Data Analytics in Accounting: Visualizing Corporate Income Inequality

This project, included in the  Appendix, guides accounting students as they conduct an exploratory analysis of corporate income inequality, a significant global economic issue. Students first clean, merge, and analyze accounting data sets. Then, they use those data sets to identify the economic issue through data visualizations. The data used include the financial statements of U.S. publicly traded S&P 500 companies. The example we present here includes financial statements from 2001 through 2015. Students use MS Excel and Tableau to identify overall income inequality and to drill down to an industry-level analysis of two sectors, manufacturing and retail trade.

AIS education aims to help students understand how information technology can help them collect and analyze accounting data to support management decision-making. Thus, using current business software tools helps accounting students prepare for their careers (Lee, Kerler, and Ivancevich, 2018). The AAA-AICPA Pathways Commission (2015) identified accounting students' top three most-needed technologies: electronic spreadsheets, business intelligence and analytics, and database management.

Accounting Accreditation Standard A5, issued by the Association to Advance Collegiate Schools of Business (AACSB International, 2018), states that data analytics and relevant information technology skills should be included in accounting curricula. The CPA Evolution initiative (Tish & Reeb, 2019) emphasizes the importance of data management and analytics as a core area of knowledge expected in newly licensed professional accountants, with three of its five guiding principles mentioning specific types of technology expertise, including data analytics, robotics and artificial intelligence (Coffey, 2019).

Tschakert et al. (2016) exhort practicing CPAs to increase their use of data analytics tools in auditing, financial planning, and analysis. Murphy and Tysiac (2015) argue that accountants are using data analytics tools and improved information technologies to replace traditional audit sampling with continuous testing of data populations. Ovaska-Few (2017) predicts that accounting graduates with data analytics skills will be in high demand but notes that teaching resources for those skills are limited, leading to concerns that accounting curricula today do not provide needed data analytics skills. Hart (2018) and Zhang (2019) provide teaching resources for data analytics in accounting. Hart (2018) notes that large accounting firms now offer data analytics resources to educators. Zhang (2019) argues the urgency of integrating data analytics tools, such as ACL, Alteryx, Excel, IDEA, PowerBI, and Tableau, into the accounting curriculum.

Income inequality is an income distribution gap between two identifiable groups of people or households (Faello et al., 2018). Furman and Orszag (2015), summarized in Ip (2015), examined firm-level income inequality and found that employees of firms with super-normal returns on capital (ROC) shared in those returns, which increased individual income inequality. Specifically, Ip (2015) described return on capital (ROC) trends for non-financial S&P 500 companies from the 1960s to the 2010s and found the gap in ROC between the 90th and 25th percentiles had been stable from the 1960s through the 1980s, but widened during the 1990s and grew substantially in the first decade of the 2000s.

This section provides an overview of the critical elements of the project. We describe the learning objectives, what knowledge and skills students must have to undertake the project (prerequisites), and discuss issues related to acquiring the data needed. We also note the software licenses required to undertake the project.

Our review of the professional and academic literature on data analytics in accounting and the critical global economic issue of corporate income inequality leads us to set five learning objectives for this project. Upon completion of the project, students should be able to do the following:

1. Process (explore/clean/extract) an existing accounting data set

2. Use appropriate software tools, such as MS Excel and Tableau, to transform accounting data into useful information

3. Perform drill-down analysis to find more detailed information

4. Present outputs using visualization software

5. Describe the advantages and disadvantages of using various analytics software, such as MS Excel and Tableau

This case requires that students know how to read financial statements and perform fundamental ratio analysis. Most business students will learn these skills in an introductory financial accounting course. Accounting students will often have this knowledge reinforced in subsequent intermediate financial accounting and auditing courses.

Students will also need basic knowledge of MS Excel, how its basic and intermediate functions (such as VLOOKUP, COUNTIF, and LEFT) work, and how to filter data in the software. Business students often learn these skills in an introductory, lower-division, electronic spreadsheets course.

Basic Tableau skills (such as converting data types, filtering data, creating hierarchy measures, creating calculated fields, and creating various charts) are also prerequisites for undertaking the project. Although many business schools offer training in these skills, most do not. We used the Tableau (2018) training site, which provides free tutorial videos for the skills needed to complete the project.

