Manipulating Dataset In SPSS

This study is mainly based on the analysis of the data on Race, Sex, Age, Income, Occupational Exposure, Education, Hearing Difficulty and Loss of Hearing of an individual. For the purpose of the analysis, certain data points have been removed from the dataset. The missing data points have been removed from the study and only people between the age group of 65 years and 85 years have been considered. A random sample of 400 people have been selected for the study instead of considering the total population.

Running Head: MANIPULATING DATASET IN SPSS
Manipulating Dataset In SPSS
Name of the Student
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1MANIPULATING DATASET IN SPSS
Introduction
This study is mainly based on the analysis of the data on Race, Sex, Age, Income,
Occupational Exposure, Education, Hearing Difficulty and Loss of Hearing of an individual.
For the purpose of the analysis, certain data points have been removed from the dataset. The
missing data points have been removed from the study and only people between the age
group of 65 years and 85 years have been considered. A random sample of 400 people have
been selected for the study instead of considering the total population.
Research Topic
The topic of this research can be stated as follows:
Are there any Race and Sex Differences in Age‐Related Hearing Loss in Puerto Rico?
Research Questions
 What is prevalence rate of hearing loss in men and women in Puerto Rico?
 What is prevalence rate of hearing loss by Race in Puerto Rico?
 What is prevalence rate of hearing losses related to define age group in the study?
Hypotheses
Null hypothesis (H0): There is no a statistically significant difference between race and sex
in age-related hearing loss in Puerto Rico population.
Alternate hypothesis (HA): There is statistically significant difference between race and sex
in age-related hearing loss in Puerto Rico population.
Data Analysis

2MANIPULATING DATASET IN SPSS
In order to test the hypothesis stated above, two different statistical tests will be
conducted. The first one is the Multiple linear regression and the second one is the Analysis
of Variance (ANOVA).
The multiple linear regression test will be used to establish the relationship between
the Race, Sex, Age, Income, Occupational Exposure, Education, Hearing Difficulty and Loss
of Hearing of an individual with loss of hearing as the dependent variable and all the other
variables as independent variables. Regression analysis is the best technique to determine the
relationship that the independent variables have with the dependent variable.
An ANOVA test has been conducted to test whether there are significant differences
between the hearing capability of the people with respect to their gender and race. ANOVA is
the best technique to test significant difference in the dependent variable with independent
variables. Here, the independent variables are the gender and race and the dependent variable
is the hearing capability. It is assumed for the purpose of the analysis that the data is
homogeneous and the assumptions of normality have been met. The null hypothesis will be
accepted if the p-value (sig) is less than 0.05 as all the tests are conducted at 5 percent level
of significance.
From the analysis of the multiple linear regression, it can be seen that 2.2 percent of
the variability in the loss of hearing can be explained by the Race, Sex, Age, Income,
Occupational Exposure, Education, Hearing Difficulty and Loss of Hearing of an individual.
The analysis has also shown that all the independent variables have shown insignificant
impact on the loss of hearing of an individual. Only the race to which the individual belong
has a significant impact on the hearing loss. Thus, it can be said that a black person has 0.97
times more chance to have hearing loss than a white person.

3MANIPULATING DATASET IN SPSS
From the results of the ANOVA test, it can be seen that significance value is less than
0.05, thus, the null hypothesis is rejected. Hence, it can be concluded that there is significant
difference in the hearing difficulty based on the race and gender of an individual.