RESEARCH ARTICLEPerceived discrimination in medical settin.docx

RESEARCH ARTICLE
Perceived discrimination in medical settings
and perceived quality of care: A population-
based study in Chicago
Maureen R. BenjaminsID
1,2*, Megan Middleton2
1 Sinai Urban Health Institute, Sinai Health System, Chicago,
Illinois, United States of America, 2 Chicago
Medical School, Rosalind Franklin University of Medicine and
Science, North Chicago, Illinois, United States
of America
* [email protected]
Abstract
Perceived discrimination in medical settings remains prevalent
within the U.S. health care
system. However, the details of these experiences and their
associations with perceived
quality of care are not well understood. Our study assessed
multiple measures of perceived
racial/ethnic discrimination in medical settings and investigated

the locations and purported
perpetrators of the discriminatory experiences within a
population-based sample of 1,543
Black, White, Mexican, Puerto Rican, and Other adults. We
used logistic regression to esti-
mate associations between perceived discrimination in the
medical setting and three quality
of care indicators. Overall, 40% of the sample reported one or
more types of perceived dis-
crimination in a medical setting, with significant differences by
race/ethnicity. Discrimination
was perceived across health settings and from a variety of
providers and staff. In adjusted
logistic regression models, individuals reporting discrimination
had more than twice the
odds of reporting fair or poor quality of care (OR = 2.4 [95%
CI: 1.4–4.3]). In addition, per-
ceived discrimination in medical settings was significantly
associated with report of not hav-
ing enough time with the physician and not being as involved in
decision-making as desired.
These findings expand our understanding of perceived
discriminatory experiences in health
care and the consequences of it for patients, providers, and

health care systems. This infor-
mation is essential for identifying future provider interventions
and improving the training of
health care professionals.
Introduction
Racial and ethnic disparities in access to, and quality of, health
care are pervasive and contrib-
ute to the persistent negative health outcomes seen among
communities of color [1–7]. Per-
ceived discrimination may underlie both disparities in health
care and health outcomes [8,9].
In particular, research is needed to better understand racial and
ethnic discrimination in the
health care setting, which likely impacts health care perceptions
and outcomes [10–13].
PLOS ONE | https://doi.org/10.1371/journal.pone.0215976
April 25, 2019 1 / 15
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Citation: Benjamins MR, Middleton M (2019)
Perceived discrimination in medical settings and
perceived quality of care: A population-based study
in Chicago. PLoS ONE 14(4): e0215976. https://
doi.org/10.1371/journal.pone.0215976
Editor: Leonidas G Koniaris, Indiana University,
UNITED STATES
Received: November 14, 2018
Accepted: April 11, 2019
Published: April 25, 2019
Copyright: © 2019 Benjamins, Middleton. This is
an open access article distributed under the terms
of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data underlying

the findings reported in the submitted manuscript
are publicly available through the Inter-university
Consortium for Political and Social Research (Sinai
Community Health Survey 2.0, Chicago, Illinois,
2015-2016 (ICPSR 37073)).
Funding: The Sinai Community Health Survey 2.0
was funded by The Chicago Community Trust
(www.cct.org), grants C2013-00630, C2014-
01723, and C2015-04294. The funders had no role
in the design of the current study, data collection
http://orcid.org/0000-0002-5478-1039
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Patient perceptions of quality are important to study because
there is a growing literature
linking them with health care outcomes [14–15]. Perceptions of
quality are now being incor-
porated into value-based incentive payments from the
government and other organizations, as
well as health care provider ratings [16]. Health care providers
also rely on patient reports of
quality to guide continuous quality improvement efforts. Given
the increasing significance of
perceived quality of care, more research is needed to understand
the factors associated with it.
Despite this, only a handful of studies have examined the
association between discrimina-
tion in health care and quality of care ratings, with most
confirming the expected negative rela-
tionship [17–20]. More broadly, previous studies have found
that perceiving discrimination
in a medical setting is associated with other health care
outcomes, such as delayed or unmet
health care needs [20–23], underutilization of mental health
services [24], and increased emer-

