NUR 705 Assignment 9.1: ANOVA Analysis

ST. Thomas University NUR 705 Assignment 9.1: ANOVA Analysis-Step-By-Step Guide

This guide will demonstrate how to complete the ST. Thomas University NUR 705 Assignment 9.1: ANOVA Analysis 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 NUR 705 Assignment 9.1: ANOVA Analysis

Whether one passes or fails an academic assignment such as the ST. Thomas University NUR 705 Assignment 9.1: ANOVA Analysis 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 NUR 705 Assignment 9.1: ANOVA Analysis

The introduction for the ST. Thomas University NUR 705 Assignment 9.1: ANOVA Analysis 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 NUR 705 Assignment 9.1: ANOVA Analysis

After the introduction, move into the main part of the NUR 705 Assignment 9.1: ANOVA Analysis 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 NUR 705 Assignment 9.1: ANOVA Analysis

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 NUR 705 Assignment 9.1: ANOVA Analysis

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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NUR 705 Assignment 9.1: ANOVA Analysis

Assignment Guidelines

Part One

Using the NUR705 Week 9 dataset (Links to an external site.), conduct an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers. (Conduct a one-way ANOVA. If the F-test is significant, use the Tukeys post-hoc test.) Assume a .05 level of significance. Complete the following:

Identify the independent and dependent variables.
Write a null hypothesis.
Write an alternative non-directional hypothesis.
Interpret your results. Guidelines for interpreting ANOVA results can be found in What to Include When Writing Up One-Way ANOVA Test Results (PDF) (Links to an external site.).

There are a few different ways to conduct an ANOVA test in SPSS. The first way is to use the “ANOVA” command. To do this, go to “Statistics” and then select “ANOVA.” After selecting this option, a dialog box will appear. Next, select the variable that you want to use as the dependent variable and click “OK.” Another way to conduct an ANOVA test in SPSS is to use the “Regression” command (van den Bergh et al., 2020). To do this, go to “Statistics” and then select “Regression.” After selecting this option, a dialog box will appear. Next, select the variable that you want to use as the dependent variable and click ” OK. When conducting an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers, one may want to first ensure that the data meets the assumption of normality. This can be done by running a goodness-of-fit test, such as the Kolmogorov-Smirnov test (Liu & Wang, 2021). If the data meet the assumption of normality, one can proceed with conducting the ANOVA. The null hypothesis for this test is that there is no difference in the Interval Depression Scores among the three groups of shift workers. The alternative hypothesis is that there is a difference in at least one of the group means. The purpose of this assignment is to conduct an ANOVA to see if there is a statistically significant difference in the Interval Depression Score among 3 groups of shift workers.

Part One

Identify the independent and dependent variables.

While conducting ANOVA test, it is necessary to determine both the dependent and independent variables. In this case, the independent variable is Shift Worked while the dependent variable is Depression Score (Interval).

Write a null hypothesis.

H0: There is no statistical significance between the depression score and the shift worked.

Write an alternative non-directional hypothesis.

H1: There is a statistical significance between the depression score and the shift worked.

Interpret your results. Guidelines for interpreting ANOVA results can be found in

Table 1: ANOVA
Shift Worked (nominal) 1=first, 2=second, 3=third
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	10.267	12	.856	1.672	.162
Within Groups	8.700	17	.512
Total	18.967	29

Table 1 shows ANOVA output between the dependent and independent variables identified in the study. The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. We therefore conclude that There is no statistical significance between the depression score and the shift worked.

Part Two

ANOVA is a statistical technique that is used to test for differences between groups. In this case, we are looking at the Interval Depression Score (IDS) among three groups of shift workers. The ANOVA analysis will help us determine if there are any significant differences between the IDS scores of the different groups (Akbay et al., 2019). To carry out the ANOVA analysis, we first need to gather data from each of the three groups of shift workers. We will need to know the mean IDS score for each group, as well as the number of people in each group. Once we have this information, we can plug it into an ANOVA calculator (there are many freely available online).

The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. We therefore conclude that There is no statistical significance between the depression score and the shift worked. There are a number of possible explanations for why there is no statistical significance between the depression score and the number of shifts worked. It could be that the sample size is too small to detect a difference, or that the relationship between depression and shift work is more complex than a simple linear relationship. Another possibility is that other factors, such as job satisfaction or social support, play a larger role in determining depression among workers who do shift work.

Conclusion

The significant value generated is 0.162 which is greater than 0.05 level of significance i.e., 0.162> 0.05, as a result, we fail to reject the null hypothesis. From the study conducted, there is no statistical significance between the depression score and the shift worked. There are a few different ways to conduct an ANOVA test in SPSS. The first way is to use the “ANOVA” command. To do this, go to “Statistics” and then select “ANOVA.” After selecting this option, a dialog box will appear.

