Question 6: Causal Inference Analysis of Determinants Affecting Mathematics Performance Objective: Determine the causal effects of various factors on students' mathematics scores. Specifically, investigate whether increasing study hours leads to higher math scores while accounting for potential confounders. Background: Understanding the causal relationships between study habits and academic performance can inform educational strategies and interventions. However, observational data often contain confounding variables that may bias the estimated effects. This analysis aims to employ causal inference techniques to estimate the true effect of study hours on math scores. Dataset Assumptions: Assume that mathnew.csv includes the following additional variables relevant to causal analysis: PreviousMathScore: The math score from the previous semester. • • • • • MotivationLevel: A self-reported measure of the student's motivation on a scale from 1 to 10. Access To Resources: Indicator of access to educational resources (Yes, No). TeacherQuality: Rating of the teacher's quality on a scale from 1 to 5. SchoolFunding: Amount of funding per student in the school. Tasks: 1. Conceptual Framework: a. Develop a causal diagram (Directed Acyclic Graph - DAG) illustrating the hypothesized relationships between Study Hours, MathScore, and potential confounders (Age, Gender, ParentalEducation, SocioEconomic Status, PreviousMathScore, MotivationLevel, AccessToResources, TeacherQuality, SchoolFunding). b. Identify the set of variables that need to be controlled for to estimate the causal effect of StudyHours on MathScore. 2. Data Preparation: • • a. Load the mathnew.csv dataset into R. b. Handle missing data using multiple imputation techniques (e.g., using the mice package). c. Convert categorical variables (Gender, Parental Education, SocioEconomic Status, AccessToResources) into appropriate numerical formats using dummy encoding or factor variables. d. Check for and address any multicollinearity issues among the predictors.

Question 6: Causal Inference Analysis of Determinants Affecting Mathematics Performance Objective: Determine the causal effects of various factors on students' mathematics scores. Specifically, investigate whether increasing study hours leads to higher math scores while accounting for potential confounders. Background: Understanding the causal relationships between study habits and academic performance can inform educational strategies and interventions. However, observational data often contain confounding variables that may bias the estimated effects. This analysis aims to employ causal inference techniques to estimate the true effect of study hours on math scores. Dataset Assumptions: Assume that mathnew.csv includes the following additional variables relevant to causal analysis: PreviousMathScore: The math score from the previous semester. • • • • • MotivationLevel: A self-reported measure of the student's motivation on a scale from 1 to 10. Access To Resources: Indicator of access to educational resources (Yes, No). TeacherQuality: Rating of the teacher's quality on a scale from 1 to 5. SchoolFunding: Amount of funding per student in the school. Tasks: 1. Conceptual Framework: a. Develop a causal diagram (Directed Acyclic Graph - DAG) illustrating the hypothesized relationships between Study Hours, MathScore, and potential confounders (Age, Gender, ParentalEducation, SocioEconomic Status, PreviousMathScore, MotivationLevel, AccessToResources, TeacherQuality, SchoolFunding). b. Identify the set of variables that need to be controlled for to estimate the causal effect of StudyHours on MathScore. 2. Data Preparation: • • a. Load the mathnew.csv dataset into R. b. Handle missing data using multiple imputation techniques (e.g., using the mice package). c. Convert categorical variables (Gender, Parental Education, SocioEconomic Status, AccessToResources) into appropriate numerical formats using dummy encoding or factor variables. d. Check for and address any multicollinearity issues among the predictors.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 11PPS

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