NURS 8201 t-Tests and ANOVA in Clinical Practice

The research chosen for this discussion was conducted to evaluate the intensive care nurse attitudes on evidence-based nursing. Nurse introduction forms and evidence based-questionnaires were used to collect data on 70 nurses in public hospitals. Evaluation of the data r using ANOVA revealed that “ the mean score based on the questionnaires was 57.20±9.06, while the highest score of attitudes was (26,97±5,50) is in the sub-dimension of beliefs and expectations towards evidence-based nursing” (Dikmen et al., 2018). A positive correlation between the data variables using ANOVA analysis suggested that nurses positively affect intensive care. However, a comparison between nurse education and duration in the intensive care unit had no significant difference.

Nurses constitute the largest group of professionals in the healthcare industry. Research on their attitudes towards evidence-based nursing in intensive care is essential to evaluate the best practices and adapt them to their practice (Liyew et al., 2020). Determining the nurse attitudes facilitates developing strategies that raise evidence-based nursing practice in the areas (Dikmen et al., 2018). Additionally, nurses need to keep updated through regular journals of scientific research methods to maintain a positive attitude towards evidence-based nursing.

The inferential statistics in the paper played a significant role in examining the rate of nurses who are positive towards evidence-based nursing practices in the intensive care unit. The use of inferential analysis helps generate a solid explanation of the situation. Conclusions are drawn based on extrapolation using descriptive statistics of the data collected. Therefore the inferential statistics strengthened the study application on evidence-based practices by identifying the relationship between the data variables collected (Hare, 2020). Evaluation of the data collected through the forms and the questionnaires suggests that nurses are positive towards evidence-based nursing in intensive care units.

Reference

Dikmen, Y., Filiz, N. Y., Tanrıkulu, F., Yılmaz, D., & Kuzgun, H. (2018). Attitudes of intensive care nurses towards evidence-based nursing. International Journal of Health Sciences and Research, 8(1), 138-143.

Hare, K. A. (2020). Evidence-based end-of-life care education for intensive care nurses (Doctoral dissertation, Walden University).

Liyew, B., Dejen Tilahun, A., & Kassew, T. (2020). Knowledge, attitude, and associated factors towards physical assessment among nurses working in intensive care units: a multicenter cross-sectional study. Critical Care Research and Practice, 2020.https://doi.org/10.1155/2020/9145105

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In a descriptive cross-sectional design survey to identify predictors of Intensive Care Unit (ICU) nurse’s practice of evidence-based practice (EBP) guidelines, the result reviewed two variables associated with why ICU nurses are not consistently practicing EBP (Ashraf et al. 2020). One hundred thirty-two participants were conveniently recruited from the ICU of two different hospitals. Self-reported questionnaires were utilized, including the EBP questionnaire and EBP barrier scale. Pearson correlation test, student t-test, and ANOVA were conducted. The analysis revealed that the mean score of ICU nurses’ EBP was 4.29 (SD = 1.50) out of 7, which was the low level of EBP. This result compared with the prior researcher at 4.3 out of 7. Attitude and knowledge were the significant predictors for the practice of EBP, while the biggest barrier to practice EBP was the setting barrier, with a mean score of 3.02 out of 4. Setting barrier was explained as lack of resources regarding health care, overload work, and low support from hospital administration to apply EBP (Ashraf, 2020).

The application of statistical analysis help researchers reveals a potential issue with why EBP is lacking in ICU. The study’s outcome added a new predictor and gave direction to how educational programs for nurses and improvement in setting barriers can enhance the practice of EBP at the workplace.

Reference

Ashraf A., Omar T., Jamal A. S., Omar, A., & Muhammad W. D. (2020). Predictors of Intensive Care Unit Nurses’ Practice of Evidence-Based Practice Guidelines. Inquiry: The Journal of Health Care Organization, Provision, and Financing, 57. https://doi.org/10.1177/0046958020902323

. To begin with, nurses are evidence-based practice (EBP) advocates and agents of evidence-based change in their clinical setting. I agree with you that nurses are also the largest health care workforce. Therefore, they can have a major impact on the provision of evidence-based care to patients. However, nurses can also face barriers to EBP in their practice setting (Shayan et al., 2019). The study you have chosen for this discussion is equally relevant and informative. On the one hand, it provides data to describe nurses’ attitudes toward EBP (Dikmen et al., 2019). On the other hand, it illustrates how descriptive and inferential statistics can be helpful in the analysis of correlations. Yet, how consistent is the statistical analysis performed in the study?

As mentioned in your post, the researchers in your chosen study used both descriptive and inferential statistics. For the latter, Dikmen et al. (2019) used ANOVA, independent t-test, and Pearson correlation. Each of these have proved useful in the analysis of statistical correlations. However, it appears that researchers often fail to include essential information about the statistical tests used, thus limiting the transparency of their research procedures. Weissgerber et al. (2018) say that researchers should not only specify the type of ANOVA used (i.e., one- or two-way ANOVA) but also include the name and level for each factor, report the F-statistic, and mention if any post-hoc tests were performed.

