Task 1

Task 1

Save Time On Research and Writing
Hire a Pro to Write You a 100% Plagiarism-Free Paper.
Get My Paper

Rubric

• CPE schedule table of tasks and timelines

• annotated bibliography of 5 recent and relevant sources

• narrative essay of your interviews with technology users and experts

Save Time On Research and Writing
Hire a Pro to Write You a 100% Plagiarism-Free Paper.
Get My Paper

• technology summary that provides the top five technologies you would recommend for transforming nursing or enhancing healthcare outcomes

• three screenshots to document your Phase 1 GoReact video reflection, that includes an image of your reflection video and an image for each of your peer responses

• a brief, written reflection summary of your video reflection below your screenshot

• completed “Summary Yrs_RN” worksheet

• completed “Summary Tot_Scores” worksheet

• completed “Summary Demographic” worksheet

• completed “Summary Responses” worksheet

• completed “Pivot Table Education Level by Work Setting”

• completed “Pivot Table Age Group by Race”

• completed “Bar Chart on the Mode” on all questions on the “Barriers Survey”

• completed “Pie Chart of Age Group”

• completed “Sunburst Chart” of Sex

• completed “Column Chart” of Education Level

• completed “Funnel Chart” of Race

• completed “Treemap Chart” of Work Setting

• three screenshots to document your Phase 3 GoReact video reflection, that includes an image of your reflection video and an image for each of your peer responses

• a brief, written reflection summary of your video reflection below your screenshot

3

Master of Science, Nursing Core

Clinical Practice Experience (CPE) Record

Student Name: _______________________________ Date: _________________________
(By submitting this CPE Record to Evaluation, you attest that you completed each required activity.)

Course Instructor Name: _________________________________________________________
Course: Informatics for Transforming Nursing Care

Welcome to the Clinical Practice Experience (CPE) for this course. The CPE for the Master of Science in Nursing program core courses consist of a variety of semi-structured activities. CPE provides the opportunity to integrate new knowledge into practice and to attain the identified professional competencies (AACN, 2016). By completing all the activities and evidence listed within this document, and earning a grade of “Competent,” you will earn 35 indirect CPE hours for this course.*

CPE Objective:

In this CPE, you will take on the role of an informatics nurse and gain an understanding of how nursing informatics supports improvements to patient outcomes and nursing practice.

In this CPE you will experience the role of a graduate degree-prepared informatics nurse in three phases:

· Phase 1:  You will research and recommend technology for nursing practice, administration or education.

· Phase 2:  You will analyze a dataset using descriptive statistics.

· Phase 3: You will visually display data using charts and graphs.

Student Instructions:

· Complete and date the required activities

· Type in your name and date the top of this form

· Type in the name of your faculty of record for this course (you assigned Course Instructor)

· Submit the completed CPE Record for evaluation

PHASE 1: TECHNOLOGY RECOMMENDATIONS

CPE Activities

Date Activity Completed

Review all of the activity and evidence requirements for this CPE, including Phase 1, Phase 2, and Phase 3. Break down each activity from each phase into strategic tasks and specific due dates in order to meet the activity deadlines. Create a CPE schedule table in your e-portfolio that lists your tasks, due dates, and estimated time needed to complete each activity.

In the course of study, you learned how current and emerging technologies have been transforming clinical practice, nursing administration, and nursing education. In your practice, you will search the WGU Library and the Internet for information on current and emerging technologies that have the potential to enhance nursing and/or healthcare. Create an annotated bibliography with at least five sources on this topic that you reviewed. (
See Sample APA Annotation
) This can be used to educate your nursing colleagues on the impact that current and emerging technologies has on healthcare delivery.

Meet with a nurse leader/manager, an informatics nurse, and/or a health information technology specialist (online or in person) to gather perspectives on current or emerging technology that may have the capacity to transform nursing practice, administration, or education. Create a one-page narrative essay of what you learned from your interviews with technology users and experts. (See
Narrative Essay Guidelines
)

From your review of relevant sources, and the interviews you conducted, select five technologies you believe have the potential to transform nursing and enhance health-related outcomes either directly or indirectly. Summarize the information for the five technologies you selected by completing the Technology Summary document.

Using
GoReact
reflect on your learning experiences while searching the literature, and interviewing a key leader, regarding emerging technologies. If you have trouble with the GoReact link, you can copy and paste the URL directly into your browser: https://lrps.wgu.edu/provision/173986831
Present an overview (less than 10 minutes) of the five technologies you selected in your Technology Summary document. Provide encouraging and constructive comments on the video reflections of two peers in GoReact.

Phase 1 CPE Evidence

(Upload the following to your e-portfolio)
:
1. CPE Schedule Table of tasks and timelines that you developed for this CPE
2. Annotated Bibliography of 5 recent and relevant sources using the “Sample APA Annotation” web link
3. Narrative Essay of your interviews with technology users and experts
4. Technology Summary that provides information on the top five technologies you would recommend for transforming nursing and/or enhancing healthcare outcomes
5. Three screenshots to document your GoReact video reflection that includes an image of your reflection video and an image for each of your peer responses
6. A brief, written reflection summary of your video reflection below your screenshot

PHASE 2: Data Analysis

CPE

Date Activity Completed

Review the CPE Schedule table created in Phase 1 to ensure you are still making progress towards meeting your timelines. Adjust the schedule table if necessary.

For this phase, you will use descriptive statistics to analyze hypothetical survey data provided to you in the Barriers Codebook file in Excel. Open the Barriers Data Analysis Instructions file for step-by-step instructions for this phase. Then, open the Barriers Codebook file and follow the instructions to analyze data and create the evidence for this phase.

Continuous Variable Analysis: Analyze data measures of central tendency (Mean, Median, Mode) and measures of dispersion (Standard Deviation, Minimum, Maximum, and Range) using the instructions. Create your own summary of the data using the Continuous Summary (Example) tab in the worksheet as an example of how to write your own analysis of RN and Tot_Scores. Save the Summary Yrs_RN and Summary Tot_Scores each as a screenshot.

Categorical-Nominal Variable Analysis: Analyze data with the COUNTIF Excel function using the instructions.Enter your results with a summary interpretation on the Summary_Demographics worksheet. Save the worksheet as a screenshot.

Categorical-Ordinal Variable Analysis: Analyze data using the Mode function using the instructions. Write an interpretation identifying high-scoring and low-scoring items on the survey. Save the Summary_Responses worksheet as a screenshot.

Count the Educational Level by Work Setting participants and for each Age Group by Race by using a Pivot Table by following the instructions.
Save the two Pivot Table worksheets each as a screenshot.

Phase 2 CPE Evidence

(Upload the following to your e-portfolio)
:
1. Screenshot of your completed Summary Yrs_RN worksheet
2. Screenshot of your completed Summary Tot_Scores worksheet
3. Screenshot of your completed Summary Demographic worksheet
4. Screenshot of your completed Summary Responses worksheet
5. Screenshot of your Pivot Table Education Level by Work Setting
6. Screenshot of your Pivot Table Age Group by Race

PHASE 3: DATA VISUALIZATION

CPE

Date Activity Completed

Review the CPE Schedule table created in Phase 1 to ensure you are still making progress towards meeting your timelines. Adjust the schedule table if necessary.

Open the Barriers Data Visualization instructions file. Follow the instructions to visually display the data by creating the following charts and graphs using the data in the Barriers Codebook file used in Phase 2. Save all graphics as screenshots by following the instructions, for your e-portfolio.
1. Bar Chart of the Mode on all questions on the Barriers Survey
2. Pie Chart of Age Group
3. Sunburst Chart of Sex
4. Column Chart of Education Level
5. Funnel Chart of Race
6. Treemap Chart of Work Setting.

Create a 5–8 minute
GoReact
video reflection of your clinical practice experience activities as you learned to use data visualization techniques to depict data. If you have trouble with the GoReact link, you can copy and paste the URL directly into your browser: https://lrps.wgu.edu/provision/173986831
During your video reflection, show examples of your work. Provide encouraging and constructive comments to the video reflections of two peers in GoReact.

