Relationships and Causation

My post 

My study elaborates on the crucial role of theory in quantitative research, specifically within the context of a Doctor of Business Administration (DBA) program. It discusses how theory underpins deductive reasoning, providing a systematic framework for understanding the relationships between variables and forecasting outcomes. It highlights the interconnections between theory, business problems, purpose statements, and research questions, emphasizing that theory forms the conceptual foundation that guides the entire research process. It provides an example of how the resource-based view (RBV) theory was applied in a DBA dissertation study on organizational capacities for employee recruitment and retention post-COVID-19, illustrating the application of theoretical frameworks in addressing specific business issues. The study’s methodology, driven by theory, aimed to explore how organizational capabilities contribute to company performance in the contemporary workforce environment, showcasing the importance of a theoretical basis in producing insights with academic and real-world relevance.

This analysis aligns with the task of distinguishing causation and correlation within the context of DBA doctoral research. It underscores the necessity of grounding research in theoretical frameworks to navigate the complexities of correlational and causal relationships. Applying this further can be done in the following ways:

• Assessing implications for professional practice: Highlighting the misinterpretation of correlational findings as causal can lead to misguided strategies and decisions in professional practice (Baig et al., 2020). A rigorous theoretical grounding can mitigate such risks by providing a more nuanced understanding of variable relationships (Coogan, 2015).
• Discussing bivariate correlation analyses and internal validity: The emphasis on theory indirectly supports the argument that bivariate correlations, while indicative of relationships, lack the depth to establish causality due to their simplicity, thus affecting internal validity (Green & Salkind, 2017).
• Extending research design for causality: My original study stated that theoretical frameworks guide hypothesis formulation and testing methodologies, implying that to examine cause-and-effect relationships, research designs should incorporate experimental or longitudinal studies that can control for external factors, as suggested by the RBV example in exploring organizational capabilities (Bleske-Rechek et al., 2015).

Incorporating these insights, my analysis would focus on the importance of theoretical underpinnings in distinguishing between causation and correlation and how this foundation can guide the design and interpretation of research findings.

References

Baig, U., Hashmi, M. A., Ali, S. B., & Zehara, S. (2020). Exploratory sequential mixed methods in doctorate research: Extended application of constructivist grounded theory. IBT Journal of Business Studies (JBS), 16(2), 284–302. https://doi.org/10.46745/ilma.jbs.2020.16.02.04 

Bleske-Rechek, A., Morrison, K. M., & Heidtke, L. D. (2015). Causal inference from descriptions of experimental and non-experimental research: Public understanding of correlation-versus-causation. The Journal of General Psychology, 142(1), 48–70. https://doi.org/10.1080/00221309.2014.977216 

Coogan, L. L. (2015). Teaching across courses: Using the concept of related markets from economics to explain statistics’ causation and correlations. B> Quest, 2015, 1–10. https://www.westga.edu/~bquest/2015/relatedmarkets2015.pdf 

Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Pearson.

Classmate 1 

Discussion Board- Relationships and Causation

Implying Causation After Using Correlation Analyses

In statistics, correlation refers to how closely two random variables are related. The degree of similarity between two data groupings is called their correlation. It is not true that one linked variable causes the other to occur. The primary similarity is that measurements of correlation and causation determine the relationships between two variables. The two conclusions, however, differ significantly in the following ways: According to Green and Salkind (2017), a causal connection is one in which a change in one variable directly impacts changes in another, whereas correlation reveals the link between two or more variables.

Results of Bivariate Correlation Analyses

The correlation that exists throughout the time between two-time series is known as dynamic correlation (Majnu, 2020). Bivariate analysis is associated with the results of causation and facilitates hypothesis testing. When two variables, say X and Y, are analyzed, the primary goal is to ascertain their empirical connection. This process is known as bivariate correlation. Because the variables may be deceptive, the bivariate correlation studies are regarded as having weak internal validity. Non-linear connections may lead to misleading results. Because there is an apparent contradiction when employing causality after correlation, bivariate analyses are highly erroneous and challenging to interpret, requiring a keen understanding of the data to grasp their significance fully.

