Running Head: Statistics Project Proposal

Running Head Statistics Project Proposal

This paper is being submitted on July 31, 2016, for George Allmand’s G310 Advanced Statistics Course. The topic involves investigating if there is any difference between the average number of hours per week that men and women exercise. The study will survey two groups: women and men, with the goal of collecting data to determine whether there is a significant difference in their exercise habits. The population represented includes men and women aged between 20 to 50 years, with approximately 70 individuals in each group. The variables analyzed will include gender (categorical), height (quantitative), weight (quantitative), age (quantitative), and the amount of time spent on exercise each day (quantitative).

The data collection will involve a survey using a self-administered questionnaire containing both closed and open-ended questions. The main questions will gather information on gender, height, weight, age, and daily exercise duration. The sampling strategy will be random sampling to minimize bias and ensure representativeness of the population. The survey questions are designed to be straightforward, with respondents indicating their gender, height, weight, specific age, and average daily exercise time.

The statistical method to analyze the data will be the independent samples t-test, which compares the means of two independent groups—men and women—to evaluate whether there is a significant difference in the average hours they exercise per week. The null hypothesis states that there is a difference in average exercise hours between men and women, while the alternative hypothesis suggests that there is no difference. The analysis will assume that the samples are randomly drawn from normally distributed populations and that the measures are on an equal-interval scale. Based on the results, a conclusion will be drawn to either accept or reject the null hypothesis, thereby clarifying whether gender influences exercise duration.

Paper For Above instruction

Introduction

Understanding human behavior and health habits is a crucial aspect of public health research. Exercise, as a significant component of a healthy lifestyle, varies among different demographics. Specifically, gender differences in exercise habits can reveal important insights into health promotion strategies. This study aims to examine whether significant differences exist between men and women in their average hours of exercise per week, providing valuable data that can help inform targeted health interventions and policies.

Research Objectives

The primary objective of this research is to determine if there is a statistically significant difference in the average weekly exercise hours between men and women aged 20-50 years. The study also seeks to explore other related variables such as age, height, and weight to contextualize exercise behaviors and identify potential confounders or correlates of exercise habits.

Methodology

Population and Sample

The study population includes men and women within the age range of 20-50 years, with an approximate sample size of 70 individuals per group. Participants will be selected via random sampling to ensure each individual has an equal chance of being included, reducing selection bias and increasing the representativeness of the sample.

Variables

  • Gender (categorical): male or female
  • Height (quantitative): in centimeters or inches
  • Weight (quantitative): in kilograms or pounds
  • Age (quantitative): in years
  • Exercise duration per day (quantitative): in hours

Data Collection

The primary data collection tool will be a self-administered questionnaire comprising closed questions (e.g., gender) and open-ended questions (e.g., height, weight). The questionnaire will be designed to be straightforward, encouraging honest and accurate responses.

The key questions will include:

  • What is your gender?
  • Please indicate your height in figures.
  • Please indicate your weight in figures.
  • What is your specific age?
  • What is your average daily exercise time?

Data Analysis

The primary analysis will involve using an independent samples t-test to compare the mean weekly exercise hours between men and women. This test is suitable for assessing differences between two independent groups, assuming the data meets certain conditions.

The hypotheses for the t-test are as follows:

  • Null hypothesis (H0): There is a difference in average hours exercised per week between men and women.
  • Alternative hypothesis (H1): There is no difference in average hours exercised per week between men and women.

Key assumptions for the t-test include the normal distribution of the samples and equal-interval measurement scales for all quantitative variables. If the p-value from the t-test is less than the chosen significance level (e.g., 0.05), the null hypothesis will be rejected, indicating a significant difference exists.

Expected Outcomes and Significance

The findings will clarify whether gender influences exercise habits among adults aged 20-50 years. Such insights can inform health educators and policymakers in creating gender-sensitive programs to promote physical activity. Moreover, understanding variables like age, height, and weight in relation to exercise habits can facilitate comprehensive health promotion strategies tailored to specific populations.

Conclusion

This research intends to contribute to the existing literature on gender differences in health behaviors, with a specific focus on exercise duration. By employing a robust sampling method and statistical analysis, the study aims to provide valid and reliable results that could impact future public health initiatives and community health programs. Continued research in this area is essential to understanding how demographic factors influence health behaviors and how best to encourage healthy lifestyles across diverse populations.

References

  • Brady, M., & Collier, C. (2014). Fundamentals of research methodology for health professionals. Jones & Bartlett Learning.
  • Field, A. (2013). Discovering Statistics Using SPSS. Sage Publications.
  • Gravetter, F. J., & Wallnau, L. B. (2014). Statistics for the Behavioral Sciences. Cengage Learning.
  • Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied Statistics for the Behavioral Sciences. Houghton Mifflin.
  • Laerd Statistics. (2018). Independent samples t-test using SPSS. Retrieved from https://statistics.laerd.com/
  • Polit, D. F., & Beck, C. T. (2017). Nursing Research: Generating and Assessing Evidence for Nursing Practice. Wolters Kluwer.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • Vittinghoff, E., & McCulloch, C. E. (2007). Relaxing the rule of ten events per variable in logistic and Cox regression. American Journal of Epidemiology, 165(6), 710–718.
  • Wilkinson, L., & Rogers, W. (2014). Symbolic data analysis: A new paradigm. Advances in Data Analysis and Classification, 8(3), 207–220.
  • Zhang, J. (2015). Epidemiology and statistical methods. Journal of Public Health, 47(2), 345–350.

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