BRFSS Data Analysis: Predictors of Poor Physical Health
This paper presents a comprehensive statistical analysis of 2019 Behavioral Risk Factor Surveillance System (BRFSS) data to identify characteristics that predict self-reported poor physical health. Using SPSS, the analysis applies independent samples t-tests, chi-square tests of independence, Pearson correlations, binary logistic regression, and multiple linear regression. Key variables examined include adverse childhood experiences (ACES), veteran status, sex, income, employment, education, smoking, exercise, alcohol use, BMI, and age. Results indicate that ACES score, employment status, income, exercise, age, and BMI are among the strongest predictors of both the likelihood and frequency of poor physical health days, while veteran status shows no significant association with either outcome.
- Graphical Description of Physical Health Days: Histogram and dot plot describe PHYSHLTH_DAYS distribution
- Group Differences in ACES Score and Alcohol Use: T-tests compare ACES and alcohol by health status
- Association Between Physical Health Status and Veteran Status: Chi-square tests association between health and veteran status
- Correlation Between Physical Health Days, ACES Score, and Veteran Status: Pearson correlations between health days and ACES/veteran
- Logistic Regression: Predictors of Reporting Poor Physical Health: Binary logistic regression identifies predictors of poor health
- Linear Regression: Predictors of Number of Poor Physical Health Days: Linear regression predicts number of poor health days
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What makes this paper effective
- Systematically matches each research question to the appropriate statistical test, with explicit justification based on variable types and independence of groups.
- Follows a consistent formal hypothesis-testing framework across all analyses — stating null and alternative hypotheses, reporting test statistics, and drawing conclusions tied to the significance level.
- Interprets regression outputs at the individual predictor level, specifying direction, magnitude, and practical meaning of each coefficient or odds ratio.
Key academic technique demonstrated
The paper demonstrates multivariable regression interpretation — both logistic and linear — by translating raw SPSS output (B coefficients, Exp(B) odds ratios, Wald statistics, and p-values) into plain-language findings for each independent variable. This technique shows the ability to move from statistical output to substantive conclusion, a core competency in quantitative health research.
Structure breakdown
The paper is organized around six sequential analysis questions, each building in complexity: it opens with univariate visualization, moves to bivariate group comparisons (t-tests and chi-square), then to correlation analysis, and concludes with two multivariable regression models. This progression from descriptive to inferential to predictive analysis mirrors the structure of a real applied research workflow using BRFSS survey data.
Graphical Description of Physical Health Days
The two graphical display options selected to describe the variable PHYSHLTH_DAYS are the histogram with normality curve and the dot plot.
The Histogram with Normality Curve
Figure 1.1 shows a histogram with a normal distribution curve. The histogram was selected because it provides a view of the central tendency, spread, and shape of the dataset, including the presence of outliers. By showing the shape of the dataset, the histogram provides an at-a-glance view of whether the dataset presents a normal distribution. The dataset presents a normal distribution, as evidenced by the single-peaked, bell-shaped normality curve, with observations spread out symmetrically around the mean. No outliers are evident from the distribution.
Figure 1.1
The Dot Plot
Figure 2.1 presents a dot plot. Like the histogram, the dot plot shows the frequency distribution of different data points in the dataset. However, the dot plot provides information on the frequency of individual values rather than a range of values as the histogram does. The dots appear as complete bars due to the large number of values attached to each data point, with longer bars representing higher frequencies. Because it focuses on individual data points, the dot plot provides a more effective way of assessing whether outliers exist than the histogram. Outliers are data points that are either extremely high or extremely low compared to the rest of the data. The dot plot shows that there are no outliers in the dataset.
Figure 2.1
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