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NURS 8201 Week 7 Discussion: Use Of Regression Analysis In Clinical Practice

Regression analysis encompasses a set of statistical methods that help estimate the relationship between a dependent variable and one or more independent variables (Sarstedt et al., 2019). It is also a type of data analysis that explains how study variables change over time and across different variables. Indeed, regression analysis allows researchers to predict and explore future outcomes (Sarstedt et al., 2019). For instance, regression analysis can help propose new treatments, determine prevention methods, or promote opportunities for learning. Linear regression is one of the analyses that is used to determine a line that best represents the general trend of a data set (Schober & Vetter, 2021). In other words, linear regression estimates the relationship between a scalar response and one or more independent variables. This discussion will select an article, examine the strengths and weaknesses of linear regression, and propose remedies to address these weaknesses.

The selected article

Chi, C., Wu, H., Huan, C., & Lee, Y. (2017). Using linear regression to identify critical demographic variables affecting patient safety culture from viewpoints of physicians and nursesLinks to an external site.Hospital Practices and Research, 2(2), 47–53. doi:10.15171/hpr.2017.12

The goals and purposes of the research study

This study aimed to identify critical demographic variables from the viewpoints of physicians and nurses that significantly influence the patient safety culture in a regional teaching hospital in Taiwan.

How is linear regression used in the study, and the results of its use

All the physicians and nurses in this hospital were invited to participate in this study. The valid number of participants was 376, comprising 42 physicians and 334 nurses. Data was collected internally through the 2014 SAQ-C from JCT (Chi et al., 2017). The questionnaire had 46 items examining their attitudes towards aspects such as job satisfaction, teamwork climate, stress recognition, emotional exhaustion, work-life balance, safety climate, and perception of management. For all the aspects except work-life balance, a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree was used (Chi et al., 2017). Since the work-life balance used a 4-point Likert to measure the frequency per week, it was excluded from this study. This study used linear regression with forward selection to analyze the data. The linear regression started with an empty set and continually added one attribute at a time. Only the attribute that gave the highest performance was added to the selection at each step. All statistical analyses were carried out using SPSS software version 18.

In this analysis, the predictor variable was, in this case, supervisors/managers. The α = 0.05, and the adjusted R-square values range between 0.048 and 0.138(Chi et al., 2017). According to the regression analysis, teamwork climate had a negative correlation with the supervisor/manager and the years of experience in the position. This means that nurses and physicians who are not in charge are less satisfied with the teamwork climate, job satisfaction, perceptions of management, stress recognition, safety climate, and working conditions (Chi et al., 2017). Similarly, physicians and nurses with many years of experience in their positions were also less satisfied. The regression analysis found that the supervisor/ manager and experience in the position negatively impacted the safety climate. This means that the healthcare workers who are not in leadership positions and those with many years of experience in their positions are less satisfied. Furthermore, the analysis found that job satisfaction is affected by variables such as experience in position, age, and supervisor/manager. More so, nurses tend to have less satisfaction in stress recognition.

Perceptions of management are negatively impacted by supervisor/manager and experience in the position. This means that the healthcare staff that do not hold supervisor/manager positions and have many years of experience in their positions are less satisfied with their perception of management. The dimension of working conditions was impacted by supervisor/ manager, years of experience, and age. Elderly employees were more satisfied with their working conditions, and those with more experience were less satisfied. Lastly, it was not possible to draw a linear regression between emotional exhaustion and the ten demographic variables. As per the regression analysis, nurses and physicians with supervisory/managerial positions and much experience had the greatest effect on the patient safety culture. Therefore, it is safe to conclude that the job position had some bearing on patient safety culture.

Strengths and weaknesses of linear regression

One of the strengths of linear regression, as shown in the article, is simplicity. This analysis is straightforward and provides a direct relationship between dependent and independent variables (Schober & Vetter, 2021). Also, linear regression is easy to interpret as it provides the direction and strength between variables. Moreover, linear regression makes it possible for researchers to analyze large sets of data using fewer resources compared to other analysis methods (Schober & Vetter, 2021). Besides, linear regression tests variables using tools such as homoscedasticity, linearity, and independence of errors.