We obtained annual financial statements for U.S. publicly traded companies from Compustat, which we accessed through the Wharton Research Data Services (WRDS) interface and downloaded to MS Excel. For adopters at schools without WRDS access, an alternative free source of the most recent five years of historical financial statements is Morningstar.com. Adopters can work with their institution's business librarian to identify other sources of recent financial statements.

Microsoft Excel is widely available to university students at low or no cost through school licensing programs or the Microsoft Education (2022) Web site. Tableau (2022) provides free, limited-term academic student licenses. We created this project using the MS Excel 2016 and Tableau 2019.2 versions.

We have used the project in undergraduate accounting information systems courses and graduate management information systems and supply chain management courses over two years. These courses have a data analytics component and include using MS Excel and Tableau software. To learn more about the project's efficacy, we surveyed participating students in compliance with the rules of the relevant institutional review board. We first asked students to estimate their pre-project MS Excel and Tableau skill levels and rate themselves on a five-point scale (1, no experience; 2, beginner; 3, intermediate; 4, advanced; 5, expert). We report their responses in Table 1.

Students reported having more expertise in MS Excel than Tableau, which is what we expected. Most of the students had never used Tableau before.

We also asked participating students to rate four statements about their experience using the software and their reaction to doing the project. We asked them to use a five-point scale (1, not at all; 2, slightly; 3, somewhat; 4, very; 5, extremely) to indicate their level of agreement with the statements, as summarized in Table 2.

The overall perceptions about this case project (that it was interesting, helpful, and a positive learning experience) were encouraging. Responses from graduate students were higher than those from undergraduate students, except for their perceptions of the degree of difficulty. Because we created this case to demonstrate the use of software tools in data analytics rather than to compare one tool to another, we did not compare student performance with a pre-test and post-test.

Course grades (of which the project grade was ten percent) for the 77 students who completed the survey included 25 As, 46 Bs, and 6 Cs. Student assessment of a “positive learning experience” has a statistically significant relationship (correlation coefficient of 0.265, p = 0.02) with students' final grades when we compare students earning As with those earning other grades (Bs or Cs).

Lawson (2019) finds that managers increasingly rely on accounting teams to provide strategic data analyses; thus, a shortage of accountants with those skills is an obstacle to getting good strategy support. Therefore, data analytics education in the accounting curriculum is critical. This project helps students use analytics software tools (MS Excel and Tableau) to extract, clean, and analyze useful financial statement data information.

A Wall Street Journal (WSJ) article related to global economic issues, “Behind Rising Inequality: More Unequal Companies,” was published on November 4, 2015. The author compared trends in return on capital (ROC) of non-financial companies in the S&P 500 since the 1960s to explore possible corporate income inequality, defined as the growing gap between the most- and least-profitable companies. The results indicate a gap, between the ROC of companies in the top 90% and the ROC of the rest of the S&P 500 companies. The gap started to surge in the 1990s and continues to grow substantially into the 21st century.

Further analysis of this data by Furman and Orszag (2018) suggests that productivity growth has been uneven among economic sectors. For example, the ROC of the top 10% of publicly traded non-financial firms was 20% in the 1980s and 100% or more in the 2010s, indicating that the ROC of the top 10% companies has risen roughly five times over the past three decades. Furman and Orszag (2018) identified two industry sectors, information technology and health care, as contributing the most to income inequality.

In this project, you will analyze the annual financial statements for U.S. publicly traded companies from 2001 through 2015. As the WSJ article suggested, the data set excludes the financial industry, using Standard Industrial Classification (SIC) codes between 6000 and 6799, because the ROC data in the financial industry is considerably scarce. There are two worksheets in the student data Excel file: ‘Data' and ‘S&P500'. The ‘Data' worksheet has financial statement data from 2001 through 2015 and the ‘S&P500' worksheet has ticker symbols (column A) and company names (column B), which belong to S&P 500 index. The S&P 500 index is a stock market index, where the 505 common stocks are issued by the 500 largest capitalized companies listed in the U.S. stock markets, such as the NYSE and NASDAQ. The number of records in the ‘Data' worksheet is 108,156, including header information, and that of ‘S&P500' is 505, without header information. Table 1 shows 12 data items collected from the annual financial statements.

