NR 449 Week 6 Discussion: Identifying and Interpreting Descriptive Statistics: 47 Pages
Examine the survey results located in the Resources section of
... [Show More] the Unit 6 Assignments page. Choose one of the items, determine the descriptive statistic that is reported, & indicate what it means. For example, “What is the highest level of education of this group?” (Please do not repeat the same topic as your classmates.)
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This week, our discussion will focus on identifying & interpreting descriptive statistics.
You may begin posting on Sunday, February 10th, 2019 for credit.
The course outcomes for this week include the following:
1. Examine the sources of knowledge that contribute to professional nursing practice.
2. Apply research principles to the interpretation of the content of published research studies.
3. Identify ethical issues common to research involving human subjects.
For this week's discussion, you will access they hypothetical survey results located in the Resources section of the Unit 6 Assignments page. The discussion will revolve around data collection & analysis, & is designed to help you report & interpret descriptive statistics. Refer back to the "rules" for looking at data in this unit's lesson. Consider how the use of different statistics gives meaning to research findings.
Please utilize the Threaded Discussion graded rubric as a guideline for posting to the discussions. These are the criteria you will be graded on which includes the use of citations & references according to current APA.
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Class,
This is just a reminder post that for this discussion thread, you are referring to the hypothetical surveyLinks to an external site. posted in this week's lesson. There are 12 questions in the survey. Since there are 22 students in this group, a maximum of two students can elect to use each question.
To avoid any confusion. you may with to post cut & past the question you opted to use at the very beginning of your post to make it easier to identify to other students.
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Class,
Question 5, "what is your gender?" has been used by 2 students already so be sure to pick a different question!
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6. What is your racial or ethnic background? Hispanic (of any race) 3.0% b. American Indian or Alaska Native 0.5% c. Asian 2.5% d. Black or African-American 15.8% e. Native Hawaiian or other Pacific Isl&er 0.2% f. White 69.1% g. Race or ethnicity unknown 8
I chose question #6 that asked about ethnicity at a nominal level data. Each race or ethnicity is represented with numbers & then calculated to get percentage values. According to Houser, nominal-level data denote categories; numbers given to these data are strictly for showing membership in a category & are not subject for mathematical calculations (Houser, 2018). A research study that has descriptive or inferential results will show summary statistics that may show percentages, rates, or derived variables. These values are calculated from the frequency data collected in the study (Houser, 2018). In statistics, frequency “means a count of the instances in which a number or category occurs in a data set” (Houser, 2018, p. 293). To analyze the descriptive & inferential result, there are four basic rules to look at data in the Results section of a study (Chamberlain College of Nursing, 2018).
I found an article, Incidence of Sinusitis Associated With Endotracheal & Nasogastric Tubes: NIS Database that used descriptive data with a nominal level of measurement. Here I analyzed the article’s Result section based on the 4 basic rules,
Rule #1 – underst&ing the purpose of the study
The two objectives were “(1) to use information in the NIS database for the years 2008 through 2013 to compare the incidence of sinusitis in patients with nasogastric tubes with the incidence in patients with an endotracheal tube only or a combination of an endotracheal tube & a nasogastric tube; & (2) to describe the extent to which the following variables were associated with the diagnosis of sinusitis in the patients specified in the preceding objective: sex, age, race, type of admission (elective or non-elective), insurance status, & died in hospital.” (Metheny, Hinyard & Mohammed, 2018, p. 25);
Rule #2 - identify the variables – independent & dependent
Independent variable was the type of tube the patient had (classified as nasogastric tube, endotracheal tube, or both nasogastric & endotracheal tubes) & the dependent variable was the diagnosis of acute sinusitis during the hospitalization.
Rule# 3 - identify how the variables are measured
Measurements used were: Unweighted & weighted frequencies, weighted percentages, & st&ard errors are reported for demographics of the full sample.
Rule #4 - the measure of central tendency & the measures of variability for the study variables (Chamberlain College of Nursing, 2018)
“Results of 1, 141,632 included cases, most (68.57%) had an endotracheal tube only, 23.02% had a nasogastric tube only, & 8.41% had both types of tubes. Sinusitis was present in 0.15% of the sample. Compared with patients with only a nasogastric tube, the risk for sinusitis was 41% greater in patients with an endotracheal tube & 200% greater in patients with both tubes” (Metheny, et. al., 2018, p. 24 ).