However, linear regression presents several weaknesses. For instance, linear regression is increasingly sensitive to outliers to the extent that a few data points can affect the model parameters inappropriately (Schober & Vetter, 2021). Also, there are instances when linear regression assumes linearity between variables. This means that the analysis may provide inaccurate results if the true relationship is non-linear. Moreover, linear regression is considered not the most effective method for analyzing complex and non-linear relationships between variables (Schober & Vetter, 2021). More so, multicollinearity issues may occur when linear regression is used to analyze highly correlated variables. This may make it difficult to understand the real effect of each dependent variable.

Remedies to address these weaknesses

The issue of outliers can be remedied by using graphical methods to identify outliers, using other techniques such as Huber regression, and replacing extreme values with less extreme ones (Sun et al., 2020). Since linear regression is not effective for complex data, a remedy may be to use polynomial regression. The remedy to multicollinearity issues is to separate highly correlated variables (Chan et al., 2022). It can also be done by calculating the Variance Inflation Factor for the variables or performing a principal component analysis, which transforms highly correlated variables into components of uncorrelated variables (Chan et al., 2022).

Importance of this study to the nursing profession

This study’s findings significantly contribute to the nursing profession by enhancing the perceptions of the patient safety culture. This is because it would provide the hospital management with insights that identify flaws in their hospitals and, in extreme situations, help redesign systems. For instance, this study revealed that physicians and nurses perceive supervisor/ manager and experience in position followed by age as the most important variables influencing the patient safety culture. Also, this study proposes activities such as mindfulness-based stress reduction programs and appreciative inquiry groups to help healthcare staff strengthen resilience and positive psychology toward negative emotions.

References

Chan, J. Y. L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z. W., & Chen, Y. L. (2022). Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics10(8), 1283.

Chi, C., Wu, H., Huan, C., & Lee, Y. (2017). Using linear regression to identify critical demographic variables affecting patient safety culture from viewpoints of physicians and nursesLinks to an external site.Hospital Practices and Research, 2(2), 47–53. doi:10.15171/hpr.2017.12

Schober, P., & Vetter, T. R. (2021). Linear Regression in Medical Research. Anesthesia and analgesia132(1), 108–109. https://doi.org/10.1213/ANE.0000000000005206Links to an external site.

Sarstedt, M., Mooi, E., Sarstedt, M., & Mooi, E. (2019). Regression analysis. A concise guide to market research: The process, data, and methods using IBM SPSS Statistics, 209-256.

Sun, Q., Zhou, W. X., & Fan, J. (2020). Adaptive huber regression. Journal of the American Statistical Association115(529), 254-265.

NURS 8201 Week 7 Discussion: Use of Regression Analysis in Clinical Practice

Discussion: Use of Regression Analysis in Clinical Practice

Regression analysis provides the researcher with an opportunity to predict and explore future outcomes. Whether it is to determine prevention methods, promote opportunities for learning, or propose new treatments, looking towards the future can have a significant impact on patient care and sustained positive patient outcomes.

This week, you explore regression analysis, paying particular attention to linear regression. Linear regression is used to “estimate the value of a dependent variable based on the value of an independent variable” (Gray & Grove, 2020). In your Discussion, you will apply your understanding of this statistical technique as it concerns use in a research study.

Photo Credit: wutzkoh / Adobe Stock

For this Discussion, you will select an article on a study to examine the strengths and weaknesses in the use of linear regression. Consider how you might remedy the weaknesses associated with the application of linear regression and reflect on how the findings of the study that you selected might contribute to various areas of your practice.

Reference: Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.

To Prepare:

  • Review the articles in this week’s Learning Resources and evaluate their use of linear regression. Select one article that interests you to examine more closely in this Discussion.
  • Critically analyze the article that you selected and consider the strengths and weaknesses described.
  • Reflect on potential remedies to address these weaknesses, and how the findings from this study may contribute to evidence-based practice, the field of nursing, or society in general.

