Correlation and regression are statistical methods used to investigate the relationship between two or more variables. Correlation measures the strength and direction of the relationship between two variables. In contrast, regression is used to predict one variable's value from another's value (Ezekiel & Fox, 1959).
The goal of the article
The article (Kniffin et al. 2015) examines the relationship between workplace parity (i.e., eating together) and firefighter performance. The authors conducted field research and found that firefighters who ate together more often had higher performance ratings were more connected to their colleagues, and were more satisfied with their jobs when compared to firefighters who ate together less often.
Reporting the test results:
In the article, the association test between the frequency of eating together and performance evaluation was significant, with Pearson's correlation coefficient r = 0.52 and p-value= 0.0001. This means that there is a positive relationship between the two variables and that the more firefighters eat together, the higher their performance ratings. Correlation test results indicate that parity in the workplace can have a positive effect on firefighter performance. As a result, organizations that want to improve the performance of their employees should encourage them to eat together more often.
Data analysis methods in these ways:
The data were analyzed using correlation or regression, as this is most relevant to the type of data collected. Data were collected using a survey and sampling, which is a quantitative data collection method. The Pearson correlation coefficient is a statistical test used to measure the strength and direction of the relationship between two quantitative variables (Schober, Boer & Schwarte, 2018).
The p-value measures the statistical significance of the correlation coefficient. A p-value of 0.0001 means a 0.0001% probability of obtaining a correlation coefficient of at least 0.52, assuming the two variables are unrelated. This is a minimal possibility, so the relationship between the frequency of eating together and performance evaluation is significant.
Correlation and regression testing in construction:
I will use correlation or regression testing in construction to investigate the relationship between different factors that can affect the quality performance of concrete buildings. For example, I use correlation testing to explore the relationship between the number of parameters in a concrete design and its accuracy. I also will use a regression test to investigate the relationship between the number of steel rebars of different diameters and the strength of the building (Koshy & Apte, 2012).
I will use correlation or regression testing to explore the relationship between factors that can affect the stability of concrete buildings, such as earthquakes, floods, and hurricanes. Correlation and regression testing are powerful tools that can be used to investigate the relationship between different factors.
Conclusion
In the article, the authors used a correlation test to investigate the relationship between the frequency of eating together and performance evaluation. They found a positive relationship between the two variables, meaning that the more firefighters ate together, the higher their performance ratings. The authors also used a regression test to predict a firefighter's performance rating based on the frequency of eating together. They found that the regression model could predict performance ratings with high accuracy (Kniffin et al. 2015).
References
Ezekiel, M., & Fox, K. A. (1959). Methods of correlation and regression analysis: linear and curvilinear.
Kniffin, K. M., Wansink, B., Devine, C. M., & Sobal, J. (2015). Eating together at the firehouse: How workplace commensality relates to the performance of firefighters. Human Performance, 28(4), 281–306. Retrieved from EBSCO multi-search
Koshy, R., & Apte, E. M. (2012). Waste minimization of construction materials on a bridge site (Cement and Reinforcement Steel)-a regression and correlation analysis. International Journal of Engineering and Innovative Technology, 2(1), 6-14.
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5), 1763-1768.