GCU quantitative core designs

by | Oct 7, 2024 | Databases

 

A researcher is interested in studying the relationship between physical activity and life satisfaction among college students. The researcher plans to administer a survey to a sample of college students and collect data on their physical activity levels and life satisfaction. Using this scenario, which GCU quantitative core designs do you think would be most appropriate for this research problem? Why? Create and provide two examples of research questions that could be addressed using the design you selected. What might the advantages and challenges be of using the identified design for this scenario? Explain including references.

A correlational design would be most appropriate for this research problem. This design is used to investigate the relationship between two or more variables. In this case, the researcher wants to examine the relationship between physical activity and life satisfaction among college students.

Research Questions:

  1. Is there a positive correlation between physical activity levels and life satisfaction among college students?
  2. Does the type of physical activity (e.g., aerobic, strength training) have a differential impact on life satisfaction among college students?

A correlational design would be most appropriate for this research problem. This design is used to investigate the relationship between two or more variables. In this case, the researcher wants to examine the relationship between physical activity and life satisfaction among college students.

Research Questions:

  1. Is there a positive correlation between physical activity levels and life satisfaction among college students?
  2. Does the type of physical activity (e.g., aerobic, strength training) have a differential impact on life satisfaction among college students?

Advantages of Correlational Design:

  • Efficient: Correlational research can be relatively efficient and cost-effective, as it often involves collecting data through surveys or existing databases.
  • Exploratory: It can be used to explore potential relationships between variables, even if the exact nature of the relationship is not fully understood.
  • Generalizability: Findings from correlational studies can sometimes be generalized to larger populations, especially if the sample is representative.

Challenges of Correlational Design:

  • Causation: Correlational research cannot establish cause-and-effect relationships. It only shows that two variables are related, not whether one causes changes in the other.
  • Confounding Variables: There may be other variables that influence both physical activity and life satisfaction, making it difficult to isolate the direct relationship between the two.
  • Self-Report Bias: Relying on self-reported data can introduce bias, as participants may overestimate or underestimate their physical activity levels or life satisfaction.

References:

  • Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

Note: While a correlational design is suitable for this research problem, it would be beneficial to consider additional research methods, such as experimental designs, to establish causality. This would involve randomly assigning participants to different physical activity groups and measuring their life satisfaction outcomes.

 

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