Phase 3 CPE Evidence

(Upload the following to your e-portfolio)
:
1. Screenshot of Bar Chart of the Mode on all questions on the Barriers Survey
2. Screenshot of Pie Chart of Age Group
3. Screenshot of Sunburst Chart of Sex
4. Screenshot of Column Chart of Education Level
5. Screenshot of Funnel Chart of Race
6. Screenshot of Treemap Chart of Work Setting
7. Three screenshots to document your GoReact video reflection with a brief presentation of each graphic and peer feedback, including an image of your reflection video and an image for each of your peer responses
8. A brief, written reflection summary of your video below your screenshot

*American Association of Colleges of Nursing. (2016). Clinical practice experiences FAQs. Retrieved from 

https://www.aacnnursing.org/CCNE-Accreditation/Resources/FAQs/Clinical-Practice

Instructionsfor Analyzing Data Using

Descriptive Statistics

Descriptive Statistics

GETTING STARTED

For this activity, you are going to analyze hypothetical data from a Barriers to Research Utilization by Nurses survey using descriptive statistics. This data has been provided for you, and step-by-step instructions on doing the analysis in excel are provided below.

BACKGROUND

When collecting online survey data, the responses are generally textual data. Look at the Data_Raw worksheet to see the survey results. This raw data must be converted to numerical data for analysis.

To accomplish this, a Codebook is created that contains the data and metadata (the information for each variable with coding instructions). For this activity, the Barriers Codebook has already been created for you. Review the Metadata worksheet to see an overview of the variables in this survey (column A), variable type (column B), level of measurement (column C), coding instructions (Response Categories, column D), and the original Survey Question (column E).

Look to see how the data have been numerically coded for you on the Data_Coded worksheet. This was done by copying the Data_Raw worksheet, then using
Find and Replace

to code the responses numerically using the MetaData worksheet information. For example, in column D (Race), White was replaced with 1, African American was replaced with

2

, Asian was replaced with 3 and Other was replaced with 4.

Now the original data has been coded, you want to retain the coded data to preserve it for additional analyses or if errors are made.

CREATE YOUR OWN DATA FILE COPY

This is where the real fun starts for this activity!

You will first copy the Data-Coded worksheet to a working file that you can use for this activity.

Desktop Excel

Office 365

Copy the Data_Coded worksheet by right-clicking on the Data_Coded worksheet, selecting
Move or Copy
(see below) and checking the
Create a Copy
box.

Copy the Data_Coded worksheet by right-clicking on the Data_Coded worksheet, selecting
Duplicate
(see below).

Rename this copied worksheet Desc_Stats by right-clicking on the tab of the copied worksheet and selecting
Rename
and typing the new name.

Right-click again, choose Tab Color, and choose a different color than red (red is a reminder to
not
use these worksheets).

Next, click on the Desc_Stats worksheet and write a formula to total the scores on the Barriers Survey for each participant. The variable you will use for this is the Tot_Scores continuous variable in column AK. You should enter this formula into cell AK2: =Sum(H2:AJ2) directly in the cell or the formula bar at the top, as shown. This formula will sum all the cells in Row 2 from Column H through Column AJ, which include all the “Q” items on the survey.

Make sure you see the formula bar displayed in this screenshot. If it is not there, click on the Formulas menu and check Show Formulas.

To avoid having to write this formula 75 more times,
Autofill the formula by holding your mouse over the small black square in the bottom right corner of cell AK2. When your mouse pointer turns into a plus sign, click and pull the plus sign down through row 77. This autofills the formula and calculates the total score, while, adjusting the formula for each row.

DESCRIBING THE DATA

Continuous Variable Analysis

For continuous variables (“Yrs_RN”, and “Tot_Scores”) you will compute measures of central tendency (Mean/Average, Median, and Mode) and measures of dispersion (standard deviation, minimum, maximum, and range). See the Continuous Summary (Example) worksheet in the Codebook for an explanation of the statistical tests you will use to analyze continuous variables, what it measures, and a sentence explaining the results.

The following table contains the formulas for these statistical tests used to describe your continuous data.

Row

Stat

Yrs_RN

Tot_Scores

78

MEAN

=AVERAGE(E2:E77)

=AVERAGE(AK2:AK77)

79

MEDIAN

=MEDIAN(E2:E77)

=MEDIAN(AK2:AK77)

80

MODE

=MODE(E2:E77)

=MODE(AK2:AK77)

81

STANDARD DEVIATION

=STDEV(E2:E77)

=STDEV(AK2:AK77)

82

MINIMUM

=MIN(E2:E77)

=MIN(AK2:AK77)

83

MAXIMUM

=MAX(E2:E77)

=MAX(AK2:AK77)

84

RANGE

=E83-E82

=AK83-AK82

85

SUM

=SUM(E2:E77)

=SUM(AK2:AK77)

86

COUNT OF RECORDS (N)

=COUNT(E2:E77)

=Count(AK2:AK77)

Enter the formulas shown above at the bottom of the columns of data for Yrs_RN and Tot_Scores in the rows indicated in the table above and then hit Enter. Remember all cell references must be enclosed with parentheses. Transfer your results to the Summary Yrs_RN and SummaryTot_Scores worksheets.

Desktop Excel

Office 365

To confirm that your formulas are correct, view the formulas in your worksheet by using the instructions below.

Hiding and protecting formulas is currently not supported in Excel for the web.
To see the formula, click on the cell (such as E78, and view the formula on the top row.

Next, write a summary interpretation for the variable analysis you did on Yrs_RN and Tot_Scores on the Summary_YrsRN and Summary_TotScores worksheets. Your summary should be similar to the one on the Continuous Summary (Example) worksheet where the variable Miles driven to work was analyzed (this is not in your dataset; it is just an example).

Great Job!

This completes the first two pieces of evidence for this CPE. Now you need to capture screenshots of both of these worksheets and save them for uploading to your e-portfolio.

To capture screenshots:

· Mac: Press Shift+Command+4. Drag the crosshairs to select the area of the screen you want to capture. After you release your mouse or trackpad button, find the screenshot on your desktop. Rename your screenshot to Summary Yrs_RN and Summary Tot_Scores so you remember what screenshots to upload to your e-portfolio.

· PC: Follow the “

Use Snipping Tool to capture screenshots directions

” to use the Microsoft Snipping Tool to capture, save, and share an image of all or part of your PC screen. The Snipping Tool is included in Windows Vista and later. Save your screenshots and name them BarChart Summary Yrs_RN and Summary Tot_Scores so you remember what screenshots to upload to your e-portfolio

Categorical-Nominal Variable Analysis

The best approach for analyzing categorical-nominal variables is to compute “frequencies” (counts) for each type of response.

Your Codebook indicates the coding values for each response, which you will need for your formula. For example, to determine how many respondents were in the first age group (1=19-29 years), you will use the
COUNTIF
function to COUNT the cells in a range IF it has the number 1 in it.

To do this, click on the Desc_Stats worksheet, then click in cell B78 and enter this formula in the cell or formula bar: =COUNTIF(B$2:B$77,1). Hit Enter immediately after entering this formula. The dollar signs next to the row references will make it possible to autofill this formula down by keeping rows 2 through 77 constant and simply changing the response code for each response type. For example, note on your Metadata worksheet that Age_Group has three categories, so you will need to autofill this formula through rows 79 and 80 and change the IF condition to =COUNTIF(B$2:B$77,2) in row 79 and =COUNTIF(B$2:B$77,3) in row 80. If you don’t use the $ in front of the row number, Excel will automatically adjust the formula to the next row, which is incorrect. There is NO dollar sign in front of the B, as you will want Excel to auto-adjust across columns, but not rows.

Next, you should
Autofill your formulas across columns C and D. You will need to skip column E and copy/paste this formula into cell F78 and autofill to column G.

Desktop Excel

Office 365

When you are done autofilling down and across, click on the Formula Menu and check Show Formulas. Your formulas should match the ones shown below for your categorical-nominal variables:

When you are done autofilling down and across, click on each cell check the formulas. Your formulas should match the ones shown below for your categorical-nominal variables:

Make sure your categorical-nominal results are formatted as whole numbers, without decimal places, as these are counts.

Desktop Excel

Office 365

First, select all cells you want to format by selecting cells B78 through D78 and then pressing the CTRL key while selecting cells F78 through G78. (The CTRL key is used when selecting cells that are not contiguous. The Shift Key is used when all cells are next to each other.)

On your Home menu, click on
Format > Format cells
.

