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below i will paste the guidlines and rubric for the essay and then after that I
below i will paste the guidlines and rubric for the essay and then after that I will include an example that the professor provided us with.
Before collecting data and drafting your research, think about the following questions.
What is the research question you try to answer with your collected data?
What is your proposition, hypothesis, argument, or prediction that folks in the field of marketing and business strategy might be interested in?
Can you test those ideas with your collected data?
Exploratory data analysis such as descriptive analysis and visualization
Inferential analysis (linear regression, logistic regression, cluster analysis, t-test, ANOVA test, factor analysis, time-series analysis)
Who will be your audience?
How do you think your findings will help managers, consultants, business owners, business analysts, and investors in the field of marketing and business strategy?
FINAL PROJECT RUBRIC
https://docs.google.com/document/d/1TLUDrx4x1ZCXJ2B8ZMeb95to0F0O2B3D/edit?usp=sharing&ouid=100023364186916917291&rtpof=true&sd=true
Final Project 19 points
Do NOT repeat what your instructor did in class and your textbook used (- 3).
Do NOT use your previous project (-5)
Abstract (2 points)
Write an engaging, thorough, and concise abstract of up to 250 words including research motivation (-1)
Motivation: what is the problem you are trying to solve? Why is this an important problem to address? (-1)
Summarize data, analysis, and findings (-1)
Introduction (3 points)
Do not use bullet points. Write the introduction logically and it should be grammatically correct. (-1)
Develop your research questions explicitly (-1)
Discuss why it is important to study or answer the research questions (-1)
Explain any background information needed to understand the context of your research questions (-1)
Data (4 points)
Describe your data
Double check whether your data is structured. If it is not structured, do NOT use it for the final project. (-4)
Structure data consists of columns (i.e., variables) and rows (i.e. observations)
If you are confused or not sure whether your data is structured or not, contact your instructor.
Data collection: data from OU library or your workplace
If you want to use Kaggle or other external data, be sure to contact your instructor.
Provide the background of your dataset (i.e., how this dataset(s) were generated) (-1.5)
Observation period and size of the dataset (-0.5)
Introduction to your key variables: Exploratory data analysis
Correlation matrix that include key variables & interpretation for your audience (-1)
Descriptive statistics of key variables and interpretation for your audience (-1)
Method (6 points)
Do not use bullet points. Write the methodology section logically and it should be grammatically correct (-1)
For graduates:
Introduce your statistical methods and explain why they are appropriate to your research. (-2)
Use at least 1 advanced analyses (-1)
logistic regression, factor analysis, cluster analysis, time-series analysis, text analysis
Multiple linear regression (e.g., regression with an interaction term) will be regarded as advanced analysis.
Use at least 2 simple analyses (-1)
Simple linear regression, t-test, ANOVA
For undergraduates:
Introduce your statistical methods and explain why they are appropriate to your research. (-2)
Use at least 2 analyses (-2)
logistic regression, factor analysis, cluster analysis, time-series analysis, text analysis
multiple linear regression (e.g., regression model with an interaction term), simple linear regression, t-test, ANOVA
Explain and interpret the results of your analyses as specifically as you can for your audience who has no idea of statistics and data analytics (-2)
Talk about notable findings related to your research question.
Highlight key numbers in a text body with visualized test results and narratives in a text body.
Even if your data does not support your arguments, it should be fine. That is another important finding.
Limitation (2 points)
Provide at least two limitations of your research & Discuss why they are limitations(-2)
Requirements (2 points)
Double-spaced 12 font-sized Times New Roman or Calibri font with 1-inch margins. (-0.5)
Page limit: 7 pages (Cover page, references, and appendix will not count against the limit.) (-0.5)
Put your graphical analysis, analysis outcomes, figures, and tables in the appendix, which does not count towards the 7-page limit (-0.5)
Be sure to signpost them (e.g., [Figure 1], See [figure 1] in Appendix, [Table 1], See [table 1] in Appendix) in a text body and match them with the items in Appendix.
Submit your manuscript in docx along with 1) “R script” and 2) “dataset” (-2)
Observations are greater than 200 (-1)
Completeness of R script (-2)
Delete all instructions when submitting the final version of your research project (-1).
Example essay:
Abstract
250-word Abstract
Introduction
Background of your research
Many organizational researchers assume that the pay gap caused by stock options, pay for performance, and incentives are effective to motivate employees to have a strong job satisfaction and be attentive to goals and results, and eventually dedicate themselves to the organization through organizational identification. For instance, Tesla, Amazon, and other tech companies in e-commerce sectors provide employees with substantial variable pay that amounts to more than 80% of total compensation.
Others have negative perspectives on the pay gap as the pay system excessively promotes internal competition, thus reducing collaboration among individuals, teams, and departments, strengthening silos, and making the organization shortsighted. Indeed, it was reported that GM or General Motors was facing low productivity issues that might be caused by the heightened internal conflicts. Some analysts argue that….
Although variable pay that an employee gets because of their performance plays a critical role in enhancing strategic fits between employees and the organization and creating a firm’s core competency, its impacts on firm performance is still a black box.
Research Questions
Managers do not understand the impact of pay gap on firm performance and the lack of understanding deters managers from designing the variable pay effectively and improving overall performance. Furthermore, ….. Indeed, many marketing and HR managers have a hard time to figure out how to design variable pay. Thus, my research question is how pay gap affects firm performance and under what conditions the impact of variable pays on the performance will be more profound.
