NUR 504 Week 5 Discussion Paper
NUR 504 Week 5 Discussion Paper
NUR 504 Week 5 Discussions 1
Demographic data is collected for every study. What is the purpose of describing the demographic data?
NUR 504 Week 5 Discussions 2
There is a tendency for novice researchers to develop their own instrument if they cannot readily find one. How might you respond to a peer or manager who asks you to help develop a new tool to collect patient data on anxiety prior to cardiac catheterization?
NUR 504 CLC EBP Literature Latest
NUR504 Week 5 Collaborative Learning Community: EBP Literature Latest
This is a CLC assignment.
Follow the instructions provided in CLC Assignment: Evidence-Based Project (EBP).
Utilize the Synthesis Table, Table of Evidence and EBA Project Evaluation Tool to complete your CLC assignment. Submit these documents for this weeks CLC assignment.
This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
You are not required to submit this assignment to Turnitin, unless otherwise directed by your instructor. If so directed, refer to the Student Success Center for directions. Only Word documents can be submitted to Turnitin.
Synthesis Table Example
Name of Article Name of Article Name of Article Name of Article
Levels of Evidence
Study Design
Variable to be studied
Variable to be studied
Variable to be studied
Compare template above to my table below. At a glance you can see the names of the most important articles I choose, the level of evidence of that study, what the design was, and what variables were important to know about in each article. As you study the table, the reader can clearly see more studies in support of BiPAP than IS along with other information that would be of interest to the provider to support standards of care.
ADDITIONAL INFO
The purpose of describing demographic data
Introduction
Some people believe that demographics are just a hot topic in the news, but no one really cares about them. However, I find that many people have a strong interest in demographics and how they can be used to predict future events. So let’s start with some basic definitions for those who aren’t familiar with this topic:
Describing demographic data
The purpose of describing demographic data is to describe the distribution of that data. The first step in describing any distribution is identifying its center and spread, as well as whether it’s symmetrical or skewed.
Discussing the purpose of describing demographic data
You’ve probably heard the phrase “describe the purpose of describing demographic data,” but what does that even mean?
Describing demographic data means giving a description of who, where and when someone was born; how old they are; what gender they are; where they live and work. Describing demographic data can also include other information such as race or ethnicity (white vs black), religion (Christian vs Muslim), political beliefs (liberal vs conservative) and more.
Property and probability
In this section, we’ll examine the difference between property and probability. We’ll also discuss how to determine if a variable is quantitative or categorical, as well as how to use these terms in your model.
Probability refers to the likelihood that an event will occur given certain conditions. For example, if you flip a coin and it lands heads up 100% of the time (or 1/2), then there’s no chance that it could land tails up instead—this would be considered impossible according to probability theory because there would be an expectation that the coin would always land heads up if flipped at least once!
In contrast to this notion of “impossible,” consider what happens when someone flips their friend’s car: even though they know their friend owns one just like theirs but has never driven one before himself due to his lack of experience with cars—and therefore might crash into something along his way home from work each day—there remains some possibility that he might crash into something while driving home tonight!
Population and sample
A population is the entire group of interest. A sample is a subset of that population, and it’s possible to take samples from different populations. In this case, we’re looking at a population that consists of all students taking AP Statistics in high school (or at least those who took the class). The sample size refers to how many elements are in our sample: if you have 100 students in your group, then it would be called “a sample” as opposed to “a sample size.”
Types of variables
There are many different types of variables. The most common ones are qualitative, quantitative and categorical.
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Qualitative variables describe the characteristics of your data. They can be described as “yes” or “no”, so they’re often used to make judgments about the value of something (for example, whether or not someone is old).
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Quantitative variables contain numbers that represent measurements on a scale from 0 to 1 (e.g., weight). These types of variables can help you measure how well something has been done in order to improve it next time around! Here’s an example: If you’re trying out new recipes at home but don’t know if they taste good yet…you could use this type of measure because if your food doesn’t look good enough then maybe nobody will want to eat it again after seeing them first hand…and then again too..then maybe there will be no money left over for other things too!!
Quantitative and categorical variables
The categories of a categorical variable are the values present in the set. For example, gender is a categorical variable because it has only two possible values: male and female. A continuous variable can have any number of distinct values, like height or weight.
In addition to these differences between quantitative and categorical variables, there are other ways to describe them: as nominal or ordinal scales (more on this later), discrete vs continuous variables and qualitative vs quantitative measures.*
Measurement level of variables
Measurement level of variables
The measurement level of a variable is the way in which it can be measured. A nominal variable has only one possible value, whereas an ordinal variable has more than one possible value and may have steps between each step. An interval variable has continuous values with no steps between them and can also have upper limits (such as population size).
Conclusion
The purpose of describing demographic data is to create a picture of the world. In order to make progress we need to have some information about our environment, and that can be done by collecting and analyzing data about people who live within it.
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