Executive Solution Overview
– 2 years ago **files (4)** – ## WK3DISCDATA-LevelsofMeasurement.docx – ## WK3DATAMediatranscript.txt – NURS_8211_WK3_DescriptiveStatistics.pptx – UrinaryTractInfection.pdf **## WK3DISCDATA-LevelsofMeasurement.docx** Levels of Measurement: Categorical vs. Continuous Data; Descriptive Statistics a…
Instant Solution Download
$6.99Immediate access to the full model answer, formatted citations, and rubric breakdown.
Need a Fresh Custom Paper?
Have this question answered from scratch according to your specific instructions and deadline.
Order on TutorsGallery →Assignment Prompt & Instructions
– 2 years ago
**files (4)**
– ## WK3DISCDATA-LevelsofMeasurement.docx
– ## WK3DATAMediatranscript.txt
– NURS_8211_WK3_DescriptiveStatistics.pptx
– UrinaryTractInfection.pdf
**## WK3DISCDATA-LevelsofMeasurement.docx**
Levels of Measurement: Categorical vs. Continuous Data; Descriptive Statistics and Probability Theory Basics
_What is the incidence of blood clots from COVID-19 in females over the age of 35?_
The above question is an example of a research question. A research question consists of three parts and guides the methods and approaches in which you will study the question to find answers. The research question includes the question, the topic, and the population or variables. In the example provided above, the question examines the prevalence of blood clots from severe COVID-19 in a selected population. From this question, the variables can be assessed, considerations can be analyzed, and populations can be sampled in order to guide the research.
For this Discussion, you will analyze a selected work to identify and analyze the variables, comparisons, and sample sizes. You will explore the potential levels of measurement for your variables and the rationale for the labels, as well as consider the advantages and challenges that you might experience in the statistical analysis.
**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.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
[**WEEKLY RESOURCES**](https://waldenu.instructure.com/courses/157739/modules/items/6373659)
Learning Resources
[Required Resources](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Bullen, P. (n.d.).
[_How to choose a sample size (for the statistically challenged)_ Links to an external site.](https://tools4dev.org/resources/how-to-choose-a-sample-size/). tools4dev. https://tools4dev.org/resources/how-to-choose-a-sample-size/
· Centers for Disease Control and Prevention. (2024, March).
[_The NHSN standard infection ratio (SIR)_ Links to an external site.](https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf). https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf
· “Overview of the Standard Infection Ratio (SIR)” (pp. 4–5)
· Dang, D., Dearholt, S. L., Bissett, K., Ascenzi, J., & Whalen, M. (2021).
_Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model & guidelines_ (4th ed.). Sigma Theta Tau International Honor Society of Nursing.
· Chapter 6, “Evidence of Appraisal: Research” (pp. 147–157)
· Salkind, N., & Frey, B. (2019).
_Statistics for people who (think they) hate statistics_ (7th ed.). SAGE Publications.
· Chapter 3, “Computing and Understanding Averages: Means to an End” (pp. 65–68)
· Chapter 5, “Creating Graphs: A Picture Really Is Worth a Thousand Words” (pp. 88–118)
· Chapter 8, “Hypotheticals and You: Testing Your Questions” (pp. 167–180)
· Chapter 9, “Probability and Why It Counts: Fun With a Bell-Shaped Curve” (pp. 181–200)
[Required Media](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Niedz, B. (2024).
_Descriptive statistics_ [Video]. Walden University Canvas. https://waldenu.instructure.com
[PowerPoint Presentation](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Document:
[Descriptive Statistics (PowerPoint presentation)](https://waldenu.instructure.com/courses/157739/files/12465906?wrap=1) [Download Descriptive Statistics (PowerPoint presentation)](https://waldenu.instructure.com/courses/157739/files/12465906/download?download_frd=1)
[Required Resources for Topic: Infections](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Beydoun, A. S., Koss, K., Nielsen, T., Holcomb, A. J., Pichardo, P., Purdy, N., Zebolsky, A. L., Heaton, C. M., McMullen, C. P., Yesensky, J. A., Moore, M. G., Goyal, N., Kohan, J., Sajisevi, M., Tan, K., Petrisor, D., Wax, M. K., Kejner, A. E., Hassan, Z., … Zenga, J. (2022).
[Perioperative topical antisepsis and surgical site infection in patients undergoing upper aerodigestive tract reconstructionLinks to an external site.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047735/).
_JAMA Otolaryngology-Head & Neck Surgery, 148_(6), 547–554. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047735/
· Sood, N., Lee, R. E., To, J. K., Cervellione, K. L., Smilios, M. D., Chun, H., & Ngai, I. M. (2022).
[Decreased incidence of cesarean surgical site infection rate with hospital‐wide perioperative bundleLinks to an external site.](https://onlinelibrary.wiley.com/doi/abs/10.1111/birt.12586). B
_irth: Issues in Perinatal Care, 49_(1), 141–146. https://onlinelibrary.wiley.com/doi/abs/10.1111/birt.12586
· Sauer, K. (2023).
