Instructionsfor Analyzing Data Using
Descriptive Statistics
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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.
Note: These instructions use right-click to access the right-click menu. If unsure how to right-click on your computer, reach out to Student Success Center, IT services, or your course instructor. For example, Ctrl/click acts as a right-click on a Mac.
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.
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Desktop Excel
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Web Excel
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. In column AK1, type the heading Tot_Scores. Next, 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)
Click on the Desc_Stats worksheet.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
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Web Excel
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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.
Note: You will format the numbers in the cells in the next section, which will fix percent and/or decimal points.
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
Web Excel
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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:
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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:
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Because these are counts, format your categorical-nominal results as whole numbers, without decimal places or percent.
Desktop Excel
Web Excel
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First, select all cells you want to format by selecting cells B78 through D78 and then pressing the CTRL key (PC) or Command key (Mac) while selecting cells F78 through G78. (The CTRL key (PC) or Command key (Mac) is used when selecting cells that are not contiguous. The Shift Key is used when all cells are next to each other.)
PC: On your Home menu, click on
Format > Format cells
.
Mac: Right click while in the highlighted cell block, click
Format cells
, and then select Number Format.
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.
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First, select the first set of cells you want to format by selecting cells B78 through D81. (The CTRL/Shift keys do not work in Web Excel
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.
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Click the ‘Show Formulas’ again to see the numbers (instead of the formulas).
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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”.
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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 similar to 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.
PC: 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.
Mac: To do this, click on the small i next to Sum of Rec ID and select Summarize by and Count, then click OK.
Pivot_Age
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 you created, 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!
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Desktop Excel
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Web Excel
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PC:
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).
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Mac:
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.
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
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Web Excel
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PC:
From the Paste menu select Paste Values > Paste Values 123. This removes the formulas from all cells but keeps the values.
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From the Paste menu select Paste Values > Paste Values 123. This removes the formulas from all cells but keeps the values.
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Mac: From the Paste menu select Paste Values. This removes the formulas from all cells but keeps the values.
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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
Web Excel
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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:
PC:
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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:
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Mac:
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Once your chart appears on your worksheet, you will want to move it to its own worksheet.
Desktop Excel
Web Excel
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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.
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Click on the + to create a new worksheet. Then select the chart and cut/paste it onto that new worksheet.
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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.
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Right Click on your new worksheet to rename it to BarChart_Q-Mode. Then click OK.
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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.
PC: 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.
Mac: When you click on any aspect of your chart, the Format Options will appear to your right.
Select Quick Layout to choose a format that has titles on both the vertical axis and horizontal axis titles.
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. Alternatively, change the Axis Title by clicking on the Axis Title, then right-click to
Format Axis Title
.
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.
Now, 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”. Right click, select
Font
, and change the font to Arial 14 or higher so it is more readable.
Chart Tools in Web Excel:
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:
·
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
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.
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
.
Desktop Excel
Web Excel
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PC: Select a simple, 2D, Doughnut, or 3D pie chart.
Mac: Select the Pie Chart, which is 2D.
If you want a 3D pie chart, select the Pie icon, then choose the 3-D pie
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Select a 2-D pie or Doughnut.
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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:
PC: 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 any way you prefer as long as it has the essential information you need to interpret the proportion for each age group.
Mac:
With your chart selected, double-click the Chart Title, and change to the title to “Barriers Survey Participants by Age”. Then right click and change the font to be a readable size. Select the legend at the bottom, right-click, click
Font
and increase the font size to be readable.
Select the pie-chart, right-click and select Add Data Labels.
Right-click again, and click
Format Data Labels.
In the box on the right, select Category Name, Value, Percentage, Show Leader Lines and position Center. Then click on the data labels, right click, and format them with a larger font size (14 pt or higher, color=light or white) so they are visible.
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.
Chart Tools in Web Excel:
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 Web Excel, or search for each type of chart to find these additional charts. If your Desktop Excel version does not have these chart options (e.g., sunburst and treemap), use Web Excel to gain access to these additional charts.
in Desktop Excel:
In Web Excel:
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
• annotated bibliography of 5 recent and relevant sources
• narrative essay of your interviews with technology users and experts
• technology summary that provides the top five technologies you would recommend for transforming nursing or enhancing healthcare outcomes
• 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
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].
