Introduction
One hundred college students will be surveyed online to prepare the raw data for analysis. Preparation would include ensuring that all the responses are recorded accurately and in the same manner. In particular, the recordings of physical activity levels must be consistent throughout the study. The surveys will include questions on the respondents’ pet ownership status and their physical activity levels over the past week. The physical activity level should be recorded using a 7-point scale, with 1 being no physical activity and 7 being very active. All other responses should be recorded the same way, and any responses with missing values should be noted. Next, the raw data should be organized into a format that is easy to analyze and visualize. This might involve using a spreadsheet to separate the respondents’ responses into categories, such as pet ownership status and physical activity levels. This data will then conclude and create recommendations for college students’ pet ownership and physical activity levels.
Procedures and Descriptive Analysis
The data collected for this study will be entered into a spreadsheet for analysis. Descriptive statistics will be used for data analysis and examination of the association between pet ownership and physical activity. First, the mean physical activity level will be calculated for each group (dog owners, cat owners, and non-pet owners). Next, correlation analysis will be used to investigate the relationship between pet ownership and physical activity level; this will help determine if there is a statistically significant association between these two variables. To examine whether there are significant differences in the physical activity and cardiovascular health of dog and cat owners, an independent samples t-test will be used. The independent samples t-test is an inferential statistical test used to determine if there are significant differences between two independent samples.
All the data analyses will be conducted using the statistical software SPSS (Statistical Package for Social Sciences). The software provides various statistical techniques, from descriptive and summary statistics to more complex inferential methods such as correlation analysis and independent samples t-tests (Abbasnasab Sardareh et al., 2021). It will allow for examining the relationship between pet ownership and physical activity levels within subgroups and for data to be visualized through graphs and charts. Şahi̇n and Aybek (2020) suggest that the software provides a user-friendly environment that simplifies data entry and analysis. These features make it a valuable tool for data analysis. With the help of SPSS, the data collected for this study will be analyzed accurately and quickly. The results of the analyses will be used to explore the association between pet ownership and physical activity level and to identify factors that could influence the relationship between these two variables. These findings will then inform recommendations for promoting physical activity among pet owners and improving the overall health of individuals who own pets.
Correlation analysis is a statistical technique used to measure the strength of linear relationships between two variables. According to Schober et al. (2018), the strength of the relationship is measured using Pearson’s correlation coefficient, which ranges from -1 to +1. Pearson’s correlation coefficient is a mathematical tool used to measure the linear relationship between two variables. A positive correlation suggests that an increase in one variable is associated with an increase in the other (Schober et al., 2018). In contrast, a negative correlation indicates that an increase in one variable is related to a decrease in the other. Such a technique can be used to investigate the association between pet ownership physical activity and cardiovascular health.
Results from a correlation analysis may indicate whether pet ownership and physical activity are associated with improved cardiovascular health and whether there is a stronger association between one or the other variables. This can help inform interventions targeting pet ownership and/or physical activity to improve cardiovascular health. For example, a positive correlation between pet ownership and improved cardiovascular health is found. In that case, interventions could focus on increasing the availability and accessibility of pet ownership to improve cardiovascular health. Similarly, if a strong negative correlation is found between physical activity and cardiovascular health, then interventions could focus on increasing physical activity to improve cardiovascular health.
Informative Descriptive Statistics
Descriptive statistics provide helpful information to understand the central tendency as well as the dispersion in the data. The mean physical activity level for each pet ownership group will be calculated using descriptive statistics; thus, it will be the most informative in answering the research question. Means and standard deviations allow for a comparison of the physical activity levels of the three groups. The t-test, used to compare the groups on physical activity measures, is also incredibly useful as it will allow any differences between the groups to be established as statistically significant. Akoglu (2018) suggests that correlation analysis is also precious, as it can be used to measure the strength of the association between pet ownership and physical activity levels. These three statistical techniques can be used to answer the research question effectively.
Conclusion
The data analysis methods proposed in this study adhere to the ethical guidelines established by the APA. The data will be collected in a systematic and unbiased manner and analyzed using established methods that are reproducible. In addition, the results of the analysis will be reported clearly and concisely, accurately reflecting the data. Furthermore, the data analysis plan will adhere to the APA’s 2010 ethical guidelines for research with human participants by protecting the rights and welfare of participants and respecting their privacy and confidentiality (Domenech Rodríguez et al., 2020). Further, the data will be collected from reliable sources, and the accuracy of the methods and results of the analysis will be evaluated through cross-checking with other data sets, professional consultation, and peer reviews. Such evaluation would ensure that the analysis is conducted in an ethically sound manner.
The data analysis plan will follow the APA’s 2010 guidelines for statistical analysis in psychological research by ensuring that statistical analyses are conducted in a way that is accurate, reliable, and valid. Citations and references to external sources will be provided for all claims, facts, and figures to ensure the results’ validity and minimize any bias or errors in the data analysis. Any discrepancies or limitations in the data will be considered, and the analysis results will be reported objectively without any attempts to misrepresent or exaggerate the findings. Finally, any conclusions drawn from the data will be presented in an accessible and understandable format. The data analysis plan adheres to APA’s guidelines and assures that ethical standards of conduct will be upheld when conducting and reporting the data analysis.
References
Abbasnasab Sardareh, S., Brown, G. T. L., & Denny, P. (2021). Comparing four contemporary statistical software tools for introductory data science and statistics in the social sciences. Teaching Statistics, 43(S1), S157–S172. Web.
Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93. Web.
Domenech Rodríguez, M. M., Gallardo, M. E., Capielo Rosario, C., Delgado-Romero, E. A., & Field, L. D. (2020). Ethical guidelines of the National Latinx Psychological Association: Background. Journal of Latinx Psychology, 8, 95–100. Web.
Şahi̇n, M., & Aybek, E. (2020). Jamovi: An easy-to-use statistical software for social scientists. International Journal of Assessment Tools in Education, 6(4), Article 4. Web.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. Web.