[_Testing for the treatment of urinary tract infections in symptomatic adult patients residing in long-term care facility: An evidence-based quality improvement project_ Links to an external site.](https://www.proquest.com/dissertations-theses/point-care-testing-treatment-urinary-tract/docview/2875242069/se-2?accountid=14872) (Publication No. 30569808) [Doctoral dissertation, Phoenix University]. ProQuest Dissertations and Theses Global. https://www.proquest.com/dissertations-theses/point-care-testing-treatment-urinary-tract/docview/2875242069/se-2?accountid=14872
**To prepare:**
· View the required media.
· It is recommended you complete the quiz prior to constructing your initial response.
By Day 3 of Week 3
**Post** a response including the following:
· Choose a research study, QI article, or EBP DNP project and interpret at least one continuous demographic variable and one categorical variable.
· Differentiate between comparisons made using descriptive statistics (e.g., the mean and standard deviation) and comparisons based on inferential statistics (e.g., a
_t_ test).
· Compare and contrast the sample sizes used in the research study, the QI project, and the DNP project in terms of type 1 and type 2 errors.
· Explain the SIR rate, how it is developed, and how organizations use it.
· Using the same articles, pick one and differentiate between one descriptive and one inferential statistic used in any one of the three studies/projects.
By Day 6 of Week 3
**Read** a selection of your colleagues’ posts and
**respond** to
**at least two** of your colleagues on
**two different days** by expanding upon their reflections, making connections to your perceptions, and offering additional insights.
**Reply**
**## WK3DATAMediatranscript.txt**
BARBARA NIEDZ: Hi, all. Dr. Niedz here
again and moving on to a brief discussion
about descriptive statistics. So we'll talk a bit about
measures of central tendency and measures of
dispersion, and you'll see how they work for
continuous variables. We'll also talk about
frequencies and percentages and how they fit for
categorical data. We'll use, revisit a
little research lingo, and talk about null directional
and non-directional hypotheses. We'll also visit the
standardized infection rate, the standardized
infection ratio measure, and how that's used in research,
quality improvement, and DNP. And we're also going to
spend a little time talking about the differences
between type one and type two measurement errors and how they
figure into research projects. Four levels of measurement, two
categorical and two continuous– very important concepts
and very important aspects of measurement. Categorical data is
usually displayed by counts and percentages. So for example, nominal
level data might be gender. Male, female, and
transgender people might fall into
those three buckets. And there's no value placed. Men are not better than women. Women are not better than men. Transgender are not
better than men or women, and the data is equal
in terms of the value of those contributions
to the data set. Whereas ordinal data is ranked. So, for example, if you
collected a demographic data on educational level
for some reason, whether you were talking
about health literacy or staff educational backgrounds,
those data are ranked. People who only
completed fifth grade and never completed grade
school have less education than those people who
completed high school who have less education
than those people who have some college as
opposed to those people who have advanced degrees. So categorical data
come in two forms– nominal and ordinal. Ordinal is ranked,
nominal is not. Continuous data is actually
from the type of data that you can do an average
with that makes sense. So for example,
interval level data, where you have some scale
that measures something– let's say perceived stress– from a number that
is as low as zero with no perceived
stress to as high as 21, where this is the most stress
you can possibly imagine. The differences
between the divisions, even though there
might be zero, is very soft because a lot of
stress compared to some stress is not exactly something that
you can multiply or divide by, whereas ratio level data is. So suppose ratio level
data– for example, income, annual income, zero– is very different from annual
income of $50,000 a year, which is half the amount
of $100,000 a year. So ratio data has equal
divisions, has an absolute zero. But they're all
useful and you're going to see some
examples of that. Measures of central tendency–
the mean, as I mentioned, is the arithmetic average. The mode is the most frequently
occurring value in a data set. And the median is
that point at which you have an equal
number of observations above and below that point. You see them represented in, in
published work and in projects. They're all very useful. The mean is subject to extremes,
and if there are outliers present in the data, the median
is a much more useful tool. Standard deviation tells you
about that variation away from the mean. The variance is simply the
standard deviation squared. And the range is the
lowest point to the highest point in the data set. So measures of central tendency
and measures of dispersion are very useful tools. The chapters that
I've selected out for every week in Salkind and
Frey are really useful tools, and understanding these concepts
takes more than a 15 minute video, and I hope you'll
consult with Salkind and Frey in order to be able to
complete assignments, in order to be able to
address the discussion questions in the classroom,
and certainly the ability to answer the knowledge
check questions. So, means and standard
deviations, very, very useful for continuous data. Counts and percentages,
also important. Percentile rankings allow
you to do some comparisons. And confidence intervals
are not exactly prediction, but it tells you something
about that spread of data and what you might
expect in terms of the relationship between
the sample and the population. So, just a little
bit of information on those four types of
concepts can be very helpful. So a picture is worth
a thousand words, and you can see the differences
between categorical data on the left with a bar
graph and continuous data on the right with a histogram. And in the histogram, you can
see that that is essentially continuous data– no spaces,
no gaps between the bars– whereas the categorical
data on the left is summarized in a bar chart. And you can see that there
are no gaps between the bars, and they are, in fact,
separate and apart. Now, this is only a portion
of the table in Sood et al that is presented on
page 144 and is actually a way to present
demographic variables and other important variables,
independent and dependent variables, in a study all
efficiently in one small table. And you can see that there are
