Assignment 3: t Tests and ANOVA NURS 8200

Assignment 3: t Tests and ANOVA NURS 8200

Walden University Assignment 3: t Tests and ANOVA NURS 8200-Step-By-Step Guide

 

This guide will demonstrate how to complete the Walden University  Assignment 3: t Tests and ANOVA NURS 8200  assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.

 

How to Research and Prepare for  Assignment 3: t Tests and ANOVA NURS 8200

 

Whether one passes or fails an academic assignment such as the Walden University   Assignment 3: t Tests and ANOVA NURS 8200 depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.

 

After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.

 

How to Write the Introduction for  Assignment 3: t Tests and ANOVA NURS 8200

 

The introduction for the Walden University   Assignment 3: t Tests and ANOVA NURS 8200 is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.

 

How to Write the Body for  Assignment 3: t Tests and ANOVA NURS 8200

 

After the introduction, move into the main part of the  Assignment 3: t Tests and ANOVA NURS 8200 assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.

 

Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.

 

How to Write the Conclusion for  Assignment 3: t Tests and ANOVA NURS 8200

 

After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.

 

How to Format the References List for  Assignment 3: t Tests and ANOVA NURS 8200

 

The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.

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Sample Answer for Assignment 3: t Tests and ANOVA NURS 8200

Independent t Test Exercises

Part 1

1. How many women were employed versus not employed in the sample?

A total 436 women were employed while 524 were not employed.

1. What is the total sample size?

The sample size is 960

1. What are the mean and standard deviation for the CES-D scores for each group?

The mean and standard deviation for CES-D scores are

 

Group Statistics
	Currently employed?	Mean	Std. Deviation
CES-D Score	No	20.8965	12.46425
	Yes	15.8239	10.13655

 

1. Interpret the Levene’s statistic. (Hint: Is the assumption of homogeneity of variance met?

The assumption of homogeneity of variance is met considering that the p-value is less than 0.05. 

1. Are equal variances assumed or not assumed?) Why?

The equal variance is not assumed because the F-statistics is 23.615 which is significantly high. 

1. What is the value of the t-statistic, number of degrees of freedom and the p-value?

 

		t	df	Sig. (2-tailed)
CES-D Score	Equal variances assumed	6.825	958	.000
	Equal variances not assumed	6.954	957.514	.000

1. Does the data support the hypothesis? Why or why not?

Yes, there is a significant difference in the levels of depression between the working women and those who are not working. The p-value is less than 0.05; therefore, the hypothesis is accepted.

Part II

1. What is the total sample size?

The sample size is 157

2. What are the mean and the standard deviation of the CES-D scores at wave 1 and wave 2?

Paired Samples Statistics
		Mean	Std. Deviation
Pair 1	CES-D Score	18.5516	11.87462
	CESD Score, Wave 1	17.8344	11.49908

3. What is the mean difference between the two time periods?

The mean diffwerence = 0.7172

4. What is the value of the t-statistic, number of degrees of freedom and the p-value(sig)?

 

		t-statistics	Degrees of freedom	P-value
Pair 1	CES-D Score – CESD Score, Wave 1	.709	156	.480

 

5. Does the data support the hypothesis? Why or why not?

Yes, the p-value is greater than p=0.05; therefore, the null hypothesis is rejected.

Part 3

The results indicate that the level of education attainment does not influence the depression level, mental health and physical health components. The p-values for the test were below 0.05 and this indicated that the characteristics in the stress level, mental and physical health components were almost similar regardless of the level of education attained. Furthermore, the assumption of equal variance was met because the F-statistics value was below 1.5.

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Week 5 ANOVA Exercises

1. What is the total sample size?

The total sample size is n=935

1. How many women were in each of the different hprobgrp groups?

 

	N
No Housing Problem	367
One Housing Problem	264
Two or More Housing Problems	304

 

1. What are the mean and standrad deviation (SD) overall satisfaction scores for each group?

	Mean	Std. Deviation
No Housing Problem	12.71	2.353
One Housing Problem	11.97	2.588
Two or More Housing Problems	10.57	2.594

1. Interpret the Levene’s statistic. (Hint: Is the assumption of homogeneity of variance met? Are equal variances assumed or not assumed?)

The assumption of homogeneity of variance is met because the p-value is lower than 0.05.