gency department visits and hospital admissions [12]. However,
the literature on discrimina-
tion in medical settings has been limited by measurement issues
and unrepresentative samples
[13]. For example, previous studies have commonly used a
single question to assess whether or
not an individual has perceived discrimination while receiving
health care [10]. In addition,
the majority of studies in this area focus on African American
populations, with limited inves-
tigation into discrimination among other groups of color and
between racial/ethnic popula-
tions typically combined into one group (e.g., Hispanics) [10,
13, 25].
Furthermore, there is a dearth of information about the
purported perpetrators and settings
of the discriminatory event [10]. One study, focusing on
discrimination due to HIV status,
assessed the perpetrators and found attributions spread among
clinical staff (more than
case managers or social workers) [26]. Another study examined
the location of perceived dis-
criminatory health care experiences and revealed that
individuals using community clinics

rather than doctor’s offices as their source of primary care
reported higher rates of discrimina-
tion [27]. However, this finding was true only for the middle
socioeconomic subgroup of
participants.
To address these gaps in the literature, our study investigates
the association of perceived
discrimination in medical settings with three measures of
perceived quality of care within a
population-based sample from selected Chicago communities.
Importantly, this sample
includes sufficient data to examine four racial/ethnic groups
(Non-Hispanic Black, Mexican,
Puerto Rican, and Non-Hispanic White) and uses multiple
measures of perceived discrimina-
tion, including a 7-item scale of discrimination in medical
settings and questions that ask
about discrimination due to insurance status and English
proficiency. Finally, we also examine
the purported perpetrators and settings of perceived
discriminatory events. This new informa-
tion can be used to inform the training of health care
professionals and the development of

policies within health care organizations to reduce perceived
discrimination, with the goals of
improved patient experience and greater health equity.
Methods
Sample
Our analysis used data from the Sinai Community Health
Survey 2.0 (Sinai Survey 2.0).
Detailed information on the sampling design and data collection
methodology is available else-
where (www.sinaisurvey.org). To summarize, the Sinai Survey
2.0 was administered by trained
interviewers from the University of Illinois at Chicago (UIC)
Survey Research Laboratory
between March 2015 and September 2016. The sample was
randomly selected from ten Chi-
cago community areas. The communities were chosen based on
geographic location and
racial/ethnic composition, representing some of the most
socially and economically challenged
neighborhoods in Chicago. The sampled area represented
approximately 386,000 individuals.
Perceived discrimination in medical settings and quality of care

April 25, 2019 2 / 15
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
http://www.sinaisurvey.org
The community areas were divided into primary sampling units
(PSU). The PSUs, and
households within them, were randomly selected using
Probability Proportionate to Size
methodology. Selected households were mailed an advance
letter with study information
before the first contact. Following this, one or two adult(s) (18
years or older) were randomly
selected from each household. An extensive protocol was
followed to maximize the number of
interviews completed, including up to ten in-person contact
attempts, up to five telephone
attempts, and materials left at the door.
Interviews were conducted face-to-face, in English or Spanish
(depending on respondent

preference) using computer-assisted personal interviewing
software. The overall response rate,
which includes some households with unknown eligibility, was
28.4% (calculated with Ameri-
can Association of Public Opinion Research’s (AAPOR)
response rate number 3) [28]. The
cooperation rate, or the proportion of respondents who
completed an interview after contact,
was 53.9% (AAPOR, response rate 4). The final sample
included 1,543 adults. The Sinai Survey
2.0 was approved by the IRBs of the University of Illinois at
Chicago (#2014–0524) and Mount
Sinai Hospital (MSH #14–17). Interviewers explained the study
to all participants, who then
had the opportunity to ask questions before signing the
informed consent document. The
dataset and questionnaires are available through the Inter-
university Consortium for Political
and Social Research website (Sinai Community Health Survey
2.0, Chicago, Illinois, 2015–
2016 (ICPSR 37073)).
Measures
Health Care Utilization. Our dependent variable, perceived

quality of care, was measured with
the following question: “Overall, how would you rate the
quality of health care you received in
the last 12 months? Would you say excellent, very good, good,
fair, or poor?” Those reporting
fair or poor care were dichotomized from those reporting
excellent, very good, or good care.
Two other, more specific, indicators of quality of care were also
asked. These were slightly
modified from the Commonwealth Fund Health Care Survey
(2001) and reflect items from
the Consumer Assessment of Healthcare Providers and Systems
(CAHPS) survey related to cli-
nician communication [29]. Both were prefaced with the
instruction to think about one’s last
visit to a doctor. The questions were: “How much did the doctor
involve you in decisions
about your care?” and “How much time did the doctor spend
with you?” For both, respon-
dents were given the following response options: “as much as
you wanted, almost as much as
you wanted, less than you wanted, or a lot less than you
wanted.” The responses were dichoto-
mized into positive and negative categories.