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| 2 |
>MetaData
| Variable |
Variable Type |
Level of Measurement |
Response Categories |
Description/Survey Question |
| StudentID |
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| N/A |
N/A N/A N/A
| Age _Grp |
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| Categorical |
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| Nominal |
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| 1 |
=1
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| 9 |
–
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| 3 |
9; 2=
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| 4 |
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| 0 |
–
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| 5 |
9; 3=
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| 6 |
0 and older
Age Group |
|
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| Sex |
Categorical Nominal
1=Male; 2=Female |
Sex
|
|
| Race |
Categorical Nominal
1=
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| White |
; 2=
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| African American |
; 3=
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| Asian |
; 4=
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| Other |
What is your race? |
|
|
| Yrs_RN |
| Continuous |
| Interval/Ratio |
Number of years |
How many years have you been an RN? |
|
|
| Setting |
Categorical Nominal
1=
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| Patient Care |
; 2=
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| Academic |
; 3=Community/Public Health; 4=Other
What is your work setting? |
| ED-Level |
Categorical Nominal
1=
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| Associate |
; 2=Bachelors; 3=Masters; 4: Doctorate
What is your highest level of education? |
| The items below relate to Barriers Survey questions on the reasons nurses do not utilize research. |
|
|
| Q1 |
Categorical
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| Ordinal |
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| 1=
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| To No Extent |
; 2=
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| To a Little Extent |
; 3=
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| To a
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| Mode |
rate Extent
; 4=
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| To a Great Extent |
Research reports/articles are not readily available. |
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| Q2 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
Implications for practice are not made clear |
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| Q3 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
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| Statistic |
al analyses are not understandable
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| Q4 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The research is not relevant to the nurse’s practice |
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| Q5 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse is unaware of the research |
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| Q6 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The facilities are inadequate for implementation |
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| Q
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| 7 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse does not have time to read research |
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| Q
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| 8 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The research has not been replicated |
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|
| Q9 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse feels the benefits of changing practice will be minimal |
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| Q
| 10 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse is uncertain whether to believe the results of the research |
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| Q
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| 11 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The research has methodological inadequacies |
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| Q
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| 12 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The relevant literature is not compiled in one place |
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| Q
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| 13 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse does not feel she/he has enough authority to change patient care procedures |
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| Q
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| 14 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse feels results are not generalizable to own setting |
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| Q
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| 15 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse is isolated from knowledgeable colleagues with whom to discuss the research |
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| Q
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| 16 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse sees little benefit for self |
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| Q
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| 17 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
Research reports/articles are not published fast enough |
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| Q
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| 18 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
Physicians will not cooperate with implementation |
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| Q
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| 19 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
Administration will not allow implementation |
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| Q
|
| 20 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse does not see the value of research for practice |
|
| Q
|
| 21 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
There is not a documented need to change practice |
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| Q
|
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| 22 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The conclusions drawn from the research are not justified |
|
| Q
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| 23 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The literature reports conflicting results |
|
| Q
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| 24 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The research is not reported clearly and readably |
|
| Q
|
| 25 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
Other staff are not supportive of implementation |
|
| Q
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| 26 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse is unwilling to change/try new ideas |
|
| Q
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| 27 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The amount of research information is overwhelming |
|
| Q
|
|
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| 28 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
The nurse does not feel capable of evaluating the quality of the research |
|
| Q
|
| 29 |
Categorical Ordinal 1=To No Extent; 2=To a Little Extent; 3=To a Moderate Extent; 4=To a Great Extent
There is insufficient time on the job to implement new ideas |
|
| Tot_Scores |
Continuous Interval/Ratio
Total scores for individual questions |
New Calculated Continuous Variable – Total of Barriers Scores |
Data_Raw
|
| Rec_ID |
| Age_Grp |
Sex Race Yrs_RN Setting
| Ed_Level |
Q1 Q2 Q3 Q4 Q5 Q6
Q7 |
Q8 |
Q9
Q12 |
Q11 |
Q12
Q13 |
Q14 |
Q15 |
Q16 |
Q17 |
Q18 |
Q19 |
Q22 |
Q21 |
Q22
Q23 |
Q24 |
Q25 |
Q26 |
Q27 |
Q28 |
Q29 |
Tot_Scores
1
|
|
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| 60 |
& older yrs
2 White 6 Patient Care
|
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| MSN |
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
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|
| 19-
|
| 39 |
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
|
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|
| 19-39 yrs |
2 African American 7 Patient Care
|
|
|
|
| DNP/PhD |
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
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|
| 40 |
–
| 59 |
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
|
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|
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|
|
| Comm/Public Hlth |
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
|
| 40-59 yrs |
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
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| BSN |
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
|
| 31 |
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
|
|
| 32 |
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
|
| 33 |
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
|
| 34 |
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
|
| 35 |
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
|
| 36 |
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
|
| 37 |
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
|
| 38 |
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
|
| 41 |
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
|
|
| 42 |
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
|
| 43 |
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
|
| 44 |
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
|
| 45 |
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
|
| 46 |
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
|
| 47 |
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
|
| 48 |
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
|
| 49 |
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
|
| 51 |
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
|
|
| 52 |
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
|
| 53 |
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
|
| 54 |
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
|
| 55 |
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
|
| 56 |
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
|
| 57 |
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
|
| 58 |
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
|
| 61 |
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
|
|
| 62 |
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
|
| 63 |
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
|
| 64 |
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
|
| 65 |
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
|
| 66 |
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
|
| 67 |
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
|
| 68 |
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
|
| 69 |
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
|
| 71 |
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
|
|
| 72 |
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
|
| 73 |
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
|
| 74 |
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
|
| 75 |
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
|
|
| 76 |
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
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
|
| 30 |
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
| 50 |
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
| 70 |
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
Continuous
|
| Sum |
mary (Example)
| Example of Descriptive Statistics for a Continuous Variable on Miles Driven to Work |
|
|
| Statistical Test |
Statistic
What it Measures |
Explanation |
|
|
| Mean |
55.9342 |
Average Miles Driven |
Participants drove an average number of 55.9 miles to work. |
|
|
| Median |
30
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 |
maximum miles is pulling the mean to the right, making the mean artificially higher).
Mode 20
| 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 |
1
Minimum value in the range |
|
|
| Self-explanatory |
|
|
| Maximum |
290
Maximum value in the range |
Self-explanatory
|
|
| 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 |
Sum
| 4251 |
The total number of Miles reported |
Self-explanatory
|
|
| Count |
76
This is the N – The number of surveys completed (or records entered) |
Self-explanatory
|
|
| Summary Interpretation |
of the Variable Miles
| 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
| Years as RN Descriptive Statistics |
Statistical Test Statistic Summary Interpretation
Mean
Median
Mode
Standard Deviation
Minimum
Maximum
Range
Sum
Count
Summary_Tot_Scores
| Barriers Total Scores Descriptive Statistics |
Statistical Test Statistic Summary Interpretation
Mean
Median
Mode
Standard Deviation
Minimum
Maximum
Range
Sum
Count
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. |