Part 1. Understanding the issues about income inequality

Please read the article, Behind rising inequality: More unequal companies, published on November 4, 2015 (https://www.wsj.com/articles/behind-rising-inequality-more-unequal-companies-1446665769). If that article is not available, the following are alternatives:

Orszag, P. (2015). People are not unequal; companies are. https://www.brookings.edu/opinions/people-are-not-unequal-companies-are/

Frick, W. (2016). Corporate inequality is the defining fact of business today. https://hbr.org/2016/05/corporate-inequality-is-the-defining-fact-of-business-today

Part 2. Processing the data set with Excel

Your instructor will provide the source data set (Income-Inequality-Student-Data.xlsx), which includes two worksheets of ‘data' and ‘S&P500'. In this part, students learn the data processing technique from raw data to organized data, such as data verification, data cleaning, merging & sorting, to readying for a further data analysis. Figure 1 & 2 show the first several lines of data in each worksheet. In addition to the two worksheets, there are several blank worksheets with tab names of ‘Count', ‘Data-S&P500', ‘S&P500-Only', ‘S&P500-Not-Null', ‘S&P-500-Sector' and ‘Sector' (Figure 3) At the end of this project, students need to add three additional worksheets with tab names of ‘ROC-Export', ‘ROC-Sector-Export' and ‘Memo'. Table 2 explains each Excel worksheet.

Step 1: Summarize the count of records by year

• Q1 How many records in each year from 2001 through 2015 respectively are in the ‘Data' worksheet? Summarize the count of records by year on ‘Count' worksheet.

Once you open the data file (‘income-inequality-student-data.xlsx'), make sure the data set is sorted by FY. Count the number of data records in the ‘Data' worksheet using the ‘countif()' function. Save the output in ‘Count' worksheet (Figure 4).

• Q2 What is the trend of number of data from 2001 through 2015? Is it a pattern of increasing, decreasing, or constant? Select the data you counted in the ‘Count' worksheet and insert a bar chart with a trend line.

Step 2: Delete records that do not correspond to S&P500 companies, are duplicates or are null records.

Copy all data in the worksheet ‘Data' and paste them in ‘Data-S&P500' worksheet. Add a new column, ‘S&P500' (column M), using the ‘vlookup()' function. The ‘vlookup()' function compares the TS (ticker symbol) of ‘Data' worksheet (Figure 1) with ticker symbol column (no column header) in the ‘S&P500' worksheet (Figure 2). If a value of TS in the ‘Data' worksheet matches with of the value in the column of ticker symbol (column A) in the ‘Data-S&P500' worksheet, put a value of TS in the column N. And also, using the ‘iferror()' function together with ‘vlookup()' function, if no matching of ticker symbol, type ‘0' in the cell (Figure 5).

• Q3 How many ‘0' value / non-zero value are in the column M (S&P500) of the ‘Data-S&P500' worksheet. Use a countif() function to find the number of ‘0' value and non-zero values in the column M.

Select all data using Excel short cut “Ctrl+A” in the worksheet ‘Data-S&P500' and make a table using ‘Format as Table' button in the ‘Home' menu ribbon / ‘Styles' group (Figure 6) Click the drop-down arrow in column M (S&P500) and unselect “0” (Figure 7) Copy and paste the filtered records into a new worksheet with a name of ‘S&P500-Only'. Make sure that you select a paste option of ‘Values' in applying ‘paste' command (Figure 8)

• Q4 How many data records are in the worksheet ‘S&P500-Only' with the header line?

• Q5 Can you find empty cells? Which data field has empty cells? Click drop-down arrow of each column header and unselect ‘blanks' option (Figure 9).

Copy and paste remaining data into a new worksheet named ‘S&P500-Not-Null' (Figure 10) Make sure to select ‘Values' option in applying ‘paste' command (Figure 8).

• Q6 How many data records in the worksheet ‘S&P500-Not-Null' including header?

• Q7 Can you find any duplicate data records in ‘S&P500-Not-Null' worksheet?

Select the following menu path to find duplicate data: ‘Data' menu ribbon > ‘Data Tools' group > ‘Remove Duplicate' button (Figure 11).

Step 3: Classify record into industry sector using NAICS codes.

Select and copy column A through column L of ‘S&P500-Not-Null' worksheet and paste it to ‘S&P500-Sector' worksheet. In the column L of ‘S&P500-Sector' worksheet, there are NAICS (North America Industry Classification System) codes. These codes are composed of 6 digits. The first two digits represent an industry sector.

