Question 6: Causal Inference Analysis of Factors Affecting Job Satisfaction Objective: Determine the causal effects of various factors on participants' JobSatisfaction. Specifically, investigate whether increasing HoursWorked leads to higher or lower job satisfaction while accounting for potential confounders. Dataset Assumptions: Assume that statsnew.csv includes the following additional variables relevant to causal analysis: . • PreviousJobSatisfaction: The job satisfaction score from the previous period. WorkEnvironment: A self-reported measure of the work environment on a scale from 1 to 10. • Training Programs: Indicator of participation in training programs (Yes, No). • • ManagerSupport: Rating of manager support on a scale from 1 to 5. CompanySize: Number of employees in the company. Tasks: 1. Conceptual Framework: a. Develop a causal diagram (Directed Acyclic Graph - DAG) illustrating the hypothesized relationships between HoursWorked, JobSatisfaction, and potential confounders (Age, Gender, EducationLevel, Income, EmploymentStatus, Marital Status, PreviousJobSatisfaction, WorkEnvironment, TrainingPrograms, ManagerSupport, CompanySize). b. Identify the set of variables that need to be controlled for to estimate the causal effect of HoursWorked on JobSatisfaction. 2. Data Preparation: . . a. Load the statsnew.csv dataset into R. b. Handle missing data using multiple imputation techniques (e.g., using the mice package). c. Convert categorical variables (Gender, EducationLevel, EmploymentStatus, Marital Status, Training Programs) 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 Factors Affecting Job Satisfaction Objective: Determine the causal effects of various factors on participants' JobSatisfaction. Specifically, investigate whether increasing HoursWorked leads to higher or lower job satisfaction while accounting for potential confounders. Dataset Assumptions: Assume that statsnew.csv includes the following additional variables relevant to causal analysis: . • PreviousJobSatisfaction: The job satisfaction score from the previous period. WorkEnvironment: A self-reported measure of the work environment on a scale from 1 to 10. • Training Programs: Indicator of participation in training programs (Yes, No). • • ManagerSupport: Rating of manager support on a scale from 1 to 5. CompanySize: Number of employees in the company. Tasks: 1. Conceptual Framework: a. Develop a causal diagram (Directed Acyclic Graph - DAG) illustrating the hypothesized relationships between HoursWorked, JobSatisfaction, and potential confounders (Age, Gender, EducationLevel, Income, EmploymentStatus, Marital Status, PreviousJobSatisfaction, WorkEnvironment, TrainingPrograms, ManagerSupport, CompanySize). b. Identify the set of variables that need to be controlled for to estimate the causal effect of HoursWorked on JobSatisfaction. 2. Data Preparation: . . a. Load the statsnew.csv dataset into R. b. Handle missing data using multiple imputation techniques (e.g., using the mice package). c. Convert categorical variables (Gender, EducationLevel, EmploymentStatus, Marital Status, Training Programs) into appropriate numerical formats using dummy encoding or factor variables. • d. Check for and address any multicollinearity issues among the predictors.
Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015
1st Edition
ISBN:9781680331141
Author:HOUGHTON MIFFLIN HARCOURT
Publisher:HOUGHTON MIFFLIN HARCOURT
Chapter4: Writing Linear Equations
Section: Chapter Questions
Problem 14CR
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Transcribed Image Text:Question 6: Causal Inference Analysis of Factors Affecting Job Satisfaction
Objective:
Determine the causal effects of various factors on participants' JobSatisfaction. Specifically,
investigate whether increasing HoursWorked leads to higher or lower job satisfaction while
accounting for potential confounders.
Dataset Assumptions:
Assume that statsnew.csv includes the following additional variables relevant to causal analysis:
.
•
PreviousJobSatisfaction: The job satisfaction score from the previous period.
WorkEnvironment: A self-reported measure of the work environment on a scale from 1 to 10.
•
Training Programs: Indicator of participation in training programs (Yes, No).
•
•
ManagerSupport: Rating of manager support on a scale from 1 to 5.
CompanySize: Number of employees in the company.
Tasks:
1. Conceptual Framework:
a. Develop a causal diagram (Directed Acyclic Graph - DAG) illustrating the hypothesized
relationships between HoursWorked, JobSatisfaction, and potential confounders (Age,
Gender, EducationLevel, Income, EmploymentStatus, Marital Status,
PreviousJobSatisfaction, WorkEnvironment, TrainingPrograms, ManagerSupport,
CompanySize).
b. Identify the set of variables that need to be controlled for to estimate the causal effect of
HoursWorked on JobSatisfaction.
2. Data Preparation:
.
.
a. Load the statsnew.csv dataset into R.
b. Handle missing data using multiple imputation techniques (e.g., using the mice
package).
c. Convert categorical variables (Gender, EducationLevel, EmploymentStatus,
Marital Status, Training Programs) into appropriate numerical formats using dummy
encoding or factor variables.
•
d. Check for and address any multicollinearity issues among the predictors.
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