By Day 3 of Week 7

Post a brief description of the article that you selected, providing its correct APA citation. Critically analyze the article by addressing the following questions:

  • What are the goals and purposes of the research study that the article describes?
  • How is linear or logistic regression used in the study? What are the results of its use?
  • What other quantitative and statistical methods could be used to address the research issue discussed in the article?
  • What are the strengths and weaknesses of the study?

Then, explain potential remedies to address the weaknesses that you identified for the research article that you selected. Analyze the importance of this study to evidence-based practice, the nursing profession, or society. Be specific and provide examples.

By Day 6 of Week 7

Read a selection of your colleagues’ responses and respond to at least two of your colleagues on two different days in one or more of the following ways:

  • Ask a probing question, substantiated with additional background information, evidence, or research.
  • ·Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
  • Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.
  • Validate an idea with your own experience and additional research.
  • Suggest an alternative perspective based on additional evidence drawn from readings or after synthesizing multiple postings.
  • Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.

Submission and Grading Information

Grading Criteria

Also Read:  NURS 8201 Week 6 Assignment: Correlations

To access your rubric:

Week 7 Discussion Rubric

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Post by Day 3 of Week 7 and Respond by Day 6 of Week 7

To Participate in this Discussion:

Week 7 Discussion

Regression analysis is one of the statistical models used in estimating the relationship between variables. The researcher has the ability to determine the effect that an independent variable has on the dependent variable (Willis & Riley, 2017). For example, an increase in one or more values on the independent variable would have an effect on the dependent variable. This paper examines regression analysis was used by an author including its weaknesses and strengths.

Article Summary

The article authored by Hatakeyama et al., (2019) aimed at finding the relationship between quality of clinical practice guideline (CPGs) and overall assessment scores. This study considered the previous studies that had been done and published between 2011 and 2015. These selected studies were subjected through an independent valuation using AGREE II. The author analyzed the results using a regression analysis. For instance, the analysis included the effect that the six domains and 23 items has on the overall assessment. The study collected a total of 206 CPGs and correlated all the domains to the items on the overall assessment to determine the strength of the relationship before taking the regression analysis on the proposed items.

Use of Regression on the Article

The author decided to subject domain 3, domain 4, domain 5, and domain 6 of the regression analysis. Domain three represented rigor of development, domain four was for clarity of presentation, domain five was for applicability and finally domain 6 was for editorial independence. The analysis was majoring on how these domains influence the overall assessment (Hatakeyama et al., 2019). The analysis showed that all the domains had a significant relationship with the overall assessment. The author also found that four different items on AGREE II, which were item 8, 15, 19 and 22 had an effect on overall assessment. The regression analysis showed that the change in one unit of the items above had a significant change on the overall assessment which in this case acted as the dependent variable (Hatakeyama et al., 2019). Therefore, the improvement of overall assessment dependent on the increase and decrease of the items that acted as independent variables in this case.

Other statistical analysis that could have been used in the study is ANOVA analysis because it shows the strength of the relationship between the items selected. Besides, it allows the researcher to determine the effect that each dependent variables have on each other and how the relationship between the dependent variables can influence the study (Fontaine et al., 2019). Use of ANOVA tests in this study could have strengthened and relayed more information on the collection of items that could have a great impact on the overall assessment.

The strength of the regression analysis is on the ability of the author to examine more than one dependent variable. According to the study the author was interested in 22 items and their effect on overall assessment. The study is able to report on the influence of 22 items more easily as compared to other methods that could have been complex (Hatakeyama et al., 2019). Despite the strength that regression analysis has on the study, the method also has its weakness it lacks the ability to examine the relationship between the independent variables considered in the study.

Conclusion

Regression analysis is a powerful tool in assessing the relationship between dependent and independent variables. The author in the selected the study has the ability to evaluate which of the 22 items have a high or low effect on the overall assessment.

