Response to my classmate Annotated Bibliography essay

DescriptionPlease response to my classmate annotated bibliography with the following Respond to two or more of your colleagues’ postings in one or more of the following ways:Address the content of each colleague’s analysis and evaluation of the topic and of the integration of the relevant resources.
Link each colleague’s posting to other colleagues’ postings or to other course materials and concepts, where appropriate and relevant.
Extend or constructively challenge your colleagues’ work.
Answer question(s) posed by your colleague(s) for further discussion.Classmate annotated bibliography
Bivariate correlation analysis is only one step in determining the cause-effect (causation) between two numeric variables. Calculating a bivariate correlation coefficient identifies whether there is simply a relationship between two numeric data sets, not whether one influences the other (Bougie & Sekaran, 2019). Commonly used correlation coefficients include Pearson, Kendall, Spearman, and the Point-Biserial. Each calculation identifies the strength of association between two variables and the relationship’s direction, ranging from +1 (perfectly positive) to -1 (perfectly negative). A correlation coefficient greater than 0.7 typically implies a strong relationship between variables (Anderson et al., 2002), a data point that researchers can use to further understand and investigate causality.
Just because a correlation is determined to be strong does not mean that A influences B. The relationship between the two variables could be caused by a third factor (or more) that affects both. For example, a new retail manager may see a 90% correlation between barbecue and cooler sales and presume that shoppers purchasing barbecues buy coolers (or vice versa). While this may be temporarily true, the manager neglected to consider that he/she started in May, and that the seasonal effect of summer beginning is primarily driving the correlation. The risk is that the manager will assume the correlation between barbecue and cooler sales implies causality, leading to sub-optimal purchasing decisions for non-summer periods. This mistaken interpretation is known as spurious or false correlation (Bougie & Sekaran, 2019). A confounding factor was not considered (weather), one of the reasons bivariate correlation analyses are considered weak in terms of internal validity.
Researchers must also be careful not to assume correlation implies causation. My doctoral study examines whether analysts issue more favorable stock recommendations for public companies that are also in-house investment banking clients. I may find a positive correlation between the timing of investment banking business (independent variable) and analyst bullishness (dependent variable), which would suggest the former influences the latter. However, my correlation finding is compromised if I fail to account for confounding factors like other analyst’s relative bullishness or the stock market cycle backdrop for the analysis time frame. I can incorporate several measures to combat this, including: (a) normalizing data for the impact of extraneous factors on my dependent variable (i.e., adjusting for consensus rating revisions); (b) isolating and evaluating other alternative explanations (i.e., adjusting for broader market sentiment); and (c) grounding my research in relevant causal theory and pre-existing empirical evidence that legitimizes study design and takeaways.References
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2002). Statistics for business and economics (8th ed.). South-Western Thomson Learning.
Bougie, R. & Sekaran, U. (2019). Research methods for business: A skill-building approach (8th ed.). John Wiley & Sons.
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