Can you do it using the faculty evaluation data set to apply skills and do the exercise?
Final project instructions
This project requires that students take the initiative, make decisions, and question themselves, revising their ideas, adding to their ideas. Students are encouraged to consulting with others – reaching out to people that can help, as well as using Google, librarians, your other professors, this course’s professor, your colleagues sitting next to you.
Requirements:
You can use
more
than one
projects/worksheets/workbooks if you desire.
1. One Word document with a memo introducing and explaining your project (see requirements for that below).
a. See full requirements below.
b. Provide a clear, practical purpose and decision to be made using evidence provided by the outcome of the analysis, articulated in the memo and in the first documentation worksheet on the spreadsheet.
2. One spreadsheet with a particular name and structure (see below) containing the following work
a. Use at least TWO the following data analysis skills using the appropriate numbers: Count vs. sum vs. average vs. percent of column vs. percent of overall.
i. Compare categories – percent differences – A, vs. B, vs. C
ii. Look at trends – increasing, decreasing, consistency, variance
iii. Do an analysis using measures of central tendency: Mean or median. Compare the mean/median between different categories.
1. CALCULATE PROPORTIONS WHEN DOING COMPARISONS: (A-B)/Average(A,B)
iv. Do an analysis using measures of distribution: Discuss the shape of the distribution, the skew, as compared to a
normal distribution
. Use one of the following techniques:
1. You can also use a histogram or a column chart grouping values in a pivot table.
2. 5 number summary/box and whiskers chart.
v. Do an analysis with variance, perhaps comparing variance among various categories.
1. Option 1: Use 5 number summary (min, 1st quartile, median, 3rd quartile, max) or Box and Whiskers chart
2. Option 2: Look at the standard deviation. Relate the standard deviation to the mean (divide the SD by the Mean for the ratio, a high percentage means a lot of variance). Is there a lot of variance in relation to the mean or a little? What might that mean?
3. CALCULATE PROPORTIONS WHEN DOING COMPARISONS:
(A-B)/Average(A,B)
b.
You are required to use at least 20 skills. These skills are listed in worksheet in the DataForMidtermSp20.xlsx file, which also has a lot of data that might be used for analysis.
i. Provide a comment in a cell near where you carried out each skill with what skill was used and briefly why.
ii. You are required to do full formatting, whether they are skills in modules 5 or 8 or not.
iii. Do NOT include among the 20 skills anything covered in modules 1-4, not 5 and 8.
c. You must include at least one chart and one table, with full formatting as we learned throughout this semester. The chart you choose must be optimal for the purpose, i.e., you should read the document on how to choose a chart posted with the assignment.
d. You can use examples of other projects but you must copy NOTHING directly. If you want to use a template or an idea from an existing worksheet, you must re-create anything you want to emulate MANUALLY in your worksheet. The instructor has ways of figuring out if you cut/paste or if you use another worksheet other than that which you create. DON’T DO IT.
3. You will be evaluated not only on whether these skills are used, but the integrity of your project. That means your project and the skills you use must make practical sense. Skills used arbitrarily and randomly for no apparent reason will not receive credit.
There is a Canvas survey that is required that will bring you through the logical steps in creating this project.
Step by step instructions:
1. Excel file submitted
a. Create a new Excel file.
b. Save it as “LastNameFirstNameFinalProject”
c. In the first worksheet, put your name and explain the purpose of your spreadsheet – who might want to use it and for what purpose. This will be very similar to what you write in your second paragraph in your email/memo (see below). Then list the major elements of your solution – what the reader will see in the following worksheets. Walk them through it, so they understand what they are looking at and the purpose, step by step. Create the following table:
Worksheet
Table/Chart
Purpose
Example
Worksheet label A
Table “title of table 1”
Identify the highest rated x, y or z
“xxxx” is the highest rated
Table “title of table 2”
Compare the average of x, y or z
“xxxx” is the highest averages
Worksheet label B
Chart “title of chart”
Line chart showing trends in xxx
“xxx” is trending up recently
d. In the next and following worksheets, provide your solution.
e. Each worksheet should have a meaningful label.
f. Each worksheet should replicate that label in row 1, formatted in Title cell format.
2. Word file submitted.
a. Write the text of an email explaining what you have done to someone who might be interest. Note that you are attaching the spreadsheet for them to look at.
b. Three paragraphs:
i. In the first paragraph, provide the reasons for why you chose your project(s), and why someone else might be interested.
