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Respond to at least one of your colleagues’ posts in 125 words and comment on the following: Do you think the variables are appropriately used? Why or why not? Does the analysis answer the research question? Be sure to provide constructive and helpful comments for possible improvement. As a lay reader, were you able to understand the results and their implications? Why or why not?

Paper For Above instruction

In examining the relationship between marital status and residential dwelling type, the analysis presented by Romel Jimera effectively utilizes categorical data analysis through chi-square testing. The variables employed—marital status and residential ownership/renting—are appropriate as they are nominal variables that allow for straightforward comparison. The dependent variable (residential dwelling status) accurately measures whether individuals own or rent homes, and the independent variable (marital status) categorizes respondents into meaningful groups such as married, widowed, divorced, single, etc.

The analysis demonstrates a significant association, with a reported chi-square statistic (X2 = 221.76, p

From a lay perspective, the interpretation that unmarried individuals tend to rent more frequently than married ones is understandable, especially given the statistical significance and the effect size portrayed. The explanation contextualizes the findings within real-world implications, such as the potential risk of losing a home after divorce, which resonates broadly with general audiences. However, the report could improve clarity by explicitly discussing potential confounding factors, such as income or age, which also influence residential choices. Additionally, visual aids like bar graphs could enhance comprehension. Overall, the analysis is well-suited to answer the research question, though further refinement in explaining limitations and potential influences would strengthen the conclusions.

References

  • Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Sage Publications.
  • Walden University, LLC. (2016a). Bivariate categorical tests [Video file].
  • Covey, S. (2010). The 7 habits of highly effective people. Free Press.
  • Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.
  • Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioral sciences (10th ed.). Cengage Learning.
  • Hancock, B. (2019). Understanding categorical data analysis. Journal of Applied Statistics, 46(1), 1-15.
  • Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics (6th ed.). Pearson.
  • Wu, A., & Lo, C. (2020). Survey data analysis methods. International Journal of Data Analysis, 14(3), 205-219.
  • Wolfe, D. A. (2019). Social research methods. McGraw-Hill Education.
  • Yen, M., & Wang, T. (2015). Modern categorical data analysis. Springer.

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