I think nominal data is pretty much easy to identify. Don’t you think so?
Reference:
Chamberlain College of Nursing (2018, May 22). Week 6: Lesson – Reading Research Literature – Results Introduction. Retrieved from https://chamberlain.instructure.com/courses/37412/pages/unit-6-lesson?module_item_id=4603014
Houser, J. (2018). Nursing research: Reading, using, & creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
Metheny, N., Hinyard, L., & Mohammed, K. (2018). Incidence of sinusitis associated with endotracheal & nasogastric tubes: NIS Database. American Journal of Critical Care, p. 24-25. Retrieved from https://doi-org.chamberlainuniversity.idm.oclc.org/10.4037/ajcc2018978
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Chona,
Great post! You made this week's concepts very easy to underst& in your discussion post.
Regarding nominal data what statistical measurements can be done with this type of data?
Professor Hobbs
Hello Professor Hobbs,
In the article mentioned on my initial post, the desired information in the study was about patients, who were then classified & measured into variables as collected into three values (patients with nasogastric (NG) tube, patients with endotracheal tubes (ET) & patients with both NG & ET tubes); the total number of patients qualified for this study were then calculated. It was then converted into a frequency data, an example of which was 782,817 patients on ET tube in a sample of 1,141,632, showing the percentage of this category would be 68.57%, a summary value that could easily be understood.
This levels of measurement help researchers find a comparison between the categorical data. The study found that patients with NG tube was 41% greater in patients with an endotracheal tube & 200% greater in patients both tubes. Using a comparative study based on nominal data can lead to further research, a study can be made to find the causes of why patients with NS tube acquire sinusitis compared to patients with ET tubes & patients with both tubes. The study can be done by reviewing health care practices rendered to patients with tubes & analyze to see any factors that may contribute to sinusitis. Once findings are found, another study can be made to determine new practices that can reduce the risk of complication. These new findings can bring new practices & interventions in healthcare services & can then be disseminated to other healthcare providers based on evidence-based practice.
To answer your question, Professor Hobbs, nominal level data can identify comparisons according to the level of measurement used that can be understood easily. The statistical summary can lead to further studies about healthcare practices that direct to better outcomes based on evidence-based practice, as illustrated in the second paragraph.
I hope I answered your question. But this helps me underst& why nominal data can be popular in healthcare studies.
Chona
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Great response,
You demonstrated that percentages can obtained from nominal data. Frequency, such as mode, can also be evaluated with nominal data as well.
Karen
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Hi everyone,
For this week’s discussion, I am picking question #9 – What is the time zone where you live in.
According to the book, there are a few possible levels of measurement used to obtain data & one of it is the nominal. Nominal level labels categories but are not ranked in a particular order. Nominal data can be counted (but not measured) for statistical purposes. Ratios, mode, percentage, & frequency are some examples of summary statistics that are suitable for nominal level (Houser, 2018). In question number 9, the timezone was labeled (East, Central, Mountain, Pacific) & the percentage(%) of the students who lived in that particular time zone is an example of a summary statistic.
The article I will discuss in relation to this week’s topic talks about methotrexate. Methotrexate is a drug used to treat certain malignancies such as acute lymphoblastic leukemia (ALL) & rheumatoid arthritis. Methotrexate toxicity can cause pancytopenia, renal toxicity, pulmonary symptoms, myelosuppression, ulceration, & mucositis to name a few ( Bidaki, Kian, Owliaey, Babaei & Feysal, 2017). This article presents an example of a nominal data because it labels the categories but doesn’t rank it in any particular order. An example of a nominal data collected in this article were patient genders. In addition, continuous variabes such as the patients’ age were also obtained.
References:
Bidaki, R., Kian, M., Owliaey, H., Babaei Zarch, M., & Feysal, M. (2017). Accidental Chronic Poisoning with Methotrexate; Report of Two Cases. Emergency (Tehran, Iran), 5(1), e67.