In the Format Cells dialog box, click
Number
and set Decimal to “0”.

For your continuous variables you should make sure there is only one decimal point.

First, select the first set of cells you want to format by selecting cells B78 through D80. (The CTRL/Shift keys do not work in Office 365).

Right click while in that cell block, and select Number Format.

In the Number Format dialog box, click
Number
and set Decimal to “0”.

For your continuous variables you should make sure there is only one decimal point.

Click the ‘Show Formulas’ again to see the numbers (instead of the formulas).

Select the second set of cells you want to format by selecting cells F78 through G80.

Right click, Select Number Format.

In the Number Format dialog box, click
Number
and set Decimal to “0”.

Analyze the frequencies of your categorical-nominal demographic data (e.g., age, sex, race, practice setting, and educational level).

After completing your analysis of the categorical-nominal demographic variables, click on the Summary_Demographics worksheet and write a summary description of the demographics of this sample population. (Hint: Select the meta-data worksheet to be reminded of how the data is coded. E.g., Age_Grp is coded as 1=19-39 yrs, 2=40-59 yrs; 3=60 and older).

Great Job!

This completes the next piece of evidence for this CPE. Now you need to capture a screenshot of this worksheet, name the file to be Summary_Demographics and save the screenshot for uploading to your e-portfolio.

Categorical-Ordinal Variable Analysis

When analyzing Categorical-Ordinal data, the MODE is the best choice, as it indicates the value that was reported most often for each of the Barriers Survey questions.

To do this, click on the Desc_Stats worksheet, then in cell H78 enter this formula =MODE(H2:H77) and hit Enter. Click back in cell H78 and
Autofill
this formula across all “Q” questions ending in cell AJ78. Your formulas should look like the ones below:

After you have determined the Mode for each question on the survey, write a summary interpretation of the questions that scored high (4) and those that scored low (1) on the Summary_Responses worksheet.

High scores indicate that the items are barriers to research utilization
to a great extent
and low scores indicate that
to no extent
is the item a barrier. For example, if the mode is 4 for Q1, you would conclude that the majority of respondents determined that nurses did not utilize research
To great extent
due to, “Research reports/articles are not readily available”.

Great Job!

This completes the next piece of evidence for this CPE. Now you need to capture a screenshot of this worksheet, name the file to be Summary_Responses and save the screenshot for uploading to your e-portfolio.

Pivot Tables

Your next analysis involves creating a matrix with counts of respondents using the
Pivot Table
function. For this activity you will look at the educational level of nurses by work setting and the age of participants by race category.

You will use your Data_Raw worksheet for your Pivot Tables, so the correct labels will appear as column and row headings. Select the part of your worksheet that you will pull your fields from, which can be done quickly by clicking in cell A1, pressing your shift-key and clicking on G77.

Next, select Insert > PivotTable.

Under Choose the data that you want to analyze, confirm that the correct worksheet and range is showing in the Select a table or range box and that the New Worksheet radio button is checked, which will place your pivot table on a new worksheet. Select OK.

This is how your new Worksheet will look:

In the Pivot Tables Field box on the right, click on the field name and drag these to the boxes at the bottom. If you check the box next to the field, instead of dragging and dropping the fields, it will automatically place it, which is not always correct. Once you drag and drop, a mark will occur in the checkbox next to the field. Drag Rec_ID to the Values box, Age_Grp to the Columns box, and Race to the Rows box.

You will have to change the function in the values box, as you need to
Count
the number of participants in each group, not Sum. To do this, click on the small arrow next to Sum of Rec ID and select Value Field Settings.

From the Value Field Settings box, select
Count
from the Summarize value field by box. Click OK.

The output for Age by Race is shown below. Right-click and Rename your worksheet Pivot_Age-Race. Right-click again, choose Tab Color, and choose a different color than red (red is a reminder to
not
use these worksheets).

Below the table, create a box by selecting several cells and then clicking on
Merge and Center
on your Home tab. In this box, include a Summary Interpretation of the findings from your pivot table.

When complete, capture a screenshot of this pivot table, name the file to be Pivot Table Age Group by Race and save the screenshot for uploading to your e-portfolio.

Terrific! You have one more Pivot Table to go!

Follow the same steps to create a Pivot Table and Summary Interpretation for Education Level by Work Setting. Choose the appropriate fields and have the Pivot Table count the number of records (Rec_ID) to determine how many are in each category.

If you put Ed_Level as the rows and Setting as columns, your Pivot Table should look like this:

If you put Settings as the rows and Ed_Level as columns, your Pivot Table would look like this:

Below the table, create a box by selecting several cells and then clicking on
Merge and Center
on your Home tab. In this box, include a Summary Interpretation of the findings from your pivot table.

Rename
your worksheet Pivot_EdxWrk. Right-click again, choose Tab Color, and choose a different color than red (red is a reminder to
not
use these worksheets).

When complete, capture a screenshot of this pivot table and interpretation, name the file to be Pivot Table Education Level by Work Setting and save the screenshot for uploading to your e-portfolio.

Conclusion

Congratulations! Using these instructions, you have just analyzed a dataset! You saw the raw data, saw how it was coded into numbers for analysis, then you conducted the analysis yourself by using descriptive statistics and pivot tables.

This activity used hypothetical data from a Barriers to Research Utilization by Nurses survey, but we hope that you can see how this same technique can be used for any kind of survey that you conduct in the future. Save these instructions for later use in your professional career.

Follow the instructions in your CPE record to upload the screenshots you saved during this activity, and then continue to Phase 3: Data Visualization.

2

Instructionsfor Presenting Data Using

Data Visualization

Data Visualization

GETTING STARTED

For this activity, you are going to continue to use the hypothetical data from a Barriers to Research Utilization by Nurses survey. Open the data file you used in Phase

2

, and follow the step-by-step instructions below to visually represent the survey data.

BACKGROUND

Data visualization refers to techniques used to communicate data or information using visual techniques such as charts, graphs, maps, dashboards and other types of graphical representations. It is a type of data analysis that communicates information clearly and efficiently and provides an accessible way to see and understand trends, outliers, and patterns in data.

Just as you learned in your data analysis activity, you must have numerical data to create various types of data visualizations. Also, the data must be in a format that supports the visualization technique you are using. For example, if you want to create a chart with a trendline to predict future values, you must have date information for each data point.

Each type of data visualization has a different purpose, and several commonly used charts that can be created in excel are described below:

· Bar Charts – presents the frequency distribution of categorical (Nominal, Ordinal) data using horizontal rectangular bars. When it is vertical, it is referred to as a column chart. Bar charts are better to use when your labels are long. The bars can be reordered to help present increasing or decreasing frequencies. For example, use to display the number of survey participants and their highest degree earned (Associates, BSN, MSN, DNP/PhD).

· Stacked Bar Charts – used to compare the frequency within each category. For example, use to compare the number of survey participants and their highest degree earned (Associates, BSN, MSN, DNP/PhD) by each setting they work in.

· Column Charts (Columns, Cones, Cylinders, and Pyramids) – presents the frequency distribution of categorical (Nominal, Ordinal) data using vertical columns, cones, cylinders, or pyramids.

· Histograms– presents the frequency distribution of continuous data (interval, ratio, some ordinal data) in bar chart format, with the bars next to each other with no gaps. These bars cannot be reordered. For example, used to present the frequency of the three age groups in the survey (19-39, 40-59, >60).

· Line Charts – used to display continuous data over time.

· Pie Charts – Displays proportional segments of a whole (100%). Use when you have five or fewer segments (or slices) such as type of education for those working in academia (Associates, BSN, MSN, DNP/PhD) or the proportion of survey participants in the three different age groups.

· Donut Charts – Like a pie chart, a doughnut chart shows the relationship of parts to a whole, but it can contain more than one

data series.

The donut chart has a cut out center, which is often used to present a trend arrow or a total #.

· Area Charts – Area charts emphasize the magnitude of change over time and can be used to draw attention to the total value across a trend. For example, use an area chart if wanting to quantify and show the change in highest degree earned over time by participants responding from an academic setting.

· X-Y (Scatter) Charts – Scatter charts show the relationships among numeric values in several data series, or plots two groups of numbers as one series of x-y coordinates. Great examples include growth charts, showing a child’s height and weight over time.