Discrimination in Medical Settings. For the primary
independent variable, we used a slightly
modified version of the Discrimination in Medical Settings
scale (DMS) [12, 19, 30, 31], which
is based on the Everyday Discrimination Scale (EDS) [32].
Questions were prefaced with the
following: “Please think about all the times in your life when
you’ve gotten health care. When
getting health care, how often have any of the following things
happened to you because of
your race, ethnicity, or color?” During the questionnaire
development, there was interest from
several researchers in adding an item about providers acting as
if they were unwilling to touch
the patient, based on conversations with community members.
Given the limited space avail-
able, the difficult decision was made to make room for this item
by substituting it for another
item from the scale. Because the item on being treated with less
courtesy was perceived to over-
lap the most with the remaining items, it was excluded. The
response scale for all items was
never, rarely, sometimes, or often. Preliminary analyses showed
that the responses had highly
skewed distributions for each question, with 74–94% of

responses falling in the “never”
April 25, 2019 3 / 15
category. Thus, the responses were dichotomized to represent
never experienced discrimina-
tion vs. ever (as done in previous uses of the scale) [12, 19].
For those reporting any lifetime experience of perceived
discrimination in a medical setting
(based on an affirmative answer to one or more of the DMS
items), as well as a health care visit
in the past 12 months, we asked about the individual involved
and the location of the most
recent time they perceived this type of discrimination. To
identify the purported perpetrator,
we asked, “Was the person who treated you this way your
doctor, a nurse, a receptionist, or
someone else?” Respondents could choose all that apply. The
location measure assessed
whether this most recent event took place in a doctor’s office,
an emergency room, a clinic, or

somewhere else. Respondents could only identify one location.
Other measures of perceived discrimination in medical settings
included a question that
assessed whether respondents felt that they had been “judged
unfairly” or “treated with disre-
spect” by the doctor or medical staff due to their race, ethnicity,
or color in the past 12 months
using a four-point Likert-like scale ranging from 1 (never) to 4
(often). Perceived discrimina-
tion based on insurance-status and English proficiency was also
assessed. Respondents were
asked to think about their experiences in health care in the last
12 months and answer yes or
no to the following questions: “Have you felt that the doctor or
medical staff you saw judged
you unfairly or treated you with disrespect because of the type
or health insurance you have, or
your ability to pay for care?” and “. . .because of how well you
speak English?” These questions
were only asked for those with a health care visit in the past 12
months.
Demographic and Socioeconomic Characteristics. Demographic
variables included age, gen-
der, and race/ethnicity (non-Hispanic White, non-Hispanic
Black, Mexican, and Puerto

Rican). Members of other racial/ethnic groups (including
Hispanic and non-Hispanic individ-
uals) were not examined in stratified analyses due to small
sample sizes. Socioeconomic vari-
ables included education, employment, and current health
insurance. Unmet health care
needs measured whether or not respondents had health care
needs in the past year that they
did not receive due to cost. Health care needs included: medical
care or surgery; a doctor’s
appointment; prescription medicine; mental health care or
counseling; dental care; and/or,
eyeglasses.
Other Covariates. Global discrimination measured personal
experiences of perceived dis-
crimination due to race, ethnicity, or color using a four-point
scale ranging from 1 (never) to 4
(often). These responses were dichotomized to reflect whether
or not the participants had ever
experienced discrimination. Self-rated health was assessed by
asking: “Would you say that in
general your health is excellent, very good, good, fair, or
poor?” This variable was dichoto-
mized into excellent, good, or very good health versus fair or

poor health.
Data analyses
We first generated descriptive statistics for the total sample and
for the largest four racial/eth-
nic groups. Next, we examined bivariate relationships between
our primary discrimination in
medical settings outcome (DMS) and the three quality variables.
Multivariate logistic regres-
sion models were also used to assess these associations,
controlling for demographic, socioeco-
nomic, unmet health care needs, and other covariates. For each
of the three outcomes, both
unadjusted and adjusted models were run. We also ran models
with interaction terms between
race/ethnicity and discrimination in health care to explore effect
modification.
The survey team also conducted a nonresponse bias analysis to
better understand potential
bias due to the low response rate and sample selection (data not
shown). Briefly, systematic
block and housing unit observations were added to the data,
along with block-group level data
from the Census Bureau’s American Community Survey. Models
were then run to identify