Houser, J. (2018). Nursing Research: Reading, Using & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
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Kristel,
Another great example using nominal data! If you were to take the same group of subjects & conduct a study using ordinal data, can you give me some examples of things that could be studied?
Professor Hobbs
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Hi Professor,
According to the textbook when compared to nominal data, ordinal data can be identified as being more than or less than one another. Since the level of measurement is still categorical, the exact level of distinctness cannot be established (Houser, 2018) To answer your question, a study related to the following can be done:
Patient satisfaction
What was their pain scale before & after intervention
These examples can be distinguished as better or less than one another, & although each one is under a category, the difference cannot be measured ( Houser, 2018).
Reference:
Houser, J. (2018). Nursing Research: Reading, Using & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
Karen Hobbs
Karen Hobbs
Feb 15, 2019 Feb 15 at 1:27pm
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Great example, Kristel!
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Ashleynicole Ndubizu
Ashleynicole Ndubizu
Feb 17, 2019 Feb 17 at 9:26pm
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Hi Kristel!
That was a good example of nominal data & I really enjoyed reviewing your articles. My topic this week also used the nominal to obtain data. As this week's lesson stated, "nominal data can be counted (but not measured) for statistical purposes. Ratios, mode, percentage, & frequency are some examples of summary statistics that are suitable for nominal level" (Houser, 2018). Nominal data is one of the easiest ways to obtain data, which is why it is most commonly used amongst researchers. Many of us use nominal data often & we don't even realize it.
Great post!
Best,
Ashley
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Reference:
Houser, J. (2018). Nursing Research: Reading, Using & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
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Collapse SubdiscussionEnajite Rowl& Mba
Enajite Rowl& Mba
Feb 13, 2019 Feb 13 at 11:29pm
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Hello Prof. Hobb & Class,
From the results of the hypothetical survey, I have chosen number 10 which applies to how many students own their residence. The variable data is nominal, a discrete classification in which the data are neither measured nor ordered (Chamberlain College of Nursing, 2018). The subjects are merely allocated to distinct categorize, therefore the students' answer can either be yes or no. According to Houser (2018), nominal level data also has no rank & no order, & the data can only be counted & not measured. The number of students who took the survey was quantitative which gives a statistical data of what percentage of the population own their own homes. The survey showed that sixty-one percent of the students own their residence & thirty-nine percent of the students do not own their residence. Based on this conclusion, most of the student’s population who attended the Chamberlain College owned their homes.
The profile of incoming college students has changed dramatically in recent years. No longer is the typical student beginning college straight from high school, nor do they attend classes full time & live on campus. Today, forty-four percent of college & university students are twenty-four years of age or older (Taro, 2011). Given this information, it can be concluded that sixty-one percent of Chamberlain’s students that own their own resident probably fall in this bracket. The other thirty-nine percent who don’t own their own resident are those just out of high school (Chamberlain College of Nursing, 2018). Not all students set out for college immediately after high school. Some enlist in the military, while some may seek immediate work to provide for their family.
Reference
Chamberlain College of Nursing. (2018). Unit 6 lesson: Reading research literature—Results introduction. Retrieved from https://chamberlain.instructure.com/courses/31413/pages/unit-6-lesson?module_item_id=3806534
Houser, J. (2018). Nursing Research: reading, using, & creating evidence. Retrieved from https://bookshelf.vitalsource.com/?#/books/9781284138887/cfi/6/4!/4/2/2/2@0:0 .Links to an external site.
Taro, C.N., & Hodson, J. B. (2011). Partners in student success. New Directions for Higher Education, 2011(153), 25-34.doi:10.1002/he.423
Collapse SubdiscussionOlukayode Ogunbanwo
Olukayode Ogunbanwo
Feb 15, 2019 Feb 15 at 9:27pm
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Hello Enajite,
You have a good post this week, Your hypothetical survey #10 has just two categories; you either own your residence or you are not the owner. Categories like this generate nominal data. According to Houser (2018), data can be collected in one of four possible levels of measurement: Nominal, ordinal, interval, or ratio. Each level is unique & requires a particular type of statistical technique Houser, (2018).
