Dev2024 michael

DESCRIPTION

@@ -1,20 +1,24 @@
-Title: Sample App - A Lengthy Title
-ShortName: Sample App
-Date: 2021-05-13
+Title: Two Period Differences-in-Differences Regression
+ShortName: Two Period DID
+Date: 2024-10-1
 Lifecycle: experimental
 Authors@R: c(
-    person(given = "Neil", family = "Hatfield", email = "neil.hatfield@psu.edu", role = c("aut", "cre")),
-    person(given = "Robert", family = "Carey", email = "rpc5102@psu.edu", role = c("aut"))
+    person(given = "Xin (Michael)", family = "Yun", email = "yun.xin@outlook.com", role = c("aut", "cre")),
+    person(given = "Neil", family = "Hatfield", email = "neil.hatfield@psu.edu", role = c("aut")),
+    person(given = "Dennis", family = "Pearl", email = "dkp13@psu.edu", role = c("aut"))
   )
-Chapter: Sample Chapter
-Description: This app is focused on the common types of xyz.
+Chapter: Chapter 14 - Causal Inference 
+Description: This app is designed to help students explore and understand the core concepts, 
+assumptions and interpretations of Two-Period Difference-in-Difference (Diff-in-Diff) Regression by experimenting with simulation and real life data.
 LearningObjectives: c(
-    "The student will learn to understand Concept A in way z.",
-    "The student will learn to understand Concept B as description y."
-  )
+    "The student will learn to review prerequisite ideas necessary for understanding Two-Period Diff-in-Diff.",
+    "The student will learn to identify and evaluate the key assumptions of the Two-Period Diff-in-Diff model.",
+    "The student will learn to interpret the coefficients and results of a Two-Period Diff-in-Diff regression.",
+    "The student will practice applying Diff-in-Diff concepts through simulations, real data, and challenge exercises."
+)
 DisplayMode: Normal
-URL: https://psu-eberly.shinyapps.io/Sample_App
-BugReports: https://github.com/EducationShinyAppTeam/Sample_App/issues
+URL: https://psu-eberly.shinyapps.io/Differences_in_Differences_Regression
+BugReports: https://github.com/EducationShinyAppTeam/Differences_in_Differences_Regression/issues
 License: CC-BY-NC-SA-4.0
 Tags: simulation
 Type: Shiny

docs/README.md

@@ -1,11 +1,12 @@
-# App Title
+# Two-Period Difference-in-Difference Regression
 
 [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/) 
 ![experimental](https://img.shields.io/badge/lifecycle-experimental-orange)
-![year](https://img.shields.io/badge/year-2021-lightgrey)
+![year](https://img.shields.io/badge/year-2024-lightgrey)
 ![new](https://img.shields.io/badge/lifecycle-newapp-brightgreen)
 
 ![App Screenshot](../docs/screenshot.png)
 
 # App Description
-Type the description of your app here
+This app is designed to help students explore and understand the core concepts, 
+assumptions and interpretations of Two-Period Difference-in-Difference (Diff-in-Diff) Regression by experimenting with simulation and real life data.

questionbank1.csv

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+question_id,question_text,choice_1,choice_2,choice_3,correct_answer,correct_feedback,incorrect_feedback
+q1,What is the primary purpose of the Parallel Trends Assumption in a Diff-in-Diff model?,To ensure that the treatment group and control group have the same trend before the intervention.,To ensure that the treatment group and control group have the same outcome after the intervention.,To control for confounding variables.,choice_1,Correct! ,Incorrect. What does the Parallel Trends Assumption ensure about the trends in the treatment and control groups before the intervention? How could these trends help isolate the treatment effect?
+q2,What does it imply if the treatment group and control group do not have similar trends before the intervention?,The Parallel Trends Assumption is violated.,The intervention has no effect.,The groups are perfectly matched.,choice_1,Correct!,"Incorrect.If the treatment and control groups do not have similar trends before the intervention, what might this indicate about the validity of the Parallel Trends Assumption? Could this affect the causal interpretation of the treatment effect?"
+q3,What does the Exchangeability Assumption imply in a Diff-in-Diff model?,There are no systematic differences between the treatment and control groups.,The control group should become the treatment group after the intervention.,The intervention must be randomly assigned.,choice_1,Correct!,"Incorrect.The Exchangeability Assumption implies no systematic differences between groups. How might such differences, if present, lead to bias in estimating the treatment effect?"
+q4,What is the expected outcome if the Exchangeability Assumption is violated in a Diff-in-Diff analysis?,The model will be biased due to confounding factors.,The results are unaffected.,The treatment effect will always be overestimated.,choice_1,Correct! ,"Incorrect.If the Exchangeability Assumption is violated, what role do confounding factors play in biasing the treatment effect? How could you address such confounding factors?"

questionbank2.csv

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+question_id,question_text,choice_1,choice_2,choice_3,correct_answer,correct_feedback,incorrect_feedback
+q1,What does the DiD estimator represent in this scenario?,The difference in reading scores between the treatment and control cities in 2023.,The additional change in reading scores in the treatment city after accounting for changes in the control city.,The average difference in reading scores across both cities before 2021.,choice_2,Correct! ,Incorrect. Why is it important to account for changes in the control city when interpreting the treatment effect? What does this adjustment tell us about the additional change in the treatment city?
+q2,"If reading scores in the control city also improved, how should this affect the interpretation of the program’s effectiveness?",It could mean that factors other than the reading program contributed to improved scores.,It confirms that the program caused the improvement.,It has no effect on the interpretation.,choice_1,Correct!,"Incorrect.If the control city also experienced an improvement in reading scores, could there be other external factors influencing the results? How would this affect the causal interpretation of the program�s effectiveness?"
+q3,Why is the Parallel Trends Assumption important in this scenario?,It ensures that both cities had identical reading scores before 2021.,"It assumes that, without the program, reading score trends would be similar in both cities.",It ensures that only the treatment city had an increase in reading scores.,choice_2,Correct! ,Incorrect.What does the Parallel Trends Assumption imply about the trends in reading scores for both cities before the intervention? How does this assumption support the validity of the Diff-in-Diff analysis?
+q4,"If a larger improvement in reading scores is observed in the treatment city after 2021, what might this suggest?",The reading program likely contributed to improved scores in the treatment city.,The control city has lower educational standards.,Random fluctuations likely caused the improvement.,choice_1,Correct!,"Incorrect.If the treatment city shows a larger improvement in reading scores after the program, what could this indicate about the program's impact? Are there alternative explanations that should be considered?"