1. What is the value of the F-statistic, number of degrees of freedom and the p-value?

	df	F	Sig.
Between Groups	2	61.674	.000
Within Groups	932

1. Is there a significant difference in the overall satisfaction level of women in each of the hprobgrp groups?

There is a significant difference in the overall satisfaction level of women in each of the hprobgrp groups.

1. Interpret the post hoc test. When interpreting the post hoc test indicate the mean and standard deviation for each group and indicate which group was signifantly higher or lower from the other. If there is no difference between two groups indicate that as well.

The turkey post-hoc test result uindicated that the level of satisfaction difefred significantly between the groups based on the housing problem. Though, there was no significant difference in level of satisfaction between those people with one housing problem and those with two or more housing problem.

Statistical analysis is a powerful tool that helps researchers gain valuable insights into a set of data and make informed decisions based on the results. Therefore, it is important for nurses and other professionals to have adequate knowledge regarding statistical analyses. There is also a need to know which statistical tests should be used based on the nature of the data set and the purpose of the analysis. Two types of statistical tests that have widely been applied in research are Analysis of Variance (ANOVA) and T-tests (Mishra et al.,2019). ANOVA is applied in to determine whether three or more groups or populations are statistically different. On the other hand, t-tests are applied to determine whether two groups are statistically different (Liang et al.,2019). Therefore, these two tests play a key role since they offer the researcher a chance to understand the nature of variations between variables in research. Therefore, the purpose of this assignment is to summarize the interpretation of the ANOVA statistics provided in the SPSS Output.

The data provided is on the overall satisfaction and material well-being. The data provided covers descriptive statistics, tests for homogeneity of variance, ANOVA and multiple comparisons. The descriptive table shows the standard deviation, mean and 95% confidence interval for the dependent variables for each separate group, which forms part of the study. From the data provided, the mean for “two or more housing problems” was 10.57, the mean for “one housing problem” was 11.97, and the mean for “No housing problem” was 12.71. The standard deviations observed for the three categories are 2.594, 2.588, and 2.353.  It is also important to note that the overall mean for all three groups represented in the study was 11.80.

Another important aspect of this data output is the test of Homogeneity of Variances. Levene’s test was used to accomplish this analysis. This analysis of the F-test when testing the null hypothesis that the variance is equal across all the groups tested (Yi et al.,2022). It is observable that the p-value obtained from Levene’s tests was 0.122, which means that they are not significantly different as the value is greater than 0.05.

The ANOVA output also showed the interaction within the group and between the groups of  “material well-being” and “overall satisfaction” as part of the statistical tests. From the results, it is evident that there was a statistically significant difference between the group means. The p-value obtained for this analysis is 0.000, a value above 0.05, indicating statistical significance. As such, the mean of material well-being and overall satisfaction is statistically significant. Nonetheless, it is not possible to have an idea of how the groups under consideration are different from each other using this test. As such, it is important to apply a computation of multiple comparisons with a Tukey post hoc test.

The next important part of the analysis is the multiple comparisons of “material well-being” and “overall satisfaction”, with a 0.05 used as the level of significance. The analysis shows that the difference between the means of the tested groups is statistically significant. As earlier indicated, a deeper study of the groups requires the use of Tukey post hoc tests, which is the test known and used in accomplishing post hoc tests on one-way ANOVA tests. Therefore, this study employed the Tukey post hoc test since it forms a vital ANOVA. When ANOVA is used to test the similarity of three or more groups’ means, the statistical significance results would show that not all the tested group means are similar (Uysal, et al., 2019).

The ANOVA output fails to identify the particular differences between the mean pairs that are significant. As such, the post hoc tests are key to determining the differences between the means of multiple groups while controlling the standard errors. The difference in overall satisfaction between one housing program and no housing problems was found to be 0.739, which is significant.  The difference in overall satisfaction between no housing problems and two or more housing problems was 2.139, which is also significant. In addition, the difference between one housing problem and two or more housing problems was 1.401, which is also significant.

It is also evident from the table that there was a statistically significant difference between one housing problem and no housing problem since the obtained p-value was 0.001. The p-value of 0.001 was obtained for the comparison of no housing problem and two or more housing problems means, which is also statistically significant. Besides, the difference between one housing problem and no housing problem was also statistically different, with a p-value of 0.001 observed.

