Seeking Empirical Validity in an Assurance of Learning System

Running head: SEARCHING FOR EMPIRICAL VALIDITY
Seeking Empirical Validity in an Assurance of Learning System
Sherry Avery, Rochell McWhorter, Roger Lirely, H. Harold Doty
College of Business and Technology
The University of Texas at Tyler
Contact Email: rmcwhorter@uttyler.edu
Note: This is the last authors’ copy of this work. The final edited, definitive, and distributed copy is
published in the Journal of Education for Business, 2014; 89: 156-164,
doi: 10.1080/08832323.2013.800467
Abstract
Business schools have established measurement tools to support their AoL systems and assess
student achievement of learning objectives. However, business schools have not required their
tools be empirically validated thus ensuring that they measure what they are intended to measure.
We propose confirmatory factor analysis (CFA) be utilized by business schools to evaluate AoL
measurement systems. We illustrate a CFA model used to evaluate the measurement tools at our
College. Our approach is in initial steps currently evaluating individual measurement tools, but
working towards developing a system that can evaluate the entire AoL measurement systems.
Keywords: AACSB, Assurances of Learning, Assessment, Confirmatory Factor Analysis
A decade ago, the Association to Advance Collegiate Schools of Business (AACSB)
International ratified new accreditation requirements including the addition of assurance of
learning (AoL) standards for continuous improvement (Martell, 2007). As part of this addition,
schools seeking to earn or maintain AACSB accreditation must develop a set of defined learning
goals and subsequently collect relevant assessment data to determine direct educational
achievement (LeClair, 2012; Sampson & Betters-Reed, 2008). The establishment of the mission-
driven assessment process requires “well-documented systematic processes to develop, monitor,

SEARCHING FOR EMPIRICAL VALIDITY 2
evaluate, and revise the substance and deliver of the curricula on learning” (Romero, 2008, p.
253).
With establishment of the 2003 AACSB standards, all schools “must develop assessment
tools that measure the effectiveness of their curriculum” (Pesta & Scherer, 2011, p. 164). As a
response to this outcomes assessment mandate, a number of schools created models to depict and
track their assessment functions (Betters-Reed, Nitkin & Sampson, 2008; Gardiner, Corbitt, &
Adams 2010; Zocco, 2011). However, the question arises as to the validity of system models for
measuring learning outcomes—does the model measure what it purports to measure—do the
learning experiences accomplish the learning goals outlined in the systems model? This question
is a very important one because once validity of a measurement system is established, then it
provides confidence in a program and quality of assurance in achieving the school’s mission
(Baker, Ni & Van Wart, 2012).
The purpose of this article is to illustrate development of an empirically-based AoL
System which may be used by other business schools seeking accreditation. Relevant literature
on this topic will be examined next.
Review of Literature
A search of the literature for an empirically-validated AoL system yielded results for
research covering either the validation of AoL tools or validation of an Aol model. Each is
discussed in the following section.
Measures of Validity in AoL Assessment Tools
Measures of validity associated with AoL learning outcomes were located in business
literature by reviewing articles that described locally-developed assessment tools and externally
validated instruments. For instance, researchers developed an assessment tool to explore

students’ self-efficacy toward service and civic participation. They utilized traditional scale
development and confirmatory factor analysis (CFA) and simultaneous factor analysis in several
populations (SIFASP) for insuring the validity and reliability of their instrument to measure AoL
criteria for ethics and social responsibility (Weber, Weber, Sleeper & Schneider, 2004). Another
tool offered was a content valid assessment exam created to measure business management
knowledge (Pesta & Scherer, 2011).
Also, a matrix presented by Harper and Harder (2009) depicts demonstrated abilities
intersected with competency clusters; the clusters were developed from literature describing
valid research into “the kinds of knowledge and skills that are known to be necessary for success
as a practitioner in the MIS field” (p. 492). However, no statistical measures of validity were
provided. Additionally, instances of use of externally validated instruments such as the revised
version of the Defining Issues Test (DIT-2) to assess ethical reasoning instruction in
undergraduate cost accounting (Wilhelm & Czyzewski, 2012) and use of the CAPSIM computer
simulation to assess business strategy integration (Garrett, Marques & Dhiman, 2012), were
found.
Measures of Validity in AoL Assessment Models
Various models have been offered for outcomes measurement as part of a processed-
based approach for meeting AoL standards (i.e. Beard, Schwieger & Surendran, 2008; Betters-
Reed, Nitkin & Sampson, 2008; Hess & Siciliano, 2007) but without statistical evidence or
discussion of validity measures. However, the search of literature found an article by Zocco
(2011) that presented a recursive model to address and document continuous and systematic
improvement and discussed validity issues surrounding the application of recursion to a process
such as AoL. Although helpful for looking at school improvement, the model does not measure

