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Andrew Spiegelman, Instructor - Quantitative Research Design

Andrew Spiegelman

Andrew is a statistician with 10 years of related work experience in research, technology, and health care. His experience includes building forecasting models such as predicting no-show patients and predicting hospital quality scores two quarters into the future. He has co-authored peer-reviewed academic medical research studies., and he specializes in communicating data science and statistical concepts to diverse audiences. He has tremendous expertise in training and mentorship in the healthcare domain.

Learn how to make scientific inferences about large populations

  • 5 hours and 20 minutes of self-paced video
  • Hands-on assignments
  • Instructor - Statistician with 10 years of related work experience in research, technology and healthcare

Duration: 4h 36m

Course Description

Quantitative “large N” research is currently the best way to make scientific inferences about large populations (people, animals, neighborhoods, etc.). Unlike qualitative “small N” research, which aims to intensely investigate the workings of a phenomenon in several cases, quantitative “large N” studies investigate phenomena among dozens, hundreds, and preferably thousands of cases, albeit less thoroughly per case than “small N” studies. Furthermore, quantitative studies rely on mathematical methods—like statistical analysis—rather than humanistic methods like interviewing (although quantitative studies might still use interviews). While “small N” studies are better able to articulate complex causal mechanisms, they are less able to make broad inferences about large populations—something one often wants to do in science. Although many people believe statistical analysis to be the most daunting and opaque part of quantitative research, in reality the greatest bulk of total study time is spent setting up the study, gathering data, and making sure data is analysis-ready. This course aims to teach the basics of how to structure and, to some extent, conduct a quantitative “large N” research study. It is designed for diverse audiences; graduate degrees are not necessary nor are degrees in particular fields. After completing the course, students should be able to lead simple research studies on their own and to assist in leading complex studies. The framework presented here can be applied to any question one wants to answer with data; it need not be an academic study intended for peer-reviewed journal publication. Studies can be conducted in businesses, schools, non-profit organizations, or anywhere else where one is dealing with mathematically measurable concepts. Perhaps even more important than being able to conduct a study, this course can help students learn how to think about everyday problems more scientifically.

What am I going to get from this course?

  • Understand the basics of how to structure and, to some extent, conduct a quantitative "Large N" research study
  • Lead simple research studies on their own, and assist in leading complex studies

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Basic understanding of Statistics will be useful, but it's not required

Who should take this course? Who should not?

  • This course is designed for a diverse audience, degrees in a particular field are not necessary


Module 1: Synopsis, Audience, and Objectives

Lecture 1 Synopsis, Audience, and Objectives

This lecture gives an overview of what will be covered throughout the course, the background of the instructor, the target audience, and the learning objectives.

Module 2: Choosing Partners

Lecture 2 Roles

This lecture will go over what types of different skill sets are needed in the process of conducting a large scale study.

Lecture 3 Examples

Examples about how to choose partners in various contexts such as academic, business, and non-profit

Lecture 4 Examples Continued

More exercises and an answer sheet to help the audience grasp the concepts better

Module 3: Generating Hypotheses

Lecture 5 Generating Hypotheses

This first lecture of the module explains the main points to consider when generating a hypothesis

Lecture 6 Exercises

This lecture provides multiple exercises to make sure you have a solid understanding of what a complete hypothesis looks like

Module 4: Sample Size

Lecture 7 Definition and Importance

An explanation of what sample size is, and why it's important

Lecture 8 Further Analysis and Examples

This lecture will go over some of the main methods used to determine the ideal sample size, and provides the audience with extensive exercises

Module 5: Sample Selection

Lecture 9 Methods of Sample Selection

This lecture will go over Randomized Control Trials, Non-Randomized Control Trials, Observational Studies, and Surveys. It will also provide examples for each of these topics.

Lecture 10 Exercises

Continuing from part 1, this lecture will give a thorough explanation of the Classroom Studies method and the Experiments method. After presenting these, extensive exercises will be provided to further the understanding of the student.

Module 6: Selection Bias

Lecture 11 Types of Selection Bias

This lecture will go over various kinds of selection bias ranging from Non-Random Sampling to Observer Selection. Example situations for each of these different types of selection biases will be provided.

Lecture 12 How to Recognize and Eliminate Selection Bias

This lecture will give you some tips and tricks to avoid selection bias. Extensive exercises will be provided at the end of the lecture.

Module 7: Preliminary Data Work

Lecture 13 Examining the Data Before the Analysis

This lecture is about eyeballing the data and making sure everything is in place before starting the experiment/analysis.

Lecture 14 Examples and Exercises

This lecture will provide further explanation of different methods of data cleaning, and conclude with various types of exercises and examples

Module 8: Hypothesis Testing

Lecture 15 Part 1

Now that you have collected the data and cleaned it, it's time to test your hypothesis. This lecture will go over some of the common methods used in hypothesis testing.

Lecture 16 Part 2

This lecture will continue from where Part 1 left off, and will provide you with multiple exercises so you can assess your level of understanding

Module 9: Final Topics

Lecture 17 Multiple Comparisons, Statistical vs. Practical Significance

This lecture will go over the problem of multiple comparisons, and why there's a difference between being statistically significant and practically significant.

Lecture 18 Positivity Bias, Reporting Results, Graphing Rules

This lecture will go over what Positivity Bias is and how it effects our daily lives, how to report the results of a study, and the basic rules of graphing the results.


7 Reviews

Gaby C

May, 2017

Quantitative research design is and interesting and informative course. I recommend it to everyone as we can learn to make scientific inferences. The course nicely explained about setting up the study, gathering data, and making it analysis-ready. It is a worth to participate in this course, as it helped my readiness to lead simple research studies on my own. It equipped me with knowledge to answer any question with data even in our day-to-day dealings dealing with mathematically measurable concepts. It helped me to learn how to think about everyday problems more scientifically.

Allen S

May, 2017

It greatly helped me in scientifically understanding the importance statistics in quantitative analysis. The way of teaching by the instructor was pretty impressive. He never lost a minute in unnecessary matters

Clinton S

May, 2017

An excellent course that teaches the basics of how to structure and conduct a quantitative research study. The instructor was good-spirited. After pursuing this course, I have become confident to lead simple research studies on my own.

Ayush K

July, 2017

It is a useful comprehensive foundation to quantitative research design. I felt that the videos and supplemental information provided valuable knowledge. This was a great class. It gave me the power to deal with any inquiry with data on daily work dealing with mathematically quantitative concepts.

Nikolai S

July, 2017

I have learned a lot from this class. Thanks to the instructor. It indeed assisted me in carefully being conscious of the important statistics in quantitative analysis.

Ritu K

July, 2017

It’s great that you have started this course with seeing the business purpose and partners for evaluating the data. I recommend this course from which one can learn scientific inferences. It was very well taught, especially about gathering data, and making analysis-ready. It encouraged my readiness to go along with basic analytic investigations.

Sampath S

July, 2017

It helped me to understand common issues more closely. Great, detailed, eye-opening class. The instructor performed very well. The methods addressed by the instructor were so helpful. He never spent time on irrelevant things.