1. Explain the added value of your spreadsheet – what information does it provide that would not otherwise be had.
2. Exactly what decision does the reader want to make, and how is this analysis going to be worth looking at, i.e., what is the added value of your project? Why would they regret not having this information?
ii. In the second paragraph, broadly explain the basic logic of your analysis, process of your use of Excel.
1. Here, you are providing an
overview of all major objects and their purpose
. “This spreadsheet provides tools a, b, and c. Tool A provides the ability to analyze x…”
2. Then,
provide a step by step walk through with an example
. For example, “If you were interested in (a particular thing), then you would look at the table or chart in this particular way, providing that information.”
iii. In the final paragraph, note that you are willing to meet about this Excel project or answer any questions.
c. Use full professional formatting
i. Meaningful subject heading
ii. Salutation
iii. Closing
iv. Single spaced, block paragraphs (space between paragraphs, NOT INDENTED).
d. Use business style:
i. Proper grammar and spelling.
ii. You are not writing in street language to a friend, but formal language to a business colleague.
1. Use a thesaurus and a dictionary – do not use vague or imprecise terms.
iii. Neutral, 3rd person tone as much as possible – avoid first or second person language, e.g., “I”, and “you”.
iv. Logical hierarchy of writing:
1. First phrase of every sentence is main point; first sentence in the paragraph is the main point.
2. First sentence in every paragraph introduces and summarizes the entire paragraph.
v. Generally, use short, precise, clearly interrelated sentences.
vi. Do not submit a first draft – rewrite at least 3 times.
For example:
Poor writing: “I’ve noticed that you were looking for places to live that would best fit your salary. Please look at the attached file in this email that may help you.”
Problems:
· Contractions are too informal.
· Please re-write minimizing first and second person language. Business communication is neutral, 3rd person, impersonal as much as possible.
· Reflect on how specific and clear you are. What does “best fit for your salary” mean? What is a “fit” for a salary? Use a dictionary and thesaurus to find more specific terms.
Better:
“This email is written regarding your interest in choosing a place to live after graduation. To that end, I provide attached an Excel file that provides critical information for that decision. The Excel analysis considers two major concerns: The salary you can expect different areas and the cost of living in that area, in terms of housing and the cost of living index.”
Example of formatting: Obviously, you would not be sending a real email with an attachment! You are submitting a Word document in this (email) format.
Ideas on projects
1. Think of a way to use Excel, or multiple ways of using Excel.
a. Data will be posted to the assignment as well as a list of sources of data.
b. Google – ‘data analytics’, ‘data analysis’ for some ideas.
c. Go through the end of chapter review projects in the textbook.
d. Google your interests and see how people are thinking about these ideas.
e. Gather data from the internet about a topic you are curious about and answer a question. Examples: Data on movies, companies, celebrities, books, albums, works of art, artists, TV shows, cable channels, homes, cars, computers, phones, countries, states, cities, foods, wines, universities, professions, hospitals, airports, airplanes, restaurants…
f. Call people you know, or simply call a professional that might be familiar with your idea and ask them for guidance -most people are willing to help.
g. Ask people who run an organization or business and ask how you can help them solve a problem with Excel.
2. Think through what that might look like in Excel – what information you need, what form it would take in Excel, what functions or skills you might need to use.
3. Find the information you need, and experiment with the best way of utilizing that information, presenting that information in order to address your issue.
4. Consult with colleagues in this class.
5. Finally, CONSULT WITH THE INSTRUCTOR.
Final project
Final project
Data analysis (today’s lecture)
20 Excel skills (same as before)
Memo (same as before)
Appropriate numbers
Count, vs. sum, vs. average, vs. percent of…
Is this unfair?
There are four women top executives and 10 men
If there are only 4 women in the company?
If there are 92% women in the company?
What would be a more reasonable number? What else would you need to know?
Proportion: Percent of column total (number of execs/total number)
4 female executives out of a total of 4 female employees = %100
10 male executives out of 203 male employees = 5%
53 female and 67 male – leads to distortion using count
Appropriate charts
Picking the right type of chart (read the PDF provided in the assignment
Example: Pie chart is used to compare parts in relation to each other in terms of a whole. It is not effective for comparing two different things. In that case a column chart would be best.
Are women in this organization discriminated against?