April 25, 2019 4 / 15
variables that independently predicted the odds of 1) the
primary respondent completing an
interview and 2) having poor health outcomes or risk factors (as
assessed by at least one nega-
tive response to 16 diverse health-related questions). Finally,
any identified variables were used
to re-estimate nonresponse-adjusted weights to account for
potential nonresponse bias and
estimates for each health indicator were compared with those
using the previously estimated
standard weights (which adjusted for gender, age, and
race/ethnicity).
Statistical significance was set at p<0.05. All analyses were
conducted in Stata 14.2 and
account for the complex survey design. Estimates are also
weighted to be representative of the
entire population from which the sample was drawn.
Results

Descriptive statistics
Demographic and socioeconomic characteristics. Sample
characteristics are shown in
Table 1 for the full sample and by racial/ethnic group. The
majority of the sample identified as
a racial or ethnic minority. Survey respondents were evenly
split between sexes and the mean
age was 42 years. Approximately three-fourths of the sample
had at least a high school degree
and the vast majority were employed. Both Whites and Blacks
had a significantly higher high
school graduation rate than Mexicans, while Blacks were more
likely to be unemployed than
Whites or Puerto Ricans. Twenty-one percent of respondents
were uninsured and one-third
had unmet health care needs due to cost in the past year. White
and Black adults were less
likely to be uninsured than Hispanics; Blacks and Mexicans had
higher rates of unmet health
needs than Whites. Almost two-thirds of respondents had ever
perceived discrimination in
general. Whites were significantly less likely to report this than
Blacks and Mexicans. Finally,

one-third reported fair or poor health.
Health care outcomes. Fifteen percent reported receiving fair or
poor quality health care
in the past year. This number was significantly lower for White
respondents compared to
Blacks and Mexicans. Seven percent reported that they were
involved in decisions less than
they wanted and 15% had less time with the doctor than they
wanted. These numbers did not
vary significantly by race/ethnicity.
Discrimination in medical settings. Overall, 40% had ever
experienced some type of
discrimination in a medical setting (DMS) (Table 2). This
differed significantly by race/eth-
nicity, with Whites being less likely to report discrimination
than the three groups of color
and Mexicans being less likely to report it than Blacks. The
scale item “received poorer ser-
vice” was most frequently reported (29%). Again, significant
racial/ethnic differences exist.
For example, only 3% of Whites reported experiencing this type
of discriminatory event,
compared to 43% of Blacks. The scale item “. . .doctor or nurse
acts as if he or she is afraid of

you” had the lowest overall affirmation rate (6%); however,
among Blacks, 14% perceived
this type of discrimination.
Overall, a smaller percentage reported discrimination in a
medical setting when it was
asked as a single question (separate from the DMS scale) and
limited to experiences in the past
year. For example, only 18% reported any level of perceived
when asked in this manner. Affirmative responses ranged from
no Whites reporting this to
one-quarter of Mexicans. Very few responded that this type of
discrimination occurred
“often”.
Purported Perpetrator. Those who responded affirmatively to
any of the DMS items and
had a health care visit in the past year were asked to identify
the perpetrator(s) of the most
recent time they perceived being discriminated against. A
substantial percentage selected each
of the four potential options (i.e. doctor, nurse, receptionist, or
someone else). The only racial/

April 25, 2019 5 / 15
ethnic difference was that Whites were significantly less likely
to report perceived discrimina-
tion from a receptionist compared to Mexicans.
Location. Similar to above, respondents who reported any type
of perceived discrimination
in the DMS and who had a health care visit in the past year
were asked about the location of
the most recent discriminatory event. Responses were evenly
distributed between the four
options of a doctor’s office, the emergency room, a clinic, and
elsewhere. Whites were less
likely to report such experiences in a clinic setting compared to
the other groups, but no
racial/ethnic differences were seen.
Judged Unfairly. Overall, 13% of those with a health care visit
in the past year reported that
they had been judged or treated unfairly by a doctor or staff
member because of their type of
insurance or ability to pay. Blacks were significantly more
likely to report this than Whites and
Mexicans. Overall, only 2% reported being treated unfairly due