References

Fontaine, G., Cossette, S., Maheu-Cadotte, M. A., Deschênes, M. F., Rouleau, G., Lavallée, A., … & Mailhot, T. (2019). Effect of implementation interventions on nurses’ behaviour in clinical practice: a systematic review, meta-analysis and meta-regression protocol. Systematic reviews8(1), 1-10. https://doi.org/10.1186/s13643-019-1227-x

Hatakeyama, Y., Seto, K., Amin, R., Kitazawa, T., Fujita, S., Matsumoto, K., & Hasegawa, T. (2019). The structure of the quality of clinical practice guidelines with the items and overall assessment in AGREE II: a regression analysis. BMC health services research19(1), 1-8. https://doi.org/10.1186/s12913-019-4532-0

Willis, B. H., & Riley, R. D. (2017). Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice. Statistics in medicine36(21), 3283-3301. https://doi.org/10.1002/sim.7372

Hello Laura. The article selected is quality and seeks to examine standardized terminologies utilized by nursing staff in electronic health records (De Groot et al., 2020). As you mentioned, a non-validated questionnaire was utilized, confirmed by a preliminary test. This was vital in ensuring the quality of the study results. One might examine the sample population used in the study, which can impact generalizability and, hence, the quality of research. The sample included 667 Dutch registered nurses and certified nursing assistants using electronic health records. One might suggest that the sample size is appropriate to allow generalizability. However, no details show that the sample is sufficient to underscore generalizability. The article fails to include any details of the sample sufficiency. However, White’s (2022) research indicated that sample size depends on the type of people participating in the study. A sufficient sample for a patient is 250 to 350, while for students, it is 500 to 600. One can, therefore, assume the study had a sufficient sample size.

References

De Groot, K., De Veer, A. J. E., Paans, W., & Francke, A. L. (2020). Use of electronic health records and standardized terminologies: A nationwide survey of nursing staff experiences. International Journal of Nursing Studies, 104, 103523. https://doi.org/10.1016/j.ijnurstu.2020.103523

White, M. (2022). Sample size in Quantitative Instrument Validation Studies: A systematic review of articles published in Scopus, 2021. Heliyon, 8(12). https://doi.org/10.1016/j.heliyon.2022.e12223

Yeom, H. E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian28(1), 48-56. https://doi.org/10.1016/j.colegn.2020.04.007

This article was authored by Yeom (2020) and focuses on hypertension among Korean patients. According to a study, Korean patients with hypertension tend to believe that their high blood pressure is caused by factors such as heredity, stress, and aging, and as a result, they are less likely to engage in self-care behaviors such as monitoring their blood pressure and eating a healthy diet. The study also found that participants who believed that hypertension was due to controllable factors were more likely to engage in self-care behaviors (Yeom, 2021). This suggests that educational interventions which focus on increasing people’s understanding of the controllable causes of hypertension may be effective at encouraging them to adopt healthier lifestyles.

Goals and Purpose of the Research

The purpose of this study was to explore the causal beliefs about hypertension and self-care behavior in Korean patients. A total of 267 participants completed a questionnaire that assessed their causal beliefs about hypertension, as well as their self-care behavior (Yeom, 2021). The results of the study showed that the most common belief about hypertension was that it is caused by stress. The second most common belief was that hypertension is hereditary. Participants who believed that hypertension is caused by stress were more likely to engage in self-care behaviors, such as monitoring their blood pressure and eating healthy foods. The main goal of the research was to determine if there is causal beliefs about hypertension and self-care behavior among Korean patients. The purpose of this discussion is to analyze the selected article and determine the use of regression analysis in clinical practice.

How Linear or Logistic Regression was used in The Study

Linear regression was used in the study to examine the relationship between causal beliefs and self-care behavior. From the linear regression analysis, the study found that, overall, patients with more linear beliefs about the cause of hypertension were more likely to engage in self-care behaviors (Wongsuriyanan et al., 2020). This suggests that educating patients on the causes of hypertension may help encourage them to better manage their condition.