This scale provides a more useful perspective
An alternative explanation to ‘discrimination: Are salary differences due to age, i.e., are women in this organization younger?
Are salary differences due to years of experience worked?
Are there systematic differences within level in the organization? Are differences in average salary due to more men at higher positions?
Do not mix separate analyses together, apples and oranges
Comparing data on different scales – examples:
Comparing GPA and working hours of male vs. female students in the same table
One possible solution: Use percent of column total
Poor use of chart: Comparing data with different scales
Poor use of chart: Comparing far too many variables
Female Montclair only Transfer Montclair only Transfer Montclair only Transfer Montclair only Transfer Freshman Junior Senior Sophmore 4 51 93 4 24 46 10 Male Montclair only Transfer Montclair only Transfer Montclair only Transfer Montclair only Transfer Freshman Junior Senior Sophmore 8 1 69 120 32 30 20 Other Montclair only Transfer Montclair only Transfer Montclair only Transfer Montclair only Transfer Freshman Junior Senior Sophmore 3
Review: Proportions
Average salary of men/women: $56,789 vs. $59,992
Difference $3,201
M/F – What is M in relation to F?
“M average salary is 95% of F”
F/M – What is F in relation to M?
“F average salary is 106% of Male” or “..6% more than male”
Percent difference (M-F)/Average(M,F) is 6%
Measures of central tendency
Mean (average)
Median (pick middle number)
Better if there are outliers, extreme numbers, skew in the data
Average salary of those in this room gets skewed if Bezos walks in… pick the median to get a better measure of the group
Example – Income in a group can vary widely, so median is frequently used
Why do we care about mean/median?
What are some common ways we think about the world using that statistic?
GPA, Earned Run Average, Batting Average
Provides information about a set or group – how they tend as a whole
What can you do with it?
The difference in GPA (numeric variable) by category
Male/Female (categorical variable
Hours worked: Average GPA by hours worked
Chunking a numeric variable to use it as a categorical
Chunks of hours worked: 0-9, 10-19, 20-29, 30-39, 40+
Pivot table – use the Grouping function on the ribbon
Alternative: Scatterplot
Measures of variance
Range, min, max
Standard deviation (from the mean)
How scattered the data is around the mean
Higher numbers mean more variance
Whether variance is high or low: SD/Mean
Higher percent means higher variance
Why do we care about variance?
Compare categories by variance
Men have more variance, and greater range of salaries – what does that mean?
Distribution
How data falls
Normal distribution as benchmark: Natural distribution given no bias, i.e., something skewing the data
Analysis of distribution: Box and Whiskers chart, 5 number summary: Min, 1st quartile, median, 3rd quartile, Max
PEWRESEARCH.ORG
Excellent source of data analysis examples, tables, charts
Excellent example of how to write with and about data analysis
Source of data: I have already provided some of their data for you to analyze yourself.
Question: Did Mayor Giuliani’s policies lead to decline in crime in NYC?
Question: Who are our students and how can we help them? What should we focus on? Are there many students living in poverty? How many students are living in poverty? Is that increasing or decreasing? Is that related to whether they are still with their parents?
Young college grads are complaining about their financial situation – is it true that they have a greater debt? What is that tend like? Does it look like there is a problem that needs to be addressed?
Has anything changed with immigration given the recent news of families being separated? Or is that simply a Trump policy?
