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.
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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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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