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Analysis of Variable Types and Statistical Tests in a Health Department Case Study

Posted on | by Kelvin

Case Study
Researchers from a local health department are conducting a study to determine factors that contribute to residents’ willingness to get the influenza vaccine. A survey was sent to 500 randomly selected residents of the town to conduct this study. Two hundred residents responded for a response rate of 40%. Researchers noticed that about 80% of the responses were from female residents, but the town itself has a ratio of 50% female/male residents.

The researchers decided to focus on determining how five factors were different between residents who had been vaccinated against influenza and those who had not been vaccinated. Accordingly, the respondents were asked to provide their vaccination status (variable 1), age (variable 2), gender (variable 3), family income (measured in dollars) (variable 4), and self-rated health (a scale from 1 to 10 with a higher number indicating feeling healthier) (variable 5). About 30% of the respondents did not respond to the income question.

Prompt
Review the above case study. Then write a brief analysis to determine the variable types and the most appropriate statistical tests for the variables in the given case study. Provide specific evidence from at least two scholarly sources to support your claims.

Specifically, you must address the following rubric criteria:

Variable Types: Identify the types of the five variables used in the given case study. Include the following details in your response:
Identify the independent and dependent variables used in the case study.
Determine the variable type for each variable.
Explain how you determined the different variable types.
Statistical Tests: Determine the most appropriate statistical test for examining the relationship between the variables. Identify which test is appropriate for determining the relationship between each of the following:
Variables 1 and 2
Variables 1 and 3
Variables 1 and 4
Variables 1 and 5
Variables 2 and 4
(Note: Assume that the first variable is the independent variable and the second is the dependent in each listed relationship. For example, in A: variable 1 is the independent variable, and variable 2 is the dependent variable)
Rationale: Discuss why each test is the most appropriate for the corresponding variable relationship. Include the following details in your response:
What will the outcome of each test tell you about the relationship between the identified variables?
How would you interpret the p value from the tests?

 

Sample Answer

Analysis of Variable Types and Statistical Tests in a Health Department Case Study

Variable Types

In the case study conducted by researchers from a local health department, five variables were considered to determine factors influencing residents’ willingness to receive the influenza vaccine. These variables included vaccination status (variable 1), age (variable 2), gender (variable 3), family income (variable 4), and self-rated health (variable 5).

Independent and Dependent Variables

1. Independent Variable: In this study, the independent variable is the vaccination status (variable 1). This variable is manipulated or controlled by the researchers to observe its effect on other variables.

2. Dependent Variables: The dependent variables are age (variable 2), gender (variable 3), family income (variable 4), and self-rated health (variable 5). These variables are measured or observed based on changes in the independent variable.

Variable Types

1. Variable 1 (Vaccination Status): Categorical Variable (Nominal)
2. Variable 2 (Age): Continuous Variable (Ratio)
3. Variable 3 (Gender): Categorical Variable (Nominal)
4. Variable 4 (Family Income): Continuous Variable (Interval)
5. Variable 5 (Self-rated Health): Ordinal Variable

Determining Variable Types

– Categorical Variables: Variables like vaccination status and gender have distinct categories with no inherent order.
– Continuous Variables: Age and family income are continuous variables that can take any value within a range.
– Ordinal Variables: Self-rated health is an ordinal variable since the values have a specific order but the differences between the values may not be uniform.

Statistical Tests

To analyze the relationships between the variables, appropriate statistical tests must be selected. Let’s consider the relationships between the independent variable (vaccination status) and each of the dependent variables.

1. Variables 1 and 2 (Vaccination Status and Age):

– Appropriate Test: Pearson’s Correlation Coefficient
– Rationale: This test will help determine if there is a linear relationship between vaccination status and age. The outcome will indicate the strength and direction of this relationship.

2. Variables 1 and 3 (Vaccination Status and Gender):

– Appropriate Test: Chi-Square Test
– Rationale: This test is suitable for examining the association between two categorical variables like vaccination status and gender. The p-value will indicate if there is a significant relationship between these variables.

3. Variables 1 and 4 (Vaccination Status and Family Income):

– Appropriate Test: Independent Samples t-test
– Rationale: This test can be used to compare the means of two groups, in this case, vaccinated and non-vaccinated residents, based on their family income. The p-value will show if there is a significant difference in income between the two groups.

4. Variables 1 and 5 (Vaccination Status and Self-rated Health):

– Appropriate Test: Mann-Whitney U Test
– Rationale: As self-rated health is an ordinal variable, this non-parametric test is suitable for comparing two independent groups based on this ordinal variable. The p-value will determine if there is a significant difference in self-rated health between vaccinated and non-vaccinated residents.

5. Variables 2 and 4 (Age and Family Income):

– Appropriate Test: Pearson’s Correlation Coefficient
– Rationale: This test can help identify if there is a linear relationship between age and family income. The correlation coefficient will indicate the strength and direction of this relationship.

Conclusion

By understanding the variable types and selecting appropriate statistical tests for analyzing the relationships between the variables in this case study, researchers can gain valuable insights into factors influencing residents’ willingness to receive the influenza vaccine. The outcomes of these tests, along with interpreting the p-values, will provide crucial information for public health interventions aimed at increasing vaccination rates in the community.

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