Salary
demanded
26,041$
26,459$
26,900$
27,116$
27,382$
27,736$
27,999$
28,670$
28,961$
29,089$
30,647$
30,863$
31,695$
31,903$
32,906$
34,084$
34,232$
34,508$
34,619$
35,019$
ListOfSkillsForFinalProject
List of skills for final project |
Chapter 5 |
5-3Freezing Rows and Columns |
5-4Creating an Excel Table |
5-4aRenaming an Excel Table |
5-4bModifying an Excel Table |
5-5Maintaining Data in an Excel Table |
5-5aAdding Records |
5-5bFinding and Editing Records |
5-5cDeleting a Record |
5-6Sorting Data |
5-6aSorting One Column Using the Sort Buttons |
5-6bSorting Multiple Columns Using the Sort Dialog Box |
5-6cSorting Using a Custom List |
5-8Session 5.2 Visual Overview: Filtering Table Data |
5-9Filtering Data |
5-9aFiltering Using One Column |
5-9bFiltering Using Multiple Columns |
5-9cClearing Filters |
5-9dSelecting Multiple Filter Items |
5-9eCreating Criteria Filters to Specify More Complex Criteria |
5-9fCreating a Slicer to Filter Data in an Excel Table |
5-10Using the Total Row to Calculate Summary Statistics |
5-11Splitting the Worksheet Window into Panes |
5-12Inserting Subtotals |
5-12aUsing the Subtotal Outline View |
5-14Session 5.3 Visual Overview: PivotTable and PivotChart |
5-15Analyzing Data with PivotTables |
5-16Creating a PivotTable |
5-16aAdding Fields to a PivotTable |
5-16bChanging the Layout of a PivotTable |
5-16cFormatting a PivotTable |
5-17Filtering a PivotTable |
5-17aAdding a Field to the FILTERS Area |
5-17bFiltering PivotTable Fields |
5-17cCreating a Slicer to Filter a PivotTable |
5-18Refreshing a PivotTable |
5-19Creating a Recommended PivotTable |
5-20Creating a PivotChart |
Chapter 7 |
7-3Naming Cells and Ranges |
7-3aUsing the Name Box to Create Defined Names |
7-3bSelecting Cells and Ranges by Their Defined Names |
7-3cCreating Defined Names by Selection |
7-3dEditing and Deleting Defined Names |
7-4Using Defined Names in Formulas |
7-4aEntering Formulas with Defined Names |
7-4bAdding Defined Names to Existing Formulas |
Chapter 8 |
8-1Session 8.1 Visual Overview: Logical Functions |
8-2Working with Logical Functions |
8-2aInserting Calculated Columns in an Excel Table |
8-2bUsing the IF Function |
8-2cUsing the AND Function |
8-2dUsing the OR Function |
8-3Using Structured References to Create Formulas in Excel Tables |
8-5Session 8.2 Visual Overview: Lookup Tables and the IFERROR Function |
8-6Creating Nested IFs |
8-7Using LOOKUP Functions |
8-7aUsing the VLOOKUP Function to Find an Exact Match |
8-7bUsing the VLOOKUP Function to Find an Approximate Match |
8-7cUsing the HLOOKUP Function to Find an Exact Match |
8-8Using the IFERROR Function |
8-10Session 8.3 Visual Overview: Conditional Formatting and Functions |
8-11Applying Conditional Formatting |
8-11aHighlighting Duplicate Values |
8-11bUsing the Conditional Formatting Rules Manager |
8-12Using Functions to Summarize Data Conditionally |
8-12aUsing the COUNTIF Function |
8-12bUsing the SUMIF Function |
8-12cUsing the AVERAGEIF Function |
>Variable INFO
82 faculty members’ evaluations conducted by students at two Texas universities.
(excellent)
1 (poor) to 5 (excellent) = , )
, )
, )
of Faculty
, )
, )
1 0.5 .2
0.5 1 0.5 2.3 1 2 5
2 0.5 5 2 2.3 1 2.3 2.5 0.5 0.5 0.5 5 0.5 age School 0
4.3 1 Texas Austin Not Tenured Minority Female English Upper 0
4.5 1 Texas Austin Tenured Not Minority Male English Upper 3.7 1 Texas Austin Tenured Not Minority Male English Upper 7