validity of the model itself. Therefore, the review of literature offered several tools and a model
with validity calculations, however, no example of an empirically-validated system was found.
Case Study: Assurances of Learning at [blinded]
During the past five years, the College of Business [removed for blinding purposes,
hereafter referred to as “College”] has conducted a complete redesign of its AACSB Assurance
of Learning system. To understand the rationale for this design change it is important to explore
several key drivers of this decision, especially in light of the fact that our prior AoL system was
cited as one or our “best practices” during our last Maintenance of Accreditation visit. At last
visit, the College operated three different and largely unrelated assessment systems: one for the
AACSB, [names of 2 other accreditation bodies removed for blinding purposes]. In some ways,
these independent assessment systems simplified accreditation reporting: each system was
tailored to the specifics of a single accrediting body and the data associated with one system
were not considered in collaboration with data collected for a different accrediting body. For
example, AACSB and the College’s assessment procedures were treated as completely
independent. This approach simplified reporting, but hindered integrating different assessment
data in the larger curriculum management process.
A second major contextual factor relevant to our AoL process was feedback from our last
AACSB visit that recommended revisions to the vision and mission statements for the College
and AACSB AoL processes. As part of that revision process, the College clarified its mission
and identified five core values.
Based largely on these contextual factors, faculty determined we were at an ideal point to
design a new single integrated assessment model to meet the needs of each of our accrediting
bodies. Further, we determined that the new system should be linked to the new mission by

incorporating the core values as learning outcomes, and that we should attempt to assess the
validity of the system in terms of both the theoretical model used to design the system and the
measurement model used to organize the data collection. These additional steps would allow
more confidence in the evidence-based changes we were making in program structure and course
curriculum. The full scale implementation began in the 2010-11 school year; our model is more
fully described next.
Faculty-Driven Process
AoL in the College is a faculty-driven process. Oversight of this process is charged to
the AoL Committee, a committee comprised of a faculty chair, the undergraduate program
director, the graduate programs coordinator and four at-large faculty members. The composition
of the Committee provides cross-sectional representation of all disciplines and programs in the
College.
The Committee works closely with our faculty to ensure that each learning objective is measured
periodically, at least twice during each five-year period but generally more often. The faculty
employ a variety of measurement strategies, including major field tests, embedded test questions,
case analyses, observation of student presentations, activity logs, simulations and other class
assignments and/or projects. Analyses of results guide the Committee in its work with the
faculty to develop and implement appropriate actions to ensure curricula and pedagogy are
managed in a manner enhancing student learning and development. Figure 1 illustrates how the

AoL assessment process operates in a continuous improvement mode.
FIGURE 1
AoL Curriculum Management Process at [Blinded]
Conceptual Framework
The AoL system in the College is based on a set of shared core values: professional
proficiency, technological competence, global awareness, social responsibility and ethical
courage as seen in Figure 2. These mission-based core values form the framework for our
comprehensive, empirically-validated AoL models for both the Bachelor in Business
Administration program and the Master of Business Administration Program, as well as other
College programs that are outside the scope of AACSB accreditation. AoL in the College has
evolved to the point where our current system is second-generation, that is, it is the culmination