Conclusion

This assignment has focused on the t-tests and ANOVA for the provided data. The provided data was mainly on overall satisfaction and material well-being. Therefore, various analyses have been performed and reported. Descriptives, Tests of Homogeneity of Variance, ANOVA and multiple comparisons have all been explored.

References

Liang, G., Fu, W., & Wang, K. (2019). Analysis of t-test misuses and SPSS operations in medical research papers. Burns & Trauma, 7. https://doi.org/10.1186/s41038-019-0170-3

Mishra, P., Singh, U., Pandey, C. M., Mishra, P., & Pandey, G. (2019). Application of student’s t-test, analysis of variance, and covariance. Annals of Cardiac Anaesthesia, 22(4), 407. https://doi.org/10.4103%2Faca.ACA_94_19

Uysal, M., Akyuncu, V., TanYıldızi, H., Sümer, M., & Yıldırım, H. (2019). Optimization of durability properties of concrete containing fly ash using Taguchi’s approach and Anova analysis.  DOI: 10.7764/RDLC.17.3.364

Yi, Z., Chen, Y. H., Yin, Y., Cheng, K., Wang, Y., Nguyen, D., … & Kim, E. (2022). Brief research report: A comparison of robust tests for homogeneity of variance in factorial ANOVA. The Journal of Experimental Education, 90(2), 505-520. https://doi.org/10.1080/00220973.2020.1789833

Sample Answer 2 for Assignment 3: t Tests and ANOVA NURS 8200

Since entering the career of nursing, I believe that most nurses would like to gather as much experience as they can to become a proficient and well-rounded staff in this profession. Being a nurse for about six years now, I have spent the last two and a half years working my way up to become an intensive care unit (ICU) nurse. Being an ICU nurse is a specialty in itself that provides many nursing with the competitive pay, comprehensive benefits, and extensive learning experience in critical level of care. As the ICU can be a stressful environment for patients and families, with established long term consequences, the impact that this unique environment can have on healthcare professionals is increasingly being recognized.

What I have noticed while being a nurse in the critical care environment, I have noticed a significant increase in our nurse turnover rates for both local and traveling nurse staff. For as long as I have been working in this hospital (in a different unit at the time), many nurses are either not trained properly and/or experiencing burnout early on in their career due particularly in the ICU unit. The exposure of  nurses within a high acuity nursing environment without the proper support from our management has led to burnout. Furthermore, I have noticed that the ICU unit is the only unit with the least amount of local nurses that stay employed for at least two years into their career life.

Most of the staff nurses that I have worked with have expressed the desires to leave off-island in search for better opportunities or change in nursing career. Our hospital is going through a constant battle with recruiting and retaining their nursing staff, specifically more significant in the ICU unit. Our medical director is currently working alongside the hospital administrators about looking for ways to address the increase burnout that the staff nurses are experiencing and construct a resilient healthcare system. For as long as I have been working in this hospital (in a different unit at the time), many nurses are either not trained properly with the advanced skills needed dealing with life threatening illnesses and/or lack the skills to tackle critically ill conditions. As a result overall, burnout causes decrease in quality of care, poor performances, increase mortality in patients, and errors in the healthcare environment.

The impact that this unique environment can have on healthcare professionals is increasing therefore, as a DNP prepared nurse, to gain a more complete understanding of critical care well-being requires commitment to measure, develops interventions, and re-measure them. An analysis variation or ANOVA tests done for each survey or experimental results are significant and help us figure out if the studies prove our hypothesis. Inferential statistics takes data from samples and make generalizations about a population. Experimental analysis using t-test, to compare the means of two groups or ANOVA (analysis of variance) to analyze the difference between the means of more than two groups, would help make estimates about the population at study (nurses) and testing hypothesis to draw conclusions (Bhandari, 2020).

One of the chosen inferential articles that describe the prevalence of burnout in the ICU healthcare assessed in the included analysis of variance study (ANOVA) through PubMed, Medline, and a web of sciences article reviews and observational study designs. Within the articles, the most commonly used instruments for data collection include the Maslach burnout inventory (MBI), professional quality of life scale, work related behavior, and experience patterns. According to a 4 large scale research study reported that the burnout prevalence rates ranges between 28%-61%; this study suggests that ICU workers were slightly (about 20%) more prone to burnout than the average healthcare (Chuang, Tseng, Lin, Lin & Chen, 2016). The following risk factors reported include: age, sex, marital status, personality traits, work experiences, work environment, workload, shift work, ethical issues, and decision making choices.