The study mentioned in your post includes just a brief discussion of both the methods and results (Dikmen et al., 2019). Therefore, it is difficult to ascertain that descriptive and inferential statistics were used appropriately, and that the researchers were able to address the risks of statistical error and bias. I agree with you that inferential statistics has strengthened the descriptive statistics provided in the article, but I think that the researchers should have been more detailed describing their data analysis procedures. Do you agree?

References

Dikmen, Y., Filiz, N.Y., Tanrikulu, F., Yilmaz, D., & Kuzgun, H. (2019). Attitudes of

intensive care nurses towards evidence-based nursing. International Journal of Health Sciences & Research, 8(1), 138-143.

Shayan, S.J., Kiwanuka, F., & Nakaye, Z. (2019). Barriers associated with evidence-based

practice among nurses in low- and middle-income countries: A systematic review. Worldviews on Evidence-Based Nursing, 16(1), 12-20. https://doi.org/10.1111/wvn.12337

Weissberger, T.L., Garcia-Valencia, O., Garovic, V.D., Milic, N.M., & Winham, S.J. (2018).

Why we need to report more than ‘Data were analyzed by t-tests or ANOVA’. eLife, 7, e36163. https://doi.org/10.7554/eLife.36163

References

Dikmen, Y., Filiz, N.Y., Tanrikulu, F., Yilmaz, D., & Kuzgun, H. (2019). Attitudes of

intensive care nurses towards evidence-based nursing. International Journal of Health Sciences & Research, 8(1), 138-143.

Shayan, S.J., Kiwanuka, F., & Nakaye, Z. (2019). Barriers associated with evidence-based

practice among nurses in low- and middle-income countries: A systematic review. Worldviews on Evidence-Based Nursing, 16(1), 12-20. https://doi.org/10.1111/wvn.12337

Weissberger, T.L., Garcia-Valencia, O., Garovic, V.D., Milic, N.M., & Winham, S.J. (2018).

Why we need to report more than ‘Data were analyzed by t-tests or ANOVA’. eLife, 7, e36163. https://doi.org/10.7554/eLife.36163

True, statistics are essential in quantitative research; they can also facilitate data analysis in a clinical setting. However, only when statistics are done appropriately, they can benefit science and advance nursing practice. Please let me add a few comments regarding the study you discussed in your post.

To begin with, the article does not include a detailed description of statistics or data analysis. According to Katzmarzyk et al. (2019), the design and methods of the study had been discussed elsewhere. I found this information in an earlier publication. In it, Katzmarzyk et al. (2013) say that the study would involve descriptive and inferential statistics, namely, multilevel random-effects models and covariate-adjusted models. The choice of the model looks appropriate, since the ISCOLE study involves numerous independent variables, such as physical activity, dietary patterns, and so on. Complex statistical models are well-suited to analyze complex correlational or causal links among variables. However, they also have limitations. For example, they can be time consuming.

The main question is whether the inferential statistics used in the study were sufficient and effective enough to identify cause-and-effect relationships. Katzmarzyk et al. (2019) note that their statistical models had limitations, making causal inferences problematic or questionable. They could have used ANOVA or t-test, but they would not be suitable in the analysis of causal relationships. According to Schober et al. (2018), correlations do not imply causality; nor do they say enough about the strength of the relationship between the independent and dependent variable. Besides, in the discussed study, data were collected at a single point of time, which could limit their utility in identifying cause-and-effect relationships.

Notwithstanding these limitations, the use of inferential statistics was beneficial, as it added to and expanded descriptive statistical results. The researchers were able to demonstrate the complexity of factors affecting childhood obesity rates across countries. They may still need additional data to address weaknesses in method and design, such as using other inferential statistics to validate the initial findings.

References

Katzmarzyk, P.T., Barreira, T.V., Broyles, S.T., Champagne, C.M., Chaput, J.P., Fogelholm,

M., Hu, G., Johnson, W.D., Kuriyan, R., Kurpad, A., Lambert, E.V., Maher, C., Maia, J., Matsudo, V., Olds, T., Onywera, V., Sarmiento, O.L., Standage, M., Tremblay, M.S., Tudor-Locke, C., Zhao, P., & Church, T.S. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health, 13, 900. https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-900

Katzmarzyk, P. T., Chaput, J. P., Fogelholm, M., Hu, G., Maher, C., Maia, J., Olds, T.,

Sarmiento, O.L., Standage, M., Tremblay, M.S., & Tudor-Locke, C. (2019). International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): contributions to understanding the global obesity epidemic. Nutrients,11(4), 848. https://dx.doi.org/10.3390/nu11040848

Schober, P., Boer, C., & Schwarte, L.A. (2018). Correlation coefficients: Appropriate use and

interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864

NURS 8201 t-Tests and ANOVA in Clinical Practice