Cohort_2_Chapters_0_1_2

01_designing-research.Rmd

@@ -7,55 +7,53 @@
 - Explain why research needs a design
 - Explain importance of a good research question
 
+## Main Goal {-}
 
-## Research Questions {-}
+**How can you do research in such a way that, when you’re done, you have an answer to your research question?**
+
+## I Have a Question
 
-- Section 1.1, p.3-5
 - A question that you plan to answer
 - Contributes to improved understanding "how the world works"
 
-
 1. Well-Defined
 2. Answerable
 3. Understandable
 
 
-## Empirical Research {-}
-- Section 1.2 p.4-5
+## Empirical Research
+
 - Any research that uses structured observations from the real world to attempt to answer questions.
-- Quantitative empirical research uses quantitative measurements (numbers, usually). 
+- Quantitative **empirical research** uses quantitative measurements (numbers, usually). 
 - Observations rather than reason
-- More data sets, fewer interviews.
+- *More data sets, fewer interviews*
 - Measurements hard to to collect precisely or interpret accurately
 - Numbers we have don’t actually answer the research question we have, what can we do?
-- Goal is to collect the right data to answer the question and design reasearch to do this
+- Goal is to collect the right data to answer the question and design research to do this
 
 
-## Why Research needs a design {-}
+## Why Research Needs a Design
 
-- Section 1.3 p.5-7 , 
-- Capable of answering the question it's trying to answer
-- Needed to evaluate first pass analysis and what to do instead
+- **Capable of answering** the question it's trying to answer
+- *Needed to evaluate first pass analysis and what to do instead*
 - Nutrition Research as example of lack of design
   1. Not having research capable to answer question
   2. Not actually answering the question
 - Leads to inconsistent results which people cannot rely on nor use
-- Could lead to too difficult a question to answer, but its still a takeway
+- Could lead to too difficult a question to answer, but its still a takeaway
 
-## Book Goals {-}
+## In This Book
 
 To Improve Confidence in crafting a research project, what kind of data needed, and calculations to perform on data
 
--First Half:
-1. Ways to build an answerable research question
-2. Think of quantitative empirical research to perform to answer question
+- First Half:
+    1. Ways to build an **answerable research question**
+    2. Think of quantitative *empirical research* to perform to answer question
 
--Second Half:
-1. "Toolbox" using observational data
-2. Tools are widely applicable to answer many questions
+- Second Half:
+    1. "Toolbox" using *observational data*
+    2. **Tools** are widely applicable to answer many questions
 
-## Discussion/Practicals {-}
-Questions, Discussions, or Examples to fill in during Book Club Meeting
 
 ## Meeting Videos {-}
 

02_research-questions.Rmd

@@ -6,73 +6,63 @@
 - Compare Data Mining and Research Questions
 - Determine if a question is good
 
-## What is a Research Question? {-}
+## What is a Research Question?
 
-- Section 2.1 p. 9-12, Section 2.3 p. 15-16
-- When answered, will improve understanding of how the world works.
-- There exists data when found results in believable answer
-- Can be Answerable with evidence: Avoid ambiguous questions such as "best"
-- It should inform theory: answer something broader than itself
+- **Research Question**
+    - *can be answered*
+    - *improve understanding* of how the world works
 
-- Theory
-  - Tells us why
-  - May be True or False - but explains why we might see outcomes
-
-- Good Question takes us from Theory to Hypothesis
-  - When answered improves ability to explain why
-  - If this is how the world works, what should I expect to observe?
-  - Gives us something new about why
-
-- Theory -> Research Question or Research Question -> Theory
-
-- Right data for right questions
-
-- Two Checks for Condition for Research Question:
-  1. Could we answer the question?
-  2. Does the question tell us about how the world works?
+- from **Theory** to **Hypothesis**
+
+<details>
+<summary>Theory</summary>
+An explanation of **why** the things we observe happen, or otherwise *generalizes* what we observe in one situation to another situation. 
+</details>
+
+<details>
+<summary>Hypothesis</summary>
+A *specific* statement about what you will observe in the data. 
+</details>
 
-- Checks if Research Question Informs Theory:
-  1. Would unexpected result change your understanding of the world?
-  2. If unexpected result doesn't change understanding, then bad question
-  3. If answered, hard to explain away if inconvenient
-
-## Data Mining vs. Research Q's {-}
-- Section 2.2 p.13 - 16
-- Data Mining is good at finding patterns and making predictions under stability
-- Not good at improving understanding nor improve theory main reason are:
-  1. Answers what's in the data , not explaining why. Correlation != Causation
-  2. Does not deal with abstraction, can see observations but not at developing theory
-  3. Results in false positives - observations found in sample but not outside of it. Random relationships eventually occur when testing everything
+- A good test for whether a research question informs theory is to imagine that you find an *unexpected result*, and then *wonder* whether it would make you change your understanding of the world
+
+## Why Start with a Question?
+
+- **data mining**: starting with the data and seeking *patterns*
+    - less useful for improving theory
+    - could create false positives
+    - doesn't generalize to abstraction
+
+- precautions
+    - try replication
+    - research design
+
+## Where Do Research Questions Come From?
+
+- theory --> hypothesis --> question
+- data --> opportunity --> question
+
+## How Do You Know if You’ve Got a Good One? 
+
+1. **Potential Results**: If you cant say something interesting from results, Question and Theory not closely linked
+2. **Feasibility**: Possible vs. realistically obtainable data
+3. **Scale**: Consider time and resource constraints
+4. **Design**: Finding a reasonable research design that can answer it
+5. **Simplicity**
+  - *Don't combine* multiple determinants into the question to answer
+  - [Multiple models](https://christophmolnar.com/books/modeling-mindsets) could lead to the same result
 