In another article review done by Kerlin, McPeake & Mikkelsen (2020), being that ICU can be a stress environment for both patients and families; the impact that this environment can have on healthcare environment is being increasingly recognized. Challenging situations, exposure to high mortality and daily difficult workloads can lead to excessive stress and resultant in moral distress, leading to burnout syndrome. This cross-sectional study, most critical care nurses experience about 81% of one or more burnout symptoms. The framework presented in this article implies that multidisciplinary and coordinated cares are essential components to high quality critical care delivery. The publications are assessed for relevance to using data to support observational study designs that examine associations between any risk factors and burnout in the ICU setting.

In a systematic meta-analysis done by Ramirez-Elvira et.al. (2021), the ANOVA is  carried out with different articles and journals from Medline and CINAHL following the PRISMA (preferred reporting item for systematic reviews and meta-analysis), with a sampling of N= 1989; there was an estimate of about 31% prevalence for high emotional exhaustion, 18% high depersonalization, and 49% low personal achievement (p.2). Furthermore, in an inferential statistical cross-sectional total population study among N=60 nurses using a self-administered MBI questionnaire resulted into a high burnout percentage of about 62% (Cishahayo, Nankundwa, Sego, & Bhengu, 2017). Burnout is measured through high emotional exhaustion (48%), high depersonalization (25%), and low personal accomplishment (50%).

On a much larger and international scale, in study done by Bhagavathula et.al. (2018), an institution based teaching hospital with a cross-sectional study conduced among healthcare providers N=500 serving about >50,000 population in Ethiopia; a questionnaire with sociodemographic details using descriptive analysis using correlation and multivariate logistic regression studied ANOVA using survey questionnaires of MBI scale. The overall prevalence of burnout is about 14%, respondents with debility was 53%, increase self criticism of about 56%, and depressive symptoms of about 46%. As a result, the nursing profession was a significant determinant for emotional exhaustion and burnout.

In conclusion, most inferential studies summarized above strengthens the application of evidenced based practices in promoting recruitment and retention policies in decreasing the risk of burnouts. Critical care courses and educational programs should be established by the support faculty to meet the needs of critical care assessments and criteria. Practice variability that necessitates changing for better conditions in a resource limited setting may excavate the underlying factors associated with nursing burnout.

 

Reference(s):

Bhagavathula, A., Abegaz, T., Belachew, S., Gebreyohannes, E., Gebresillassie, B., & Chattu, V.

(2018). Prevalence of burnout syndrome among healthcare professionals working at Gondar University Hospital, Ethiopia. Journal of Educational Health Promotion 7(145). Retrieved from https://www.jehp.net/article.asp?issn=2277-9531;year=2018;volume=7;issue=1;spage=145;epage=145;aulast=Bhagavathula

Bhandari, P. (2020). An introduction to inferential statistics. Scribbr Statistics. Retrieved

https://www.scribbr.com/statistics/inferential-statistics/

Chuang, C., Tseng, P., Lin, C., Lin, K. & Chen, Y. (2016). Burnout in the intensive care unit

professionals. Medicine Baltimore Journal. 95(50). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268051/

Cishahayo, E., Nankundwa, E., Sego, R., & Bhengu, B. (2017). Burnout among nurses working

in critical care settings: A case of a selected tertiary hospital in Rwanda. International Journal of Research in Medical Sciences. 5(12). Retrieved from https://www.msjonline.org/index.php/ijrms/article/view/4101

Kerlin, M., Mc Peake, J., & Mikkelsen, M. (2020). Burnout and joy in the profession of critical

care medicine. BMC Critical Care Journal. 24(98). Retrieved from  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7092567/

Ramirez-Elvira, S., Romero-Bejar, J., Suleiman-Martos, N., Gomez-Urquiza, J., Monsalve-

Reyes, C., Canadas-Delafuentes, G…Albendin-Garcia, L. (2021). Prevalence, risk factors, and burnout levels in intensive care unit: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health. 18.Retrieved from www.mdpi.com/pdf  

Sample Answer 3 for Assignment 3: t Tests and ANOVA NURS 8200

This is quite insightful. The analysis of quantitative research studies involves consideration of different inferential statistics. In many cases, the analysis starts with descriptive statistics that show the attributes of different variables in the study. The study that you selected was quantitative, and from the analysis of the variables that have been selected, t-tests and ANOVA can be applied in comparing the means of different groups (Emerson, 2017). The accuracy, validity, and applicability of the research outcomes significantly depend on the choice of the inferential statistics to be applied. The sample size used in the study is representative of the population under the study. In other words, the sample of 184 represents the attributes of the population under the study.

