Cheap essays, research papers, dissertations.T-TESTS AND ANOVA NURS 8201
Sample Answer for T-TESTS AND ANOVA NURS 8201 Included After Question
You are a DNP-Prepared nurse tasked with evaluating patient care at your practice compared to patient care at affiliated practices. You have noticed that a key complaint from your patients concerns the wait times associated with each patient visit. Based on these complaints, you have decided to compare the wait times at your practice to the wait times at affiliated practices. After recording the wait times at each practice, for 50 individual patients at each practice, you are now prepared to analyze your data. What approach will you use to analyze the data?
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In the scenario provided, you might decide to use, the Analysis of Variance (ANOVA) approach. “ANOVA is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable” (Gray & Grove, 2020). ANOVA is often a recommended statistical technique, as it has low chance of error for determining differences between three or more groups.
For this Assignment, analyze the ANOVA statistics provided in the ANOVA Exercises SPSS Output document. Examine the results to determine the differences and reflect on how you would interpret these results.
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:
T-TESTS AND ANOVA NURS 8201
Review the Week 5 ANOVA Exercises SPSS Output provided in this week’s Learning Resources.
Review the Learning Resources on how to interpret ANOVA results to determine differences.
Consider the results presented in the SPSS output and reflect on how you might interpret the results presented.
The Assignment: (2–3 pages)
Summarize your interpretation of the ANOVA statistics provided in the Week 5 ANOVA Exercises SPSS Output document.
Note: Interpretation of the ANOVA output should include identification of the p-value to determine whether the differences between the group means are statistically significant.
Be sure to accurately evaluate each of the results presented (descriptives, ANOVA results, and multiple comparisons using post-hoc analysis)
Reminder: The College of Nursing requires that all papers submitted include a title page, introduction, summary, and references. The Sample Paper provided at the Walden Writing Center provides an example of those required elements (available at https://academicguides.waldenu.edu/writingcenter/templates/general#s-lg-box-20293632). All papers submitted must use this formatting.
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A Sample Answer For the Assignment: T-TESTS AND ANOVA NURS 8201
Title: T-TESTS AND ANOVA NURS 8201
Next week, you will continue your exploration of quantitative data. You will explore correlations and consider when it is best to utilize this statistical approach for quantifying relationships between variables.
Next Week
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
A Sample Answer 2 For the Assignment: T-TESTS AND ANOVA NURS 8201
Title: T-TESTS AND ANOVA NURS 8201
Gray and Kim’s research paper Palliative care needs of direct care workers caring for people with intellectual and developmental disabilities was chosen for the research (2019). The research sampled 149 direct care workers (DCW) from rural and suburban areas of a Midwestern state in the United States of America who were involved in providing palliative care to people with intellectual and developmental disabilities (PWIDD). They were chosen using a convenience sampling method. The sources of data are self-administered surveys in hard copy or online. In the research, both t-tests and analysis of variance were used to examine whether there was a significant difference between the two and three groups of data, respectively (Gray & Grove, 2020). For example, the former was used to establish whether there was a statistically significant difference between two locations (rural and suburban) in palliative care experience score, palliative care training score, and palliative care training-need score, whilst the ANOVA was used to determine whether there was a statistically significant difference between three job titles (DSP, front-line supervisor, and manager) in palliative care experience score, palliative care training score, and palliative care training-need score. Similarly, Hilvert et al. (2021) employed ANOVA to answer the first question, which is to determine group differences in the TAP (the tense and agreement productivity) total scores. In contrast to Gray and Kim (2019), Donovan and Payne used the ANOVA statistics to find whether there was a significant effect of affective and normative commitment on the WEC and GJS, followed by post hoc analysis, whereas Lee and Hong (2019) employed only regression to determine factors influencing spousal stress. Using t-tests, Gray and Kim (2019) found that participants in rural areas had significantly less palliative experience and less palliative care training than those in suburban areas. Using ANOVA, the researchers also determined that there was a statistically significant difference in experience with palliative care by job title and that palliative care training varied by race. According to post hoc analysis (Scheffe pairwise comparison) followed after ANOVA, DSP had less experience than managers, and Black/African American participants received more training than white participants. Using inferential statistics like t-test and ANOVA brought value (statistical significance) to the research study by providing evidence-based findings that were not due to chance acting alone. These results indicated that rural DCWs lacked palliative care expertise and training in comparison to their suburban counterparts and that white DCWs had less training than black or African American DCWs.
References
Donovan, L. M., & Payne, C. L. (2021). Organizational commitment of nurse faculty teaching in accelerated baccalaureate nursing programs. Nursing Education Perspectives, 42(2), 81–86. doi:10.1097/01.NEP.0000000000000764
Gray, J. A., & Kim, J. (2020). Palliative care needs of direct care workers caring for people with intellectual and developmental disabilities. British Journal of Learning Disabilities, 48(1), 69–77. doi:10.1111/bld.12318
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.