of an assessment of the AoL system itself. Many of the best features of the prior system were
retained, including assessment of discipline-based knowledge, communication skills, and the use
of quantitative tools and business technology. The result of this process is a value-based
conceptual framework whose efficacy can be tested empirically using confirmatory factor
analysis. To our knowledge, our College is the first AACSB-accredited program to design an
empirically-validated AoL system. Figure 2 depicts the conceptual framework of our AoL
system for the BBA program.
Figure 2
Method
Data Collection. The faculty developed ten learning objectives to support the five
learning goals of the College. A measurement tool was designed for each objective, such as the
Major Field Test or rubrics. Assessment was conducted within required core business courses
that included students across all College of Business majors. Students were generally either
junior or seniors in one of the business majors. Results were then collected and compiled
centrally in an administrative function within the College.
College of Business & Technology
Creating Leaders for a Better Tomorrow
Learning Goal:
Graduates have the
knowledge and
communication skills to
succeed in the business
profession.
Ethical
Courage
Social
Responsibility
Global
Awareness
Technological
Competence
Professional
Proficiency
Learning Goal:
Graduates use the
information sources and
tools associated with
their chosen
profession.
Learning Goal:
Graduates incorporate
global considerations in
business activities.
Learning Goal:
Graduates evaluate the
social consequences of
alternative outcomes
when making business
decisions.
Learning Goal:
Graduates understand
ethical considerations
and their impacts.
Cognitive:
GA1 - Students
demonstrate
awareness of global
issues and
perspectives.
Behavioral:
GA2 - Students are
knowledgeable of
global issues and
perspectives that
may impact
Behavioral:
SR1 - Students
exhibit an
understanding of
the social
consequences of
Cognitive:
EC1 - Students
understand legal
and ethical
concepts.
Behavioral:
EC2 - Students
make ethical
decisions.
Behavioral:
TC2 - Students are
able to use
business software,
data sources, and
tools.
Cognitive:
TC1 - Students
understand
information
systems and their
role in business
enterprises.
Behavioral:
PP3 - Students are
able to deliver a
presentation that is
focused, well-
organized, and
includes the
appropriate verbal
and nonverbal
behaviors.
Cognitive:
PP1 - Students
demonstrate that
they are
knowledgeable
about current
business theory,
concepts,
methodology,
terminology and
practices.
PP2 - Students can
prepare a business
document that is
focused, well-
organized, and
mechanically
correct.
Bachelor of Business Administration Updated: 7/11/2012

Analysis Approach. Several of the measurement tools included a number of items that
collectively assessed the specific learning objective. Confirmatory factor analysis (CFA) was
conducted to assess empirical validity of the items measures and learning objectives. CFA was
chosen for assessment because it tests how well the measured variables represent the constructs
(Hair, Black, Babin, Anderson & Tatham, 2005).
The items that comprise each construct are identified prior to running the CFA analysis.
We then confirm or reject that the items properly reflect the construct, in this case the learning
objective. CFA was conducted on six of the learning objectives. We were unable to run CFA
for some learning objectives because they were either measured by a single item, the sample size
was too small, or data was binary, therefore negating the applicability of using CFA. Table 1
details the learning objectives, measurement tools, and when the CFA was conducted. In the
following section, we discuss the general approach used in the CFA analysis. Then, we follow up
with two examples of the CFA analysis; the first example is empirically valid, and the second
example was not empirical valid. We used a combination of the software tools SPSS and AMOS

to support the analysis.
For each of the CFA analyses, we followed a 3-step approach documented in many of the
leading academic journals: (1) review of the raw data, (2) assessment of model fit, and (3)
assessment of construct validity. Prior to the CFA analyses, we reviewed the data for sample

size, outliers, missing data, and normality. We determined if the sample size was adequate for
the model based on suggested requirements that range from 5 to 20 observations per variable.
(Hair et al., 2005). The existence of outliers along with their potential impact on normality and
the final results were assessed at both the univariate and multivariate level by reviewing the
Mahalanobis distance (D2) calculation for each case. Next, we identified missing data and
assessed the potential impact on analysis. A case that has a substantially different value from
other D2 calculations is a potential outlier. Then, we identified the amount of missing data and
assessed the potential impact on the analysis. Finally, we assessed normality by reviewing both
skewness and kurtosis at the univariate and multivariate level. Values of 0 represent a normal
distribution. For skewness, less than 3 is acceptable (Chou & Bentler, 1995; Kline, 2005). For
kurtosis, Kline (2005) stated that less than 8 is reasonable with greater than 10 indicating a
problem and over 20 an extreme problem.
We evaluated how well the data fit the measurement model using Analysis of Moment
Structures (AMOS) software (see http://www.utexas.edu/its/help/spss/526). We used maximum-
likelihood estimations which is a widely used approach and is fairly robust to violations of
normality and produces reliable results under many circumstances (Hair et al., 2005; Marsh,
Balla & McDonald, 1998). First, we evaluated the Chi-Square (χ2) statistic which measures the
difference between the observed and estimated covariance matrices. It is the only statistic that
has a direct statistical significance and is used as the basis for many other fit indices (Hair et al.,
2005). Statistical significance in this case indicates that an error of approximation or estimation
exists. Many researchers question the validity of the chi-square statistics (Bentler, 1990), so if it
is significant additional indices should be used to assess overall evaluate model fit. The root
mean square error of approximation (RMSEA) is a standardized measure of the lack of fit of the