to how well they spoke English.
Whites were significantly less likely to report this type of
perceived discrimination compared
to other groups.
Bivariate analyses
Fig 1 displays results from the bivariate analyses. Those
reporting DMS were more than twice
as likely to rate their quality of care as fair or poor compared to
those with no DMS (24% [95%
CI: 18–30%] versus 9% [95% CI: 6–13%], respectively). Those
reporting DMS were also signifi-
cantly more likely to be less involved in decisions than they
wanted (11% [95% CI: 8–15%])
Table 1. Demographic, health, and health care characteristics by
race/ethnicity
a
.
Total NH White NH Black Mexican Puerto Rican
Percent (95% CI) Percent (95% CI) Percent (95% CI) Percent
(95% CI) Percent (95% CI)
Demographics
Male 50 (46–54) 54 (43–65) 45 (40–50) 54 (49–60) 49 (38–59)
Age (mean) 42 (40–44) 47 (44–50) 42 (39–45) 39 (37–41) 45
(41–49)

English as primary language 86 (81–89) 100
b
100
b
70 (62–77) 89 (74–96)
Married 38 (33–42) 51 (39–63) 20 (15–27) 45 (38–52) 34 (23–
46)
Socioeconomic Status
High school degree or more 73 (68–78) 99 (97–100) 78 (71–84)
62 (52–70) 68 (51–81)
Unemployed 10 (8–12) 3 (1–8) 16 (11–21) 8 (5–12) 4 (2–9)
Access to Care
Health insurance 79 (75–83) 93 (87–97) 89 (84–93) 66 (58–73)
66 (58–73)
Any unmet health care needs 33 (29–38) 19 (12–27) 42 (35–49)
32 (35–49) 33 (22–47)
General Discrimination
Any discrimination 64 (59–69) 37 (26–48) 73 (66–79) 67 (60–
74) 63 (47–76)
Self-Rated Health
Fair or poor 33 (29–37) 18 (10–28) 34 (26–43) 37 (31–42) 31
(20–45)
Health Care Quality Outcomes
Perceived quality of care as fair/poor 15 (12–19) 3 (1–6) 16
(12–22) 20 (14–28) 11 (5–25)

Not involved in decisions 7 (5–9) 8 (5–14) 6 (4–9) 8 (5–13) 5
(2–12)
Little time with doctor 15 (12–19) 10 (6–17) 15 (10–22) 17 (12–
25) 18 (10–32)
N 1,543 c 219 536 521 151
Notes: NH = non-Hispanic; CI = confidence interval
a Sinai Community Health Survey 2.0 (2015–2016, ten
Community Areas in Chicago, IL). All statistics weighted for
clustered survey sampling design.
b
No confidence interval.
c
Total sample includes individuals in racial/ethnic groups other
than those displayed separately.
https://doi.org/10.1371/journal.pone.0215976.t001
April 25, 2019 6 / 15
versus 4% [95% CI: 3–7%], respectively)) and have less time
with the doctor than they wanted
compared to those reporting no DMS (21% [95% CI: 17–27%])

versus 10% [95% CI:7–16%],
respectively).
Logistic regression analyses
We report unadjusted and adjusted odds ratios in Table 3. The
findings from the six logistic
regression models indicate that DMS is significantly associated
with all three quality of care
outcomes (i.e. overall perceptions of health care quality as fair
or poor, less involvement in
health care decisions than desired, and less time spent with
one’s physician than desired). In
Table 2. Perceived discrimination in medical settings and
related factors by race/ethnicity, Sinai Community Health
Survey 2.0 (2015–2016, ten Community Areas
in Chicago, IL)
a
.
Overall NH White NH Black Mexican Puerto Rican
Percent (95% CI) Percent (95% CI) Percent (95% CI) Percent
(95% CI) Percent (95% CI)
Discrimination in Medical Settings (ever)
Any Type of Discrimination in Medical Settings 40 (35–45) 13
(8–20) 56 (48–63) 39 (32–46) 44 (31–58)
Doctor or nurse is not listening to you 24 (20–28) 4 (2–11) 34

