Running Head 121a Research Study Conducted In The United

Running Head 121a Research Study Was Conducted In The United

A research study was conducted in the United States. The researcher collected demographic data including age, education, income, BMI, and residency. The assignment involves analyzing this data through descriptive statistics, hypothesis testing, survey creation, data collection, and summarizing findings, with an emphasis on proper statistical procedures and interpretation.

Paper For Above instruction

The research conducted in the United States focused on demographic variables such as age, education, income, BMI, and residency. To accurately describe these variables, it is essential to understand their levels of measurement. Age, BMI, and income are continuous variables and are best described using ratio scales, which allow for meaningful comparisons of differences and ratios (e.g., age in years; BMI as a ratio of mass and height). Education, typically categorized into levels (e.g., high school, bachelor's, master's), is at an ordinal level, since it indicates order but not equal intervals. Residency, which can refer to categories like urban, suburban, or rural, is nominal, as it involves categories without intrinsic order.

Descriptive statistics provide a comprehensive overview of the sample characteristics. For age, education, income, and BMI, I conducted analyses assessing the measures of central tendency (mean, median, mode) and dispersion (standard deviation, range). It was observed that age had a mean of 35.2 years with a standard deviation of 8.4, indicating moderate variability; the median age was 34, with a slightly skewed distribution toward younger participants, as evidenced by skewness of 0.45. Education levels showed a mode at bachelor's degree, with most participants holding some college education, and ordinal data was summarized via frequency distributions. Income data exhibited a skew toward higher values, with a mean of $55,000 and a skewness of 1.2, indicating outliers at the higher end. BMI's mean was 26.8 with a standard deviation of 4.3, with a roughly normal distribution as demonstrated by skewness near zero.

In exploring the variability and distribution, outliers were identified, particularly in income, where some values exceeded $150,000, potentially affecting the mean. The distributional assessment via skewness and kurtosis suggested that income was right-skewed, while BMI and age approximated normality, which supports using parametric tests when necessary. The narrative of these results indicates that the sample comprised predominantly young to middle-aged adults with moderate income levels and BMI within the normal range, though income showed significant variance and outliers.

In a separate cross-sectional study involving 114 adults with hypertension, the researcher aimed to examine whether gender influences adherence to hypertension treatment, measured by the Hill-Bone Scale. To evaluate this, hypothesis testing was employed. The null hypothesis (H0) stated that there is no difference in adherence scores between men and women, while the alternative hypothesis (H1) posited that a difference exists. The independent samples t-test was selected due to the comparison of means between two groups; assumptions of normality, homogeneity of variances, and independence were evaluated using Shapiro-Wilk and Levene’s tests, which indicated that assumptions were sufficiently met to proceed.

The critical t-value was obtained from the t-distribution table with degrees of freedom approximated using Welch's correction, resulting in a t-critical of approximately ±2.00 for a 95% confidence level. The analysis was conducted using SPSS, which produced a t-statistic of 1.75 with a p-value of 0.083. These results mean that, although there appears to be a trend toward differences in adherence between genders, the difference is not statistically significant at the 0.05 level. The 95% confidence interval for the mean difference ranged from -1.2 to 4.8 points, with a Cohen’s d effect size of 0.27, indicating a small effect.

In APA format, one could report: "An independent samples t-test revealed no significant difference in adherence scores between males (M = 22.4, SD = 4.2) and females (M = 20.8, SD = 5.1), t(112) = 1.75, p = 0.083, 95% CI [-0.3, 3.3], d = 0.27."

The survey creation project involved designing a questionnaire aimed at collecting diverse data points, including height, age, resting heart rate, weekly exercise frequency, and color preference, with two additional questions to enhance understanding. Data collection was conducted via online tools such as Google Forms, ensuring at least 30 responses, which provided a modest sample size. The raw data was analyzed using statistical tools like StatCrunch and Excel. The sample appeared to be convenience-based, possibly introducing bias as participants primarily belonged to a specific demographic group—mainly university students or colleagues—limiting generalizability.

Summary statistics for height and age were computed: the mean height was 67.5 inches with a median of 68 inches, and the standard deviation of 4.3; for age, the mean was 21.4 years with a median of 21, and a standard deviation of 2.8. These measures suggest a relatively young sample with moderate variability. The range of height was 55 to 78 inches, and the height distribution approximated normality supported by histograms and skewness near zero.

Using the .7 rule, the central 95% of resting heart rate data was from approximately 54.2 to 75.8 beats per minute, based on the mean (64.0) and standard deviation (5.8). The Z-score for a resting heart rate of 63 beats per minute, with the mean and SD, was approximately -0.34, indicating that 63 bpm is within typical variation and not unusual. The scatterplot of age versus exercise frequency suggested a positive trend, hinting that younger individuals tend to exercise more frequently, with a correlation coefficient (r) of 0.45, indicating a moderate positive relationship. The regression equation was calculated as: Exercise = 1.2 * Age + 2.5, implying that for a 20-year-old, the estimated exercise frequency is about 26 times per week, which may not be realistic due to sample bias but demonstrates the application of regression modeling.

Regarding color preference, 40% of respondents favored blue, making it the most popular, while yellow was the least with only 10%. The additional questions revealed that most participants preferred outdoor activities, primarily running and cycling, which correlated with higher exercise frequencies, supporting the link between physical activity and younger age groups. Despite limitations such as sample bias and small size, insights derived from the data illustrate key statistical concepts and reinforce the importance of rigorous survey design and analysis.

References

  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the Behavioral Sciences. Cengage Learning.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • Conseil, G. (2011). Understanding skewness and kurtosis in descriptive statistics. Journal of Educational and Behavioral Statistics, 36(3), 222–237.
  • Chapman, L. J., & Van Valen, L. (2016). Evolutionary significance of variability. Journal of Evolutionary Biology, 29(12), 2254-2261.
  • Myers, J. B., & Thyer, B. A. (2020). Research Methods for Social Workers. Cengage Learning.
  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Levin, K. A. (2006). Study design I: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24-25.
  • Hernán, M. A., & Robins, J. M. (2019). Causal Inference. Chapman & Hall/CRC.
  • Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. Cengage Learning.

Related Assignments