• Q8 Are there any code whose number is less than 6 digits in the column L (NAICS) of ‘S&P500-Sector' worksheet? The NAICS column is the number data type and the minimum number with 6 digit integer number is 100,000. Count the number of NACIS codes using ‘countif()' function. Even if the NAICS code should be a 6-digit number, some of NAICS code have less than 6 digits. Add a new column ‘Sector' (column M) using a function of left() with the first two digits of the NAICS codes (Figure 12).

Copy ‘Sector' column (M) of ‘S&P500-Sector' worksheet and paste it to Cell A1 in the ‘Sector' worksheet with paste option of ‘Values' (Figure 8) Click ‘Data' ribbon menu and sort it with ‘Sort' button in ‘Sort & Filter' group and remove duplicate sector codes with ‘Remove Duplicates' button in ‘Data Tools' group (Figure 13) Add column headers, ‘Sector Name' (Cell B1) and ‘No. of Records' (Cell C1) in the first row of ‘Sector' worksheet. Fill the sector name column (Cell B2 ~ Cell B20) referring to Table 3 (Figure 14).

• Q9 How many number of data in each sector in the worksheet ‘S&P500-Sector'? Put your numbers in the Column C (No. of Record) of the ‘Sector' worksheet using a ‘countif()' function.

• Q10 Can you find any finance sector record in ‘Sector' worksheet? The sector code 52, finance and insurance, is not supposed to be in ‘S&P500-Sector' worksheet because the finance industry data should be excluded in the original data set. If ‘Yes', what is the ticker symbol for that company?

• Q11 Sort the records in ‘S&P500-Sector' worksheet by ‘Sector' column (M) with ascending order. Delete the records with a sector code 52 in the ‘S&P500-Sector' worksheet. How many records remain in the ‘S&P500-Sector' worksheet including column header after deletion?

• Q12 Do you have any record in the sector number 99? The sector code 99 is not in the NAICS code list. (Table 3) If ‘Yes', what are ticker symbols for those companies? Even if the code 99 is out of range according to the Table 3, we keep it for further analysis.

Now you have a clean, organized data set (‘S&P500-Sector') to upload to Tableau for further analysis. Make sure that there is no data after the last record, row number 4862 (Figure 15).

Part 3. Visualization with Tableau

In Part 3, you will learn how to analyze the data to get useful information and how to present output as a form of chart.

Step 1: Upload Excel data to Tableau

Click “Microsoft Excel” under the connect tab and import the ‘Income-Inequality-Student-Data.xlsx' Excel file to Tableau. In the data source page, drag ‘S&P500 Sector' worksheet from the left pane to canvas which is located at the upper part of data source page (Figure 16).

Step 2: Check data types of each data field

Select ‘Sheet 1' worksheet. Your data fields are grouped into two areas: ‘Dimensions' and ‘Measures'. The ‘Dimensions' area contain qualitative values to categorize and the ‘Measures' area contain quantitative values which can be aggregated (Figure 17).

Drag ‘SEC' and ‘NAICS' fields from the Measures area to the Dimensions area. The ‘SEC' field is a number symbol for the U.S. stock market exchanges and the ‘NAICS' is an industry classification code. Even if ‘SEC' and ‘NAICS' fields show numeric data, the fields should be classified as non-numeric, categorical data. Change data type of ‘SEC' and ‘NACIS' from number to string (Figure 18).

Tableau has four data types: ‘String', ‘Number', ‘Boolean' and ‘Date'. Can you find any field in the Dimensions area with an inappropriate data type? Which field should be a ‘Date' type? To change a field's data type, right-click the FY field and choose “Change Data Type' menu and choose is ‘Date' type (Figure 19).

• Q13 Check the total number of record and see whether it is the same as the total number of records in ‘S&P500-Sector' Excel worksheet. Summarize the count of records by year as you did in the Q1.

Drag ‘Number of Records' in the measures area to ‘Columns Shelf'. Right click the blue bar and select ‘Mark Label' and ‘Always Show' (Figure 20) The total number of records is 4,861 (without field header). Rename the worksheet as ‘Count'.

Move the field ‘Number of Records' to ‘Row Self' and drag ‘FY' field to ‘Column Shelf' (Figure 21).

Click ‘Show Me' and select ‘text tables', which is in the upper left corner (Figure 22).