Other Quantitative and Statistical Methods Could Be Used To Address the Research Issue Discussed In the Article

It is possible to test causal beliefs on self-care intention and medication compliance by using ANOVA instead of linear regression. This would involve creating a model that includes both the independent and dependent variables, as well as a measure of the strength of the relationship between them (i.e., the correlation coefficient) (Liang et al., 2020). Doing this would allow you to determine whether there is a significant relationship between the two variables, after accounting for the variance in both.

Strengths and Weaknesses of the Study

One of the strengths of the study is that it was conducted in a real-world setting with a large sample size. However, the study has several weaknesses, including that it did not control for dietary intake or other lifestyle factors that could have influenced self-care behavior. Additionally, the study did not measure blood pressure or biomarkers of hypertension, which would have been useful to confirm the relationship between causal beliefs and self-care behavior (Yeom, 2021). One of the remedies to the above weakness is the formulation of the control study to confirm the correlation between causal beliefs and self-care. Another remedy is to accurately measure and record hypertension among patients that have been selected for the study.

The results of this study can help inform evidence-based practice by providing information on the most important factors that influence self-care behavior among patients with hypertension. This study is important because it provides empirical evidence for the importance of causal beliefs about hypertension and self-care behaviour (Yeom, 2021). For example, the findings of this study can be used to help inform evidence-based practice in the management of hypertension.

Conclusion

The purpose of this study was to explore the causal beliefs about hypertension among Korean patients and to examine the relationships between these causal beliefs and self-care behaviors. Regression analysis was used to determine the relationship between the variable. The results of this study can help inform evidence-based practice by providing information on the most important factors that influence self-care behaviour among patients with hypertension.

References

Liang, J., Bi, G., & Zhan, C. (2020). Multinomial and ordinal Logistic regression analyses with multi-categorical variables using R. Annals of Translational Medicine8(16). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475459/

Wongsuriyanan, C., Phattharayuttawat, S., & Ratta-apha, W. (2020). The Prevalence of Type D Personality and Correlations between Medication Self-Efficacy and Self-Care Behavior in Patients with Hypertension. https://doi.org/10.21203/rs.2.20328/v1

Yeom, H. E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian28(1), 48-56. https://doi.org/10.1016/j.colegn.2020.04.007

 

Name: NURS_8201_Week7_Discussion_Rubric

  Excellent

90–100

Good

80–89

Fair

70–79

Poor

0–69

Main Posting:

Response to the Discussion question is reflective with critical analysis and synthesis representative of knowledge gained from the course readings for the module and current credible sources.

40 (40%) – 44 (44%)

Thoroughly responds to the Discussion question(s).

Is reflective with critical analysis and synthesis representative of knowledge gained from the course readings for the module and current credible sources.

No less than 75% of post has exceptional depth and breadth.

Supported by at least three current credible sources.

35 (35%) – 39 (39%)

Responds to most of the Discussion question(s).

Is somewhat reflective with critical analysis and synthesis representative of knowledge gained from the course readings for the module.

50% of the post has exceptional depth and breadth.

Supported by at least three credible references.

31 (31%) – 34 (34%)

Responds to some of the Discussion question(s).

One to two criteria are not addressed or are superficially addressed.

Is somewhat lacking reflection and critical analysis and synthesis.

Somewhat represents knowledge gained from the course readings for the module.

Cited with fewer than two credible references.

0 (0%) – 30 (30%)

Does not respond to the Discussion question(s).

Lacks depth or superficially addresses criteria.

Lacks reflection and critical analysis and synthesis.

Does not represent knowledge gained from the course readings for the module.

Contains only one or no credible references.

Main Posting:

Writing

6 (6%) – 6 (6%)

Written clearly and concisely.

Contains no grammatical or spelling errors.

Adheres to current APA manual writing rules and style.

5 (5%) – 5 (5%)

Written concisely.

May contain one to two grammatical or spelling errors.

Adheres to current APA manual writing rules and style.

4 (4%) – 4 (4%)

Written somewhat concisely.

May contain more than two spelling or grammatical errors.

Contains some APA formatting errors.