References

Akbay, L. O. K. M. A. N., Akbay, T., Osman, E. R. O. L., & Kilinc, M. (2019). Inadvertent Use of ANOVA in Educational Research: ANOVA is not A Surrogate for MANOVA. Journal of Measurement and Evaluation in Education and Psychology, 10(3), 302-314. https://doi.org/10.21031/epod.524511

Liu, Q., & Wang, L. (2021). t-Test and ANOVA for data with ceiling and/or floor effects. Behavior Research Methods, 53(1), 264-277. https://link.springer.com/article/10.3758/s13428-020-01407-2

Van den Bergh, D., Van Doorn, J., Marsman, M., Draws, T., Van Kesteren, E. J., Derks, K., … & Wagenmakers, E. J. (2020). A tutorial on conducting and interpreting a Bayesian ANOVA in JASP. LAnnee psychologique, 120(1), 73-96. https://www.cairn.info/revue-l-annee-psychologique-2020-1-page-73.htm?ref=doi

JASP One-Way ANOVA Screencast

JASP One-Way ANOVA Transcript (Links to an external site.)

Note: Remember that the dependent variable needs to be measured on a continuous/interval level, not a categorical level. Be sure to select the correct dependent variable when conducting your analysis.

For part one of the assignment, submit screenshots of the items above. It is best to copy these and put them in a Word document.

Part Two

For part two of the assignment:

Prepare a short narrative to describe the ANOVA analysis. Your narrative should use of APA formatting.
This narrative should be approximately one paragraph, double-spaced.

Submission

Submit your assignment and review full grading criteria on the Assignment 9.1: ANOVA Analysis page.

Lecture: ANOVA

Slide 1

Okay, in this lecture we’re going to talk about one-way analysis of variance or ANOVA for short.

Slide 2

No dialogue

Slide 3

Now with ANOVA everything is similar to T-tests but with one small change. Now we can compare more than two groups. You can’t do that with T-tests. You can’t compare A to B, B to C, A to C. You can’t do that with a single research question. So if you’re looking at more than two groups, three, four, five, six, seven or more groups, you use an ANOVA.

Slide 4

Now with a simple ANOVA, the null hypothesis just looks like this, Mu1 equals Mu2 equals Mu3, all the way down to however many groups you are comparing. And the alternative is that at least one mean score is different from at least one of the other mean scores.

Slide 5

Okay, the ANOVA is conceptualized in kind of a different way. So I want to review that with you right now. If we have three groups in

healthcare, a classic way to compare patient groups is by age group. So we can put people into one of three: a pediatric, adult, or geriatric age group. Now, if we look at whatever the dependent variable is, height, weight, whatever variable we’re looking at, we can look at the variations or the variance of that dependent variable measure in two classic ways.

Slide 6

This first is called within group variance or sometimes it’s referred to as error. That’s the variation of the differences of scores within the particular groups. So within the pediatric group, again whatever measure we’re using, there are probably high scores and low scores. Within the adult group, there’s variance. Within the geriatric group, again, variations exists. We’ll have high scores and low scores. That’s the variance we’re not interested in. So we’re going to refer to that as error.

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Slide 7

Now what we’re interested in is the second type of variance and that is called the between group variance. That’s how geriatric, a particular score in the geriatric group differs from a particular score in the adult group, or pediatric versus adult, or however many comparisons you want to make. But that variance between those two groups that’s what we’re interested in.

Slide 8

So the F-statistic and the F-statistic is the coefficient we use for ANOVA. That coefficient is simply the ratio of between group variance over within group variance. So believe it or not, those differences are normally distributed. So if we put what we’re interested in in the numerator and the error in the denominator, if that numerator gets large enough that suggests that the between group differences are larger than what chance could explain so that we have a real difference. So again we put, the F-statistic puts the between group differences in the numerator and the within group differences in the denominator and looks at that particular ratio.

Slide 9

Now the F-test, if we have a significant F-test that just tells us that there is a difference. It doesn’t tell us where the difference lies. Again, to get a significant Fscore that just says that at least one group differs from at least one of the other groups. So we need to do a post hoc test that will tell us the specific nature of the difference, where the difference lies. And there are a whole family of post hoc tests. For this class we’re just going to use kid of the default post hoc test called the least squared differences, or the LSD, post hoc test.

Slide 10

So let’s look at an example. And I’m sorry this isn’t a healthcare example, but it’s a pretty simple one. If we look at ACT scores, that ACT test you take in high school, and compare it, those scores between three classic types of school, high schools: rural high schools, suburban high schools, and urban high schools. So in this case our null hypothesis is there is no significant difference between the mean ACT scores of students from rural, suburban and urban schools.

Slide 11

Okay let’s look at our SPSS printout. We see that ratio I talked about the F-ratio of the between group and the within group differences. And then we see the Fscore, but what we’re interested in is that significant score. We want it to be below .05. So we see that it is. It’s .008, that’s certainly well below .05. So we have a significant difference in ACT scores between the three types of schools, but we don’t know where that particular difference, where do the differences lie. So we need to perform a post hoc test to learn more.