both categorical variables– for example, ethnicity,
with 1,086 participants, broken down into the counts
by different groupings– Asians, Hispanic, Black,
Hispanic, white, and others– and also the percentages
of the sample. You could easily
do this manually to just figure out
those percentages, but it's awfully nice
when they're presented like this in a published piece. You can also see that the
age and gestational age in this study are
presented as the mean and the standard deviation plus
or minus 1 standard deviation. So you get an idea of what the
spread of data might look like. Median BMI and the range– again, it's an ordinal
level of measurement. And so the mean
might make good sense for the way that is
presented and time in minutes also
presented in the average with the standard deviation. So, can be a very efficient and
useful way of summarizing data. There are three
types of hypotheses that are actually
usually implicit and not stated unless
you were reading a PhD dissertation from Proquest. The null hypothesis
states that there is no relationship,
no association, between the variables,
between the dependent and the independent variables. The alternate hypothesis states
that there is a relationship or association between those
independent and dependent variables, and hypothesis
can go in one direction or not, depending on the
nature of that comparison. And that can also be
an important concept. So here's an example. Take a look at the study
that's in the resources for this week by Bedouin
and others from 2022. It's a study about perioperative
prophylaxis practices and surgical site infections. And the null hypothesis
would essentially say there is no association. There is no
relationship, and there's no differences in
perioperative prophylaxis practices and the SSI rate. However, an alternate
hypothesis would posit a relationship between
those two types of variables in a specific type
of surgical procedure and would perhaps even be
more specific in electing a certain type of
prophylaxis over another. So, null, alternative,
directional, or non-directional hypotheses. So the standardized
infection rate or the standardized
infection ratio, the SIR, is a very useful tool that's
been developed by the NHSN that allows us to take a
look at infections and make some comparisons on
the basis of risk adjustment. So how sick are the
patients, and what is the predicted rate of
infection for a particular type of surgical procedure? It compares the actual
rate to the prediction. So a SIR of greater
than 1 indicates more hospital acquired
infections than predicted, and an SIR error
rate of less than 1 indicates fewer hospital
acquired infections than predicted. So I played around with the
literature and the SIR rate as I was preparing
these slides and videos, and I live in New Jersey. And so I just said,
well, let me see what I can find about this
that's publicly available. And there is, in New Jersey,
the State Department of Health publishes by hospital
comparisons for different types of surgeries across
the state and provides hospital-specific SIR rates
for these different surgical procedures. So I challenge you to
look in your own state and see what you can find out
in terms of published SIR rates. And if you can't find anything,
look up New Jersey Department of Health, and you'll be able
to see a variety of hospitals in New Jersey. So we typically use probability
theory and the normal curve in order to be able to
understand prediction, and our statistics, our
statistical analysis that we'll move into
starting next week, will allow us to define
a critical value, the point at which we can make
a decision about rejecting the null hypothesis,
that there is no difference or no
association, in favor of the alternate hypothesis that
says essentially, if you were to repeat the test with
a different sample, you'd likely get a similar
result 95% of the time or 99% of the time, depending
on what that p value is, or whether it is in this
part of the curve, where 95% of the values are, that allows you to see that
difference between rejecting and not rejecting the
hypothesis, the null hypothesis. There is actually excellent
information in Salkind and Frey on pages 215 to 217
on this concept. Now, one characteristic that
you can see in the SOOD et al article is about whether
or not there's a P value. If there's a P
value, you can infer from that there is an
inferential test of significance that is being used. And you can see this in
table two in Sood et al. The last key point I want
to make in this video is about measurement error. A type one error is
the type of error that is made when statistical
significance is found, but it's not really there. And your protection against this
is the all important p value. Type two error is
the type of error that you make when you
don't find significance, but it's really there. And sample size is the biggest
protection that we have. Key points in this
video on this slide. Don't forget the
value of the reading.
**NURS_8211_WK3_DescriptiveStatistics.pptx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk3-descriptivestatistics-pptx)
**UrinaryTractInfection.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/urinarytractinfection-pdf-10222459)
**UrinaryTractInfection.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/urinarytractinfection-pdf-10222459)
**## WK3DISCDATA-LevelsofMeasurement.docx**
Levels of Measurement: Categorical vs. Continuous Data; Descriptive Statistics and Probability Theory Basics
_What is the incidence of blood clots from COVID-19 in females over the age of 35?_
The above question is an example of a research question. A research question consists of three parts and guides the methods and approaches in which you will study the question to find answers. The research question includes the question, the topic, and the population or variables. In the example provided above, the question examines the prevalence of blood clots from severe COVID-19 in a selected population. From this question, the variables can be assessed, considerations can be analyzed, and populations can be sampled in order to guide the research.
For this Discussion, you will analyze a selected work to identify and analyze the variables, comparisons, and sample sizes. You will explore the potential levels of measurement for your variables and the rationale for the labels, as well as consider the advantages and challenges that you might experience in the statistical analysis.
**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.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
[**WEEKLY RESOURCES**](https://waldenu.instructure.com/courses/157739/modules/items/6373659)
Learning Resources
[Required Resources](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Bullen, P. (n.d.).