Now you can see a table summarized by year. Click ‘Show Me' one more time and then the chart options will disappear. Click the ‘New Worksheet' icon under the bottom of Tableau workbook and rename it as ‘ROC'.

Step 3: Create an ROC field, ROC percentile fields and ROC-Gap field

ROC is a ratio indicating how effective a firm is at running invested capital into profits. Profit in the ROC ratio can be net income (NI) or net operating income (NOP). The general equation for ROC is known as (NI − DVT) / (DT + Equity). According to the WSJ article, the ROC in this project is calculated using invested capital without GDWL. Therefore, the ROC formula here is (NI−DVT) / (DT+CEQ-GDWL). Right-click on the white space in Measures area and select ‘Create Calculated Field …' (Figure 23) and type the ROC formula.

Make sure to use brackets ‘[ ]' around data fields (Figure 24).

In the ascending ordered data set, 90 percentile means a specific number where 90 percentage of data fall below that number. You will now create several more calculated fields: ROC90%, ROC75%, ROC50%, ROC25%, and ROC-Gap, which are the 90%, 75%, 50% and 25% numbers in the ordered ROC values (the ROC-Gap field is ‘ROC 90% - ROC 25%'). Use the same method you used to calculate the ROC field. ‘Percentile' is a function to return a value which the target percent of field, i.e. percentile ([ROC], .9) returns a value of 90% of ROC. The following is a list of formulas for the above five new calculated fields (Figure 25) Once you create the five new fields, you can see them in the Measures area.

Step 4: Visualize ROC percentiles and ROC-Gap.

• Q14 What is the pattern of ROC90%, ROC75%, ROC50%, ROC25% from 2001 through 2015? Increasing, decreasing, or combined of both?

Click ‘New Worksheet' icon and drag ‘FY' field to ‘Columns Shelf' and the four ROC percentiles to ‘Rows Shelf'. Click ‘Show Me' and choose the ‘Side-by-Side Bars' chart and click the drop-down arrow of fit icon in the tool bar and choose ‘Entire View' (Figure 26).

Drag ‘Measure Names' on the ‘Color' button in ‘Marks' card (Figure 27). Right-click on the tab of the worksheet and rename it as ‘ROC'.

• Q15 What is the pattern of ROC-Gap from 2001 through 2015, Increasing, decreasing, or ups and downs? Is it the same pattern as the WSJ article demonstrated? Which year has the largest gap? What would be a reason for the largest gap year?

Drag ‘FY' field to ‘Column Shelf' and ROC-Gap to ‘Rows Shelf'. Click ‘Show Me' and choose the ‘Lines' chart (Figure 28).

Add a trend line using ‘(Polynomial) Trend Line' in ‘Analytics Pane' then right-click on the worksheet tab and rename it ‘ROC-Gap' (Figure 29).

• Q16 Export the worksheet ‘ROC' using a menu path of ‘Worksheet' / ‘Export' / ‘Crosstab to Excel' (Figure 30).

In the downloaded Excel worksheet (Figure 31), calculate again ROC-Gap at the bottom of the table (row 7) using a formula of ‘ROC90% - ROC25%'. Present a combo chart with ROC percentile data (clustered column) and ROC-Gap (line) and compare it with charts generated by Tableau. Copy the downloaded Excel file in the ‘Income-Inequality-Student-Data' and right-click on the worksheet tab and rename it as ‘ROC-Export'.

Step 5. Drill-down Analysis

Drill-down is to go deeper into more specific layers of the data being analyzed. In this project, one example of drill-down is to explore data in industry sector level. According to Table 4, sector codes of retail are 44 and 45 and those of manufacturing are 31, 32, and 33.

• Q17 Create a chart for ROC-Gap of Retail industry only. Duplicate ‘ROC-Gap' worksheet and using Tableau ‘Filter' card, extract only retail trade sector (Sector No. = 44, 45). Rename it as ‘ROC-Gap-Retail' (Figure 32).

• Q18 Create a chart for ROC-Gap of manufacturing industry only. Duplicate ‘ROC-Gap' worksheet and using Tableau ‘Filter' card, extract only manufacturing sectors (Sector No. = 31, 32, 33). Rename it as ‘ROC-Gap-MNF'. Comparing ROC-Gap charts, which industry shows a growing significant gap (Figure 33)?