0 (0%) – 3 (3%)

Not written clearly or concisely.

Contains more than two spelling or grammatical errors.

Does not adhere to current APA manual writing rules and style.

Main Posting:

Timely and full participation

9 (9%) – 10 (10%)

Meets requirements for timely, full, and active participation.

Posts main Discussion by due date.

8 (8%) – 8 (8%)

Meets requirements for full participation.

Posts main Discussion by due date.

7 (7%) – 7 (7%)

Posts main Discussion by due date.

0 (0%) – 6 (6%)

Does not meet requirements for full participation.

Does not post main Discussion by due date.

First Response:

Post to colleague’s main post that is reflective and justified with credible sources.

9 (9%) – 9 (9%)

Response exhibits critical thinking and application to practice settings.

Responds to questions posed by faculty.

The use of scholarly sources to support ideas demonstrates synthesis and understanding of learning objectives.

8 (8%) – 8 (8%)

Response has some depth and may exhibit critical thinking or application to practice setting.

7 (7%) – 7 (7%)

Response is on topic and may have some depth.

0 (0%) – 6 (6%)

Response may not be on topic and lacks depth.

First Response:

Writing

6 (6%) – 6 (6%)

Communication is professional and respectful to colleagues.

Response to faculty questions are fully answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

5 (5%) – 5 (5%)

Communication is mostly professional and respectful to colleagues.

Response to faculty questions are mostly answered, if posed.

Provides opinions and ideas that are supported by few credible sources.

Response is written in standard, edited English.

4 (4%) – 4 (4%)

Response posed in the Discussion may lack effective professional communication.

Response to faculty questions are somewhat answered, if posed.

Few or no credible sources are cited.

0 (0%) – 3 (3%)

Responses posted in the Discussion lack effective communication.

Response to faculty questions are missing.

No credible sources are cited.

First Response:

Timely and full participation

5 (5%) – 5 (5%)

Meets requirements for timely, full, and active participation.

Posts by due date.

4 (4%) – 4 (4%)

Meets requirements for full participation.

Posts by due date.

3 (3%) – 3 (3%)

Posts by due date.

0 (0%) – 2 (2%)

Does not meet requirements for full participation.

Does not post by due date.

Second Response:
Post to colleague’s main post that is reflective and justified with credible sources.
9 (9%) – 9 (9%)

Response exhibits critical thinking and application to practice settings.

Responds to questions posed by faculty.

The use of scholarly sources to support ideas demonstrates synthesis and understanding of learning objectives.

8 (8%) – 8 (8%)

Response has some depth and may exhibit critical thinking or application to practice setting.

7 (7%) – 7 (7%)

Response is on topic and may have some depth.

0 (0%) – 6 (6%)

Response may not be on topic and lacks depth.

Second Response:
Writing
6 (6%) – 6 (6%)

Communication is professional and respectful to colleagues.

Response to faculty questions are fully answered, if posed.

Provides clear, concise opinions and ideas that are supported by two or more credible sources.

Response is effectively written in standard, edited English.

5 (5%) – 5 (5%)

Communication is mostly professional and respectful to colleagues.

Response to faculty questions are mostly answered, if posed.

Provides opinions and ideas that are supported by few credible sources.

Response is written in standard, edited English.

4 (4%) – 4 (4%)

Response posed in the Discussion may lack effective professional communication.

Response to faculty questions are somewhat answered, if posed.

Few or no credible sources are cited.

0 (0%) – 3 (3%)

Responses posted in the Discussion lack effective communication.

Response to faculty questions are missing.

No credible sources are cited.

Second Response:
Timely and full participation
5 (5%) – 5 (5%)

Meets requirements for timely, full, and active participation.

Posts by due date.

4 (4%) – 4 (4%)

Meets requirements for full participation.

Posts by due date.

3 (3%) – 3 (3%)

Posts by due date.

0 (0%) – 2 (2%)

Does not meet requirements for full participation.

Does not post by due date.

Total Points: 100

Name: NURS_8201_Week7_Discussion_Rubric