· Bubble Charts – Similar to a scatter chart representing the x-y relationship, the bubble (or circle) represents an additional dimension of data represented by the bubble’s size (z). For example, comparing life expectancy (x) by country (y) by healthcare spending (z) OR number of comorbidities (x) by healthcare spending (y) by number of patients (z).

· Funnel Charts – Used to show the progression of data.

· Sunburst Charts – Similar to a donut chart, but adds in a hierarchy for each ring.

· Treemap Charts – Used to present the relative size of two or more items and is organized hierarchically. An example is a chart displaying participants work settings.

VISUALLY REPRESENTING DATA

For this activity, begin by copying your Desc_Stats worksheet and rename the copied worksheet Data_Visual. This is IMPORTANT, as you need to preserve your Desc_Stats worksheet!

Desktop Excel

Office 365

Copy the Desc_Stats worksheet by right-clicking on the Desc_Stats worksheet, selecting
Move or Copy

(see below) and checking the
Create a Copy
box.

Copy the Desc_Stats worksheet by right-clicking on the Desc_Stats worksheet, selecting
Duplicate
(see below).

Next, remove the formulas from this worksheet by first selecting all cells, as shown below.

Click on the
Copy
button under the Home tab to copy all cells and then click
Paste
.

Desktop Excel

Office 365

From the Paste menu select Paste Values > Paste Values 123. This removes the formulas from all cells but keeps the values.

From the Paste menu select Paste Values > Paste Values 123. This removes the formulas from all cells but keeps the values.

This next step in setting up the data for use in data visualization is to remove the data used to calculate the totals, and just leave the totals along with the labels (or headings).

To do this, remove all the records between rows 2 through 77, while maintaining the column headings in row 1 and the calculated values in rows 78-86.

Place your cursor on row 2, then click the left mouse button and drag down to select all rows through 77. Right-click anywhere on the row numbers and select
Delete
. Are you left with 10 rows?

Next, add the labels for each value and change the heading to be more readable. Refer to the Metadata worksheet for what to label each row.

Right click to insert columns, to make room for these labels. Then
Home/Merge and Center
the heading cells for the demographics to create a heading for the group. For example, Change Age_Grp to Age Group and Ed_Level to Education Level. You can delete Yrs_RN and Tot_Scores columns, as these are continuous variables.

BAR CHART

The first chart you will create is a bar chart of the Mode responses on the survey. This will allow you to quickly see high and low scoring questions. Select all the labels and values from Q1 through Q29 Row 2. This might be cells K1:AM2 or L1:AN2 depending on whether or not you deleted the first column in the prior steps.

Desktop Excel

Office 365

Click on the Insert and select the Column Chart drop down arrow. Then select one of the unstacked vertical bar charts under 2D or 3D horizonal Bar, as indicated below:

Click on the Insert and select the Bar drop down arrow. Then select one of the unstacked vertical bar charts under 2D as indicated below:

Once your chart appears on your worksheet, you will want to move it to its own worksheet.

Desktop Excel

Office 365

Click on the chart and then the
Move Chart
button to place this chart on its own worksheet. You can also select a design for your chart by clicking one of the designs under the Chart Design section.

Click on the + to create a new worksheet. Then select the chart and cut/paste it onto that new worksheet.

When the
Move Chart
dialog box opens, select New Sheet and type in the name of the new sheet, BarChart_Q-Mode. Then click OK.

Right Click on your new worksheet to rename it to BarChart_Q-Mode. Then click OK.

Now it is time to format the Bar Chart.

Chart Tools in Desktop Excel:

Enter the title for your chart by double-clicking in the
Chart Title
box and typing the title “Barriers to Research Utilization Survey Responses”. Use your Font options to change the size, font, and color to whatever size, font, and color you like.

Note the number of the question in the left vertical axis (the Y axis). This allows you to refer back to the actual question on your Metadata worksheet to interpret high-ranking and low-ranking questions.

When you click on any aspect of your chart, the Format Options will appear to your right.

Add
Chart Elements
by clicking on the Plus Sign and selecting Axis Titles and Data Labels. Double click on the Axis boxes and type in the title for the vertical axis (the Y axis) Barriers Question and the horizontal axis (the X axis) Mode of Responses. Click in the axis box and select the text to change the font size or style. Choose a
Chart Style
for your chart by clicking on the paintbrush. You may design your chart anyway you choose by clicking on a chart element and using the Format Option that open for each element in the window to the right of the chart.

Click on Axis Title on the vertical axis (the Y axis) and replace with “Question Number” and click on Axis Title on the horizontal axis (the X axis) and replace with “Response”. Change the font to Arial 14 or higher so it is more readable.

Chart Tools in Office 365:

Enter the title for your chart by clicking in the
Tell me what you want to do
box and type Title Above Chart. Select the option, and enter the Title text “Barriers to Research Utilization Survey Responses”.

After you have inserted a Chart title, click on the right hand side to change the size, font, and color to whatever size, font, and color you like.

Add
Chart Elements
such as a horizontal and vertical axis title by selecting the
Tell me what you want to do
box and type Primary Vertical Axis Title and type in Barriers Question. Then type Primary Horizontal Axis title and type in Mode of Responses.

Click on the axis on the right and select the Axis Title to change the font size or style.

When your chart is complete, congratulate yourself on creating a terrific Bar Chart!

This completes the first piece of evidence for Phase 3 of this CPE. Now you need to capture a screenshot of BarChart_Q-Mode worksheet and save it for uploading to your e-portfolio.

To capture a screenshot:

· Mac: Press Shift+Command+4. Drag the crosshairs to select the area of the screen you want to capture. After you release your mouse or trackpad button, find the screenshot on your desktop. Rename your screenshot to BarChart_Q-Mode so you remember what screenshot to upload to your e-portfolio.

·

PC: Follow the “

Use Snipping Tool to capture screenshots directions

” to use the Microsoft Snipping Tool to capture, save, and share an image of all or part of your PC screen. The Snipping Tool is included in Windows Vista and later. Save your screenshot and name it BarChart_Q-Mode so you remember what screenshot to upload to your e-portfolio

PIE CHART

Select the worksheet Data_Visual. Next, create a Pie Chart of the different groups of participants by Age Group to quickly see the proportional distribution of responses for each group. Begin by selecting the Label and Counts for Age Group by clicking on the title and dragging down through the labels and counts.

Select the Age Group label and values. This might be cells A1:B4 or B1:C4 depending on whether or not you deleted the first column in the prior steps.

Then on your Insert Tab, and in the Recommended Charts area select
Pie Chart
. Select a simple, 2D, Doughnut, or 3D pie chart. While the chart is selected, move your chart to a new worksheet and rename it Chart_Age.

You will need to do some additional work on this chart to make it meaningful, as the data for each category is missing.

Chart Tools in Desktop Excel:

With your chart selected, click the
Plus
sign and check
Data Labels

-> More Options
. In the Format Data Labels box, click on
Label Options
and select the
Chart
symbol. Select Category Name, Value, Percentage, Show Leader Lines and position Center. Click on the data labels and format them with a larger font size (14 pt or higher, color=light or white) so they are visible. Add in a chart title of “Barriers Survey Participants by Age” and change the font to be readable. Select the legend at the bottom and increase the font size to be readable.

You may design your pie chart in anyway you prefer as long as it has the essential information you need to interpret the proportion for each age group.

Chart Tools in Office 365:

With your chart selected, enter the title for your chart by clicking in the
Tell me what you want to do
box and type Title Above Chart. Select the option, and enter the Title Text “Barriers Survey Participants by Age”.

Next, enter data labels by clicking in the
Tell me what you want to do
box and type Chart Data Labels. Choose which Data Labels you want such as Center.

Format your data labels, by selecting on the Data Labels drop down, and choosing Category name, Value, and Best Fit for Label Position.

You may design your pie chart in any way you prefer as long as it has the essential information you need to interpret the proportion for each age group.

When your chart is complete, congratulate yourself on a terrific job!

This completes the next piece of evidence for Phase 3 of this CPE. Now you need to capture a screenshot of the pie chart and save it for uploading to your e-portfolio.

OTHER KINDS OF CHARTS

Now that you know how to create the bar and the pie charts, create the rest of the charts on your own, and format them using your personal preferences.