(29–41) 23 (18–30) 28 (16–43)
Treated with less respect 25 (20–29) 3 (1–9) 34 (27–42) 25 (19–
31) 29 (17–44)
Received poorer service 29 (24–34) 3 (1–10) 43 (35–51) 28
(22–34) 33 (21–48)
Doctor or nurse acts better than you 18 (15–22) 7 (4–11) 26
(20–32) 15 (10–20) 23 (13–39)
Doctor or nurse acts as if you are not smart 18 (14–22) 7 (3–13)
29 (23–36) 14 (10–19) 26 (14–42)
Doctor or nurse acts as if he or she is afraid of you 6 (4–9) - -
b
14 (10–21) 4 (2–7) 5 (2–11)
Doctor or nurse did not want to touch you 8 (6–10) - -
b
14 (11–19) 6 (4–10) 5 (2–11)
Treated Unfairly When Getting Medical Care (past year)
c
Never 82 (77–85) 100 (98–100) 80 (73–85) 76 (68–83) 85 (69–
93)
Rarely 11 (8–15) - -
b
10 (7–15) 17 (11–24) 8 (2–22)
Sometimes 6 (4–9) - -

b
8 (5–14) 6 (3–11) 7 (2–23)
Often 1 (0–3) - -
b
2 (1–4) 2 (1–4) - -
b
Purported Perpetrator of Most Recent Discriminatory Event
de
Doctor 25 (19–33) 29 (10–58) 30 (19–45) 22 (13–36) 16 (7–31)
Nurse 30 (24–36) 21 (7–48) 31 (23–41) 24 (17–34) 39 (19–63)
Receptionist 23 (17–29) 7 (3–19) 18 (10–30) 29 (22–37) 26
(11–51)
Someone else 22 (17–28) - -
b
25 (18–34) 23 (16–33) 13 (6–27)
Location of Most Recent Discriminatory Event
e
Doctor’s office 29 (22–36) 19 (8–37) 33 (22–48) 25 (15–38) 34
(18–55)
Emergency room 30 (23–38) 44 (19–73) 32 (20–47) 31 (22–41)
28 (12–52)
A clinic 20 (15–25) - -
b

19 (13–28) 18 (12–28) 17 (7–36)
Somewhere else 22 (17–28) 31 (12–60) 16 (10–24) 26 (18–37)
21 (7–50)
Judged or Treated Unfairly by Doctor or Staff
c
Because of type of insurance or ability to pay 13 (11–17) 7 (4–
12) 22 (16–29) 10 (6–15) 15 (7–32)
Because of how well you speak English 2 (0–8) - -
b
4 (2–7) 11 (7–15) 5 (2–13)
N 1,543 219 536 521 151
Notes: NH = non-Hispanic; CI = confidence interval
a
All statistics weighted for clustered survey sampling design.
b
Insufficient cell size (n<5).
c
Limited to those with a health care visit in past 12 months.
d
Respondents could choose more than one answer.
e
Limited to those with a health care visit in past 12 months and
who reported a discriminatory event.

April 25, 2019 7 / 15
the three unadjusted models, individuals who report any
perceived discrimination in a medical
setting have two to three times higher odds of reporting each of
the negative health care quality
outcomes.
In the three adjusted models, two of the odds ratios are slightly
attenuated, but all remain
significant. Specifically, individuals reporting perceived
had more than twice the odds of reporting fair or poor quality of
care (OR = 2.4 [95% CI: 1.4–
4.3]). Perceived discrimination in medical settings was also
significantly associated with not
Fig 1. Three measures of health care quality by lifetime
experience of perceived discrimination in medical settings
ab

.
a Sinai
Community Health Survey 2.0 (2015–2016, ten Community
Areas in Chicago, IL). All statistics weighted for clustered
survey
sampling design.
b
“Any Perceived Discrimination in Medical Settings” indicates
an affirmative response to any item in the
Discrimination in Medical Setting scale.
https://doi.org/10.1371/journal.pone.0215976.g001
Table 3. Unadjusted and adjusted odd ratios for the association
of perceived discrimination in medical settings with three health
care quality outcomes
a
.
Perceived Quality of Care (Fair/Poor) Not Involved in Decisions
Little Time with Doctor
OR (95% CI) OR (95% CI) OR (95% CI)
Discrimination in Medical Settings
Unadjusted Models 3.2 (1.9–5.3) 2.9 (1.6–5.2) 2.4 (1.4–4.2)
n = 1,397 n = 1,529 n = 1,530
Adjusted Models
b
2.4 (1.4–4.3) 2.4 (1.3–4.4) 2.6 (1.5–4.6)