Examining True Cause-and-Effect Relationship

A researcher may use an ANOVA data analysis to extend and change a study design to explore a real cause-and-effect connection, potentially dispelling all the contracts found in the bivariate analysis results. ANOVA data analysis is used when a researcher needs to establish a strong, genuine cause-and-effect link since it eliminates all the inconsistencies discovered in a bivariate study. A statistical method for determining the difference between two or more means is called analysis of variance (ANOVA) (Green & Salkind, 2017). The degree to which a researcher is confident that a cause-and-effect link that emerged in a study is not the result of other causes is known as internal validity. Therefore, a strategy that produces accurate research evidence is preferable to one that produces inaccurate results.

References

Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson.

Majnu J., Yihren W., Manjari N., Aparna, T., & Ferbinteanu, J. (2020). Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm.Entropy,22(617),617. https://doi:10.3390/e22060617Links to an external site. 

Classmate 2 

In the context of a DBA doctoral research study focusing on soft skills development among corporate professionals, it’s crucial to distinguish between causation and correlation. This distinction not only shapes the interpretation of research findings but also guides the design and validity of the study. When considering the implications for professional practice, it is essential to understand the risks associated with implying causation from merely correlational analyses, such as bivariate correlation. Bleske-Rechek et al. (2015) highlight the prevalent misunderstanding of the distinction between correlation and causation, a misconception that often permeates professional domains. In a corporate setting, implying that a specific soft skill training program causes an increase in productivity, based solely on correlational evidence, can lead to misdirected strategies and resource allocation. Such misattribution of causation can divert attention and funds from potentially more effective interventions, leading to a general skepticism about the value of soft skills development when the expected outcomes are not realized.

The internal validity of a study, or its ability to establish a causal relationship between variables, is compromised when relying on bivariate correlation analyses. Green and Salkind (2017) note that correlation does not imply causation but rather that two variables move together in a specific pattern. This type of analysis is particularly vulnerable to the third-variable problem, where an external variable, not considered in the analysis, influences both variables under study, leading to a spurious correlation. In soft skills development, this could mean that factors like prior educational background might confound the perceived effectiveness of training programs and actual job performance. Additionally, bivariate correlations cannot determine the direction of the relationship, adding another layer of complexity to the interpretation of results in a corporate training context.

To robustly examine cause-and-effect relationships and move beyond the limitations of correlation, the research design needs to be carefully crafted. Experimental designs, where participants are randomly assigned to treatment or control groups, can be particularly effective. In studying soft skills development, such a design could involve administering a training program to one group (treatment) while withholding it from another (control), followed by comparing the subsequent job performance between the two groups. Alternatively, a longitudinal study design, observing professionals over time and measuring changes in soft skills and job performance at multiple intervals, could help establish a timeline of cause and effect. This approach is beneficial for examining long-term effects and establishing the temporal precedence of the cause before the effect. Employing more sophisticated statistical methods like structural equation modeling or instrumental variables can also enhance the ability to infer causation. These methods allow for the modeling of complex relationships and control for unobserved confounding factors, thereby offering a more nuanced understanding of causal relationships (Coogan, 2015).

While bivariate correlation analyses can provide valuable insights, they are inherently limited in establishing causality. Recognizing and addressing this limitation is critical in professional practice to avoid decision-making based on incomplete or misleading information. By incorporating experimental designs, longitudinal studies, or advanced statistical methods, researchers can better explore the intricate cause-and-effect relationships inherent in soft skills development among corporate professionals.

Bleske-Rechek, A., Morrison, K. M., & Heidtke, L. D. (2015). Causal inference from descriptions of experimental and non-experimental research: Public understanding of correlation-versus-causation. Journal of General Psychology, 142(1), 48–70. doi:10.1080/00221309.2014.977216

Coogan, L. L. (2015). Teaching across courses: Using the concept of related markets from economics to explain statistics’ causation and correlation. B>Quest, 1–10.

Green, S. B., & Salkind, N. J. (2017). Using SPSS for Windows and Macintosh: Analyzing and understanding data (8th ed.). Upper Saddle River, NJ: Pearson.