data to the model (Steiger, 1990). It is fairly robust in terms of small sample size; i.e. 250 or
less. Thresholds of .05 to .08 have been suggested with Hu and Bentler (1999) recommending a
cutoff close to .06. The Bentler-Bonnet (1980) Non-Normed Fit Index (NNFI) was used because
it also works well with small sample sizes (Bedeian, 2007). Generally .90 or better is considered
adequate fit with Hu and Bentler (1999) suggesting a threshold of .95 or better for good fit. The
non-normed fit index (NNFI) and comparative fit index (CFI) are incremental fit indices in that
they assess model fit by referring a baseline model (Bentler 1990; Bentler & Bonett, 1980; Hu &
Bentler, 1999). The NNFI and CFI generate values between 0 and 1 with .90 or greater
representing adequate fit (Hu & Bentler, 1999; Bedeian 2007).
The final step was to assess construct validity which is the extent to which a set of
measured items accurately reflect the theoretical latent construct the items were designed to
measure (Hair et al., 2005). The standardized factor loadings should be statistically significant
and .5 or higher (Hair et al., 2005). Convergence validity was assessed by calculating the
average variance extracted (AVE) and construct reliability (CR). The average percent of
variance extracted among a set of construct items is a summary indicator of convergence. AVE
of .5 or higher suggests adequate convergence. Less than 5 indicates that on average more error
remains in the items than variance explained by the latent construct (Fornell & Larcker, 1981).
High CR reliability indicates that the measures consistently represent the same latent construct.
Values of .6 or 7 are acceptable, with .7 or higher indicating good reliability (Nunnally, 1978).
Results. The purpose of this article is to illustrate the method we used to assess empirical
validity of our learning objectives to aid other business schools in their AoL journey. It is not
our goal to suggest that our measurement tools or learning objectives should be universally
adopted. Therefore for illustration of our process, we will limit our discussion to the overall fit

of our constructs and then discuss in detail two examples of how we used CFA; an example of a
valid measure (business knowledge) and an example of a measure that requires some
modifications (oral communication).
Table 2 documents the model fit indices for the CFA analyses performed for six of the
learning objectives. The learning objectives for business knowledge, written communication,
global awareness context, region, and perspectives are valid for construct reliability and model
fit. Therefore we are reasonably confident that these objectives adequately measure the learning
goals established by the College. The model fit for Oral Communication was below suggested
thresholds.
Table 2
Fit Indices
Composite
reliability
Variance
Extracted
χ2
RMSEA RMSR NNFI CFI
Learning Objective
Business Knowledge (n=218)
.853 .409 61.35** .077 11.66 .932 .949
Written Communication (n=147) .798 .498 6.163** .119 .011 .926 .975
Oral Communication (n=161) .62 .246 120.24** .148 .033 .693 .770
Global Awareness Context (n=151) .85 .397 19.65** .089 .336 .958 .975
Global Awareness Region (n=151) .89 .562 8.183 .000 .192 1.003 1.000
Global Awareness Perspectives (n=90) .96 .73 96.643** .208 .031 .867 .905
**p<.05
Business knowledge. Students take the ETS Major Field Test for the Bachelor’s Degree
in Business (MFT) in a capstone class in their senior year. The MFT is a widely used
standardized exam for business students (www.ets.org/mft). The capstone class is required for all
business majors and the MFT is administered in all sections of the capstone class thus ensuring
that all business majors participate in the assessment prior to graduation. To ensure that students
take the exam seriously and give their best effort, their results are reflected in their course grade.
Two hundred and eighteen responses were obtained from the exams administered in 2010 –
2011. Nine composite scores from the exam are used to assess the overall business knowledge

of the student. (See Table 3 for a listing of these nine items.) A review of the data found no
missing data or significant outliers. The sample size divided by the number of responses to the
number of variables (218/9 = 24) was well above the recommended threshold range of 5 to 20.
The kurtosis and skewness statistics were less than the recommended thresholds of 8 and 3,
which indicates only a slight departure from normality. Therefore we were reasonably confident
in proceeding to the next phase of the analysis, evaluating model fit by running a confirmatory
factor analysis on the item measures.
The χ2
was significant, however the RMSEA was .077, below the recommended threshold
of .08. The RMSEA is parsimonious in that it considers the impact of the number of variables in
their calculations, so it is a better indicator of model fit than χ2. The NNFI and CFI was .932
and .949, well above the recommended threshold of .90. Overall, the model fit is acceptable.

In assessing construct validity of the items, we noted that all items were statistically
significant, however one item measure, quantitative analysis, fell below the recommended
loading of .50. The composite reliability of .853 was well above the recommended threshold of
.6 and the variance extracted was slightly below the recommended threshold at .50. Overall
there is evidence that the item measures adequately reflect the latent construct of business
knowledge. However, further analysis is needed to determine the cause of the low factor loading
of quantitative analysis. We provided Figure 3 as a visual representation of the CFA model for
this construct.
Oral communication. The students’ oral communication skills were measured by a rubric
assessed oral presentation assignment administered in the business communication course, which
is part of the required core curriculum. The business communication professor for all sections of
the class completed the assessment. There is only one business communication professor, thus
ensuring consistency of the measurement process. The rubric is comprised of nine item measures
(see Table 3 for a list of these items). Data was obtained from the 2010-2012 assessments which
resulted in 161 observations for the CFA of the oral communication construct. A review of the
data found two missing observations for the item measure conclusion and one missing

observation for eye contact. Because the impact of missing data was small, we used the mean
imputation method for the missing observations. We also identified one potential outlier; we
deleted the case on a trail run and found that it did not have a significant impact on normality or
the results. The sample size ratio (161/9 = 17.9) is in the recommended threshold range of 5 to
20. The multivariate kurtosis statistic of 23.393 was well above the recommended thresholds of
8, which provides evidence of a departure from normality. The univariate skewness statistics
were below the threshold of 3.
Because of the non-normal distribution, we attempted to run an asymptotically
distribution-free estimation method to contact the CFA. Unfortunately this resulted in an
inadmissible solution because of the existence of a negative error variance. Therefore we used
the Maximum-Likelihood estimation technique which often provides reasonable results with
departures from normality. The χ2
was significant, however the adjusted chi-square ratio (χ2
/df)
was 4.453, below the recommended threshold of 5. The RMSR was .033 well below the
recommended threshold of .10 while the RMSEA was .145, above the recommended threshold of
.08. The NNFI and CFI was .693 and .770, below the recommended threshold of .90. Our
overall assessment is the model fit is poor. The RMSEA, NNFI, and CFI are impacted by the
model complexity which could be an indication that the number of variables in the model
affected model fit.
Two of the items were not statistically significant: conclusion and fillers. Only two of
the nine items were greater than the recommended threshold of .50; projection and pace. The
composite reliability of .62 met the recommended threshold of .6. The variance extracted of .246
was well below the recommended threshold of .50. Our conclusion is that the item measures do
not accurately reflect the latent construct oral communications.

Discussion
The purpose of this article is to highlight an AACSB-accredited program as a case study
of its design of an empirically-validated AoL system and to demonstrate how empirical validity
improved our AoL system. When appropriate, we used confirmatory factor analysis to validate
the measurement instruments used to assess student achievement of the learning objectives
established by the faculty and stakeholders of the College. We provided a description of the CFA
process used to assess the empirical validity of the learning objectives. Finally we illustrated the
process by discussing the results of the validation process on two learning objectives.
For the business knowledge learning objective, we found that both the model fit and
construct reliability are valid. However, we noted that factor loading for quantitative analysis
was much lower than the other item measures. In reviewing the raw scores, not surprisingly, we
found that our students do not perform as well in the quantitative analysis topic when compared
to the other topics covered by the MFT indicating that even though we had a valid measure of
business knowledge, our students need improvement in their quantitative skills. These result
prompted us to examine the curriculum of the class where much of the quantitative methods are
taught.

For the oral communication learning objective, we found that the model fit was poor,
construct reliability was low, and many of the item measures from the rubric did not load. These
results prompted us to examine the measurement tool used to assess achievement oral
communication competency. Corrective action includes a review of the rubric and also the
process used to collect the data.
Conclusion and Limitations. We found value in, and therefore will continue to
empirically validate the AoL learning objectives using confirmatory factor analysis. The
validation process has increased the support of the AoL system by faculty that understand and
are trained in the research process. We have received positive feedback from the AACSB and
higher education associations on our validation process. Most importantly, it provides
confidence in the tools we are using to measure student achievement of the learning objectives.
Now that the process and the supporting models are in place, it will be relatively simple to
continue the validation process in order to continually improve. Because of the method we used
to capture the assessments, we are able to use the same validation process for both AACSB and
SACS accreditations.
We are continually striving to improve our validation process. One limitation is that we
are unable to simultaneously assess the empirical validity of the entire model of the learning
objectives. Assessment is conducted by class, rather than individual student across all classes.
Therefore we do not have data for one student for all learning objectives. To address this
limitation, we are evaluating both in-house and commercially developed databases to track
student data across classes and semesters. For example, the University of Central Florida (UCF)
developed an in-house database for tracking of student data (Moskal, Ellis, & Keon, 2008).
Taskstream is a commercially available database for tracking data (https://www.taskstream.com).

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