Nominal level data are those that denote categories & have no rank order, the numbers given shows membership & are not subject to mathematical calculations Houser, (2018). Your sample survey falls under nominal level data. You either own your residence or you do not. Summary statistics appropriate for this level of measurement are frequency, percentage, rates, ratio, & mode Houser, (2018).
Reference
Houser, J. (2018). Nursing Research: Reading, Using, & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
Collapse SubdiscussionKaren Hobbs
Karen Hobbs
Feb 16, 2019 Feb 16 at 9:24am
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Olukayode,
You did a great job clarifying the use of nominal data in research that was used in Ena's post! Nominal data, although not useful for statistical evaluation, is great to present in a research study when describing the sample population. Nice work!
Karen
Enajite Rowl& Mba
Enajite Rowl& Mba
Feb 17, 2019 Feb 17 at 11:34pm
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Hello Prof. Hobbs & Olukayode,
Thank you for your feedback on my post.
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Collapse SubdiscussionKayla Gagnon
Kayla Gagnon
Feb 10, 2019 Feb 10 at 7:25pm
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Question #5
What is your gender?
Male 7.5%
Female 92.3%
For this weeks discussion, I used question #5 on the Unit 6 Assignments hypothetical survey page. When determining what type of descriptive statistic this question is I referred to the textbook. I concluded that this is nominal data. The definition of nominal data that is presented in the textbook is "...data that denote categories & have no rank or order; numbers given to these data are strictly for showing membership in a category & are not subject to mathematical calculations. Summary statistics appropriate for this level of measurement are frequency, percentages, rates, ratios, & mode" (Houser, 2018, pg.292). In addiiton, to the deffinition, the textbook gives an example of this type of level of measurement. They give the example of a patient on fall precautions. They state that this is an example of numerical data because a patient is either on fall precautions or they are not (Houser, 2018). Similarly, for the question I picked, a patient is either female or male, so depending on what gender they associate with shows membership to a specific category.
In the research article, Discussing Spirituality with Patients: A Rational & Ethical Approach, by McCord et.al, (2004) the authors present several examples of nominal data. The examples presented in the study included: sex (male vs female), marital status (single, married, divorced, separated, widowed, partner), race (White, Black, Asian/Pacific Isl&er, Hispanic, mixed white/black, or other) , the respondent (patient, adult companion), & site (private practice or residency site) (McCord et.al, 2004). I feel like a majority of demographic statistics can be presented in nominal data. What do you guys think?
Resources:
Houser, J. (2018). Nursing Research: Reading, Using, & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
McCord, et.al, (2004). Discussing Spirituality with Patients: A Rational & Ethical Approach. Annals of Family Medicine, 2, 356–361.
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Karen Hobbs
Karen Hobbs
Feb 13, 2019 Feb 13 at 9:22am
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Kayla,
Great post! Just to clarify for the rest of the group...
The nominal data cannot be used to calculate ratios from the data obtained. However, it can be used just to show ratios. For example, "two-thirds of the participants were caucasian". You cited Houser appropriately, but I just wanted to ensure we are all on the same page.
Thanks!
Professor Hobbs
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Collapse SubdiscussionOlukayode Ogunbanwo
Olukayode Ogunbanwo
Feb 15, 2019 Feb 15 at 9:16pm
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Hello Kayla
Great post, your pick (survey # 5) is clearly a nominal level data because you are either a male or a female. The appropriate summary statistics for this level of measurement are percentage, frequency, & rates, Houser, (2018).
A closer look at those percentages shows they do not sum up to 100% (we have 99.8%); is the 0.2% representing people who are both male & female at the same time? I think that would have represented the third category of people.
References
Houser, J. (2018). Nursing Research: Reading, Using, & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
Karen Hobbs
Karen Hobbs
Feb 16, 2019 Feb 16 at 9:24am
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Ha, good catch=)
Karen
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Collapse SubdiscussionMelissa Castro
Melissa Castro
Feb 10, 2019 Feb 10 at 9:24pm
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According to Houser (2018), “descriptive statistics use numbers narratively, in tables, or in graphic displays, to organize & describe the characteristics of a sample.”
For the purpose of this discussion, I will be using question #9 from the lesson. The question is:
What is the time zone where you live?