You may need to open the All Charts in Desktop Excel, Other Charts in Office 365, or search for each type of chart to find these additional charts.

in Desktop Excel:

In Office 365:

Remember these steps: 1) Select your data on the Data_Visual worksheet, 2) Select your chart, 3) Move and name your chart, 4) Title the chart “Barrier Survey Participants by “_____”, 5) Format fonts and labels to your preference and 6) Create and save screenshots of each chart for uploading to your e-portfolio.

1. Sunburst Chart of Sex

2. Column Chart of Education Level

3. Funnel Chart of Race

4. Treemap Chart of Work Setting

CONCLUSION

Congratulations! Using these instructions, you have just visually displayed the data in a dataset! You saw the raw data, saw how it was coded into numbers for analysis, then in Phase 2 you conducted the analysis by using descriptive statistics and pivot tables. In phase 3, you visually represented this data in a variety of charts.

This activity used hypothetical data from a Barriers to Research Utilization by Nurses survey, but we hope that you can see how this same technique can be used for any kind of data that you want to visually represent in the future. Save these instructions for later use in your professional career. Follow the instructions in your CPE record to upload these screenshots into your e-portfolio.

2

Summary of Technology Recommendations

[Your Name, Credentials]

[Date]

Technology #1

Recommended Technology: [Enter the technology name with a brief description]

Purpose: (Describe what need this technology meets and how it will enhance nursing practice, administration, or education]

Potential Use and Users: [Explain how the technology will be used and the stakeholders who will benefit from the technology]

Technology Acceptance: [Include information on the ease of use and the usefulness of this technology].

Technology #2

Recommended Technology: [Enter the technology name with a brief description]
Purpose: (Describe what need this technology meets and how it will enhance nursing practice, administration, or education]
Potential Use and Users: [Explain how the technology will be used and the stakeholders who will benefit from the technology]
Technology Acceptance: [Include information on the ease of use and the usefulness of this technology].

Technology #3

Recommended Technology: [Enter the technology name with a brief description]
Purpose: (Describe what need this technology meets and how it will enhance nursing practice, administration, or education]
Potential Use and Users: [Explain how the technology will be used and the stakeholders who will benefit from the technology]
Technology Acceptance: [Include information on the ease of use and the usefulness of this technology].

Technology #4

Recommended Technology: [Enter the technology name with a brief description]
Purpose: (Describe what need this technology meets and how it will enhance nursing practice, administration, or education]
Potential Use and Users: [Explain how the technology will be used and the stakeholders who will benefit from the technology]
Technology Acceptance: [Include information on the ease of use and the usefulness of this technology].

Technology #5

Recommended Technology: [Enter the technology name with a brief description]
Purpose: (Describe what need this technology meets and how it will enhance nursing practice, administration, or education]
Potential Use and Users: [Explain how the technology will be used and the stakeholders who will benefit from the technology]
Technology Acceptance: [Include information on the ease of use and the usefulness of this technology].

2

>MetaData

N/A N/A N/A

=1

9; 2=

9; 3=

0 and older

Categorical Nominal

Sex

Categorical Nominal

; 2=

; 3=

; 4=

Categorical Nominal

; 2=

; 3=Community/Public Health; 4=Other

Categorical Nominal

; 2=Bachelors; 3=Masters; 4: Doctorate

Categorical

; 2=

; 3=

rate Extent

; 4=

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

al analyses are not understandable

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent

Continuous Interval/Ratio

Variable Variable Type Level of Measurement Response Categories Description/Survey Question
StudentID N/A
Age _Grp Categorical Nominal 1 9 3 4 0 5 6 Age Group
Sex 1=Male; 2=Female
Race 1=

White African American Asian Other What is your race?
Yrs_RN Continuous Interval/Ratio Number of years How many years have you been an RN?
Setting 1=

Patient Care Academic What is your work setting?
ED-Level 1=

Associate What is your highest level of education?
The items below relate to Barriers Survey questions on the reasons nurses do not utilize research.
Q1 Ordinal 1=

To No Extent To a Little Extent To a

Mode To a Great Extent Research reports/articles are not readily available.
Q2 Implications for practice are not made clear
Q3 Statistic
Q4 The research is not relevant to the nurse’s practice
Q5 The nurse is unaware of the research
Q6 The facilities are inadequate for implementation
Q

7 The nurse does not have time to read research
Q

8 The research has not been replicated
Q9 The nurse feels the benefits of changing practice will be minimal
Q

10 The nurse is uncertain whether to believe the results of the research
Q

11 The research has methodological inadequacies
Q

12 The relevant literature is not compiled in one place
Q

13 The nurse does not feel she/he has enough authority to change patient care procedures
Q

14 The nurse feels results are not generalizable to own setting
Q

15 The nurse is isolated from knowledgeable colleagues with whom to discuss the research
Q

16 The nurse sees little benefit for self
Q

17 Research reports/articles are not published fast enough
Q

18 Physicians will not cooperate with implementation
Q

19 Administration will not allow implementation
Q

20 The nurse does not see the value of research for practice
Q

21 There is not a documented need to change practice
Q

22 The conclusions drawn from the research are not justified
Q

23 The literature reports conflicting results
Q

24 The research is not reported clearly and readably
Q

25 Other staff are not supportive of implementation
Q

26 The nurse is unwilling to change/try new ideas
Q

27 The amount of research information is overwhelming
Q

28 The nurse does not feel capable of evaluating the quality of the research
Q

29 There is insufficient time on the job to implement new ideas
Tot_Scores Total scores for individual questions New Calculated Continuous Variable – Total of Barriers Scores

Data_Raw

Sex Race Yrs_RN Setting

Q1 Q2 Q3 Q4 Q5 Q6

Q9

Q12

Q22

Tot_Scores

1

& older yrs

2 White 6 Patient Care

To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To a Great Extent 0

2

yrs

2 White 4 Patient Care MSN To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent 0

3

2 African American 7 Patient Care

To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent 0

4 19-39 yrs 2 White 5 Patient Care Associate To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To No Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent 0
5 19-39 yrs 2 White 3 Patient Care MSN To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Great Extent To a Moderate Extent To No Extent 0
6

yrs

1 White 3 Patient Care Associate To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent 0

7 19-39 yrs 2 Asian 6

DNP/PhD To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent 0

8 19-39 yrs 1 White 4 Academic MSN To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Great Extent To a Great Extent To No Extent To a Great Extent To a Great Extent To a Great Extent 0
9 19-39 yrs 2 White 4 Academic MSN To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Great Extent To a Moderate Extent To No Extent 0
11 19-39 yrs 2 White 7 Patient Care MSN To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent 0
12

2 White 17 Academic Associate To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Moderate Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent 0

12 40-59 yrs 2 African American 17 Comm/Public Hlth

To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To No Extent To a Great Extent To a Little Extent To a Little Extent To No Extent To a Great Extent To No Extent To a Little Extent To a Great Extent To a Great Extent 0

13 19-39 yrs 1 White 3 Academic Associate To No Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Great Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent 0
14 19-39 yrs 2 White 8 Patient Care MSN To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent 0
15 19-39 yrs 2 White 6 Patient Care Associate To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent 0
16 40-59 yrs 2 White 11 Academic Associate To a Little Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To No Extent To No Extent To a Little Extent To a Great Extent To No Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent 0
17 19-39 yrs 2 Other 7 Patient Care DNP/PhD To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent 0
18 19-39 yrs 1 White 3 Patient Care Associate To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent 0
19 60 & older yrs 2 White 13 Patient Care MSN To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent 0
21 60 & older yrs 1 White 16 Patient Care BSN To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent To a Little Extent To a Great Extent 0
22 19-39 yrs 2 White 3 Patient Care Associate To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent 0
22 19-39 yrs 2 Asian 8 Patient Care BSN To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent 0
23 19-39 yrs 2 African American 11 Academic DNP/PhD To No Extent To No Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Great Extent To No Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent 0
24 19-39 yrs 1 African American 6 Patient Care BSN To No Extent To No Extent To No Extent To No Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To No Extent To a Little Extent To No Extent To No Extent To a Great Extent 0
25 19-39 yrs 2 White 5 Patient Care Associate To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent 0
26 19-39 yrs 2 White 5 Patient Care MSN To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To No Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To No Extent To No Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent 0
27 19-39 yrs 1 Asian 5 Patient Care MSN To a Moderate Extent To a Little Extent To a Great Extent To No Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent 0
28 19-39 yrs 2 White 8 Academic BSN To No Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Great Extent To No Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Moderate Extent 0
29 19-39 yrs 2 Asian 9 Comm/Public Hlth BSN To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 2 Asian 8 Other BSN To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent 0