[_How to choose a sample size (for the statistically challenged)_ Links to an external site.](https://tools4dev.org/resources/how-to-choose-a-sample-size/). tools4dev. https://tools4dev.org/resources/how-to-choose-a-sample-size/
· Centers for Disease Control and Prevention. (2024, March).
[_The NHSN standard infection ratio (SIR)_ Links to an external site.](https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf). https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf
· “Overview of the Standard Infection Ratio (SIR)” (pp. 4–5)
· Dang, D., Dearholt, S. L., Bissett, K., Ascenzi, J., & Whalen, M. (2021).
_Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model & guidelines_ (4th ed.). Sigma Theta Tau International Honor Society of Nursing.
· Chapter 6, “Evidence of Appraisal: Research” (pp. 147–157)
· Salkind, N., & Frey, B. (2019).
_Statistics for people who (think they) hate statistics_ (7th ed.). SAGE Publications.
· Chapter 3, “Computing and Understanding Averages: Means to an End” (pp. 65–68)
· Chapter 5, “Creating Graphs: A Picture Really Is Worth a Thousand Words” (pp. 88–118)
· Chapter 8, “Hypotheticals and You: Testing Your Questions” (pp. 167–180)
· Chapter 9, “Probability and Why It Counts: Fun With a Bell-Shaped Curve” (pp. 181–200)
[Required Media](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Niedz, B. (2024).
_Descriptive statistics_ [Video]. Walden University Canvas. https://waldenu.instructure.com
[PowerPoint Presentation](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Document:
[Descriptive Statistics (PowerPoint presentation)](https://waldenu.instructure.com/courses/157739/files/12465906?wrap=1) [Download Descriptive Statistics (PowerPoint presentation)](https://waldenu.instructure.com/courses/157739/files/12465906/download?download_frd=1)
[Required Resources for Topic: Infections](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Beydoun, A. S., Koss, K., Nielsen, T., Holcomb, A. J., Pichardo, P., Purdy, N., Zebolsky, A. L., Heaton, C. M., McMullen, C. P., Yesensky, J. A., Moore, M. G., Goyal, N., Kohan, J., Sajisevi, M., Tan, K., Petrisor, D., Wax, M. K., Kejner, A. E., Hassan, Z., … Zenga, J. (2022).
[Perioperative topical antisepsis and surgical site infection in patients undergoing upper aerodigestive tract reconstructionLinks to an external site.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047735/).
_JAMA Otolaryngology-Head & Neck Surgery, 148_(6), 547–554. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047735/
· Sood, N., Lee, R. E., To, J. K., Cervellione, K. L., Smilios, M. D., Chun, H., & Ngai, I. M. (2022).
[Decreased incidence of cesarean surgical site infection rate with hospital‐wide perioperative bundleLinks to an external site.](https://onlinelibrary.wiley.com/doi/abs/10.1111/birt.12586). B
_irth: Issues in Perinatal Care, 49_(1), 141–146. https://onlinelibrary.wiley.com/doi/abs/10.1111/birt.12586
· Sauer, K. (2023).
[_Testing for the treatment of urinary tract infections in symptomatic adult patients residing in long-term care facility: An evidence-based quality improvement project_ Links to an external site.](https://www.proquest.com/dissertations-theses/point-care-testing-treatment-urinary-tract/docview/2875242069/se-2?accountid=14872) (Publication No. 30569808) [Doctoral dissertation, Phoenix University]. ProQuest Dissertations and Theses Global. https://www.proquest.com/dissertations-theses/point-care-testing-treatment-urinary-tract/docview/2875242069/se-2?accountid=14872
**To prepare:**
· View the required media.
· It is recommended you complete the quiz prior to constructing your initial response.
By Day 3 of Week 3
**Post** a response including the following:
· Choose a research study, QI article, or EBP DNP project and interpret at least one continuous demographic variable and one categorical variable.
· Differentiate between comparisons made using descriptive statistics (e.g., the mean and standard deviation) and comparisons based on inferential statistics (e.g., a
_t_ test).
· Compare and contrast the sample sizes used in the research study, the QI project, and the DNP project in terms of type 1 and type 2 errors.
· Explain the SIR rate, how it is developed, and how organizations use it.
· Using the same articles, pick one and differentiate between one descriptive and one inferential statistic used in any one of the three studies/projects.
By Day 6 of Week 3
**Read** a selection of your colleagues’ posts and
**respond** to
**at least two** of your colleagues on
**two different days** by expanding upon their reflections, making connections to your perceptions, and offering additional insights.