A. Eastern
B. Central
C. Mountain
D. Pacific
This question is a type of nominal-level data because it denotes a category (time zone) but does not have a rank order (Houser, 2018). According to the lesson, variables in nominal-level data simply represent a “membership to that category” (Chamberlain College of Nursing, 2018). In question #9, the Chamberlain students can only be categorized in one of the four options; they can either live on the eastern, central, mountain, or pacific time zone.
In the research article Post-traumatic Stress Disorder & Self-reported Outcomes After Traumatic Brain Injury in Victims of Assaultby Bown, Belli, Qureshi, Davies, Toman, & Upthegrove (2019), the authors “aimed to examine the influence of assault on self-reported outcomes.” When obtaining the participants demographics, they used nominal-level data. Examples of nominal data used in the study include Categories for ethnicity: White, Asian, Other, & categories for the severity of the assault: Severe, Moderate, Mild.
References:
Bown, D., Belli, A., Qureshi, K., Davies, D., Toman, E., & Upthegrove, R. (2019). Post-traumatic Stress Disorder & Self-reported Outcomes After Traumatic Brain Injury in Victims of Assault. PLoS ONE, 14(2), 1-14. https://doi-org.chamberlainuniversity.idm.oclc.org/10.1371/journal.pone.0211684Links to an external site.
Chamberlain College of Nursing (2018, May 22). Week 6: Lesson – Reading Research Literature – Results Introduction. Retrieved from https://chamberlain.instructure.com/courses/37412/pages/unit-6-lesson?module_item_id=4603014
Houser, J. (2018). Nursing research: Reading, Using, & Creating Evidence. Burlington, MA: Jones & Bartlett Learning.
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Collapse SubdiscussionKayla Gagnon
Kayla Gagnon
Feb 12, 2019 Feb 12 at 3:40pm
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Melissa,
You did a great job explaining & providing examples of nominal-level data. I felt like the textbook as well a the lesson provided great information about each type of data & really helped when trying to figure out what to write for this weeks post. In addition, you found a great research article that gave excellent examples of normal data. I found the article to be very beneficial & interesting to read due to the fact that I am taking Mental Health right now.
Tying in what we have learned & wrote about in previous discussion posts this session, I think the most efficient way to gather nominal data would be through a survey or questionnaire data collection tool/method. I think this because this information is very cut & dry (either you are apart of a category or you are not). As we all know surveys/ questionnaires are a fast & efficient way to ask yes/no as well as "check the boxes that apply to you" questions. Can you think of another data collection methods that could be used to gather nominal data?
Again great post keep up the awesome work!
Kayla
Collapse SubdiscussionMelissa Castro
Melissa Castro
Feb 13, 2019 Feb 13 at 3:02pm
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Hi Kayla,
I'm glad you found the article helpful & that it can help you for your class. I found it very interesting as well. As for what you said about nominal data, I have to agree with you. Nominal data is the most efficient & most helpful way of gathering data which is why it's the one that's mostly used in comparison to ordinal or ratios. To answer your question, I don't think that nominal data can be used in something other than surveys or questionnaires. I was thinking that maybe they can be used through interviews, but in this case, the researcher would ask questions that have multiple choices, this way the interviewee can only have a certain number of options to answer from.
Karen Hobbs
Karen Hobbs
Feb 15, 2019 Feb 15 at 1:33pm
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Melissa & Kayla,
Many students have been utilizing reports from agencies like AHRQ for their projects. In terms of gathering nominal data, such as gender, the data can also be obtained from patient charts.
Good discussion!
Professor Hobbs
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Collapse SubdiscussionIfure Inyangotu
Ifure Inyangotu
Feb 11, 2019 Feb 11 at 9:13am
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Question #5
What is your gender?