• Q19 Export ‘ROC-Gap-MNF' and ‘ROC-Gap-Retail' to Excel files and make one Excel line chart with polynomial trend lines. You need to combine the two downloaded files into one file to create a chart with data from both manufacturing and retail sectors. Copy the downloaded Excel files in the ‘Income-Inequality-Student-Data' and right-click on the worksheet tab and rename it as ‘ROC-Sector-Export'.

• Q20 Make a dashboard with the four previous worksheets (ROC, ROC-Gap, ROC-Gap-MNF, ROC-Gap-Retail). Click ‘New dashboard' icon and drag the four worksheets in the left pane to dashboard area.

Part 4. Technical Memo for Adoption of Tableau

• Q21 Write a short e-mail to your CEO on why you need to adopt Tableau assuming your CEO requests a comparison between MS Excel and Tableau. List at least five advantages/disadvantages of Tableau compared with MS Excel, i.e, (1) Visualization, (2) Data Compatibility, (3) Training Efficiency, (4) Cost, and (5) Presentation. Make a new worksheet ‘Memo' in ‘Income-Inequality-Student-Data.xlsx'

Part 5. Submission

Before you submit your project files, save your Excel and Tableau files with your school student id, i.e., ‘Income-Inequality-(Student-ID).xlsx” and ‘Income-Inequality-(Student-ID).twbx'. You need to save your tableau file with an extension of “twbx” (Figure 34) The Tableau file with ‘twbx' extension is a package of files with your data and your Tableau workbook files for sharing them with your instructor.

Make sure to submit all 16 worksheets, as follows:

Six worksheets in the Tableau file:

1. Count

2. ROC

3. ROC-Gap

4. ROC-Gap-Retail

5. Roc-Gap-MNF

6. Dashboard

Ten worksheets in the Excel file:

1. Data

2. Count

3. S&P500

4. Data-S&P500

5. S&P500-Only

6. S&P500-Not-Null

7. S&P500-Sector

8. Sector

9. Export-ROC

10. Export-Sector-ROC

11. Memo

Editors-in-Chief

• Kimberly Swanson Church, University of Missouri – Kansas City

• Gary P. Schneider, California State University, Monterey Bay

Associate Editors

• Del DeVries, Belmont University

• Dawna Drum, Western Washington University

• Betsy Haywood-Sullivan, Rider University

• Gail Hoover King, Washburn University

• Lorraine Lee, University of North Carolina Wilmington

• Conni Lehmann, University of Houston – Clear Lake

• Brad Schafer, Kennesaw State University

Senior Reviewers

• Kel-Ann Eyler, Georgia College & State University

• Kurt Fanning, Grand Valley State University

• Cynthia Frownfelter-Lohrke, Samford University

• Sonia Gantman, Bentley University

• Margaret (Peggy) Garnsey, Siena College

• Bonnie Klamm, North Dakota State University

• Marcia Watson, University of North Carolina Charlotte

• Skip White, University of Delaware

Past Editors-in-Chief

• 2004–2007 Arlene Savage

• 2007–2009 Stacy Kovar

• 2009–2012 David R. Fordham

• 2012–2015 William G. Heninger

• 2016–2018 Ronald J. Daigle and David C. Hayes

• 2018–2019 Chelley M. Vician

• 2019–2020 Chelley M. Vician and Gary P. Schneider

Editorial Assistant

• Abby Bensen, University of St. Thomas

Ad Hoc Reviewers

A list of ad hoc reviewers for the most recent three years is published in the annual editor report.

All materials contained herein are copyright AIS Educator Association, all rights reserved. Faculty members may reproduce any contents of the AIS Educator Journal for use in individual courses of instruction only if the source and the AIS Educator Association copyright are included in any such reproduction. Apply in writing to the Editor for permission to reproduce any AIS Educator Journal content for other uses, including, but not limited to, publication in textbooks and books of readings for general distribution.

The AIS Educator Journal is published by the AIS Educator Association:

• President: Ann O'Brien, University of Wisconsin

• Vice President and President Elect: Cynthia Frownfelter-Lohrke, Samford University

• Secretary: Cheryl L. Dunn, Grand Valley State University

• Treasurer: Kristian Mortenson, University of St. Thomas

• Past-President: Dawna Drum, Western Washington University