**Reply**
**## WK3DATAMediatranscript.txt**
BARBARA NIEDZ: Hi, all. Dr. Niedz here
again and moving on to a brief discussion
about descriptive statistics. So we'll talk a bit about
measures of central tendency and measures of
dispersion, and you'll see how they work for
continuous variables. We'll also talk about
frequencies and percentages and how they fit for
categorical data. We'll use, revisit a
little research lingo, and talk about null directional
and non-directional hypotheses. We'll also visit the
standardized infection rate, the standardized
infection ratio measure, and how that's used in research,
quality improvement, and DNP. And we're also going to
spend a little time talking about the differences
between type one and type two measurement errors and how they
figure into research projects. Four levels of measurement, two
categorical and two continuous– very important concepts
and very important aspects of measurement. Categorical data is
usually displayed by counts and percentages. So for example, nominal
level data might be gender. Male, female, and
transgender people might fall into
those three buckets. And there's no value placed. Men are not better than women. Women are not better than men. Transgender are not
better than men or women, and the data is equal
in terms of the value of those contributions
to the data set. Whereas ordinal data is ranked. So, for example, if you
collected a demographic data on educational level
for some reason, whether you were talking
about health literacy or staff educational backgrounds,
those data are ranked. People who only
completed fifth grade and never completed grade
school have less education than those people who
completed high school who have less education
than those people who have some college as
opposed to those people who have advanced degrees. So categorical data
come in two forms– nominal and ordinal. Ordinal is ranked,
nominal is not. Continuous data is actually
from the type of data that you can do an average
with that makes sense. So for example,
interval level data, where you have some scale
that measures something– let's say perceived stress– from a number that
is as low as zero with no perceived
stress to as high as 21, where this is the most stress
you can possibly imagine. The differences
between the divisions, even though there
might be zero, is very soft because a lot of
stress compared to some stress is not exactly something that
you can multiply or divide by, whereas ratio level data is. So suppose ratio level
data– for example, income, annual income, zero– is very different from annual
income of $50,000 a year, which is half the amount
of $100,000 a year. So ratio data has equal
divisions, has an absolute zero. But they're all
useful and you're going to see some
examples of that. Measures of central tendency–
the mean, as I mentioned, is the arithmetic average. The mode is the most frequently
occurring value in a data set. And the median is
that point at which you have an equal
number of observations above and below that point. You see them represented in, in
published work and in projects. They're all very useful. The mean is subject to extremes,
and if there are outliers present in the data, the median
is a much more useful tool. Standard deviation tells you
about that variation away from the mean. The variance is simply the
standard deviation squared. And the range is the
lowest point to the highest point in the data set. So measures of central tendency
and measures of dispersion are very useful tools. The chapters that
I've selected out for every week in Salkind and
Frey are really useful tools, and understanding these concepts
takes more than a 15 minute video, and I hope you'll
consult with Salkind and Frey in order to be able to
complete assignments, in order to be able to
address the discussion questions in the classroom,
and certainly the ability to answer the knowledge
check questions. So, means and standard
deviations, very, very useful for continuous data. Counts and percentages,
also important. Percentile rankings allow
you to do some comparisons. And confidence intervals
are not exactly prediction, but it tells you something
about that spread of data and what you might
expect in terms of the relationship between
the sample and the population. So, just a little
bit of information on those four types of
concepts can be very helpful. So a picture is worth
a thousand words, and you can see the differences
between categorical data on the left with a bar
graph and continuous data on the right with a histogram. And in the histogram, you can
see that that is essentially continuous data– no spaces,
no gaps between the bars– whereas the categorical
data on the left is summarized in a bar chart. And you can see that there
are no gaps between the bars, and they are, in fact,
separate and apart. Now, this is only a portion
of the table in Sood et al that is presented on
page 144 and is actually a way to present
demographic variables and other important variables,
independent and dependent variables, in a study all
efficiently in one small table. And you can see that there are
both categorical variables– for example, ethnicity,
with 1,086 participants, broken down into the counts
by different groupings– Asians, Hispanic, Black,
Hispanic, white, and others– and also the percentages
of the sample. You could easily
do this manually to just figure out
those percentages, but it's awfully nice
when they're presented like this in a published piece. You can also see that the
age and gestational age in this study are
presented as the mean and the standard deviation plus
or minus 1 standard deviation. So you get an idea of what the
spread of data might look like. Median BMI and the range– again, it's an ordinal
level of measurement. And so the mean
might make good sense for the way that is
presented and time in minutes also
presented in the average with the standard deviation. So, can be a very efficient and
useful way of summarizing data. There are three
types of hypotheses that are actually
usually implicit and not stated unless
you were reading a PhD dissertation from Proquest. The null hypothesis
states that there is no relationship,
no association, between the variables,
between the dependent and the independent variables. The alternate hypothesis states
that there is a relationship or association between those
independent and dependent variables, and hypothesis
can go in one direction or not, depending on the
nature of that comparison. And that can also be
an important concept. So here's an example. Take a look at the study
that's in the resources for this week by Bedouin
and others from 2022. It's a study about perioperative
prophylaxis practices and surgical site infections. And the null hypothesis
would essentially say there is no association. There is no
relationship, and there's no differences in
perioperative prophylaxis practices and the SSI rate. However, an alternate
hypothesis would posit a relationship between