n = 1,243 n = 1,362 n = 1,364
Notes:
a Sinai Community Health Survey 2.0 (2015–2016, ten
Community Areas in Chicago, IL). All statistics weighted for
clustered survey sampling design.
b
Adjusted for age, sex, race/ethnicity, marital status, education,
employment, insurance, unmet health care needs, general
discrimination, and self-rated health.
Excludes members of Other race/ethnic category.
April 25, 2019 8 / 15
https://doi.org/10.1371/journal.pone.0215976.g001
being as involved in decision-making as desired (OR = 2.4 [95%
CI: 1.3–4.4]) and not having
enough time with the physician (OR = 2.6 [95% CI: 1.5–4.6]).
Results from models including the interaction terms between
race/ethnicity and discrimi-
nation in health care (not shown) suggested that effect

modification was likely for two of the
three quality outcomes (perceived quality of care and
involvement in decisions). In these mod-
els, discrimination in health care was most strongly associated
with the quality outcomes for
Blacks and Mexicans. However, the small sample sizes
precluded stratifying models by race/
ethnicity.
Nonresponse bias
In the nonresponse bias analyses (not shown), two variables—
concentrated economic disad-
vantage (percent unemployed, percent female-headed families
with children, percent Black,
and percent below the poverty level) and concentrated
immigration (percent Hispanic and
percent foreign-born)—were associated with both nonresponse
and at least one of the 16
selected health measures. When comparing estimates for each
health indicator using the stan-
dard weights and the nonresponse-adjusted weights, all
estimates were similar (i.e. none of the
nonresponse-adjusted health indicator estimates fell outside the
95% confidence intervals of

the standard weighted estimates).
Discussion
A positive patient experience is increasingly recognized as an
essential element of high quality
health care [33]. Accordingly, perceptions of quality of care are
now included in determina-
tions of governmental funding and provider ratings. Emerging
literature has found that per-
ceptions of low-quality care may be linked to poorer health care
outcomes (such as delayed
care and non-compliance with treatment) as well as objective
measures of quality of care [14,
22]. However, little is known about the predictors of patient
quality ratings. Our study adds
to the evidence by exploring how frequently perceived
discrimination in medical settings is
reported, who is allegedly perpetrating it, where it happens, and
how it is associated with per-
ceived quality measures.
Overall, 40% of adults in this sample responded affirmatively to
one or more items on the
DMS, with significant differences by race/ethnicity. Receiving
poorer service was the most fre-

quently reported issue, with over 40% of Blacks reporting this
type of discriminatory event.
Few patterns were seen regarding the purported perpetrators and
locations identified. More
specifically, doctors, nurses, receptionists, and others were all
sources of perceived discrimina-
tion. Similarly, substantial percentages of the population
reported perceived discriminatory
events at each type of clinical location. Finally, in adjusted
models, reporting one or more
types of discrimination in a medical setting was significantly
associated with reporting fair or
poor quality of care, not having enough time with the physician,
and not being as involved in
decision-making as desired.
Comparison with previous findings
Previous studies using the DMS scale reflect a wide range of
prevalence rates, from 8% of
female veterans [34] to over 60% of those included in a
convenience sample of African Ameri-
can adults in northern Ohio [12] and a sample of HIV patients
[29]. Consistent with the cur-
rent literature, we found that the perception of race-related fear
from doctors or nurses toward

patients was the least reported type of perceived discrimination
[12, 30, 34, 35]. In previous
studies, perceiving that providers were “not listening” was the
most commonly reported item
April 25, 2019 9 / 15
on the DMS [12, 19, 29, 30, 34]; however, in our study,
receiving “poorer services” was most
frequently reported.
Not surprisingly, our study found lower levels of perceived
discrimination in health care
(18%) when using a single-item measure and when limiting
events to the past 12 months.
Many previous cross-sectional studies on general populations
(also using single-item question)
have reported far lower rates of health care discrimination,
ranging from 1% to 9%, compared
to studies using the DMS [17, 22, 24, 36–44]. Most of these had
predominantly White sample
populations [22, 24, 36–39]. In contrast, a cross-sectional study

