Article Critique Assignment
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Lamar University
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Article Critique Assignment
Introduction or Article Summary
Cook & Welfare (2018) are experienced professionals in the field of counseling and supervision. Ryan M. Cook is a Counselor Education at the University of Alabama. He has written several publications on counseling and supervision. On the other hand, Laura E. is a Welfare Associate Professor of Counselor Education at Virginia Tech. Likewise, Welfare has also published several works mostly on counseling and supervision. That said, Cook & Welfare (2018) set to explore the factors that best contribute to intentional nondisclosure by Counselors-in-Training (CITs) while onsite supervision. The researchers focused on three factors: the social judgment about the supervisor, the supervisee’s attachment styles, and the supervisory working alliance (SWA). Cook & Welfare (2018) identified weaknesses with the existing research that they used an inadequate sample of CITs. Likewise, the authors also identified that most studies focus on the supervisee attachment style and SWA. Moreover, the authors also identified that some of the research with these two factors have results with mixed findings. Consequently, the authors set to explore the degree to which these factors contribute to nondisclosure by Counselors-in-Training. The authors also added another factor, the social judgment about one’s supervisor.
The researchers recruited the research participants through fifteen key informants at fourteen institutions. To conduct the research, 173 eligible students were invited to which 152 turned up, yielding to a response rate of 87.86%. Likewise, the authors carried out data cleaning that led to the exclusion of six participants. This resulted in 146 participants being sampled. The research results were analyzed using SPSS software, after which the authors carried out stepwise multiple regression analysis. The researchers concluded that the three factors being investigated relate to intentional nondisclosure. Therefore, since CITs nondisclosure is an issue of concern that might affect the Welfare of the clients, Cook & Welfare (2018), counselor educators and supervisors are responsible for creating an environment that promotes disclosure.
Major strengths
The researchers build this study on existing researches, which enhances its credibility. This has helped the researchers in creating the theoretical framework that forms a foundation for this study. Cook & Welfare (2018) have reviewed several sources in this study, which has helped them identify gaps. In this case, the researchers identified that the existing researches used inadequate samples that cannot be used for generalization. This helped the authors avoid duplicating the already existing literature. Repetition in research is deemed unethical, especially when the answers are already known. Moreover, the authors could relate their research to the existing literature; thus, proving why their research is important. In more detail, by reviewing several previously done studies, the authors were able to justify their research.
Likewise, the authors conducted their research with integrity. Firstly, before conducting the research, Cook & Welfare (2018) sought approval from an institutional review board. The researchers were also guided by the ethical principles of research: beneficence (do good) and Nonmaleficence (do not harm) (AVAC, 2014). This research’s core principles were obtained through the following means: Cook & Welfare (2018) protected privacy and confidentiality by enhancing anonymity. This ethical principle ensures that no personally-identifying information is collected. This principle encourages research participants to cooperate. Likewise, the researchers respected the participants’ autonomy. In this research, the researchers had invited 173 eligible students to the research, but only 152 turned up.
The researchers did not make follow-ups on why the twenty-one respondents failed to show up for the survey. Therefore, the research participants had the right to withdraw from the study.
Moreover, the researchers did not discriminate against the research participants based on their race and ethnicity. The research incorporated respondents from African American, Hispanic/Latino, whites, Native Americans, and multiracial. Thus, Cook & Welfare (2018) ensured their research reflected the population’s diversity being investigated. This helped the researchers eliminate potential bias caused by using small samples that do not incorporate diversity. For instance, several ethnicity factors could have influenced the judgment factor with the supervisee being investigated; hence, with diversity, potential biases caused by relying on populations from specific ethnicities were eliminated.
The researchers also strived to have accurate and consistent data by carrying out data cleaning to remove all noisy data. In this case, results from six respondents were excluded due to incomplete information. Having incomplete data from some respondents could have impacted the research results, rendering the study insignificant.
Major Weaknesses
This research consisted of several weaknesses, as discussed below. Firstly, the use of expectation–maximization (EM) procedure algorithm to predict the expected missing values with the respondents weakened this research. Researchers are often faced with missing data while conducting research. Most algorithms cannot execute if there are some missing values; therefore, researchers must be prepared to handle such situations. The researchers often have two solutions to this problem: they can either choose to drop the respondents with missing data, which could reduce the number of respondents, or loss of crucial data. The second option involves studying the patterns with other observations and input data based on this predictability. However, this method also has its drawbacks since it increases the risk of losing data integrity. More specifically, the guessed values are from assumptions and not from the actual observations.
Also, Cook & Welfare (2018) tested the missing data’s randomness using the missing completely at random (MCAR) test. This method works on the assumption that the “data are missing at random (MAR)” (Rhoads, 2012 p.2). This helps the researchers to avoid making bias inferences while predicting the values. Regardless, the results from MCAR tests are assumptive, which impacts the integrity of the research.
Another problem with this research is the use of small size for generalization. Since it is not practical to sample the entire population, researchers sample the respondents to represent these populations. That said, due to resource constraints, some researchers choose to have a small sample size. Using a small sample size to make inferences decreases the research’s power and increases the margin of error. Consequently, researchers choose to use large samples to minimize variability, which impacts the results. In this case, Cook & Welfare (2018) used a small sample size, which cannot generalize the results. Although the authors tried to address the small samples issue with the previously done research, they also fell into the same trap. Moreover, this study’s research results were consistent with the previously done studies, which proves the ineffectiveness of using a small sample size.
Likewise, the use of multiple regression in this research had its shortcomings. This method is effective as it enables the researchers to test multiple variables on the expected outcomes. However, this method is limited as it increases the risk of Type I error, which occurs when a researcher rejects the null hypothesis when it’s actually true. Moreover, the use of self-reports by the participants also limits this study. To be more specific, the motivations behind the respondents during the study cannot be identified. The respondents might exaggerate their answers or get embarrassed to reveal the truth leading to lying. Furthermore, the respondents might appear too good during the study, while in reality, this is not the case.
Future areas to Consider
The researchers should consider the use of large sample size in their research to minimize variability. Likewise, Cook & Welfare (2018) should also search for alternatives to self-reported questionnaires. Moreover, these researchers should reconsider the way to handle missing data in their research.
Reflection and Conclusion
This article focused on the factors that best contribute to intentional nondisclosure by Counselors-in-Training (CITs) while onsite supervision. Despite the various limitations of this research, the researchers were able to find a relationship between the factors being investigated and the willingness to disclose. That said, I found social justice’s role to be the most intriguing factor among the three discussed by Cook & Welfare (2018). I was surprised to learn that most supervisees secretly assess their supervisors’ morals, competence, and sociability, influencing their willingness to disclose. In more detail, I was surprised to learn that the trust that the CITs have in their supervisors influences their willingness to disclose. For instance, if the supervisor regularly criticizes them, they are unlikely to disclose if mistakes happen while attending to the clients.
I am very excited to have contributed to the ongoing conversation regarding this article. Critiquing this article has been both complex and fun to work on. I believe that the complexity of this article has boosted my confidence to work on future articles.
References
AVAC. (2014, February 20). Principles of Research Ethics. AVAC. https://www.avac.org/principles-research-ethics
Cook, R. M., & Welfare, L. E. (2018). Examining Predictors of Counselor-in-Training Intentional Nondisclosure. Counselor Education and Supervision, 57(3), 211–226. https://doi.org/10.1002/ceas.12111
Rhoads, C. H. (2012). Problems with Tests of the Missingness Mechanism in Quantitative Policy Studies. Statistics, Politics, and Policy, 3(1). https://doi.org/10.1515/2151-7509.1012