The research process adheres to the required standards; however, one of the questions that I would like to ask is: How was the data used in the study collected, and what was the study’s hypothesis? Student t-test is normally applied when comparing the means of two continuous variables. There are different forms of t-test, including independent sample t-test, paired sample t-test, and one-sample t-test. The choice of the t-test to apply in the data analysis process depends on the types of variables and data. For instance, the application of an independent sample t-test requires one continuous variable and one categorical variable. However, there need to be two variables under consideration. ANOVA is commonly applied in cases where there are more than two variables or two groups to be compared (Franscisco, 2017). Finally, Pearson correlation could be applied in the research process to compare the relationship that exists between two variables. The correlation coefficient is normally used to indicate the relationship between variables under consideration.

References

Emerson, R. W. (2017). ANOVA and t-tests. Journal of Visual Impairment & Blindness, 111(2), 193-196. https://journals.sagepub.com/doi/abs/10.1177/0145482X1711100214?journalCode=jvba

Franscisco, (2017). Difference between T-TEST and ANOVA. Difference between Similar Terms and Objects. http://www.differencebetween.net/miscellaneous/difference-between-t-test-and-anova/.

Sample Answer 4 for Assignment 3: t Tests and ANOVA NURS 8200

Article Description

With the COVID-19 pandemic, many colleges had to change their format to an online program instead of an in-person format. The article that I have selected discusses the switch to the online format and how nursing students feel about this change. It took a quantitative approach utilizing an online form to collect data from students to discover if they are satisfied with the online format and if there are any barriers that have been identified with this learning style.

Summary of Sample Studied

The sample that was studied includes 219 nursing students. These nursing students are both undergraduate and graduate students. They are attending university in India.

Data Sources

The data sources come from a questionnaire that the participants filled out. This questionnaire asked the demographic profile of the student filling out the questionnaire, their overall satisfaction with the program being online, and barriers that have been identified with the program being online. The satisfaction portion of the questionnaire had 15 items for study participants to fill out with categories ranging from highly dissatisfied to highly satisfied. The barriers portion had 20 items for study participants to fill out with always, sometimes, and never as options.

Inferential Statistics used and associated findings

Inferential statistics help a researcher to draw conclusions from a sample to a population (Guetterman, 2019). Inferential statistics were used to analyze the data. This was used to help find the association of online learning and barriers related to the sociodemographic variables.

Purpose and value of this research

The value of this research is helping to determine if the online learning model that many institutions were forced to adopt during the pandemic is something that can continue instead of moving back to the in-person model. By utilizing an online-only approach to learning, institutions can then employ instructors around the country instead of being limited to just the immediate vicinity that the institution is located in. This can also help the institution cut down on overhead costs by owning/renting fewer buildings.

Did using inferential statistics strengthen or weaken this study’s application?

Utilizing inferential statistics is beneficial to researchers because they are able to make assumptions on data by using a smaller population, thus saving time and resources (Testbook, 2023). Using inferential statistics strengthened this study’s application. By better understanding what the barriers are related to online-only learning, institutions can help fix these to make online learning more beneficial to their students. For example, one of the biggest barriers that was noted in this study was a physical barrier such as eye strain. By identifying that this is a barrier that many students are noting with online classes, institutions can help to educate how to prevent eye strain and can also work to decrease eye strain themselves as well.

References

Guetterman T. C. (2019). Basics of statistics for primary care research. Family medicine and community health, 7(2), e000067. https://doi.org/10.1136/fmch-2018-000067

Kumar, A., Kalal, N., Rana, N., Vyas, H., Choudhary, V., & Rani, R. (2021). Online learning in nursing students: Satisfaction and barriers. Journal of education and health promotion, 10, 411. https://doi.org/10.4103/jehp.jehp_1221_20

Testbook. (2023, May 23). Inferential Statistics: Definition, Types, Formulas with Examples. Testbook. https://testbook.com/maths/inferential-statistics#:~:text=Inferential%20statistics%20enables%20us%20to