4.3 1 Texas Austin Tenured Not Minority Female English Upper 1 Texas Austin Not Tenured Not Minority Female English Upper 4.2 1 Texas Austin Tenured Not Minority Male English Upper 1 Texas Austin Not Tenured Not Minority Female English Upper 51 3.4 1 Texas Austin Tenured Not Minority Female English Upper 4.3 1 Texas Austin Not Tenured Not Minority Female English Upper 1 Texas Austin Not Tenured Not Minority Male English Upper 3.1 1 Texas Austin Not Tenured Minority Male Non-English Upper 4.0 1 Texas Austin Not Tenured Not Minority Male English Upper 4.83 1 Texas Austin Tenured Not Minority Male English Upper 4.00 3.4 1 Texas Austin Tenured Not Minority Male Non-English Upper 37 5.50 2.9 1 Texas Austin Not Tenured Not Minority Female English Upper 4.17 4.5 1 Texas Austin Tenured Not Minority Male English Upper 4.0 1 Texas Austin Not Tenured Not Minority Female English Upper 4.33 3.8 1 Texas Austin Not Tenured Not Minority Male English Lower 4.33 4.3 1 Texas Austin Tenured Not Minority Female English Upper 4.33 3.4 1 Texas Austin Not Tenured Not Minority Female English Upper 4.83 3.4 1 Texas Austin Not Tenured Not Minority Female English Upper 3
1 Texas Austin Not Tenured Minority Female Non-English Upper 4.3 1 Texas Austin Tenured Not Minority Male English Upper 4.17 4.4 1 Texas Austin Tenured Not Minority Male English Upper 4.6 1 Texas Austin Not Tenured Not Minority Female English Upper 4.2 1 Texas Austin Tenured Not Minority Male English Upper 3.00 3.3 1 Texas Austin Tenured Not Minority Male English Upper 3.00 4.4 1 Texas Austin Tenured Not Minority Male English Upper 2.3 1 Texas Austin Not Tenured Not Minority Female English Upper 4.33 3.5 1 Texas Austin Not Tenured Not Minority Male English Upper 7
4.7 1 Texas Austin Tenured Not Minority Male English Upper 5.50 3.9 1 Texas Austin Tenured Not Minority Male English Lower 4.3 1 Texas Austin Tenured Not Minority Male English Upper 3.8 1 Texas Austin Tenured Minority Female English Upper 4.6 1 Texas Austin Tenured Not Minority Male English Upper 3.5 1 Texas Austin Not Tenured Not Minority Male English Upper 3.4 1 Texas Austin Not Tenured Not Minority Female English Lower 4.83 4.5 1 Texas Austin Tenured Not Minority Male English Lower 4.7 1 Texas Austin Not Tenured Not Minority Male English Upper 3.5 1 Texas Austin Tenured Not Minority Female English Upper 4.0 1 Texas Austin Not Tenured Not Minority Female English Lower 4.4 1 Texas Austin Not Tenured Not Minority Female English Upper 4.2 1 Texas Austin Tenured Not Minority Male English Upper 6.50 4.5 1 Texas Austin Tenured Not Minority Female English Upper 3.8 1 Texas Austin Not Tenured Not Minority Female English Lower 3.1 1 Texas Austin Not Tenured Not Minority Male English Upper 3.67 3.7 1 Texas Austin Not Tenured Not Minority Male English Lower 4.0 1 Texas Austin Tenured Not Minority Female English Lower 62 3.1 1 Texas Austin Tenured Not Minority Female English Upper 3.7 1 Texas Austin Tenured Not Minority Female English Upper 4.2 1 Texas Austin Tenured Not Minority Male English Upper 60 1.67 1 Texas Austin Tenured Not Minority Male English Upper 4.0 1 Texas Austin Tenured Not Minority Female English Upper 3.5 1 Texas Austin Tenured Not Minority Male English Lower 3.8 1 Texas Austin Tenured Minority Female English Lower 3.3 1 Texas Austin Tenured Not Minority Male English Lower 4.0 1 Texas Austin Tenured Not Minority Female English Lower 3.3 1 Texas Austin Not Tenured Minority Female Non-English Upper 4.17 4.1 0 Other Tenured Not Minority Male English Upper 2.67 4.9 0 Other Tenured Not Minority Male English Upper 3.17 3.4 0 Other Tenured Minority Male English Upper 4.33 3.0 0 Other Tenured Not Minority Male Non-English Upper 2.33 4.3 0 Other Tenured Not Minority Male English Upper 0 Other Tenured Not Minority Female English Upper 2.33 3.7 0 Other Tenured Not Minority Female English Upper 3.8 0 Other Not Tenured Minority Female English Lower
2
A random sample of
1
by students at Texas universities.
Source:
Variables
Faculty Evaluation
1 (poor) to
5
Age of Faculty
in years
Fac. Attractiveness Avg.
1 (low) to 9 (high)
Course Evaluation
School
1=
Texas Austin
0
Other
Rank of Faculty
(
Tenured
Not Tenured
Ethnicity of Faculty
(
Minority
Not Minority
Gender of Faculty
(
Female
Male
First Langu
age
(
English
Non-English
Course Level
(
Lower
Upper
ForBoxPlot
2.