19-39 yrs 1 White 4 Comm/Public Hlth BSN To a Moderate Extent To No Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent 0

32 60 & older yrs 2 White 16 Other BSN To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent 0

19-39 yrs 2 Asian 5 Academic Associate To a Little Extent To a Moderate Extent To a Great Extent To No Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent 0

40-59 yrs 1 White 8 Comm/Public Hlth BSN To No Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Moderate Extent To a Great Extent To No Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Great Extent To No Extent To a Little Extent To a Moderate Extent To a Great Extent 0

40-59 yrs 2 African American 26 Comm/Public Hlth Associate To No Extent To No Extent To a Little Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Great Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent 0

40-59 yrs 2 White 7 Academic Associate To No Extent To No Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To No Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent 0

40-59 yrs 2 White 14 Patient Care Associate To No Extent To a Little Extent To a Moderate Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent To No Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 1 White 2 Academic Associate To No Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To No Extent To a Moderate Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To No Extent To a Little Extent To a Moderate Extent 0

39 60 & older yrs 2 White 18 Other Associate To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To No Extent To a Great Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 2 White 4 Patient Care Associate To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent 0

40-59 yrs 1 African American 23 Other Associate To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent 0

42 60 & older yrs 2 White 14 Patient Care BSN To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To a Great Extent To No Extent To No Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To No Extent To a Great Extent 0

19-39 yrs 2 Asian 5 Patient Care Associate To a Little Extent To a Little Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Great Extent To No Extent To No Extent To No Extent To a Great Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Great Extent 0

40-59 yrs 2 White 15 Comm/Public Hlth BSN To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Moderate Extent To No Extent To a Moderate Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To No Extent To No Extent To No Extent To a Moderate Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To No Extent 0

40-59 yrs 1 Other 8 Patient Care DNP/PhD To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 2 White 4 Patient Care MSN To a Little Extent To a Little Extent To a Little Extent To No Extent To No Extent To a Moderate Extent To a Great Extent To No Extent To No Extent To No Extent To No Extent To a Little Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To No Extent To a Moderate Extent To No Extent To No Extent To a Moderate Extent To a Great Extent 0

40-59 yrs 2 White 8 Academic Associate To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To No Extent To a Great Extent To a Little Extent To a Great Extent 0

19-39 yrs 2 White 5 Patient Care MSN To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To No Extent To No Extent To No Extent To No Extent To No Extent To a Little Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Little Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 2 White 6 Other DNP/PhD To a Moderate Extent To a Little Extent To a Great Extent To No Extent To a Great Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent 0

40-59 yrs 1 Asian 6 Academic BSN To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent 0

19-39 yrs 2 African American 7 Patient Care BSN To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To No Extent To a Great Extent To a Great Extent To No Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent 0

52 60 & older yrs 2 White 6 Patient Care MSN To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To No Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To No Extent To a Great Extent To a Great Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent 0

19-39 yrs 2 White 13 Patient Care BSN To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To No Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent 0

19-39 yrs 2 White 27 Patient Care MSN To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 2 African American 5 Patient Care BSN To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent 0

19-39 yrs 1 White 5 Other BSN To No Extent To a Little Extent To a Moderate Extent To No Extent To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent 0

19-39 yrs 2 White 19 Patient Care MSN To No Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent 0

19-39 yrs 2 African American 4 Comm/Public Hlth BSN To a Little Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To No Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent 0

59 19-39 yrs 2 White 4 Academic BSN To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent 0

19-39 yrs 2 White 2 Patient Care Associate To a Moderate Extent To a Moderate Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To No Extent To a Little Extent To a Great Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 1 African American 2 Patient Care Associate To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent 0

62 60 & older yrs 2 White 28 Academic BSN To No Extent To No Extent To a Great Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To No Extent To a Little Extent To No Extent To No Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Great Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent 0

40-59 yrs 1 White 3 Patient Care BSN To a Moderate Extent To a Little Extent To a Great Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Little Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent 0

19-39 yrs 2 White 3 Academic BSN To a Little Extent To No Extent To a Moderate Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To No Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent 0

19-39 yrs 2 White 23 Patient Care BSN To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent 0

19-39 yrs 2 White 22 Academic BSN To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Little Extent To a Moderate Extent To a Great Extent 0

19-39 yrs 2 White 26 Patient Care BSN To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent 0

19-39 yrs 2 White 13 Patient Care BSN To a Great Extent To a Great Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Great Extent To No Extent To No Extent To a Moderate Extent To No Extent To a Moderate Extent To a Moderate Extent 0

19-39 yrs 2 White 17 Patient Care BSN To a Moderate Extent To a Little Extent To a Moderate Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To No Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Moderate Extent To a Moderate Extent To a Little Extent 0

19-39 yrs 2 White 8 Patient Care Associate To a Moderate Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Moderate Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To a Moderate Extent To a Great Extent To a Great Extent To a Moderate Extent To a Moderate Extent 0

60 & older yrs 1 White 19 Patient Care BSN To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To a Great Extent To a Great Extent 0

72 40-59 yrs 2 White 8 Comm/Public Hlth BSN To a Little Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To No Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To No Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent 0

19-39 yrs 2 African American 8 Comm/Public Hlth BSN To a Moderate Extent To No Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To a Moderate Extent To a Little Extent To No Extent To No Extent To No Extent To a Little Extent To a Great Extent To a Little Extent To a Little Extent To No Extent To No Extent To No Extent To No Extent To No Extent To No Extent To No Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Little Extent To a Great Extent 0

19-39 yrs 1 Asian 11 Comm/Public Hlth BSN To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Little Extent To a Moderate Extent To a Great Extent To a Great Extent To a Great Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To a Little Extent To a Great Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent To a Moderate Extent 0

19-39 yrs 2 African American 3 Other Associate To No Extent To a Little Extent To a Moderate Extent To a Little Extent To No Extent To a Great Extent To a Little Extent To a Little Extent To No Extent To No Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent To No Extent To No Extent To a Moderate Extent To a Moderate Extent To a Great Extent To No Extent To a Great Extent To No Extent To a Little Extent To No Extent To a Great Extent To No Extent To a Moderate Extent To No Extent To a Great Extent 0

19-39 yrs 2 White 12 Patient Care MSN To a Little Extent To a Little Extent To a Moderate Extent To a Little Extent To a Moderate Extent To No Extent To a Little Extent To No Extent To a Little Extent To a Little Extent To No Extent To a Moderate Extent To a Great Extent To a Little Extent To a Moderate Extent To a Moderate Extent To No Extent To a Great Extent To a Moderate Extent To a Little Extent To a Moderate Extent To a Little Extent To a Little Extent To a Great Extent To a Moderate Extent To a Moderate Extent To a Little Extent To a Great Extent To a Great Extent 0

Rec_ID Age_Grp Ed_Level Q7 Q8 Q12 Q11 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q22 Q21 Q23 Q24 Q25 Q26 Q27 Q28 Q29
60 MSN
19-

39
19-39 yrs DNP/PhD
40 59
Comm/Public Hlth
40-59 yrs
BSN
31
32
33
34
35
36
37
38
41
42
43
44
45
46
47
48
49
51
52
53
54
55
56
57
58
61
62
63
64
65
66
67
68
69
71
72
73
74
75
76

