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Walden University PUBH 8546 Advanced Analysis of Community Health Data and Surveillance in Public Health - SPP Research Questions, Statistical Analysis Plan, And Variables

Part 1: Variables

One of the key variables selected from the dataset in this study on total tobacco consumption is the number of times each male and female participant uses tobacco. This is a continuous variable that can be analyzed using measures of dispersion such as descriptive statistical analysis and graphical representations, including histograms.

Converting a Continuous Variable to a Categorical Variable

To facilitate the analysis, a continuous variable was transformed into a categorical variable. The age variable, initially recorded as an exact number, was grouped into categories:

  • <=14 years
  • 14–18 years

Below is the recategorization of the dataset:

Male 14.00 <=14 years
Male 16.00 14-18 years
Female 14.00 <=14 years
Male 18.00 14-18 years
Female 15.00 14-18 years
Male 17.00 14-18 years
Female 14.00 <=14 years
Female 17.00 14-18 years
Male 18.00 14-18 years
Male 15.00 14-18 years
Female 16.00 14-18 years
Male 15.00 14-18 years
Female 17.00 14-18 years
Male 18.00 14-18 years
Female 15.00 14-18 years
Male 14.00 <=14 years
Female 14.00 <=14 years
Female 18.00 14-18 years
Male 16.00 14-18 years
Female 16.00 14-18 years

Process of Converting the Variable

The transformation of the variable was carried out by analyzing the continuous age data to determine the minimum and maximum values and the distribution of participants across different age groups. Using descriptive statistics, the age data was categorized into predefined ranges. The transformation process involved:

  • Selecting the continuous age variable
  • Using the “Transform” function and selecting “Recode into a Different Variable”
  • Assigning a new output variable
  • Labeling the new categorical variable as “Age Group”

This method ensured the dataset was structured appropriately for categorical analysis.

Frequency Table for the New Variable

Age GroupFrequencyPercentValid PercentCumulative Percent
<=14 years525.0%25.0%25.0%
14-18 years1575.0%75.0%100.0%
Total20100%100%100%

Descriptive Statistics for the New Variable

The descriptive analysis revealed a positive dispersion in the dataset. The difference between the mean and maximum values was greater than the difference between the mean and minimum values, shaping the distribution in graphical representations (Kaliyadan et al., 2019).

VariableNMinimumMaximumMeanStandard Deviation
Age2014.0018.0015.851.49649
Gender20121.500.513
Age Group20121.750.444

Part 2: Research Questions and Statistical Analysis Plan

Tobacco use has long been a public health concern, particularly among adolescents and young adults in the United States. This issue is more prevalent in low-income communities, where factors such as socioeconomic status and environmental influences contribute to higher smoking rates (Friend et al., 2021). The impact of tobacco use on heart disease, stroke, lung cancer, and type 2 diabetes underscores its significance as a major public health issue. Despite policy interventions and government efforts, tobacco consumption among youth continues to rise, making it difficult to fully control (Leshargie et al., 2019).

Research Question

What is the association between socioeconomic risk factors, environmental risk factors, and age with tobacco consumption outcomes?

Study Population and Rationale

The target population for this study consists of youths aged 14–18 years. Liu et al. (2019) highlighted that adolescents in low-income neighborhoods are more vulnerable to tobacco use due to easy access and peer pressure. Many young individuals face stressors at home and school, further increasing their likelihood of engaging in smoking behaviors (Luis et al., 2019).

List of Variables

The study will analyze the following variables:

  • Independent Variable: Tobacco consumption
  • Dependent Variable: Gender
  • Confounding Variables: Age, socioeconomic status, and environmental risk factors

Socioeconomic and environmental risk factors are quantitative variables that will be measured using an interval scale. Age will be measured on an ordinal scale, while gender will be a nominal variable. These variables will help identify key risk factors contributing to youth tobacco use.

Rationale for Variable Selection

The selected variables provide essential insights into the relationship between socioeconomic status, environmental factors, and tobacco consumption. Including gender as a dummy variable allows for a comparative analysis of smoking behavior across different demographics. The study aims to determine:

  • The impact of socioeconomic status on tobacco consumption
  • How age influences smoking habits
  • The role of environmental risk factors in tobacco use among youth

Statistical Analysis Plan

The analysis will involve descriptive and inferential statistics to examine the relationships between variables.

  1. Descriptive Methods

    • Evaluating maximum, minimum, mean, and standard deviation of the dataset
    • Analyzing frequency distributions of categorical variables
  2. Statistical Tests

    • Chi-square test to assess relationships between categorical variables
    • T-test to compare differences in tobacco consumption across gender groups
    • ANOVA to evaluate the impact of multiple risk factors on smoking behavior

These statistical tests will allow for a comprehensive understanding of the data and will help determine whether socioeconomic and environmental factors are significant predictors of tobacco consumption among youth (Mishra et al., 2019).

References

Friend, K. B., Lipperman-Kreda, S., & Grube, J. W. (2021). The impact of local US Tobacco Policies on youth tobacco use: A critical review. Open Journal of Preventive Medicine, 1(2), 34. https://doi.org/10.4236/ojpm.2011.12006

Kaliyadan, F., & Kulkarni, V. (2019). Types of Variables, Descriptive Statistics, and Dermatology Online Journal Otology online journal, 10(1), 82–86. https://doi.org/10.4103/idoj.IDOJ_468_18

Leshargie, C. T., et al. (2019). The impact of peer pressure on cigarette smoking among high school and university students in Ethiopia: A systematic review and meta-analysis. PLoS ONE, 14(10). https://doi.org/10.1371/journal.pone.0222572

Mishra, P., et al. (2019). Application of Student’s t-test, analysis of variance, and covariance. Annals of Cardiac Anesthesia, 22(4), 407–411. https://doi.org/10.4103/aca.ACA_94_19

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