3
0.5
2.3
2.3
1.5
3.7
3.7 1
3.7 1.5
4
4.2
4.2 1.5
4.5
4.5 1
4.5 1.5
5 0.5
5 1
5 1.5
5 1
3.7 0.5
4.5 0.5
3.7 1.5
4.5 1.5
ForBoxPlot2
2.3 0.5 2.3 2
2.3 1 2.3
2.5
2.3 1.5 2.3 3
3.7 0.5
3.6
3.7 1 3.6 2.5
3.7 1.5 3.6 3
4.2 0.5
4.1
4.2 1 4.15 2.5
4.2 1.5 4.15 3
4.5 0.5 4.5 2
4.5 1 4.5 2.5
4.5 1.5 4.5 3
4.9
4.9 1 5 2.5
4.9 1.5 5 3
4.9 1 5 2.5
3.7 0.5 3.6 2
4.5 0.5 4.5 2
3.7 1.5 3.6 3
4.5 1.5 4.5 3
ForBoxPlot3
2.1
2.1 1
2.1 1.5
3.5
3.5 1
3.5 1.5
4 0.5
4 1
4 1.5
4.3
4.3 1
4.3 1.5
5 1
5 1.5
2.1 1
5 1
3.5 0.5
4.3 0.5
3.5 1.5
4.3 1.5
Fac Eval DATA
fac_eval
attr_avg
course_eval
SchoolCODE
rank
ethnicity
gender
language
course level
4.7
36
5.0
4.6
59
3.0
4.1
51
3.
33
4.5
40
3.1
4.8
31
7.33
4.4
4.4
62
5.
50
4.4 33
4.17
4.0
3.4
4.00
4.5 33
4.
67
4.0
47
5.50
3.9
3.6
35
4.83
4.1
37
4.33
4.1
42
3.8
3.5
49
2.9
4.6
45
4.4
56
2.50
4.0
48
4.9
46
3.5
57
3.7
52
3.4
29
2.8
3.3
4.3 62
3.00
4.5
64
4.8
34
7.83
4.4
58
3.83
4.4 52 4.83 4.0 1 Texas Austin Tenured Minority Male Non-English Upper
3.6
73
4.6
70
2.3
41
5.17
4.3
63
4.9 47
2.6
4.2
39
4.2 47 4.33 4.0 1 Texas Austin Not Tenured Minority Female English Lower
4.3
54
2.33
3.9
44
6.50
3.9 47 2.33 3.8 1 Texas Austin Tenured Minority Male English Upper
4.1 62 3.00 4.0 1 Texas Austin Tenured Not Minority Male English Upper
4.8
60
3.67
3.3 37
6.17
4.4 42 4.00 4.7 1 Texas Austin Not Tenured Not Minority Male English Upper
4.3 35 4.83 4.2 1 Texas Austin Tenured Not Minority Male English Upper
3.3 39
8.17
4.0 49 6.50 3.9 1 Texas Austin Tenured Not Minority Male English Upper
4.5
61
4.9 33
7.00
3.7 58
4.67
3.9 56 3.83 3.7 1 Texas Austin Tenured Not Minority Female English Upper
4.4 50
3.17
4.4 52 3.17 4.1 1 Texas Austin Tenured Not Minority Male English Upper
4.5 33
5.83
4.3 57
5.67
4.8
38
4.1 34
1.67
3.5 34
6.67
4.0
32
3.8 42 6.17 3.9 1 Texas Austin Tenured Not Minority Female English Lower
4.1
43
3.33
3.7 35 3.67 3.3 1 Texas Austin Not Tenured Not Minority Male Non-English Upper
3.2
3.50
4.2 42 2.67 4.0 1 Texas Austin Tenured Not Minority Male English Upper
4.5 39 5.67 4.3 1 Texas Austin Tenured Not Minority Male English Upper
3.8 52
6.00
3.7 52 6.50 3.4 1 Texas Austin Tenured Not Minority Female English Upper
4.5 52 2.33 4.3 1 Texas Austin Not Tenured Not Minority Female English Upper
3.8 64 2.33 3.7 1 Texas Austin Tenured Not Minority Male English Upper
4.5 50
7.17
2.4
2.2
3.0 51 5.17 3.0 1 Texas Austin Tenured Not Minority Female English Upper
4.5 43 3.50 4.3 1 Texas Austin Not Tenured Not Minority Male English Lower
4.6 50 3.33 4.6 1 Texas Austin Not Tenured Minority Male English Lower
3.5 52 5.83 3.5 1 Texas Austin Tenured Not Minority Male English Upper
4.8 51 6.17 4.6 1 Texas Austin Tenured Not Minority Male English Lower
4.2 38 3.33 3.7 1 Texas Austin Tenured Not Minority Male English Lower
4.0 47 5.17 3.8 1 Texas Austin Tenured Not Minority Female English Lower
3.7 43 4.17 3.6 1 Texas Austin Tenured Minority Female English Lower
4.5 38 2.50 4.4 1 Texas Austin Not Tenured Not Minority Female English Lower
4.7 43 4.33 4.1 1 Texas Austin Tenured Not Minority Male English Lower