Male 7.5%
Female 92.3%
In this unit 6 section, I will like to write on question 5, “What is your gender?” This question is considered the nominal level of measurement. The nominal level of measurement can be either binary or multinational. Binary has only two categories such as male & female while multinational has more than two categories such as marital status: married, divorced, never married, widowed, separated. But the key thing here is that there is no logical order to the categories (Karen Grace-Martin 2018). Nominal level data are those that denote categories & have no rank order: In nominal data, mathematical calculations are not required, numbers given are strictly for showing membership in a category (Houser 2018). Nominal data can be counted, but are not measured, so they can be summarized using statistics that represent counts. (Houser 2018. This summarized statistic can be found in Table 11.1 pg. 292 of our textbook showing the different levels of measurement & the appropriate summary statistics. The distribution is frequency & percentage & the central tendency is the mode, median & mean. The frequency is the number of time a category occurs in a data set. The mode is the most frequently occurring value in the set. These provide less detail about the data set but it is the only measure of central tendency that can be applied to nominal data. (Houser, 2018).
References
Houser, J. (2018). Nursing Research: Reading, Using & Creating Evidence (4th ed.). Sudbury, MA: Jones & Bartlett Learning.
Grace-Martin, K. (2008-2018). When a variable's level of measurement isn't obvious. Retrieved April 03 2018 from https://www.theanalysisfactor.com/level-of-measurement-not-obvious/Links to an external site.
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Collapse SubdiscussionKaren Hobbs
Karen Hobbs
Feb 16, 2019 Feb 16 at 9:29am
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Ifure,
Beautiful description on the use of nominal data! I am currently taking a statistics course in my doctoral program & had to clarify this info with my group in class last night, basically verbatim how you explained it! That should make you feel pretty good=)
Karen
Ifure Inyangotu
Ifure Inyangotu
Feb 17, 2019 Feb 17 at 3:20pm
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Hello Professor,
Thank you so very much.
Ifure
(1 like)
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Enajite Rowl& Mba
Enajite Rowl& Mba
Feb 17, 2019 Feb 17 at 11:57pm
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Hello ifure,
Your information is correct & appreciated. Christensen (2017) conducted a historical review . While we’ve all heard of famous women in nursing, there are equally famous men in nursing. Men have been nurses for more than 2000 years (Christensen, 2017). Up until Florence Nightingale, nursing was viewed as a less than desirable profession, with most nursing being conducted in prisons. Nurses were less than reputable, being drunk, drug addicted, & prostitutes many times. Nightingale transformed the job into a female profession.
Reference
Christensen, M. (2017). Men in nursing: The early years. Journal of Nursing Education & Practice, 7(5), 94-104.
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Rachel Johnson
Rachel Johnson
Feb 11, 2019 Feb 11 at 12:36pm
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11. Please indicate how prepared you felt to enter nursing school.
a. Extremely prepared: 15%
b. Prepared: 37%
c. Neither prepared or unprepared: 28%
d. Unprepared: 15%
e. Extremely unprepared: 5%
For this week's discussion, I chose to focus on question #11 from the hypothetical survey. The question asked hypothetical participants to indicate how prepared they felt when they entered nursing school. This type of question collects results that are categories, but also have an added characteristic of rank order. This is also known as ordinal data (Houser, 2018). As explained by the textbook, ordinal data "differs from nominal data in that categories for a variable can be identified as being less than or greater than one another" (Houser, 2018). This week's lesson uses the question above as an example & further breaks each answer into point values. The lesson explains that the reply of 'extremely prepared' is assigned 5 points, & the reply 'extremely unprepared' is assigned 1 point (Chamberlain College of Nursing, 2019). If someone claimed they felt 'prepared' to enter nursing school, they would be assigned a score of 4. If someone claimed they felt 'unprepared', they would be assigned a score of 2. By assigning each answer a point value, the variables can be identified as being less than or greater than a different variable, as mentioned above.
One of the articles I had previously discussed also used ordinal data. The research article, "Opioid Overdose Prevention in Family Medicine Clerkships" gathered data by using a 4-point Likert scale. The possible responses were "not important, slightly important, important, & very important" (Gano et al, 2018). After the participants were given the survey, statistical analysis was used to analyze the data & turn the numbers into meaning. This week's lesson also explains how statistics is needed in order aggregate the data & make sense of the data received (Chamberlain, 2019). I also found it useful to use process of elimination when determining what kind of data was collected. Since the question of how students felt when they entered nursing school was not related to gender, race, or ethnicity, it couldn't be nominal data. Also, since it didn't deal with specific numbers such as temperature, pulse, weight, height, as discussed in the Web-Ex from last week, it wouldn't be considered interval or ratio data. This only left ordinal. But, looking at the definition & explanation of what ordinal data is, it makes sense.