Hilvert, E., Hoover, J., Sterling, A., & Schroeder, S. (2020). Comparing tense and agreement productivity in boys with fragile X syndrome, children with developmental language disorder, and children with typical development. Journal of Speech, Language and Hearing Research, 63(4), 1181–1194. doi:10.1044/2019_JSLHR-19-00022
Lee, J., & Hong, S. (2019). Factors influencing stress in spouses of hospitalized women diagnosed with preterm labor. Korean Journal of Women Health Nursing, 25(4), 459–473. doi:10.4069/kjwhn.2019.25.4.459
Week 6
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Excellent Good Fair Poor
Summarize your interpretation of the frequency data provided in the output for respondent’s age, highest school grade completed, and family income from prior month. 32 (32%) – 35 (35%)
The response accurately and clearly explains, in detail, a summary of the frequency distributions for the variables presented.
The response accurately and clearly explains, in detail, the number of times the value occurs in the data.
The response accurately and clearly explains, in detail, the appearance of the data, the range of data values, and an explanation of extreme values in describing intervals that sufficiently provides an analysis that fully supports the categorization of each variable value.
The response includes relevant, specific, and appropriate examples that fully support the explanations provided for each of the areas described.
28 (28%) – 31 (31%)
The response accurately summarizes the frequency distributions for the variables presented.
The response accurately explains the number of times the value occurs in the data.
The response accurately explains the appearance of the data, the range of data values, and explains extreme values in describing intervals that provides an analysis which supports the categorization of each variable value.
The response includes relevant, specific, and accurate examples that support the explanations provided for each of the areas described.
25 (25%) – 27 (27%)
The response inaccurately or vaguely summarizes the frequency distributions for the variables presented.
The response inaccurately or vaguely explains the number of times the value occurs in the data.
The response inaccurately or vaguely explains the appearance of the data, the range of data values, and inaccurately or vaguely explains extreme values.
An analysis that may support the categorization of each variable value is inaccurate or vague.
The response includes inaccurate and irrelevant examples that may support the explanations provided for each of the areas described.
0 (0%) – 24 (24%)
The response inaccurately and vaguely summarizes the frequency distributions for the variables presented, or it is missing.
The response inaccurately and vaguely explains the number of times the value occurs in the data, or it is missing.
The response inaccurately and vaguely explains the appearance of the data, the range of data values, and an explanation of extreme values, or it is missing.
An analysis that does not support the categorization of each variable values is provided, or it is missing.
The response includes inaccurate and vague examples that do not support the explanations provided for each of the areas described, or it is missing.
Summarize your interpretation of the descriptive statistics provided in the output for respondent’s age, highest school grade completed, race and ethnicity, currently employed, and family income from prior month. 45 (45%) – 50 (50%)
The response accurately and clearly summarizes in detail the interpretation of the descriptive statistics provided.
The response accurately and clearly evaluates in detail each of the variables presented, including an accurate and complete description of the sample size, the mean, the median, standard deviation, and the size and spread of the data.
40 (40%) – 44 (44%)
The response accurately summarizes the interpretation of the descriptive statistics provided.
The response accurately explains evaluates each of the variables presented, including an accurate description of the sample size, the mean, the median, standard deviation, and the size and spread of the data.
35 (35%) – 39 (39%)
The response inaccurately or vaguely summarizes the interpretation of the descriptive statistics provided.
The response inaccurately or vaguely evaluates each of the variables presented, including an inaccurate or vague description of the sample size, the mean, the median, the standard deviation, and the size and spread of the data.
0 (0%) – 34 (34%)
The response inaccurately and vaguely summarizes the interpretation of the descriptive statistics provided, or it is missing.
The response inaccurately and vaguely evaluates each of the variables presented, including an inaccurate and vague description of the sample size, the mean, the median, the standard deviation, and the size and spread of the data, or it is missing.
Written Expression and Formatting – Paragraph Development and Organization:
Paragraphs make clear points that support well-developed ideas, flow logically, and demonstrate continuity of ideas. Sentences are carefully focused—neither long and rambling nor short and lacking substance. A clear and comprehensive purpose statement and introduction is provided which delineates all required criteria. 5 (5%) – 5 (5%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity.
A clear and comprehensive purpose statement, introduction, and conclusion is provided which delineates all required criteria.
4 (4%) – 4 (4%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity 80% of the time.
Purpose, introduction, and conclusion of the assignment is stated, yet is brief and not descriptive.
3 (3%) – 3 (3%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity 60%–79% of the time.
Purpose, introduction, and conclusion of the assignment is vague or off topic.
0 (0%) – 2 (2%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity < 60% of the time. No purpose statement, introduction, or conclusion was provided. Written Expression and Formatting – English writing standards: Correct grammar, mechanics, and proper punctuation 5 (5%) – 5 (5%) Uses correct grammar, spelling, and punctuation with no errors. 4 (4%) – 4 (4%) Contains a few (1 or 2) grammar, spelling, and punctuation errors. 3 (3%) – 3 (3%) Contains several (3 or 4) grammar, spelling, and punctuation errors. 0 (0%) – 2 (2%) Contains many (≥ 5) grammar, spelling, and punctuation errors that interfere with the reader’s understanding. Written Expression and Formatting – The paper follows correct APA format for title page, headings, font, spacing, margins, indentations, page numbers, parenthetical/in-text citations, and reference list. 5 (5%) – 5 (5%) Uses correct APA format with no errors. 4 (4%) – 4 (4%) Contains a few (1 or 2) APA format errors. 3 (3%) – 3 (3%) Contains several (3 or 4) APA format errors. 0 (0%) – 2 (2%) Contains many (≥ 5) APA format errors. Total Points: 100
