Module 2: Research Lab1_Multiple Regression Analysis

Module 2: Research Lab 1 – Multiple Regression Analysis

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• Due Jun 26 by 11:59pm
• Points 75
• Submitting a file upload

Purpose

This assignment is intended to help you learn to do the following:

• Assess the assumptions of multiple regression analysis.
• Conduct multiple regression analysis.
• Interpret the results of multiple regression analysis.

Overview

Regression analysis mathematically describes the relationship between a set of independent variables and a dependent variable. There are numerous types of regression models that you can use. This choice often depends on the kind of data you have for the dependent variable and the type of model that provides the best fit. 

The terminology related to regression analysis is:

• Dependent variable or target variable: Variable to predict.
• Independent variable or predictor variable: Variables to estimate the dependent variable.
• Outlier: Observation that differs significantly from other observations. It should be avoided since it may hamper the result.
• Multicollinearity: Situation in which two or more independent variables are highly linearly related.
• Homoscedasticity or homogeneity of variance: Situation in which the error term is the same across all values of the independent variables.

There are different types of regression analysis. To determine which type of regression analysis you need to use, first you need to determine which type of dependent variable you have.

You can read the Introduction to Regression Analysis (Links to an external site.) to understand which type of regression analysis is your best choice.

The best way to learn and develop skills in applying statistical techniques is by doing. To this end, practice exercise is intended to aid you in applying the techniques of multiple regression.

Preparation

Before you work on this assignment, read the following:

• Tabachnick and Fidell (2019), Ch. 5.
• Williams, K.M., Nathanson, C. & Paulhus, D. (2010). Identifying and profiling scholastic cheaters: their personality, cognitive ability and motivation (Links to an external site.). Journal of Experimental Psychology: Applied, 16(3), 293-307.
  • This article is related to regression analysis. It will give you some insights into how regression analysis has been applied in a published journal article.

Action Items

Consider the following dataset with several variables: Regression Dataset_Personality.xlsx 

Download Regression Dataset_Personality.xlsx

For this assignment, your focus should be on these seven variables: negative affect, positive affect, openness to experience, extraversion, neuroticism, trait anxiety, and self-esteem. Use self-esteem as the dependent variable and negative affect, positive affect, openness to experience, extraversion, neuroticism, and trait anxiety predictor variables

Based on the provided dataset, address the following:

1. Write out a hypothesis you could test using the variables in the dataset
2. Clean the data and check whether there are any missing values. Discuss how will you deal with missing values if any are present?
3. Are there any outliers in the cleaned dataset? Discuss how will you deal with outliers if any are present?
4. Check statistical assumptions. What will you recommend if there are any violations?
5. Run a stepwise regression on the dataset.
6. Do the predictors correlate statistically significantly and practically with the dependent variable?
7. What is the R and adjusted R Square for all predictors? Interpret the values.
8. What variable(s) provide a significant unique contribution(s)?
9. Write out the prediction equation.
10. Write one to two paragraphs of how you will report this results in finding section of a dissertation or journal article.

Rubric

Module 2: Research Lab 1 – Multiple Regression Analysis

 

Criteria	Ratings	Pts
This criterion is linked to a Learning OutcomeStatistical Analysis	75 to >68.0 pts Proficient Answers, or responses, or computations (if required) for the learning activities are accurate and indicate a comprehensive understanding about what the analysis requires.   68 to >60.0 pts Acceptable Answers, or responses, or computations (if required) for the learning activities are mostly accurate and indicate a basic understanding about what the analysis requires.   60 to >0 pts Unacceptable Answers, or responses, or computations (if required) for the learning activities are inaccurate and indicate a lack of understanding about what the analysis requires.	75 pts
Total Points: 75

 

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