References:
Chamberlain College of Nursing (2019). Week 6: Lesson - Reading Research Literature - Results Introduction. Retrieved from https://chamberlain.instructure.com/courses/37412/pages/unit-6-lesson?module_item_id=4603014
Houser, J. (2018). Nursing research: Reading, using, & creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett.
Gano, L., Renshaw, S. E., Hern&ez, R. H., & Cronholm, P. F. (2018). Opioid Overdose Prevention in Family Medicine Clerkships: A CERA Study. Family Medicine, 50(9), 698–701. https://doi.org/10.22454/FamMed.2018.757385
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Collapse SubdiscussionOlukayode Ogunbanwo
Olukayode Ogunbanwo
Feb 11, 2019 Feb 11 at 2:20pm
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Hello professor Hobbs & the class
I choose item number seven "what is your family status" for this weeks discussion. The variables falls under nominal data, because each represent different categories of family, with no other assigned. Being married for example, does not make the participant greater than 'never married' or 'divorced participants' Houser, (2018). Nominal data are analyzed using descriptive statistics like frequencies & percentage. The descriptive statistics use in item seven is the percentage. Descriptive statistics do not make inference, it only describe characteristics of the participants ( Chamberlain College of Nursing, 2019). From the table, one can conclude that majority of Chamberlain nursing students are married while very few are never married this is based on the value given on the table namely married 70.5% widowed/divorced/separated 18.1% never married 9.2%.
The use of descriptive statistics is valuable when there is need to better organize large data without compromising accuracy. It present quantitative description of the data in a more organized & manageable form Vetter, (2017).
References
Chamberlain College of Nursing (2019). Week 6: Lesson – Reading Research Literature – Results Introduction. Retrieved from https://chamberlain.instructure.com/courses/37412/pages/unit-6-lesson?module_item_id=4603014
Houser, J. (2018). Nursing research: Reading, Using, & Creating Evidence. Burlington, MA: Jones & Bartlett Learning.
Vetter, T.R.,(2017). Descriptive statistics: Reporting the answers to the 5 basic questions of who, what, why, when, where, & a sixth, so what ? Anesthesia & Analgesia, 125(5), 1797-1802.
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Collapse SubdiscussionAida Fall
Aida Fall
Feb 11, 2019 Feb 11 at 8:56pm
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Hello Professor & class,
8. Do you have children?
9. No children 56.2%, b. One child 24.9%, c. two children 11.5%, d. Three or more children 5.6%.
Item # 8 is the one I chose to talk about.
According to Houser (2018), there are four levels of measurement
Nominal :Categorical data/labels/no mathematical properties
Ordinal: Categorical data that are ranked
Interval: Data that are ranked with equal interview
Ratio Data: Interval level data that have a true zero.
Quantitative research directs health care decision makers with statistics numerical data collected from different measurements or observation that describe the features of specific population sample. The level of measurement that I’m going to talk about ratio . Ratio is a tool of quantitative research that works equally like the interval scale; the only difference is that the ratio scale has an absolute zero point meaning there no number below the zero. I understood that over 56% of the 65 students in Chamberlain chosen for the survey does not have children, & the lowest percentage belongs to the group with three or more children.
I previously used a research study titled “ Opioids in Adolescents’ Homes: Prevalence, Caregiver Attitudes, & Risk Reduction Opportunities” that also uses ratio to illustrate the risks of having the leftovers of opioid in the home. Garbutt, J. M.et al. writes “ Our study findings can inform a campaign to reduce access to opioids in the home”(2019).
References
Houser, J. (2018). Nursing research: Reading, using & creating evidence (4th ed.). Sudbury, MA: Jones & Bartlett.
Garbutt, J. M., Kulka, K., Dodd, S., Sterkel, R., & Plax, K. (2019). Opioids in Adolescents’ Homes: Prevalence, Caregiver Attitudes, & Risk Reduction Opportunities [Abstract]. Academic Pediatrics, 19(1), 103-108. doi: 10.1016/j.acap.2018.06.012 [Show Less]