Hypotheses and contributions of this study
First, this study will suggest the relationship between pay gap and firm performance and provide insights into the relationship. Second, I will suggest the organizational condition that the effect will be more profound. Third, managers will be able to identify the most optimal proportion of variable pay to an employee’s total compensation through the decision making tool.
Based on the possible positive and negative relationships of variable pay and firm performance, I hypothesize that there will be an inverse U-shaped relationship between two variables. In other words, the impact will increase firm performance until the maximum level of the performance is reached after which additional variable pay will lower the performance gradually. I have three rationales for it: First,…….. second, ……………, and third………………. I think there will be three contributions this research can make. First, this research will help the organization lower …… Second, it will …………. Third, HR managers will benefit from …. Fourth, …..
Data
Description of my dataset
I will empirically test my hypothesis using the Major League Baseball data. The use of the MLB data is commonly used for management studies. There are several reasons. First, using athletic organizations for research enables management researchers to test competitive behaviors with a large size of highly accurate observations over years, which were generated from an empirically controlled setting. Second, professional sports leagues offer a unique context to capture talent movement and the resulting variable pay made by these executives within the context of a highly competitive and specialized industry. The Lahman dataset contains the Major League Baseball data that has been produced since 1876. Regarding the structure of the data, I will use a 20-year observation period from 1980 to 2000, because…… Total observations are 280.
Introduction to my key variables
The following four variables will be used in this research: pay gap, firm performance, league affiliation, and audience per game.
Independent variable: pay gap
This variable measures…… I calculate the pay gap by calculating standard deviation of annual salary within the MLB team. The min, 1st quartile, median, 3rd quartile, and max of this variable are $663546.9, $3460747.2, $5084271.8, $6795280.7, and $10109769.8. The histogram indicates the variable is normally distributed (See Figure-1).
Dependent variable: firm performance
This variable measures ….. I used the winning percentage calculated as dividing the number of winning games by the total number of the game in a season. The descriptive statistics ….
Control variables: audience per game & League affiliation
The first control variable measures …. The purpose…. The descriptive statistics….
Exploratory data analysis
To better understand how those variables are correlated, I conducted exploratory data analysis. Figure 2 provides the visualized correlation matrix. It shows that….
METHOD
Analysis and interpretations
To test my hypothesis, first I used a linear regression model, as my dependent variable is continuous…. Please find the test result in the appendix page (see Table 1). Table 1 shows the impact of pay gap on firm performance. From the table, I found that the coefficient of pay gap was statistically significant at the significance level of 0.05, meaning that….. Furthermore, residual standard errors were …… adjusted r square …. Prediction interval….. and other statistics….
As shown in Table 2, I adopted a quadratic regression model to test the relationship. p-value …., adjusted r squared ….., predictive interval …… For the interpretation, I first look at the coefficient of my independent variable and its p-value.
I found the quartic regression model best represents the relationship between the two variables because… To visualize the relationship between an independent and dependent variable, I draw the fittest line on the scatterplot. The fittest line graphically shows the overall relationship between …., outliers, and … In addition, it captures… I am attaching the graphic outcome in the appendix page. Please refer to Figure 2.
Interpretation
I used linear, quadratic, cubic, and quartic regression models. …….
Limitations
My research findings provide important implications that might be helpful for my audience, HR managers. However, it also has two limitations. First, ………………. Second, ……………….
Appendix
Figure 1
Figure 2
Figure 3
Table 1
# Linear regression model
> summary(lm(wp~sd, data=e))
Call:
lm(formula = wp ~ sd, data = e)
Residuals:
Min 1Q Median 3Q Max
-0.168527 -0.049722 0.007093 0.052165 0.131244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.809e-01 1.310e-02 36.715 summary(lm(wp~I(sd)+I((sd)^2), data=e))
Call:
lm(formula = wp ~ I(sd) + I((sd)^2), data = e)
Residuals:
Min 1Q Median 3Q Max
-0.162461 -0.050431 0.004256 0.050335 0.123376
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.246e-01 2.679e-02 15.853 summary(lm(wp~I(sd)+I((sd)^2)+I((sd)^3), data=e))
Call:
lm(formula = wp ~ I(sd) + I((sd)^2) + I((sd)^3), data = e)
Residuals:
Min 1Q Median 3Q Max
-0.162388 -0.050471 0.004377 0.050460 0.123217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.260e-01 4.692e-02 9.078 summary(lm(wp~I(sd)+I((sd)^2)+I((sd)^3)+I((sd)^4), data=e))
Call:
lm(formula = wp ~ I(sd) + I((sd)^2) + I((sd)^3) + I((sd)^4),
data = e)
Residuals:
Min 1Q Median 3Q Max
-0.161411 -0.051713 0.003127 0.047745 0.126160
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.177e-01 7.665e-02 4.145 5.29e-05 ***
I(sd) 1.519e-07 7.646e-08 1.987 0.0485 *
I((sd)^2) -4.603e-14 2.549e-14 -1.806 0.0727 .
I((sd)^3) 6.032e-21 3.423e-21 1.763 0.0797 .
I((sd)^4) -2.840e-28 1.596e-28 -1.780 0.0768 .
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0661 on 175 degrees of freedom
Multiple R-squared: 0.06179, Adjusted R-squared: 0.04035
F-statistic: 2.881 on 4 and 175 DF, p-value: 0.02415
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