Data_Coded

Rec_ID Age_Grp Sex Race Yrs_RN Setting Ed_Level Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29
4 1 2 1 5 1 1 3 1 2 2 2 4 1 2 3 3 3 4 4 4 1 3 2 3 4 1 1 1 2 2 4 3 4 4 3
6 2 1 1 3 1 1 1 2 2 3 1 2 3 3 2 1 2 2 2 3 3 3 2 2 2 3 1 2 2 3 3 2 2 4 2
10 2 2 1 17 2 1 4 3 3 2 4 2 4 2 2 2 3 2 3 2 3 3 1 2 3 3 1 3 1 3 3 4 2 4 4
13 1 1 1 3 2 1 1 1 2 1 1 1 3 2 2 2 2 2 4 2 2 2 1 4 3 2 1 1 2 2 3 2 3 3 2
15 1 2 1 6 1 1 3 3 3 2 4 3 4 4 4 4 1 4 4 2 2 1 2 2 3 2 2 2 2 3 3 2 4 3 4
16 2 2 1 11 2 1 2 1 3 1 2 2 3 1 1 1 1 2 4 1 2 1 1 2 2 1 1 1 2 1 1 1 3 3 4
18 1 1 1 3 1 1 2 2 3 1 2 3 3 2 1 2 2 2 3 3 3 2 2 2 3 1 2 2 3 3 2 2 4 2 4
22 1 2 1 3 1 1 2 1 2 2 3 3 4 2 1 3 2 3 4 2 2 4 3 4 2 3 4 2 2 2 2 3 3 3 4
25 1 2 1 5 1 1 2 2 2 2 3 3 4 1 2 2 1 1 3 3 2 2 1 2 2 2 3 1 1 2 3 3 2 4 4
33 1 2 3 5 2 1 2 3 4 1 4 3 4 2 3 1 2 3 3 2 4 4 2 2 3 3 3 2 3 4 3 3 4 4 4
35 2 2 2 26 3 1 1 1 2 1 3 1 2 1 2 1 1 2 3 1 2 2 2 4 3 2 1 1 1 2 4 4 2 3 3
36 2 2 1 7 2 1 1 1 4 2 3 4 4 3 2 2 1 3 4 4 3 3 1 3 3 2 2 2 3 3 4 3 4 4 4
37 2 2 1 14 1 1 1 2 3 1 3 3 4 1 2 3 1 1 3 3 2 2 1 1 1 2 3 1 3 2 2 3 4 3 4
38 1 1 1 2 2 1 1 1 3 2 2 1 3 3 4 3 1 3 4 2 1 2 1 2 2 3 3 2 4 4 3 4 1 2 3
39 3 2 1 18 4 1 2 3 3 2 3 1 4 1 2 2 2 3 4 3 3 2 2 3 4 2 1 1 2 2 2 3 2 3 4
41 1 2 1 4 1 1 2 2 3 3 3 3 4 3 2 3 3 4 4 3 3 2 3 4 3 2 2 3 4 2 3 3 2 2 4
42 2 1 2 23 4 1 2 3 4 3 4 3 4 2 3 2 2 3 3 3 2 3 2 2 2 3 3 2 2 2 3 3 4 3 4
43 1 2 3 5 1 1 2 2 4 2 3 3 4 2 2 2 3 1 2 2 2 2 1 2 4 1 1 1 4 4 2 1 2 3 4
47 2 2 1 8 2 1 2 2 3 2 2 3 4 2 3 2 2 3 4 2 2 3 2 3 4 2 2 2 4 3 2 1 4 2 4
61 1 2 1 2 1 1 3 3 1 2 3 3 2 3 2 1 3 4 2 2 4 3 2 4 4 3 4 1 2 4 3 2 4 3 4
62 1 1 2 2 1 1 4 3 3 2 2 3 4 2 2 2 2 2 3 3 1 2 1 3 3 2 2 2 3 2 3 3 2 2 3
71 1 2 1 8 1 1 3 3 4 4 4 4 4 3 3 2 4 3 4 3 3 2 2 4 3 3 3 2 4 4 3 4 4 3 3
75 1 2 2 3 4 1 1 2 3 2 1 4 2 2 1 1 3 2 4 4 1 1 3 3 4 1 4 1 2 1 4 1 3 1 4
12 2 2 2 17 3 2 4 4 4 4 4 3 4 1 3 2 1 1 3 4 4 3 2 4 2 1 4 1 1 1 4 1 2 4 4
20 1 2 3 8 1 2 4 4 3 3 3 4 4 4 3 2 4 4 4 4 4 4 4 4 3 3 3 3 4 4 3 4 3 3 3
21 3 1 1 16 1 2 3 3 3 2 4 2 4 2 4 4 3 4 4 3 3 4 2 4 4 4 4 3 2 4 4 4 2 2 4
24 1 1 2 6 1 2 1 1 1 1 2 1 3 1 1 1 1 2 3 1 1 3 1 2 2 1 2 1 1 1 1 2 1 1 4
28 1 2 1 8 2 2 1 2 3 1 2 2 1 4 1 1 1 2 3 2 2 2 3 2 2 1 1 2 3 3 1 1 1 3 3
29 1 2 3 9 3 2 2 2 3 2 4 3 4 3 3 4 3 4 2 2 2 3 2 1 2 1 1 3 3 3 3 3 4 3 4

1 1 1 4 3 2 3 1 4 2 2 2 1 2 2 1 1 1 3 2 1 1 2 3 2 1 1 1 1 3 1 2 3 3 3

31 1 2 3 8 4 2 3 2 2 1 2 1 2 2 2 2 2 3 2 2 2 2 3 1 1 1 2 1 1 1 2 2 2 2 2
32 3 2 1 16 4 2 3 4 3 3 4 3 4 4 3 3 1 4 4 4 3 1 1 1 1 3 4 1 1 4 3 2 4 4 3
34 2 1 1 8 3 2 1 2 3 2 3 4 4 2 2 2 2 3 4 3 2 1 1 3 4 1 2 1 2 1 4 1 1 3 4
40 3 2 1 14 1 2 2 2 1 3 2 3 4 1 2 1 1 1 4 1 1 2 1 2 1 2 1 1 2 2 3 3 2 1 4
44 2 2 1 15 3 2 2 3 2 1 2 1 3 1 3 2 1 3 3 3 3 1 1 1 3 1 3 1 2 1 3 1 2 1 1

1 2 2 7 1 2 3 2 4 2 1 4 4 1 1 1 2 2 4 2 2 2 2 1 4 2 3 3 3 4 4 4 4 4 3

51 2 1 3 6 2 2 3 2 4 3 4 3 2 2 3 3 2 3 4 3 4 3 2 2 3 3 4 2 3 4 2 3 4 4 3
53 1 2 1 13 1 2 3 2 4 2 4 4 4 1 3 2 2 1 4 2 3 3 2 4 4 2 3 1 1 3 3 3 3 2 4
55 1 2 2 5 1 2 2 2 3 3 4 2 3 2 1 2 2 4 4 3 2 2 2 2 3 2 3 2 2 3 3 4 3 3 2
56 1 1 1 5 4 2 1 2 3 1 3 2 4 2 4 4 2 3 4 2 3 3 3 4 4 3 4 3 3 3 4 4 4 4 4
58 1 2 2 4 3 2 2 2 2 1 3 3 1 1 2 2 1 2 1 2 3 1 1 3 2 2 3 2 1 1 2 3 3 2 2
59 1 2 1 4 2 2 3 2 3 2 4 3 2 1 3 2 2 4 4 4 4 3 3 4 4 3 2 2 3 4 4 4 4 4 2
60 3 2 1 28 2 2 1 1 4 2 2 2 4 1 2 1 1 3 4 2 3 3 1 4 1 2 1 1 1 1 3 3 4 4 4
63 2 1 1 3 1 2 3 2 4 2 3 2 4 1 2 2 1 3 4 2 4 2 2 4 2 3 3 2 2 4 4 3 4 4 4
64 1 2 1 3 2 2 2 1 3 1 3 1 2 1 2 3 2 3 3 2 1 2 1 1 1 1 2 1 2 3 2 3 3 3 2
65 1 2 1 23 1 2 3 2 4 3 4 4 4 3 3 3 3 4 4 3 4 3 2 3 2 3 3 3 3 3 3 2 4 4 3
66 1 2 1 22 2 2 4 3 4 2 3 4 2 3 3 4 4 3 4 3 2 3 2 4 4 3 3 4 3 4 2 4 2 3 4
67 1 2 1 26 1 2 2 2 4 3 4 3 3 3 3 2 3 3 4 3 4 4 3 4 4 3 4 3 3 4 3 4 4 4 3
68 1 2 1 13 1 2 4 4 4 2 4 4 3 2 3 3 1 4 3 2 3 2 3 3 2 1 1 1 4 1 1 3 1 3 3
69 1 2 1 17 1 2 3 2 3 1 3 1 2 2 1 2 2 3 2 1 1 1 2 3 1 1 2 1 2 3 2 1 3 3 2

