Data Analysis Plan for Age-Related Hearing Loss in Puerto Rico

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RUNNINGHEAD: Data Analysis Plan
Scholar-Practitioner Project
Advanced Analysis of Community Health
Data and Surveillance in Public Health
Data Analysis plan

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Data Analysis Plan
Research Topic
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.
Alternative Hypothesis
H1: There is statistically significant difference between race and sex in age–related hearing loss
in Puerto Rico population.
The dataset used for the analysis process is from a random sample population in Puerto Rico. It
contains 128 variables collected for 14408 random individuals, however in the analysis; eight
variables are used to determine the relationship between age-related hearing loss, race, and sex.
To investigate the research question two statistical tests will be conducted during data analysis:
i. Analysis of Variance
ii. Multiple linear regression

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Data Analysis Plan
An ANOVA (analysis of covariance) test is conducted to examine statistical significant
difference between age-related hearing loss (dependent variable) by race and sex (independent
variables). When determining whether there exists mean differences in researching relationship
between continuous dependent variable with an independent variable from different discrete
groups, we conduct ANOVA statistical analysis for its sufficiency. When conducting ANOVA
discrete groups will be age, gender, race, frequency of noise, occupational exposure, income, and
socio-economic status while the analysis dependent variable will be loss of hearing.
Assumptions assessed:
i. Homogeneity
ii. Normality
In assumption of normality supposes the test variables to be normally distributed; the test uses
Kolmogorov-Smirnov test. Whereas, Variance homogeneity supposes that the groups have
identical variance errors, evaluated using Levene’s test for uniformity of variance errors. In
testing the probability of rejecting the null hypothesis to ensure the differences did not happen by
chance, the t test will be conducted to determine p-value. Where if p<0.05then we accept the null
hypothesis.
Additionally, in order to determine the relationship between independent variables (age, gender,
race, frequency of noise, occupational exposure, income, and socio-economic) and independent
variable (loss of hearing), the linear regression will be a sufficient statistical analysis. In the
linear regression analysis, the independent variable will be the predictor variable and the
independent variable be the criterion variable.
Regression equation used: