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Textbooks for Responsible Data Analysis in ExcelNathan Garretta

a Woodbury University, Burbank, California, USAPublished online: 26 Feb 2015.

To cite this article: Nathan Garrett (2015): Textbooks for Responsible Data Analysis in Excel, Journal of Education forBusiness, DOI: 10.1080/08832323.2015.1007908

To link to this article: http://dx.doi.org/10.1080/08832323.2015.1007908

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Textbooks for Responsible Data Analysis in Excel

Nathan Garrett

Woodbury University, Burbank, California, USA

With 27 million users, Excel (Microsoft Corporation, Seattle, WA) is the most common

business data analysis software. However, audits show that almost all complex spreadsheets

have errors. The author examined textbooks to understand why responsible data analysis is

taught. A purposeful sample of 10 textbooks was coded, and then compared against

spreadsheet development best practices. The results show a wide range of approaches, and

reveal that none of the 10 books fully cover the methodologies needed to create well-

rounded Excel data analysts. There is a need to re-evaluate the teaching approaches being

used in office application courses.

Keywords: data analysis, Excel, spreadsheet, textbooks

Excel (Microsoft Corporation, Seattle, WA) is the most

popular data analysis tool used in business, with estimates

suggesting 27 million users (Scaffidi, Shaw, & Myers,

2005). Excel fills a vital role in most companies, with 80–

90% of firms using spreadsheets in mission-critical finan-

cial reporting and forecasting (Panko & Port, 2012).

The problem is that Excel, for all its ubiquity, is almost

always used badly. A synthesis of audit reports (Kruck,

2006) show that 75% find an error rate above 35%, with a

median value of 50%. Other audit approaches have found

that 85–100% of Excel spreadsheets contain errors (Panko

& Port, 2012).

Few companies test spreadsheets thoroughly, and those

with formal policies are rarely followed (Panko & Port,

2012). This has resulted in a range of horror stories, from

the inadvertent release of sensitive information, to range

errors reversing GPD growth rates, or even incorrect bids

that lose millions of dollars (European Spreadsheet Risks

Interest Group, 2014).

Worryingly, experience with Excel is not a predictor of

success. Comparing master of business administration

(MBA) students with minimal Excel experience against

those with more than 250 hours shows no difference in

error rates (Panko & Sprague, 1999). Professional research-

ers are not exempt. The conclusions from (Reinhart & Rog-

off, 2010) were featured in the Wall Street Journal, NPR,

The Economist, and BusinessWeek, and were heavily cited

in justifying austerity policies (Coy, 2014). However, the

underlying Excel data set contained a range error, that

when corrected, changed high-debt country growth rates

from –0.3% to C2.6% (Konczal, 2013).

Why do experienced Excel users not have lower error

rates? One suggestion may be the way Excel is taught as an

application, and not as programming. Vandeput (2009)

explained this:

[T]he use of software in order to carry out a task is often

considered like a practical process devoid of any intelligent

approach. . . . For instance, the use of a word processing

program is considered by lots of people like a sequence of

elementary commands. . . . So the trainer will insist on the

graphical elements of the environment (menus, buttons,

checkboxes . . . ) and on graphical aspects of the process

results. (p. 2)

Excel users struggle with deep knowledge. New Excel

tends to be locked in the menu bar or menu items and have

a button pushing mentality (Tort, 2010). While able to do

superficial manipulations, they have trouble with tasks

requiring deep knowledge, particularly with formulas (Tort,

2010). Questions posted in online forums show that users

struggle with foundation issues, such as how to set up a

problem, and ask fewer feature-based questions (Chambers,

Sommers, & Scaffidi, 2012).

Spreadsheet courses should focus on turning students

into professional Excel analysts. Instead of training that

focuses on surface-level graphic elements or new features,

we should encourage the teaching of professional

Correspondence should be addressed to Nathan Garrett, Woodbury

University, School of Business, 7500 N. Glenoaks Boulevard, Burbank,

CA 91504, USA. E-mail: Nathan.Garrett@woodbury.edu

JOURNAL OF EDUCATION FOR BUSINESS, 0: 1–6, 2015

Copyright� Taylor & Francis Group, LLC

ISSN: 0883-2323 print / 1940-3356 online

DOI: 10.1080/08832323.2015.1007908

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quantitative and analytic skills. There is a significant differ-

ence between professional and amateur approaches, as the

following quote from Weinberg (1998) explains:

The amateur [programmer], being committed to the results

of the particular program for his own purposes, is looking

for a way to get the job done. If he runs into difficulty, all

he wants is to surmount it—the manner of doing so is of lit-

tle consequence. Not so, however, for the professional [pro-

grammer]. He may well be aware of numerous ways of

circumnavigating the problem at hand. . . . But his work

does not stop there; it begins there. It begins because he

must understand why he did not understand, in order that he

may prepare himself for the programs he may someday

write which will require that understanding. (p. 125)

Excel courses should teach logical thinking, and not but-

ton pushing. This view is supported by a study showing that

training in Excel was linked to an increase in logical skills,

as measured by the ETS Diagramming Relationships test

(Kruck, Maher, & Barkhi, 2003). Importantly, students’

logical skills were related with their success in producing

error-free spreadsheets.

This project examines a selection of books to see how

they teach Excel. Do they teach software development

methodologies? Which best practices are covered? Which

books target superficial features and button clicking, and

which target deep knowledge and skills?

LITERATURE REVIEW

Research literature on Excel data analysis can be separated

into lifecycle methodology and spreadsheet features

sections.

Lifecycle Methodology

Spreadsheet modeling is the combination of technical skills

(executing a narrowly defined task), as well as a craft skill

(prototyping and simplifying a complex problem) (Powell

& Baker, 2013). As users develop a spreadsheet, they are

actively engaged in a problem exploration and discovery

process (Nardi & Miller, 1990, 1991).

As a result, prespecification of the spreadsheet may not

be advisable, or even possible. As Ronen, Palley, and Lucas

(1989) said, “one of the major advantages of spreadsheets is

their ease of use . . . [a]dvocating more formal approaches to

spreadsheet design may be viewed by some as a step back-

wards” (p. 84). An agile process (Powell & Baker, 2013)

has the following repeating phases: explore the mess,

search for information, identify a problem, search for solu-

tions, evaluate solutions, and implement solutions.

Because Excel relies on spatial organization, properly

laying out cells can be a way of conveying information

(Bewig, 2005; Rajalingham, Chadwick, & Knight, 2001;

Rajalingham, Chadwick, Knight, & Edwards, 2000; Ronen

et al., 1989). Some common approaches to improving this

layout can be called block structuring.

Block structuring can be done through a variety of

approaches:

� Use a separate sheet for input, calculation, and outputs

(Bewig, 2005);

� Only refer to cells above and to the left (Powell &

Baker, 2013; Read & Batson, 1999); or

� For each row, use only a single formula copied over

from left to right (Bewig, 2005; Read & Batson,

1999).

There is empirical support for the usefulness of a block

structuring approach. Surveys have found that heavier users

of Excel followed these rules to a larger degree than novice

users (Baker & Powell, 2006). Experiments with students

have shown that following block rules doubles their rate of

error detection (Rajalingham et al., 2001). Several popular

financial modeling methodologies also recommend block

structuring (Grossman & Ozluk, 2010).

Testing is an essential part of a methodology. Finding

errors is difficult, with individual code inspections of indi-

vidual cells finding only 63% of errors, and group inspec-

tions catching only 83% of errors (Panko, 1999). Cell-by-

cell inspection by a group is the only proven technique

to catch most of the errors (Panko, 2000). There is a range

of common problems, but they often involve formulas and

cell reference errors (Hendry & Green, 1994).

High Excel error rates are a natural result of human

errors rates and cognition (Panko, 2000). Humans average

between 2–5% errors on tasks in general, and when consid-

ering the number of formulas in a complex sheet, this natu-

rally results in significant numbers of errors. Unlike

writing, where a single error may lie unnoticed, a spread-

sheet’s cumulative nature means that an error in any part of

the chain results in cascading errors toward final results.

Research on error rates has shown that no studies of spread-

sheets has shown that errors are rare or of low significance

(Powell, Baker, & Lawson, 2008).

A significant problem with spreadsheet development is

developer overconfidence (Panko, 2000, 2003). In particu-

lar, novice users rely too much on quantitative data, have

little abstract conceptualization, and do not check their own

work (Powell & Willemain, 2006; Willemain & Powell,

2006).

Fortunately, addressing student overconfidence can be

effective. Panko (2003) was able to reduce overconfidence

by providing a warning as to the error rates in solving an

Excel problem. This decreased the rate of solutions with

errors from 93% to 73%.

Last, documentation of Excel spreadsheets is an essen-

tial part of any methodology. Documentation may be

expressed as a how-to sheet (Bewig, 2005; Read & Batson,

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1999), or in comment cells (Powell & Baker, 2013). While

spreadsheets may start with a single user, many are shared

in larger groups (Nardi & Miller, 1990, 1991). Documenta-

tion is key to ensuring spreadsheets remain free from error.

Spreadsheet Features

Most spreadsheets are used to store lists of data (Chambers

& Scaffidi, 2010). A sample of 400 spreadsheets from the

End-Users Shaping Effective Software (EUSES) Spreadsheet

Corpus provides a detailed breakdown (Chambers, Scaffidi,

& Sommers, 2010). A total of 56% of the spreadsheets were

used for data entry, contained tabular data, and frequently

had no formulas. 25% of the spreadsheets were used as data-

bases, contained mostly text tabular data, and had no formu-

las or charts. The remaining 19% were used for data

visualization, data entry, or a combination of purposes.

One survey asked Excel users what features they used

most (Lawson, Baker, Powell, & Foster-Johnson, 2009).

Ranked from most popular to least, they included the fol-

lowing: if, data sort, chart wizard, find and replace, lookup,

financial functions, conditional formatting, macros, formula

auditing tools, pivot tables, data tables, solver, and goal

seek. Another study found that the following features were

used occasionally or higher: if, data sort, chart wizard, find

and replace, financial functions, and the function wizard

(Baker & Powell, 2006). Features used at lower rates

included conditional formatting, macros, formula auditing,

pivot tables, data table tool, solver, and goal seek (Baker &

Powell, 2006).

Beyond simply identifying the most commonly used fea-

tures, how can we best use formulas, shortcuts, and

formatting?

First, formulas are frequently the most error-prone sec-

tions of workbooks. As a result, a number of guidelines pro-

mote ways to improve their auditability. Some of the most

basic guidelines include the following:

� Break complex formulas into multiple cells (Gross-

man & Ozluk, 2010; Powell & Baker, 2013; Read &

Batson, 1999);

� Do not hard-code constants into formulas (Powell &

Baker, 2013); and

� Protect formula so users cannot manually overwrite

them with hard-coded variables (Panko, 2000).

Second, keyboard shortcuts can promote low error rates

and speed development time. FAST and OPERIS method-

ologies stress the need to learn keyboard shortcuts to enable

best practices and reduce errors (Grossman & Ozluk,

2010). Shortcuts are particularly useful in creating block

structures, and encourage users to have the same formulas

throughout a single column or row.

Third, formatting is an effective way to signal data and

meaning, as opposed simply providing decoration (Gross-

man & Ozluk, 2010; Powell & Baker, 2013; Raffensperger,

2001). For example, formulas may use a consistent back-

ground color, and cells linking to other sheets a different

font. Borders, font, and colors can all act as signals.

STUDY DESIGN

This goal of this study was to catalog the range of ways that

Excel is taught. It was not designed to provide a representa-

tive sample of textbooks, or to evaluate the effectiveness

of the different approaches. Instead, its purpose was to cata-

log the range of approaches, and compare these approaches

to the existing literature on best practices.

I chose all textbooks suitable for a first semester course

on Microsoft Office. I excluded books that were exclusively

quick references, second or third courses, or touch-based

interface only.

Data Gathering

This project began by selecting a purposeful sample of

Microsoft Office textbooks. A list was created through

searches of retailers, publishers, and Office Application

course syllabi. This list included both college-focused

books, as well as more practitioner-oriented and popular

press introductions. After creating a list, a subset was

selected for analysis. The subset (shown in Table 1)

included at least one textbook from each major publisher or

series, and included a range of authors.

Coding

I read and coded each book. The coding scheme was further

developed during the process, which required each book to

be analyzed at least twice to ensure accuracy. These codes

used are described in the following list.

Pedagogical approach.

� Conceptual: Do books explain the concepts behind a

feature, or only give a step-by-step process? Books

are coded as conceptual (no step-by-step instructions

are given), mixed, and step by step (no conceptual

information is given).

� Implementation details: Do books assume that Excel

works properly? Books are coded as assume all fea-

tures work, limitations given, and limitations and

work-around given.

� Lifecycle: Do books follow a lifecycle model, or do

they only provide individual illustrations? Books are

coded as lifecycle, extended example, or illustration.

TEXTBOOKS FOR RESPONSIBLE DATA ANALYSIS IN EXCEL 3

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Lifecycle approach.

� Lifecycle: Do books model a lifecycle approach?

� Block structuring: Do books explain how to layout

data? Are assumptions separated from calculations,

and outputs place on a separate sheet?

� Documentation: Do books explain the need to com-

ment a spreadsheet?

� Test: Do books show how to test or debug a

spreadsheet?

RESULTS

The following section presents the results of the coding

analysis.

Pedagogical Approach

The books have diverse pedagogical approaches, but can be

separated into three groups (shown in Table 2).

A third use a lifecycle approach with mixed step-by-step

and conceptual explanations. These books guide students

through problem description, design requirements, data

input, visualization, and printing. Two examples of this

approach include Vermaat (2013) and O’Leary and

O’Leary (2013).

The second third use illustrative examples and con-

ceptual explanations. These comprehensive references

did not present overall development methodologies.

Instead, they described the value of individual features,

and give limitations and work-arounds. Conner and

MacDonald’s (2013) is good example of this approach.

For example, it is the only text that explains that sub-

tracting a larger time from a smaller time will result in

a ##### error message.

The remaining books use a variety of approaches. For

example, Gaskin, Martin, Graviett, Marks, and Geoghan

(2013) used a lifecycle approach, but has step-by-step

explanations with minimal conceptual information.

Lifecycle Methodology

Lifecycle. Only two books model and explain a life-

cycle development approach. While some books show a

lifecycle approach, most do not explain why a structured

approach is important.

Many of the features needed for accurate documentation

are ignored. For example, only three books present the

comment feature. Only one book stresses the need for

documentation.

Most disappointingly, only one book suggests auditing

spreadsheets for errors. None of the books present error

rates, or provide examples of what can go wrong when a

spreadsheet is shared among multiple developers.

Block structuring. Unfortunately, the textbooks fall

short in providing guidelines for good spatial layout. While

good practices are sometimes modeled, their justification

and explanation are almost entirely absent. While three sep-

arate assumptions from calculations, only Vermaat (2013)

explains why this is important. Two books separate calcula-

tions from output, but rely upon the reader to understand

why.

Nine of the books show how to manage summary data

sheets, but two do not show how to manage lists. Pivot

tables are also mainly absent, with only four books showing

how they work.

Using spreadsheets as what-if tools is common, with five

books providing a walkthrough of changing input parame-

ters to impact output cells.

Documentation. Almost without exception, books do

not show how to document a spreadsheet. Only Vermaat

(2013) explained the need for documentation, or shows for-

matting as conveying meaning (instead of as decoration).

The comment feature is only shown in three books. Using

styles to show the purpose of cells is the most popular docu-

mentation feature. Seven books present information about

cell styles, though they are generally explained as decora-

tion and not documentation.

TABLE 1

Books Analyzed

Publisher Book title

Cengage Microsoft Office 2013: Illustrated Introductory, First Course, 1st ed. (Beskeen, 2013)

Cengage Discovering Computers & Microsoft Office 2013: A Fundamental Combined Approach (Vermaat, 2013)

McGraw Hill The O’Leary Series: Microsoft Office 2013: A Case Approach (O’Leary & O’Leary, 2013)

Pearson GO! With Microsoft Office 2013 (Gaskin, Martin, Graviett, Marks, & Geoghan, 2013)

For Dummies Office 2013 for Dummies (Wang, 2013)

For Dummies Office 2013 All-In-One for Dummies (Weverka, 2013)

Wiley Office 2013 Bible: The Comprehensive Tutorial Resource (Bucki, Walkenbach, Alexander, Kusleika, & Wempen, 2013)

O’Reilly Media Office 2013: The Missing Manual (Conner & MacDonald, 2013)

Microsoft Press Microsoft Office Professional 2013 Step by Step (Step By Step (Microsoft)) (Melton et al., 2013)

Microsoft Press Microsoft Office 2013 Inside Out (Bott & Siechert, 2013)

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Test. None of the books present information on error

rates. Only Vermaat (2013) strongly supported the need to

audit an Excel sheet for errors. When it comes to error mes-

sages, the books tend to be more successful. Half explain

all of the basic # error messages (such as #ref or ######).

A majority of the books teach debugging tools. Half show

how to print formulas, and seven show the controlC»shortcut that reveals formulas. Some strategies for data val-

idation and protection are shown. Six books show how to

hide a sheet, and three how to protect a sheet. Four show

the data validation feature.

CONCLUSION

How well do the books teach professional spreadsheet anal-

ysis? This analysis has found a surprising range of

approaches, but they can be roughly grouped into three

categories.

First, some books teach Excel primarily in terms of fea-

ture-oriented step-by-step processes. For example, Gaskin

et al. (2013) and Wang (2013) walk the user through a series

of steps, but rarely explain why or when to use each feature.

While users with a background in data management and soft-

ware development may find these texts useful, novice users

without this background will be left with significant gaps.

Second, the best reference books (Bucki, Walkenbach,

Alexander, Kusleika, & Wempen, 2013; Conner & Mac-

Donald, 2013; Melton et al., 2013) show why, when, and

how to use individual features. But, they do not show how

to combine these features into a larger design methodology.

Third, some books teach solid methodology. Unfortu-

nately, the two best methodology books do not show how

to use Excel for managing lists (and ignore the filter and

sort functions). This is a critical gap, since Excel is gener-

ally used to manage lists.

All of the analyzed books require supplements. These

include the following:

� Lifecycle methodology,

� Excel error rates, auditing, and documentation

approaches,

� Data normalization, and

� Block formatting rules.

Teaching Excel as a point-and-click tool, or only exam-

ining individual features in isolation, results in amateur

excel programmers who understand features, but do not

know how to tie these features together. The universally

high error rates found in the field show the need for improv-

ing the state of instruction. Curriculum needs to reflect a

professional approach. Without this shift, amateur work

will continue to be the norm.

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TABLE 2

Pedagogy, Conceptual, and Implementation Details

A3. Lifecycle

Approach Illustration Extended example LifecycleP

A1. Conceptual Step by step (Wang, 2013) (Gaskin et al., 2013) 2

Mixed (Weverka, 2013) (Melton et al., 2013) (Beskeen, 2013; O’Leary &

O’Leary, 2013; Vermaat,

2013)

5

Conceptual (Bott & Siechert, 2013; Bucki

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3

P5 1 4 10

P

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2013)

4

Limitations given (Melton et al., 2013) (O’Leary & O’Leary, 2013) 2

Limitations and work-around

given

(Bott & Siechert, 2013; Bucki

et al., 2013; Conner &

MacDonald, 2013;

Weverka, 2013)

4

P5 1 4 10

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