Module 1 Discussion Forum: Provide Two Real-World Survey Que

Module 1 Discussion Forumprovide Two Real World Survey Questions That

Provide two real-world survey questions that would be useful to you in a professional application or in your everyday life by addressing the following: (i) Your first should be a question associated with a categorical (qualitative) variable. Explain the measuring scale associated with the question and if the data collected is cross-sectional or time series. What might you be able to infer about the data you would collect? (ii) Your second should be a question associated with a quantitative variable. Explain the measuring scale associated with the question. Also, determine whether the variable associated with the survey question is discrete or continuous and if the data collected is cross-sectional or time series. What might you be able to infer about the data you would collect? Be sure to support your statements with logic and argument, citing any sources referenced. Post your initial response early, and check back often to continue the discussion. Be sure to respond to your peers’ and instructor’s posts, as well. 2 to 3 paragraphs.

Paper For Above instruction

Effective data collection through surveys is essential for both professional decision-making and everyday life insights. I propose two survey questions that exemplify this: one focusing on a categorical (qualitative) variable and the other on a quantitative variable. These questions are designed to gather actionable insights while illustrating the importance of understanding measurement scales, data types, and the nature of data collection over time or cross-sectionally.

Question 1: Categorical (Qualitative) Variable

“What is your preferred mode of transportation?” with response options such as “Car,” “Public Transit,” “Bicycle,” “Walking,” or “Other.” This question employs a nominal measurement scale, as the categories have no inherent order. The data collected would be cross-sectional, capturing individuals' preferences at a single point in time, rather than over an extended period. Analyzing such data could reveal the most popular transportation modes within a community or demographic group, aiding urban planners and local governments in resource allocation and infrastructure development. Although these preferences can change over time, a cross-sectional snapshot provides valuable immediate insights into current habits and preferences.

Question 2: Quantitative Variable

“How many hours do you spend on recreational activities per week?” This question uses a ratio measurement scale, as the responses are numerical and have a meaningful zero point, allowing for meaningful ratios. The variable is discrete because the number of hours is counted in whole numbers; responses like 3 hours or 5 hours are typical, while fractional hours (such as 3.5 hours) might still be recorded, but generally, the data are considered discrete unless precise time measurement is used. The data collected would typically be cross-sectional—gathered from individuals at a specific point in time—though it could also be part of a longitudinal study if collected repeatedly over periods. Analyzing this data can shed light on leisure activity trends, inform health and wellness initiatives, or help employers understand employee work-life balance.

References

  • Ferguson, G. A. (2018). Statistical analysis in psychology and education (8th ed.).
  • Moore, D. S., McCabe, G. P., & Craig, B. A. (2017). Introduction to the Practice of Statistics (9th ed.).
  • Salkind, N. J. (2016). Statistics for People Who (Think They) Hate Statistics (6th ed.).
  • Hinkelmann, K., & Kempthorne, O. (2008). Design and Analysis of Experiments.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.).
  • Walpole, R. E., Myrick, P. M., Myrick, J. G., & Myers, R. H. (2012). Probability & Statistics for Engineers & Scientists (9th ed.).
  • Agresti, A., & Finlay, B. (2009). Statistical Methods for the Social Sciences (4th ed.).
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences.
  • Levine, D. M., Stephan, D. F., Krehbiel, T. C., & Berenson, M. L. (2018). Statistics for Managers Using Microsoft Excel.
  • Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences.

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