between)

Lecture 8: TUTORIAL: Spot Relationships Between a Categorical IV and Numerical DV (Between)

Lecture 9: How to Spot Relationships Between a Categorical IV and Numerical DV (Within)

Lecture 10: TUTORIAL: Spot Relationships Between a Categorical IV and Numerical DV (Within)

Lecture 11: How to Spot Relationships Between a Categorical IV and Numerical DV (Mixed)

Lecture 12: TUTORIAL: Spot Relationships Between a Categorical IV and Numerical DV (Mixed)

Lecture 13: How to Spot Relationships Between a Numerical IV and Categorical DV

Lecture 14: TUTORIAL: Spot Relationships Between a Numerical IV and Categorical DV

Lecture 15: How to Spot Relationships Between a Categorical IV and Categorical DV

Lecture 16: TUTORIAL: Spot Relationships Between a Categorical IV and Categorical DV

Lecture 17: Data Preparation STEP 1: Import and Format Your Data

Lecture 18: TUTORIAL: Importing and Formatting for Between-subject Designs

Lecture 19: A Link to Free Software for Data Preparation

Lecture 20: TUTORIAL: Importing and Formatting for Within-subject Designs

Lecture 21: Data Preparation STEP 2: How to Deal With Missing Values

Lecture 22: TUTORIAL: How to Deal With Missing Values

Lecture 23: Data Preparation STEP 3: Handle Outliers (Tricky!)

Lecture 24: TUTORIAL: How to Handle Outliers

Lecture 25: SUMMARY: Everything You Need to Know About Finding Patterns in Your Data

Chapter 7: Data Analysis 2: Which Patterns Can You Generalize to The Population?

Lecture 1: Generalizing From Sample to Population

Lecture 2: The Two Principles Behind Generalizing From Single Variables (Principle 1)

Lecture 3: The Two Principles Behind Generalizing From Single Variables (Principle 2)

Lecture 4: The Three Principles Behind Generalizing Relationships Between Variables

Lecture 5: The One Number You Need to Generalize Your Findings

Lecture 6: How to Use Inferential Statistics (Step-by-step)

Lecture 7: Download The Decision Chart For Statistical Tests

Lecture 8: Generalizing a Correlation

Lecture 9: TUTORIAL: Generalizing a Correlation

Lecture 10: Generalizing a Mean Difference Between Groups (ANOVA)

Lecture 11: TUTORIAL: Generalizing a Mean Difference Between Groups (ANOVA)

Lecture 12: Generalizing a Mean Difference Between Moments in Time (RM ANOVA)

Lecture 13: TUTORIAL: Generalizing a Mean Difference Between Moments in Time (RM ANOVA)

Lecture 14: Generalizing From a Mixed Design (RM ANOVA)

Lecture 15: TUTORIAL: Generalizing From a Mixed Design (RM ANOVA)

Lecture 16: Generalizing a Continuous Effect on Probabilities (Logistic Regression)

Lecture 17: TUTORIAL Generalizing a Continuous Effect on Probabilities (Logistic Regression)

Lecture 18: Generalizing a Difference Between Probabilities (Chi Square Test)

Lecture 19: TUTORIAL: Generalizing a Difference Between Probabilities (Chi Square Test)

Lecture 20: Avoid Mistakes! Two Common Mistakes and How to Prevent Them

Lecture 21: SUMMARY: Everything You Need to Know about Generalizing to the Population

Chapter 8: Select your Sample Size And Maximize Statistical Power

Lecture 1: The Two Types of Statistical Errors

Lecture 2: Two Ways to Minimize Statistical Errors

Lecture 3: How to Determine The Right Sample Size (Power Analysis)

Instructors

André Klapper, PhD
Researcher, Neuroscientist, Psychologist

Rating Distribution

1 stars: 10 votes
2 stars: 14 votes
3 stars: 82 votes
4 stars: 357 votes
5 stars: 702 votes

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