those two types of variables in a specific type
of surgical procedure and would perhaps even be
more specific in electing a certain type of
prophylaxis over another. So, null, alternative,
directional, or non-directional hypotheses. So the standardized
infection rate or the standardized
infection ratio, the SIR, is a very useful tool that's
been developed by the NHSN that allows us to take a
look at infections and make some comparisons on
the basis of risk adjustment. So how sick are the
patients, and what is the predicted rate of
infection for a particular type of surgical procedure? It compares the actual
rate to the prediction. So a SIR of greater
than 1 indicates more hospital acquired
infections than predicted, and an SIR error
rate of less than 1 indicates fewer hospital
acquired infections than predicted. So I played around with the
literature and the SIR rate as I was preparing
these slides and videos, and I live in New Jersey. And so I just said,
well, let me see what I can find about this
that's publicly available. And there is, in New Jersey,
the State Department of Health publishes by hospital
comparisons for different types of surgeries across
the state and provides hospital-specific SIR rates
for these different surgical procedures. So I challenge you to
look in your own state and see what you can find out
in terms of published SIR rates. And if you can't find anything,
look up New Jersey Department of Health, and you'll be able
to see a variety of hospitals in New Jersey. So we typically use probability
theory and the normal curve in order to be able to
understand prediction, and our statistics, our
statistical analysis that we'll move into
starting next week, will allow us to define
a critical value, the point at which we can make
a decision about rejecting the null hypothesis,
that there is no difference or no
association, in favor of the alternate hypothesis that
says essentially, if you were to repeat the test with
a different sample, you'd likely get a similar
result 95% of the time or 99% of the time, depending
on what that p value is, or whether it is in this
part of the curve, where 95% of the values are, that allows you to see that
difference between rejecting and not rejecting the
hypothesis, the null hypothesis. There is actually excellent
information in Salkind and Frey on pages 215 to 217
on this concept. Now, one characteristic that
you can see in the SOOD et al article is about whether
or not there's a P value. If there's a P
value, you can infer from that there is an
inferential test of significance that is being used. And you can see this in
table two in Sood et al. The last key point I want
to make in this video is about measurement error. A type one error is
the type of error that is made when statistical
significance is found, but it's not really there. And your protection against this
is the all important p value. Type two error is
the type of error that you make when you
don't find significance, but it's really there. And sample size is the biggest
protection that we have. Key points in this
video on this slide. Don't forget the
value of the reading.
**NURS_8211_WK3_DescriptiveStatistics.pptx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk3-descriptivestatistics-pptx)
**UrinaryTractInfection.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/urinarytractinfection-pdf-10222459)
**## WK3DISCDATA-LevelsofMeasurement.docx**
Levels of Measurement: Categorical vs. Continuous Data; Descriptive Statistics and Probability Theory Basics
_What is the incidence of blood clots from COVID-19 in females over the age of 35?_
The above question is an example of a research question. A research question consists of three parts and guides the methods and approaches in which you will study the question to find answers. The research question includes the question, the topic, and the population or variables. In the example provided above, the question examines the prevalence of blood clots from severe COVID-19 in a selected population. From this question, the variables can be assessed, considerations can be analyzed, and populations can be sampled in order to guide the research.
For this Discussion, you will analyze a selected work to identify and analyze the variables, comparisons, and sample sizes. You will explore the potential levels of measurement for your variables and the rationale for the labels, as well as consider the advantages and challenges that you might experience in the statistical analysis.
**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.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
[**WEEKLY RESOURCES**](https://waldenu.instructure.com/courses/157739/modules/items/6373659)
Learning Resources
[Required Resources](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Bullen, P. (n.d.).
[_How to choose a sample size (for the statistically challenged)_ Links to an external site.](https://tools4dev.org/resources/how-to-choose-a-sample-size/). tools4dev. https://tools4dev.org/resources/how-to-choose-a-sample-size/
· Centers for Disease Control and Prevention. (2024, March).
[_The NHSN standard infection ratio (SIR)_ Links to an external site.](https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf). https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf
· “Overview of the Standard Infection Ratio (SIR)” (pp. 4–5)
· Dang, D., Dearholt, S. L., Bissett, K., Ascenzi, J., & Whalen, M. (2021).
_Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model & guidelines_ (4th ed.). Sigma Theta Tau International Honor Society of Nursing.
· Chapter 6, “Evidence of Appraisal: Research” (pp. 147–157)
· Salkind, N., & Frey, B. (2019).
_Statistics for people who (think they) hate statistics_ (7th ed.). SAGE Publications.
· Chapter 3, “Computing and Understanding Averages: Means to an End” (pp. 65–68)
· Chapter 5, “Creating Graphs: A Picture Really Is Worth a Thousand Words” (pp. 88–118)
· Chapter 8, “Hypotheticals and You: Testing Your Questions” (pp. 167–180)
· Chapter 9, “Probability and Why It Counts: Fun With a Bell-Shaped Curve” (pp. 181–200)
[Required Media](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Niedz, B. (2024).
_Descriptive statistics_ [Video]. Walden University Canvas. https://waldenu.instructure.com
[PowerPoint Presentation](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Document:
[Descriptive Statistics (PowerPoint presentation)](https://waldenu.instructure.com/courses/157739/files/12465906?wrap=1) [Download Descriptive Statistics (PowerPoint presentation)](https://waldenu.instructure.com/courses/157739/files/12465906/download?download_frd=1)
[Required Resources for Topic: Infections](https://waldenu.instructure.com/courses/157739/pages/week-3-learning-resources?module_item_id=6373659)
· Beydoun, A. S., Koss, K., Nielsen, T., Holcomb, A. J., Pichardo, P., Purdy, N., Zebolsky, A. L., Heaton, C. M., McMullen, C. P., Yesensky, J. A., Moore, M. G., Goyal, N., Kohan, J., Sajisevi, M., Tan, K., Petrisor, D., Wax, M. K., Kejner, A. E., Hassan, Z., … Zenga, J. (2022).
