Is Good Research Valuable In Scholarly Or Practitioner Setti ✓ Solved
Whether In A Scholarly Or Practitioner Setting Good Research
Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will perform an article critique on correlation and bivariate regression. The goal is to obtain constructive feedback to improve the research and its interpretation.
To prepare for this Discussion: Review the Learning Resources and the media programs related to correlation and regression. Search for and select a quantitative article specific to your discipline and related to correlation or regression. By 1/20/2021, write a 3- to 5-paragraph critique of the article. In your critique, include responses to the following:
- What is the research design used by the authors?
- Why did the authors use correlation or bivariate regression? Do you think it’s the most appropriate choice? Why or why not?
- Did the authors display the data? Do the results stand alone? Why or why not?
- Did the authors report effect size? If yes, is this meaningful?
Paper For Above Instructions
In the context of research, correlation and bivariate regression analysis serve as critical tools for understanding relationships between variables. This paper critiques a selected quantitative article to evaluate its research design and the appropriateness of the statistical methods used. The primary objective is to assess the validity of the authors' choices and the effectiveness of their data presentation.
Research Design
The research design employed in the chosen article is essential for establishing how the authors conducted their analysis. In this case, the authors utilized a quantitative approach to explore the correlation between two numeric variables. The research design was presumably a cross-sectional study, allowing the authors to gather data at a single point in time. Such a design is beneficial for examining relationships but may not capture changes over time.
Justification for Statistical Method
The choice between using correlation analysis or bivariate regression stems from the research question at hand. Correlation analysis is designed to measure the strength and direction of a relationship between two variables but does not imply causation. In contrast, bivariate regression not only assesses the relationship but also accounts for the impact of one variable on another, providing deeper insight into predictive modeling. In the article, the authors opted for bivariate regression, likely justified by their intent to predict outcomes based on independent variables. This choice is appropriate as it extends beyond mere correlation to infer causational insights.
Data Presentation
Effective data presentation is crucial for comprehending the results. In the reviewed article, the authors displayed their data using graphs and tables, which enhanced the readers' ability to grasp complex relationships visually. However, the adequacy of these visual representations lies in whether they clearly convey the data patterns and statistical significance. The results should be able to stand alone, meaning that readers should understand the findings without reliance on external context. In this regard, the article succeeded as the results were presented alongside adequate explanations and statistical commentary.
Effect Size Reporting
Effect size is a vital statistic that adds depth to the interpretation of results. Reporting effect size allows researchers to understand the practical significance of findings, beyond mere statistical significance. The authors of the selected article did report effect sizes, and these figures were indeed meaningful as they provided insight into the strength of the relationships observed. Such information aids practitioners and scholars in determining the real-world implications of research findings.
Conclusion
In summary, the critique of the selected article emphasizes the importance of research design, statistical method selection, data presentation, and effect size in quantitative research. Correlation and bivariate regression serve essential roles in uncovering relationships between variables and providing insights that can influence both scholarly inquiry and practical application. By obtaining peer feedback, researchers can refine their approaches and enhance the clarity and impact of their findings.
References
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