Slide 12

So we’re going to run a least squared differences test and what that test does is it pulls out each variable and compares it to the remaining variable. So we have three variables so there’s going to be three comparisons, where in each comparison, each variable is pulled out and compared to the other two. SPSS makes it very easy for us. If there is a significant difference, it will put an asterisk next to the mean difference. So there’s some redundancies in the post hoc test. If A is significantly different than B, B is significantly different than A. So you have to kind of tease out those redundancies. What I do is I look at each of the comparisons and I see is there is a box where there’s two asterisks, and we see that does exist in the first box, rural. Rural is significantly different than both urban and suburban. I look at the other two boxes and I see that’s just the reverse, and so I know that the difference lies between rural and the other two. But I still don’t know where the difference is, or is the difference higher or lower. So I’m going to ask for a means plot and I can see visually where the differences lie.

Slide 13

So I ask for both a means plot and a list of descriptive statistics. So I see the rural, suburban, and urban numbers shown there graphically, and I see that rural is significantly lower than both urban and suburban. And I get those exact scores there: the rural ACT is a 19.66 mean score, and then suburban a 24, and urban a 25.

One small note. I don’t want to insult anybody that went to a rural high school. This is made up data. I’m not suggesting that rural high schools or poor or less intelligent students attend those schools. So this is just made up data.

Week 9: Comparing Group Means—Analysis of Variance

Lesson 1: Comparing Group Means—Analysis of Variance

Introduction

This week, you will learn more complex statistical testing. Most research studies need to compare more than two groups. For example, if you want to compare outcomes in three or four groups, you need a different statistical test.

Multiple-group comparison with a continuous variable measurement for categorical groups is done with an Analysis of Variance test, or ANOVA for short.

Learning Outcomes

At the end of this lesson, you will be able to:

Understand how the number of groups and variables impact the choice of statistical tests to compare differences.
Understand the purpose of multiple comparison testing.
Use JASP to compute a one-way ANOVA.
Correctly report findings of statistical tests in APA style.

Before attempting to complete your learning activities for this week, review the following learning materials:

Learning Materials

Read the following in your Kim, Mallory, & Vallerio (2022) Statistics for evidence-based practice in nursing textbook:

Chapter 11, “Tests for Comparing Group Means: Part I” pages 230–245

Read the following in your Polit & Beck (2021) Nursing research: Generating and assessing evidence for practice textbook:

Chapter 18, “Inferential Statistics” pages 396 (starting at “Testing Mean Differences with Three or More Groups”) through 400

Against All Odds: One-Way ANOVA

Review the presentation by Dr. Pardis Sabeti to learn about ANOVA:

Sabeti, P. (Host), & Villiger, M. (Writer/Producer/Director). (2014). One-way ANOVA (Links to an external site.) [Video Unit 31]. Against All Odds: Inside Statistics. Retrieved from Annenberg Learner (Links to an external site.). (Closed captioning is provided.)

One-Way ANOVA Transcript (Links to an external site.)

Lecture: ANOVA

Review the lecture to learn more about ANOVA.

Lecture: ANOVA Transcript (Links to an external site.)

Analysis of Variance (ANOVA)

Review the video on Analysis of Variance (ANOVA).

Analysis of Variance (ANOVA) Transcript (Links to an external site.)

Introduction to One-Way ANOVA

Review the video on one-way ANOVA.

Introduction to One-Way ANOVA Transcript

Assignment 9.1: ANOVA Analysis Rubric
Criteria	Ratings	Pts
Part 1: JASP Dataset	5 to >1 pts Meets Expectations Completed screenshots of statistical output for your ANOVA is present. 1 to >0 pts Does Not Meet Expectations Completed screenshots are not included.	4.5 / 5 pts
Part 2: Narrative	5 to >4 pts Meets Expectations Narrative includes a description of your sample and analysis. Narrative includes the identification of independent and dependent variables, a null hypothesis, an alternative non-directional hypothesis, and an interpretation of your results. 4 to >1 pts Nearly Meets Expectations Narrative includes a description of your sample and analysis. Narrative includes some of the following: • Identification of independent and dependent variables • A null hypothesis, an alternative non-directional hypothesis • An interpretation of your results 1 to >0 pts Does Not Meet Expectations Narrative does not include a description of your sample and analysis. Narrative does not include the identification of independent and dependent variables, a null hypothesis, an alternative non-directional hypothesis, and an interpretation of your results. Comments Please see the comments on your paper. You did not correctly identify the 2 groups that differed.	4 / 5 pts
Documentation and Mechanics	5 to >4 pts Meets Expectations No errors in grammar, spelling, punctuation, or sentence structure. 4 to >1 pts Nearly Meets Expectations Few errors in grammar, spelling, punctuation, or sentence structure. 1 to >0 pts Does Not Meet Expectations Numerous and distracting errors in grammar, spelling, punctuation, or sentence structure. Comments APA had some errors related to acronyms and numbers – remember the APA basics so you don’t miss points.	4.5 / 5 pts
Total Points: 13