3 1 1 19 1 2 2 2 3 1 2 2 3 1 2 2 1 3 2 2 2 2 1 2 3 2 1 1 1 3 3 4 2 4 4

72 2 2 1 8 3 2 2 3 3 2 3 3 4 2 1 2 1 2 2 2 1 2 1 1 2 2 2 1 2 3 3 3 2 2 4
73 1 2 2 8 3 2 3 1 3 1 2 1 3 2 1 1 1 2 4 2 2 1 1 1 1 1 1 1 2 3 2 2 4 2 4
74 1 1 3 11 3 2 2 3 3 3 2 2 4 3 3 2 2 3 4 4 4 2 1 2 2 2 4 1 1 3 2 3 3 3 3
1 1 2 1 6 1 3 2 2 3 1 3 3 3 3 2 2 2 2 3 2 2 2 1 3 3 1 2 2 2 2 2 1 3 2 4
2 1 2 1 4 1 3 2 2 3 2 2 3 2 2 3 2 2 2 4 3 3 2 2 3 3 2 2 2 2 3 3 2 3 2 2
5 1 2 1 3 1 3 3 1 2 2 2 4 1 2 3 3 3 4 4 4 1 3 2 3 4 1 1 1 2 2 4 3 4 4 1
8 1 1 1 4 2 3 3 2 4 2 4 2 4 2 2 2 3 4 3 2 1 1 2 3 2 1 1 2 2 4 4 1 4 4 4
9 1 2 1 4 2 3 4 3 4 2 3 2 4 2 1 2 2 3 3 2 1 1 2 2 2 1 2 2 2 3 2 1 4 3 1
11 1 2 1 7 1 3 2 3 3 3 4 4 4 3 3 2 1 3 4 3 3 3 3 2 2 2 3 3 3 3 3 2 3 4 4
14 1 2 1 8 1 3 2 2 3 2 4 3 2 2 4 3 3 2 3 4 4 1 2 2 2 4 3 3 3 3 3 4 3 3 3
19 1 2 1 13 1 3 2 2 2 2 3 2 2 3 1 2 2 3 3 2 2 1 2 3 3 1 2 2 2 3 2 1 3 3 3
26 1 2 1 5 1 3 2 2 4 3 2 3 2 2 1 3 1 1 4 3 4 2 3 4 3 1 1 3 4 4 4 4 4 4 4
27 1 1 3 5 1 3 3 2 4 1 1 1 1 3 2 1 1 1 2 1 1 2 3 2 1 1 1 1 3 2 1 1 3 2 2
46 1 2 1 4 1 3 2 2 2 1 1 3 4 1 1 1 1 2 4 2 1 2 1 1 2 2 3 1 1 1 3 1 1 3 4
48 1 2 1 5 1 3 3 2 3 2 1 1 4 2 2 2 1 3 1 1 1 1 1 2 2 1 1 1 2 2 1 1 1 3 4
52 1 2 1 6 1 3 3 3 4 4 4 2 4 3 4 2 1 4 4 3 2 3 1 4 4 2 4 3 3 4 2 4 4 4 4
54 1 2 1 27 1 3 4 3 3 2 3 3 4 3 3 3 2 3 4 3 4 2 3 3 2 2 2 3 2 3 4 4 3 3 4
57 1 2 1 19 1 3 1 3 4 3 4 2 3 2 2 2 2 3 4 2 3 3 2 3 2 2 2 1 2 3 3 3 4 4 3
76 1 2 1 12 1 3 2 2 3 2 3 1 2 1 2 2 1 3 4 2 3 3 1 4 3 2 3 2 2 4 3 3 2 4 4
3 1 2 2 7 1 4 3 3 4 4 4 3 3 3 4 4 3 2 4 3 4 4 3 3 4 4 3 3 4 4 4 4 4 4 4
7 1 2 3 6 3 4 3 2 4 2 4 2 3 1 1 2 2 3 3 2 2 2 1 2 1 1 1 1 1 3 2 2 4 4 3
17 1 2 4 7 1 4 2 2 3 3 4 2 3 3 2 1 2 2 3 2 2 2 1 3 2 2 2 3 3 3 3 3 4 3 2
23 1 2 2 11 2 4 1 1 3 2 4 2 3 4 3 4 1 3 4 3 3 2 2 4 4 1 3 4 4 3 4 2 4 4 3
45 2 1 4 8 1 4 2 2 3 3 3 4 4 3 2 2 2 3 3 3 4 2 3 4 4 2 3 2 2 3 4 4 3 3 4
49 1 2 1 6 4 4 3 2 4 1 4 2 4 3 3 3 3 4 4 2 3 2 2 3 4 1 2 2 3 3 3 3 4 4 3
30
50
70

Continuous

Sum

mary (Example)

Statistic

30

maximum miles is pulling the mean to the right, making the mean artificially higher).

Mode 20

1

290

Self-explanatory

Sum

Self-explanatory

76

Self-explanatory

of the Variable Miles

Example of Descriptive Statistics for a Continuous Variable on Miles Driven to Work
Statistical Test What it Measures Explanation
Mean 55.9342 Average Miles Driven Participants drove an average number of 55.9 miles to work.
Median The middle point of the values of Miles The midpoint of all the Miles recorded is 30, which means 1/2 of the Miles are below this and 1/2 are above. Compare the Median to the Mean. There is a large difference, which indicates that the mean may be skewed due to extreme values (for example, the

290
The value of Miles reported most often Participants reported driving 20 Miles to work more often than any other value.
Standard Deviation 65.7055 How widely spread the Miles values are. It is the avearge distance from the mean. Each value of “# of Miles Driven to Work” differs from the mean of 55.9 by an average of 65.7 Miles! There is a great deal of variability in the miles reported.
Minimum Minimum value in the range Self-explanatory
Maximum Maximum value in the range
Range 289 The spread of Miles values This is the lowest number of Miles reported (1) subtracted from the highest (290), which tells us that there is a very wide spread in the values
4251 The total number of Miles reported
Count This is the N – The number of surveys completed (or records entered)
Summary Interpretation
Summary statistics on the variable “Miles” were obtained using Microsoft Excel Descriptive Statistics. In a dataset of 76 records, the variable Miles had a mean of 55.9 miles (SD +/- 65.7), a median of 30 and a mode of 20. The range of values was from 1 to 290 miles with a total of 4,251 miles being driven by nurses to work. When comparing the Mean to the Median (55.9 vs. 30), there is a large difference, indicating that the average is skewed due to higher values in the dataset distorting the true mean.

Summary_Yrs_RN

Statistical Test Statistic Summary Interpretation
Mean
Median
Mode
Standard Deviation
Minimum
Maximum
Range
Sum
Count
Years as RN Descriptive Statistics

Summary_Tot_Scores

Statistical Test Statistic Summary Interpretation
Mean
Median
Mode
Standard Deviation
Minimum
Maximum
Range
Sum
Count

Barriers Total Scores Descriptive Statistics

Summary_Demographics

Summary Description of Sample Characteristics
In this box, include a summary description of the sample population demographics. For example: In this sample population (N=76), the largest age-group was the 19-29 year-old group with 55 participants. Write similar interpretations for all of the categorical-nominal variables that describe the study population.

Summary_Responses

Summary Description of Survey Items Mode Responses
In this box, include a summary interpretation of the high-scoring and low-scoring items on the survey indicating those items that are the greatest barriers to the use of research by nurses and those that did not influence on research utilization by nurses.

Calculate your order
Pages (275 words)
Standard price: $0.00
Client Reviews
4.9
Sitejabber
4.6
Trustpilot
4.8
Our Guarantees
100% Confidentiality
Information about customers is confidential and never disclosed to third parties.
Original Writing
We complete all papers from scratch. You can get a plagiarism report.
Timely Delivery
No missed deadlines – 97% of assignments are completed in time.
Money Back
If you're confident that a writer didn't follow your order details, ask for a refund.

Calculate the price of your order

You will get a personal manager and a discount.
We'll send you the first draft for approval by at
Total price:
$0.00
Power up Your Academic Success with the
Team of Professionals. We’ve Got Your Back.
Power up Your Study Success with Experts We’ve Got Your Back.

Order your essay today and save 30% with the discount code ESSAYHELP