4.3 57 3.00 4.3 1 Texas Austin Tenured Not Minority Male English Upper
4.0 51
6.33
4.8 45 3.33 4.8 1 Texas Austin Not Tenured Not Minority Male English Lower
3.5 57
2.83
3.3 47 6.67 3.6 1 Texas Austin Not Tenured Not Minority Female English Lower
4.2 54
6.83
4.8 58 7.83 4.7 1 Texas Austin Not Tenured Not Minority Male English Lower
4.9 42 7.83 4.9 1 Texas Austin Tenured Not Minority Male English Lower
4.5 33 5.83 4.3 1 Texas Austin Not Tenured Not Minority Male English Lower
3.3 62
2.00
3.3 35 7.83 3.4 1 Texas Austin Not Tenured Minority Female English Lower
3.6 61 3.33 3.6 1 Texas Austin Tenured Not Minority Male English Lower
4.1 52
4.50
3.7 60 4.33 3.9 1 Texas Austin Not Tenured Not Minority Female Non-English Upper
4.5 32 6.83 4.3 1 Texas Austin Not Tenured Not Minority Male English Lower
3.5 42
5.33
4.8 38 4.67 4.4 0 Other Not Tenured Minority Female English Upper
2.8 59 3.50 2.1 0 Other Tenured Not Minority Male English Upper
3.4 57 3.33 3.2 0 Other Tenured Not Minority Male Non-English Upper
4.5 43 3.17 4.2 0 Other Tenured Not Minority Female English Upper
4.5 35 5.67 4.4 0 Other Not Tenured Minority Female English Upper
4.5 62 5.50 4.3 0 Other Tenured Not Minority Male English Upper
3.5 35 4.17 3.3 0 Other Not Tenured Not Minority Female English Upper
4.5 50 4.00 4.5 0 Other Tenured Not Minority Female English Upper
4.5 37 4.67 4.3 0 Other Not Tenured Not Minority Female English Upper
5.0 48 5.33 5.0 0 Other Tenured Not Minority Male English Lower
4.3 37 4.83 4.0 0 Other Not Tenured Minority Male Non-English Upper
3.5 39 4.33 3.5 0 Other Tenured Not Minority Male English Upper
3.8 44 4.83 3.8 0 Other Tenured Minority Male English Upper
3.6 52 3.00 3.6 0 Other Tenured Not Minority Female Non-English Upper
3.2 38 6.00 3.1 0 Other Not Tenured Not Minority Female English Upper
4.6 46 4.17 4.3 0 Other Tenured Not Minority Male English Lower
4.3 58 2.67 4.2 0 Other Tenured Not Minority Female English Upper
4.4 50 4.33 4.2 0 Other Tenured Not Minority Male English Lower
4.5 48 4.00 4.0 0 Other Tenured Not Minority Female English Lower
3.4 56 4.33 3.2 0 Other Tenured Minority Female English Upper
4.4 54 4.00 4.2 0 Other Tenured Not Minority Female English Upper
3.4 33 3.00 3.3 0 Other Not Tenured Minority Female Non-English Upper
3.6 64 3.00 3.4 0 Other Tenured Not Minority Male Non-English Lower
4.3
68
4.1 36 6.17 3.9 0 Other Not Tenured Minority Female English Upper
4.3 60 3.83 4.0 0 Other Tenured Not Minority Male English Lower
4.2 54 4.83 3.9 0 Other Tenured Minority Male Non-English Upper
4.3
75
3.6
69
2.3 43 3.67 2.3 0 Other Not Tenured Minority Female English Upper
3.4
66
3.2 49 2.67 2.8 0 Other Tenured Not Minority Male English Upper
4.4 41 6.00 4.4 0 Other Tenured Not Minority Male English Upper
4.2 49 4.33 4.0 0 Other Not Tenured Minority Female English Lower
4.6
55
4.0 46 5.83 3.7 0 Other Tenured Minority Female English Upper
3.3 49 2.33 3.3 0 Other Tenured Minority Male English Upper
4.7 62 3.67 4.3 0 Other Tenured Not Minority Male Non-English Lower
3.3 39 6.33 3.5 0 Other Not Tenured Not Minority Male English Upper