[Perioperative topical antisepsis and surgical site infection in patients undergoing upper aerodigestive tract reconstructionLinks to an external site.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047735/).
_JAMA Otolaryngology-Head & Neck Surgery, 148_(6), 547–554. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047735/
· Sood, N., Lee, R. E., To, J. K., Cervellione, K. L., Smilios, M. D., Chun, H., & Ngai, I. M. (2022).
[Decreased incidence of cesarean surgical site infection rate with hospital‐wide perioperative bundleLinks to an external site.](https://onlinelibrary.wiley.com/doi/abs/10.1111/birt.12586). B
_irth: Issues in Perinatal Care, 49_(1), 141–146. https://onlinelibrary.wiley.com/doi/abs/10.1111/birt.12586
· Sauer, K. (2023).
[_Testing for the treatment of urinary tract infections in symptomatic adult patients residing in long-term care facility: An evidence-based quality improvement project_ Links to an external site.](https://www.proquest.com/dissertations-theses/point-care-testing-treatment-urinary-tract/docview/2875242069/se-2?accountid=14872) (Publication No. 30569808) [Doctoral dissertation, Phoenix University]. ProQuest Dissertations and Theses Global. https://www.proquest.com/dissertations-theses/point-care-testing-treatment-urinary-tract/docview/2875242069/se-2?accountid=14872
**To prepare:**
· View the required media.
· It is recommended you complete the quiz prior to constructing your initial response.
By Day 3 of Week 3
**Post** a response including the following:
· Choose a research study, QI article, or EBP DNP project and interpret at least one continuous demographic variable and one categorical variable.
· Differentiate between comparisons made using descriptive statistics (e.g., the mean and standard deviation) and comparisons based on inferential statistics (e.g., a
_t_ test).
· Compare and contrast the sample sizes used in the research study, the QI project, and the DNP project in terms of type 1 and type 2 errors.
· Explain the SIR rate, how it is developed, and how organizations use it.
· Using the same articles, pick one and differentiate between one descriptive and one inferential statistic used in any one of the three studies/projects.
By Day 6 of Week 3
**Read** a selection of your colleagues’ posts and
**respond** to
**at least two** of your colleagues on
**two different days** by expanding upon their reflections, making connections to your perceptions, and offering additional insights.
**Reply**
**## WK3DATAMediatranscript.txt**
BARBARA NIEDZ: Hi, all. Dr. Niedz here
again and moving on to a brief discussion
about descriptive statistics. So we'll talk a bit about
measures of central tendency and measures of
dispersion, and you'll see how they work for
continuous variables. We'll also talk about
frequencies and percentages and how they fit for
categorical data. We'll use, revisit a
little research lingo, and talk about null directional
and non-directional hypotheses. We'll also visit the
standardized infection rate, the standardized
infection ratio measure, and how that's used in research,
quality improvement, and DNP. And we're also going to
spend a little time talking about the differences
between type one and type two measurement errors and how they
figure into research projects. Four levels of measurement, two
categorical and two continuous– very important concepts
and very important aspects of measurement. Categorical data is
usually displayed by counts and percentages. So for example, nominal
level data might be gender. Male, female, and
transgender people might fall into
those three buckets. And there's no value placed. Men are not better than women. Women are not better than men. Transgender are not
better than men or women, and the data is equal
in terms of the value of those contributions
to the data set. Whereas ordinal data is ranked. So, for example, if you
collected a demographic data on educational level
for some reason, whether you were talking
about health literacy or staff educational backgrounds,
those data are ranked. People who only
completed fifth grade and never completed grade
school have less education than those people who
completed high school who have less education
than those people who have some college as
opposed to those people who have advanced degrees. So categorical data
come in two forms– nominal and ordinal. Ordinal is ranked,
nominal is not. Continuous data is actually
from the type of data that you can do an average
with that makes sense. So for example,
interval level data, where you have some scale
that measures something– let's say perceived stress– from a number that
is as low as zero with no perceived
stress to as high as 21, where this is the most stress
you can possibly imagine. The differences
between the divisions, even though there
might be zero, is very soft because a lot of
stress compared to some stress is not exactly something that
you can multiply or divide by, whereas ratio level data is. So suppose ratio level
data– for example, income, annual income, zero– is very different from annual
income of $50,000 a year, which is half the amount
of $100,000 a year. So ratio data has equal
divisions, has an absolute zero. But they're all
useful and you're going to see some
examples of that. Measures of central tendency–
the mean, as I mentioned, is the arithmetic average. The mode is the most frequently
occurring value in a data set. And the median is
that point at which you have an equal
number of observations above and below that point. You see them represented in, in
published work and in projects. They're all very useful. The mean is subject to extremes,
and if there are outliers present in the data, the median
is a much more useful tool. Standard deviation tells you
about that variation away from the mean. The variance is simply the
standard deviation squared. And the range is the
lowest point to the highest point in the data set. So measures of central tendency
and measures of dispersion are very useful tools. The chapters that
I've selected out for every week in Salkind and
Frey are really useful tools, and understanding these concepts
takes more than a 15 minute video, and I hope you'll
consult with Salkind and Frey in order to be able to
complete assignments, in order to be able to
address the discussion questions in the classroom,
and certainly the ability to answer the knowledge
check questions. So, means and standard
