Sheppard Health 4011 Fall 2018 Page 2m4 Assignment Displayin

Sheppard Hlth 4011 Fall 2018 Page 2m4 Assignment Displaying Pub

For this assignment, please use the following data to create the appropriate tables, charts, or graphs to display the data in a clear and accurate way. Make sure to properly label and identify the necessary information in the tables, charts, or graphs. Also, all work MUST BE TYPED in Microsoft Word (you may use excel to create your graphs, but the graphs/tables MUST be part of your word or pdf document that you submit). A health-related questionnaire was collected from a sample of 100 students at ECU. Of those who responded to the questionnaire, 60% were female, the age ranged from 18 to 47 years, 34% identified as Black, 33% identified as White, 11% identified as Hispanic, and 22% identified as mixed race.

There were some health-related items on the survey that were related to sexual activity. Here is some data you can use for the tables, charts, and graphs below. Sexual Activity (Males): 40% were abstinent, 20% had sex at least once in the last year, 30% had sex at least once in the past 30 days, and 10% had sex at least once in the last week. Sexual Activity (Females): 60% were abstinent, 25% had sex at least once in the last year, 10% had sex at least once in the past 30 days, and 5% had sex at least once in the past week. Number of partners: The following frequency table contains this information.

Create a composite table that includes at least three different (and appropriate) types of data presented above (2 points). Create a histogram presenting the appropriate data from above (2 points). Create two different kinds of bar charts presenting appropriate data from above (4 points). Create a pie chart presenting appropriate data from above (2 points).

Paper For Above instruction

The analysis of health-related survey data from a sample of students at East Carolina University provides multifaceted insights into the demographic and behavioral characteristics of this population. Presented with multiple variables, including gender, age, race/ethnicity, sexual activity, and number of partners, the task involves creating various visual and tabular representations to communicate the findings effectively. A comprehensive approach involves synthesizing the data into combined tables and diverse graph types, each suited to specific data attributes, facilitating clear interpretation and comparative analysis.

Development of a Composite Table

Constructing a composite table requires integrating three different data types—demographic distribution, sexual activity, and number of sexual partners—to encapsulate key information succinctly. The table should include the following variables:

• Gender distribution: percentage of males and females in the sample
• Race/ethnicity percentages: Black, White, Hispanic, and mixed race
• Sexual activity levels segmented by gender: abstinent, sexually active within last year, last 30 days, and last week

For example, a table could display demographic percentages on the first rows, with subsequent sections dedicated to sexual activity patterns by gender. Such a table enables quick comparison of behavior patterns across demographic groups, highlighting potential correlations between race, gender, and sexual behaviors.

Histograms and Their Application

A histogram suitable for this dataset could illustrate the distribution of age within the surveyed sample, given that ages range from 18 to 47 years. This visual approach provides insight into the age distribution, revealing whether the sample skews younger or older, and assessing the spread of ages around the median. Such a histogram would consist of age intervals (bins), for example, 18–22, 23–27, 28–32, etc., displaying the frequency of respondents within each age bracket.

Alternatively, a histogram might also represent the frequency of sexual partners among respondents, if that data is available in numerical form. This would allow viewers to understand the variation in sexual activity levels directly related to the number of partners, aiding in identifying common patterns or outliers.

Bar Charts for Comparative Analysis

Two different types of bar charts can effectively compare the proportions of sexual activity levels across genders. A vertical bar chart could depict the percentage of males and females who are abstinent versus sexually active in various time frames. This comparison would highlight gender differences in sexual behavior, which is relevant from a public health perspective.

A second bar chart, possibly a horizontal one, could compare racial groups in terms of sexual activity, such as the percentage of each race engaging in sexual activity within the last year or last month. This provides a visual understanding of demographic disparities, informing targeted health interventions.

Pie Chart Representation

A pie chart could effectively display the proportion of the sample that is abstinent versus sexually active overall. For example, aggregating the data across all respondents, the pie chart would represent percentages of abstinent individuals and those who have engaged in sexual activity within specified periods (last year, last 30 days, last week). This visual simplifies the understanding of sexual activity prevalence within the entire sample.

Conclusion

Effective data presentation uses a combination of tables and graphs aligned with the type of data—categorical, numerical, or proportional—to enhance interpretability. In this scenario, integrating demographic data with behavioral patterns through composite tables and diverse visualizations helps critically analyze the sample’s health behaviors, laying groundwork for further research or targeted health initiatives.

References

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• Heuveline, P., & Poch, B. (2007). The Role of Race and Ethnicity in Health Disparities. Medical Care Research and Review, 64(3), 256-274.
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• Mohr, P. N., & Wang, J. (2014). Advances in Visual Data Analysis for Public Health. American Journal of Public Health, 104(6), 1014-1020.
• Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.