Marketing Research Report: Employee Satisfaction and Personality

This marketing research report analyzes the relationship between employee personality scores and overall job satisfaction. The analysis uses data from SPSS software, examining six personality traits and three job satisfaction measures. The report includes descriptive statistics, paired sample statistics to compare job satisfaction across employment lengths, and regression analysis to model the relationship between personality and job satisfaction. The findings reveal the reliability of job satisfaction scales, significant differences in satisfaction levels, and potential multicollinearity issues in the regression model. The report concludes with recommendations for future analysis, particularly regarding the use of variables that do not exhibit multicollinearity. The report is structured as a market research report, suitable for presentation in a professional setting.

Marketing Research Report
Aishwarya Vijay Sarfare
18029307
48

Table of Contents
Executive summary.....................................................................................................................................2
Introduction.................................................................................................................................................2
1 Descriptive statistics.................................................................................................................................3
2 Paired sample statistics............................................................................................................................4
3 Regression analysis...................................................................................................................................5
Conclusion and recommendations..............................................................................................................6
References...................................................................................................................................................7
Executive summary
In this market research report the relationship between personalities score of a company’s
employees and the overall satisfaction with job is analyzed. The reliability of the overall job
satisfaction scale of the three items is checked and then their mean and standard deviation is
calculated. The comparison between the overall satisfactions with job among the three groups
concerning the length of employment is also determined. The significant difference among the
three groups is also analyzed. Furthermore a regression analysis which is based on machine
learning is performed and the results interpreted accordingly. The problem arising due to multi-
collinearity is again checked and addressed. A strategic guideline on the regression analysis is
provided in this marketing research report. Therefore all the mentioned data analysis has been
done in SPSS software and the results presented in this research report.
Introduction
The market report analyzes the relationship between the overall satisfaction with job and the
personality score of a company’s employees of the six items. The six items include active, afraid,

determined, scared, and strong and upset however the three items of the overall satisfaction
include overall satisfaction1, overall satisfaction 2 and overall satisfaction 3. The data from the
SPSS software contain eleven variables and 243 observations. The variable case ID has two
missing values and this is because the values have been deleted as instructed in the assignment
file. The analysis is conducted to assist a market researcher who has an interest in knowing the
connection between personality score of a company’s staffs and the overall satisfaction with the
employment.
1 Descriptive statistics
Findings and discussions the reliability of the overall job satisfaction scales is checked and the
average for the three items that is overall satisfaction 1, overall satisfaction 2 and overall
satisfaction 3 are calculated. Moreover their mean and standard deviation is reported below.
Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Overall satisfaction1 243 1 7 4.77 1.111
Overall satisfaction2 243 1 7 4.75 1.139
Overall satisfaction3 243 1 7 3.19 1.108
Valid N (listwise) 243
Table 1
The table 1 above shows the descriptive statistics of the three items. The mean of overall
satisfaction 1, overall satisfaction 2 and overall satisfaction 3 are 4.77, 4.75 and 3.19
respectively. Their standard deviation is 1.111 for overall satisfaction 1, 1.139 for overall

satisfaction 2 and 1.108 for overall satisfaction 3. The minimum and maximum for these
attributes is 1 and 7 respectively. The total numbers of observations are 243 which indicate that
there was no any missing value in these variables. The variable overall satisfaction 3 had the
least mean and standard deviation and the variable overall satisfaction 1 had the highest mean
while the variable overall satisfaction 2 had the highest standard deviation. The mean for the
variables overall satisfaction 1 and satisfaction 2 are relatively close. In addition the standard
deviation of the three variables is also relatively close. From table 1 the mean for the variables
are greater than their respective standard deviation and therefore this mean that most of the
dataset from this variable are dispersed away from the mean.
2 Paired sample statistics
To compare the overall job satisfaction among three groups based on their length of employment
and then to comment on the statistical significant difference among the groups.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Overall satisfaction1 4.77 243 1.111 .071
year_of_employment 1.92 243 .765 .049
Table 2
From table 2 the standard deviation for the variable overall satisfaction 1 and year of
employment is determined. The p-value is less than the significance level which is 0.05 and
therefore the null hypothesis is rejected and conclusion made that there is no statistical
significant difference between the variables overall satisfaction 1 and year of employment.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Overall satisfaction2 4.75 243 1.139 .073
year_of_employment 1.92 243 .765 .049
Table 3

Table 3 above shows the standard deviation of the variables overall satisfaction 2 and year of
employment. The p-value is less than the significance level of 0.05 and therefore the null
hypothesis is rejected.
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 Overall satisfaction3 3.19 243 1.108 .071
year_of_employment 1.92 243 .765 .049
Table 4
In table 4 the standard deviation for overall satisfaction 3 and year of employment is calculated.
The p-value is found to be less than 0.05 and therefore conclusion is made that there is no
statistical significance difference between these variables.
3 Regression analysis
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .375a .141 .119 1.043
a. Predictors: (Constant), upset, determined, scared, strong, active,
afriad
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 42.029 6 7.005 6.444 .000b
Residual 256.523 236 1.087
Total 298.551 242
a. Dependent Variable: Overall satisfaction1
b. Predictors: (Constant), upset, determined, scared, strong, active, afriad

Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 3.940 .645 6.108 .000
active .139 .084 .128 1.662 .098
afriad -.409 .188 -.426 -2.183 .030
determined .029 .091 .024 .322 .748
scared .122 .181 .131 .674 .501
strong .081 .044 .123 1.856 .065
upset .009 .075 .008 .121 .904
a. Dependent Variable: Overall satisfaction1
Table 5
From table 5 above the R-square is 0.141 or 14% and the adjusted R-square is 0.119 or 12%. It is
noticed that both R-square and adjusted R-square are small and this shows that the model is
fairly good for the given data (Sainami,2013). The p-value for the coefficient of the variables
active, scared, determined, strong and upset are greater than the significant level of 0.05 and
therefore they faces the multi-collinearity problem. The p-value of the variable afraid is less the
significance level of 0.05 and therefore it is recommended to be used for prediction in this
analysis. The regression model can be given as follows
Overall satisfaction 1=3.94+0.139*active-
0.409*afraid+0.029*determined+0.122*scared+0.081*strong+0.009*upset
In the model above the variable overall satisfaction are the dependent variable and the variables
active, afraid, determined, scared, strong and upset are the independent variables (Pandis, 2016).
Conclusion and recommendations
The descriptive statistics of the variable overall job satisfaction among the three groups is
calculated and the findings reported. The overall job satisfaction among groups regarding their
length of employment is determined using a paired t-test and it is found that there is no statistical
significance difference between them. The regression model is analyzed and it is found that the
independent variables except the variable afraid face the problem of multi-collinearity. I would

recommend that the variable in the regression analysis that faces multi-collinearity not to be used
in the model.

References
Sainani, K 2013, Understanding linear regression, Statistically speaking, vol. 5, pp. 10631068
doi: 10.1016/j.pmrj.2013.10.002
Pandis, N 2016, Linear regression, American Journal of Orthodontics and Dentofacial
Orthopedics, vol. 149, no. 3, pp. 431-434 doi: 10.1016/j.ajodo.2015.11.019