-- Can lead to Research Questions
-  - Come to data without a theory, noticed interesting data patterns
-  - Confirm it holds up in other data aka replication of data patterns 
-
-
-## Considerations for a good Research Q {-}
-- Section 2.3 p. 15-16, Section 2.4 p. 16-18
-- Sources of Questions:
-  - Curiosity
-  - Theory
-    - If this is what I expect the world to work, what would I expect to see in the world?
-  - Opportunity
-    - What questions would this data allow me to answer?
-
-- Research Questions tells us why hypothesis to test
-
-1. Potential Results
-  - If you cant say something interesting from results, Question and Theory not closely linked
-2. Feasibility
-  - Possible vs. Realistically Obtainable Data
-3. Scale
-  - Consider time, resource constraints
-4. Design
-  - Finding a reasonable research design that can answer it
-5. Simplicity
-  - Don't combine multiple determinants into the question to answer
-
-## Discussion/Practicals {-}
-Questions, Discussions, or Examples to fill in during Book Club Meeting
+## Actionable {-}
+
+"To transform vague stakeholder requests into actionable causal projects, work through these four questions together:
+
+1. What is the *endgame* of the project?
+2. What is the **core question** or hypothesis we are addressing?
+3. What *decisions* or actions will be taken based on our insights?
+4. How will we **measure** project success on our end?"
+---Robson Tigre, [Everyday Causal Inference](https://www.everydaycausal.com/planning-your-analysis.html#four-essential-clarifying-questions)
+
+
 
 ## Meeting Videos {-}
 

index.Rmd

@@ -13,7 +13,7 @@ description: "This website is the product of the Data Science Learning Community
 
 # Introduction {-}
 
-This is a companion for the book[_The Effect: An Introduction to Research Design and Causality_](https://theeffectbook.net/index.html) by Nick Huntington-Klein (copyright January 5, 2022).
+This is a companion for the book[_The Effect: An Introduction to Research Design and Causality (Second Edition)_](https://theeffectbook.net/index.html) by Nick Huntington-Klein (published July 11, 2025).
 
 -  These are **shared slides!**
    -  Developed by the [Data Science Learning Community](https://dslc.io).
@@ -39,29 +39,50 @@ This is a companion for the book[_The Effect: An Introduction to Research Design
    -  We'll discuss even if presenter unavailable
 -  Meetings = **1 hour.**
 -  **Goal:** 1 chapter/week
+
+
+<!--
 -  Ok to split overwhelming chapters
 -  Ok to combine short chapters
 -  If we need to **slow down** and discuss, **let me know.**
-   -  Most likely someone has the same question 
+   -  Most likely someone has the same question
    -  We are all here to learn 
+-->
 
-## Introductions {-}
+## Icebreaker {-}
 
-If you feel comfortable sharing, unmute or raise your hand!
+If you feel comfortable sharing, in the Zoom chat:
 
--  **Who** are you?
--  **Where** are you joining from?
--  **Previous clubs?** (DSLC or other)
--  **How long** have you been using R?
--  **What** are you most looking forward to learning?
+-  *Who* are you?
+-  *Where* are you joining from?
+-  *Previous clubs?* (DSLC or other)
+-  *How long* have you been using R and/or Python?
+-  *What* are you most looking forward to learning?
 
 ## git and GitHub {-}
 
--  **Git/GitHub** (version control) = **valuable skill.**
-   -  Discussed more in **Chapter 21.**
--  If you present, best to **integrate your materials** into this bookdown version.
+-  **Git/GitHub** (version control) = *valuable skill.*
+-  If you present, best to *integrate your materials* into this bookdown version.
 -  You'll need a **GitHub account** to submit modifications.
--  **Full instructions** in [GitHub repository](https://github.com/r4ds/bookclub-rpkgs) ("repo").
+-  Full instructions in [GitHub repository](https://github.com/r4ds/bookclub-rpkgs) ("repo").
+
+## Book Design {-}
+
+- Part 1: The Design of Research
+    - emphasis on **research design**
+- Part 2: Toolbox
+    - **causal diagram** framework
+- Audiences: data scientists, business analysts, researchers, non-researchers
+
+## Levels of Understanding {-}
+
+1. Attend book club meetings
+2. Watch the author's [video series](https://nickchk.com/theeffectvideos.html)
+3. Present a few meetings
+4. Work through the [reading homework](https://github.com/NickCH-K/TheEffectAssignments)
+5. Work through the [coding homework](https://github.com/NickCH-K/TheEffectAssignments)
+
+<!--
 
 ## Group Question 1 {-}
 
@@ -117,3 +138,5 @@ to
 
 -  INSERT BOOK-SPECIFIC CONTENT STARTING HERE
 -  AND THEN ADD SLIDES FOR CHAPTER 0 IF APPLICABLE
+
+-->