deviations, very, very useful for continuous data. Counts and percentages,
also important. Percentile rankings allow
you to do some comparisons. And confidence intervals
are not exactly prediction, but it tells you something
about that spread of data and what you might
expect in terms of the relationship between
the sample and the population. So, just a little
bit of information on those four types of
concepts can be very helpful. So a picture is worth
a thousand words, and you can see the differences
between categorical data on the left with a bar
graph and continuous data on the right with a histogram. And in the histogram, you can
see that that is essentially continuous data– no spaces,
no gaps between the bars– whereas the categorical
data on the left is summarized in a bar chart. And you can see that there
are no gaps between the bars, and they are, in fact,
separate and apart. Now, this is only a portion
of the table in Sood et al that is presented on
page 144 and is actually a way to present
demographic variables and other important variables,
independent and dependent variables, in a study all
efficiently in one small table. And you can see that there are
both categorical variables– for example, ethnicity,
with 1,086 participants, broken down into the counts
by different groupings– Asians, Hispanic, Black,
Hispanic, white, and others– and also the percentages
of the sample. You could easily
do this manually to just figure out
those percentages, but it's awfully nice
when they're presented like this in a published piece. You can also see that the
age and gestational age in this study are
presented as the mean and the standard deviation plus
or minus 1 standard deviation. So you get an idea of what the
spread of data might look like. Median BMI and the range– again, it's an ordinal
level of measurement. And so the mean
might make good sense for the way that is
presented and time in minutes also
presented in the average with the standard deviation. So, can be a very efficient and
useful way of summarizing data. There are three
types of hypotheses that are actually
usually implicit and not stated unless
you were reading a PhD dissertation from Proquest. The null hypothesis
states that there is no relationship,
no association, between the variables,
between the dependent and the independent variables. The alternate hypothesis states
that there is a relationship or association between those
independent and dependent variables, and hypothesis
can go in one direction or not, depending on the
nature of that comparison. And that can also be
an important concept. So here's an example. Take a look at the study
that's in the resources for this week by Bedouin
and others from 2022. It's a study about perioperative
prophylaxis practices and surgical site infections. And the null hypothesis
would essentially say there is no association. There is no
relationship, and there's no differences in
perioperative prophylaxis practices and the SSI rate. However, an alternate
hypothesis would posit a relationship between
those two types of variables in a specific type
of surgical procedure and would perhaps even be
more specific in electing a certain type of
prophylaxis over another. So, null, alternative,
directional, or non-directional hypotheses. So the standardized
infection rate or the standardized
infection ratio, the SIR, is a very useful tool that's
been developed by the NHSN that allows us to take a
look at infections and make some comparisons on
the basis of risk adjustment. So how sick are the
patients, and what is the predicted rate of
infection for a particular type of surgical procedure? It compares the actual
rate to the prediction. So a SIR of greater
than 1 indicates more hospital acquired
infections than predicted, and an SIR error
rate of less than 1 indicates fewer hospital
acquired infections than predicted. So I played around with the
literature and the SIR rate as I was preparing
these slides and videos, and I live in New Jersey. And so I just said,
well, let me see what I can find about this
that's publicly available. And there is, in New Jersey,
the State Department of Health publishes by hospital
comparisons for different types of surgeries across
the state and provides hospital-specific SIR rates
for these different surgical procedures. So I challenge you to
look in your own state and see what you can find out
in terms of published SIR rates. And if you can't find anything,
look up New Jersey Department of Health, and you'll be able
to see a variety of hospitals in New Jersey. So we typically use probability
theory and the normal curve in order to be able to
understand prediction, and our statistics, our
statistical analysis that we'll move into
starting next week, will allow us to define
a critical value, the point at which we can make
a decision about rejecting the null hypothesis,
that there is no difference or no
association, in favor of the alternate hypothesis that
says essentially, if you were to repeat the test with
a different sample, you'd likely get a similar
result 95% of the time or 99% of the time, depending
on what that p value is, or whether it is in this
part of the curve, where 95% of the values are, that allows you to see that
difference between rejecting and not rejecting the
hypothesis, the null hypothesis. There is actually excellent
information in Salkind and Frey on pages 215 to 217
on this concept. Now, one characteristic that
you can see in the SOOD et al article is about whether
or not there's a P value. If there's a P
value, you can infer from that there is an
inferential test of significance that is being used. And you can see this in
table two in Sood et al. The last key point I want
to make in this video is about measurement error. A type one error is
the type of error that is made when statistical
significance is found, but it's not really there. And your protection against this
is the all important p value. Type two error is
the type of error that you make when you
don't find significance, but it's really there. And sample size is the biggest
protection that we have. Key points in this
video on this slide. Don't forget the
value of the reading.
**NURS_8211_WK3_DescriptiveStatistics.pptx**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/nurs-8211-wk3-descriptivestatistics-pptx)
**UrinaryTractInfection.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/urinarytractinfection-pdf-10222459)