4.7 44 4.33 4.6 0 Other Not Tenured Minority Male English Upper
3.7 60 4.67 3.5 0 Other Tenured Minority Female English Upper
3.2 57 3.83 3.1 0 Other Tenured Not Minority Female English Upper
4.4 52 3.17 4.0 0 Other Tenured Not Minority Female English Upper
3.8 54 3.17 4.0 0 Other Tenured Not Minority Male English Upper
4.6 35 5.83 4.5 0 Other Not Tenured Not Minority Female English Upper
4.2 59 5.67 4.1 0 Other Tenured Not Minority Male English Upper
4.7 39 6.83 4.5 0 Other Tenured Not Minority Female English Upper
4.1 36 1.67 3.9 0 Other Not Tenured Not Minority Female English Upper
3.6 36 6.67 3.0 0 Other Not Tenured Not Minority Male English Lower
4.7 34 3.83 4.5 0 Other Not Tenured Not Minority Male English Upper
4.1 44 6.00 3.9 0 Other Tenured Not Minority Female English Upper
4.6 45 3.33 4.2 0 Other Tenured Not Minority Female English Upper
3.6 37 3.67 3.4 0 Other Not Tenured Not Minority Male Non-English Lower
2.9 61 3.50
2.7
4.2 44 3.00 4.0 0 Other Tenured Not Minority Male English Upper
4.5 41 5.67 4.3 0 Other Tenured Not Minority Male English Upper
3.7 54 5.50 3.9 0 Other Tenured Not Minority Female English Upper
3.7 54 5.33 3.6 0 Other Tenured Not Minority Female English Upper
4.0
53
3.9 67 2.50 3.6 0 Other Tenured Not Minority Male English Upper
3.7 52 6.00 3.7 0 Other Tenured Not Minority Male Non-English Upper
2.5 63 2.00 2.6 0 Other Tenured Not Minority Male English Upper
3.0 53 5.17 3.0 0 Other Tenured Minority Female English Upper
4.6 45 3.50 4.6 0 Other Not Tenured Not Minority Male English Lower
4.5 52 3.33 4.5 0 Other Tenured Minority Male English Lower
4.1 54 5.33 4.2 0 Other Tenured Not Minority Male English Upper
4.9 53 5.17 4.7 0 Other Tenured Not Minority Male English Lower
4.4 40 3.33 4.0 0 Other Tenured Not Minority Male English Lower
3.6 49 5.17 3.4 0 Other Tenured Not Minority Female English Lower
2.7 45 4.17 2.8 0 Other Tenured Minority Female English Lower
4.4 40 4.50 4.2 0 Other Tenured Minority Female English Lower
3.7 45 4.33 3.4 0 Other Tenured Not Minority Male English Upper
3.7 58 3.00 3.6 0 Other Tenured Not Minority Male Non-English Upper
4.5 53 5.50 4.6 0 Other Tenured Not Minority Female English Upper
4.8 47 3.33 4.6 0 Other Tenured Not Minority Male English Lower
4.2 59 2.83 4.1 0 Other Tenured Not Minority Male English Lower
4.0 49 4.50 3.9 0 Other Tenured Not Minority Female English Lower
3.8 56 6.83 3.8 0 Other Tenured Minority Female English Lower
4.9 60 4.17 4.9 0 Other Tenured Not Minority Male English Lower
3.9 44 7.33 3.9 0 Other Tenured Not Minority Male English Upper
4.5 35 5.83 4.1 0 Other Not Tenured Not Minority Male English Lower
3.0 67 2.50 3.0 0 Other Tenured Not Minority Male English Lower
3.7 37
7.67
4.3 63 3.33 3.9 0 Other Tenured Not Minority Male English Lower
4.8 54 4.67 4.7 0 Other Tenured Not Minority Female English Lower
4.4 62 4.33 4.0 0 Other Not Tenured Not Minority Female Non-English Upper
4.5 34 7.00 4.3 0 Other Not Tenured Not Minority Male English Upper
4.1 44 5.67 4.1 0 Other Not Tenured Minority Female Non-English Lower