conducted in Chicago in 2002
yielded a higher prevalence of 22% [20].
Consistent with much of the previous literature, our study found
that Blacks reported the
highest rates of health care discrimination (56%) using the DMS
scale [10, 45, 39, 43, 44]; how-
ever, rates among Puerto Ricans and Mexicans far exceeded
previous findings at 45% and
40%, respectively. Few studies have estimated prevalence rates
within the large and diverse
Latino population [17, 41]. One study of Puerto Ricans in
Boston found that 12% had experi-
enced discrimination in medical settings [46]. Another found
nearly equal rates of perceived
healthcare discrimination between adults of Mexican and Puerto
Rican descent, at roughly
24% [20].
Perceived discrimination and perceived quality of health care
The current study found that DMS is associated with three
measures of quality of care (related
to overall perceived quality, involvement with decisions, and
time spent with physician), in
line with the existing studies. For example, one study found that

perceiving discrimination
while receiving health care was the primary predictor of the
variance in quality of care ratings
between Black and White patients [17], while another found that
perceived discrimination in
health care (not necessarily due to race/ethnicity) was related to
perceived quality of care for
foreign-born (but not U.S. born) Hispanics [18]. Most existing
studies on perceived discrimi-
nation in health care and quality outcomes have used single
item discrimination measures [17,
18, 30, 43, 47]. As an exception, a study using a small sample
of Black and White veterans
found that a multi-item scale of discrimination in health care
was associated with quality of
diabetes care [19].
Strengths and limitations
The present research builds upon the literature by examining the
prevalence of this type of dis-
crimination for adults of Mexican and Puerto Rican descent,
which addresses recent calls to
add to the limited body of health care discrimination research
on these growing populations

[25, 27, 41]. In addition, we were able to enlist the more
detailed DMS tool in order to attain
more actionable and specific data regarding perceived
discriminatory experiences in health
care settings. This study is also among the first to assess the
locations and purported perpetra-
tors of perceived discrimination in health care. It is critical to
understand that the patient expe-
rience, and perceptions of quality, are impacted at all stages of
the clinical process, from ease of
scheduling to interactions with office staff (who are often a
patient’s first point of contact), to
physician encounters [48]. By studying the purported
perpetrators and locations reported in
the context of discriminatory events, we are able to more clearly
understand the patient experi-
ence and, consequently, collectively work to improve patient
care and health outcomes
through targeted interventions.
It should be noted that these data, which were collected using a
probability sample of ten
diverse Chicago communities, do not represent greater Chicago
or U.S. populations. As dis-
cussed above, the overall response rate was low (28.4%,

AAPOR response rate type 3) [28]. We
April 25, 2019 10 / 15
weighted all statistics to make the results representative of the
sampling frame to account for
this. Moreover, the cooperation rate, which only includes those
individuals with whom inter-
viewers made contact, was almost double (53.9%, AAPOR
cooperation rate type 4) [28]. These
rates reflect the general decline in response rates seen
nationally. As with all surveys, we must
consider whether participants differ from non-participants in
characteristics related to our
variables of interest (or the relationships between them). Our
nonresponse bias analysis did
not show evidence of this type of bias in these data. However,
we were unable to specifically
test for differences in levels of perceived discrimination or
healthcare satisfaction between
responders and nonresponders. One might hypothesize that
individuals with higher levels of

perceived discrimination in medical settings and lower levels of
healthcare satisfaction would
be less likely to participate than other individuals, particularly
given that our survey was con-
ducted by a research center that is part of a healthcare system.
If this was the case, our numbers
underestimate the extent of perceived discrimination in our
healthcare system, while overesti-
mating levels of satisfaction with care.
Another of our study’s weaknesses is intrinsic to the measure of
perceived discrimination.
Self-reported discrimination measures reflect only what subjects
are able to identify and recall,
and willing to report. Moreover, several measures related to
were limited to those who reported a health care visit in the past
year. The use of odds ratios as
a measure of association may overstate the association, given
that both the outcome and expo-
sure are prevalent. Finally, the cross-sectional nature of the data
precluded investigation of
causal relationships.
Practical implications

By illuminating associations between patients’ perceptions of
discrimination in medical set-
tings and quality of care outcomes, health care systems can be
incentivized and guided in the
development of systemic improvement measures. Such
interventions, including surveillance,
training, and policies, can be further informed by data regarding
the types of behaviors and
clinical scenarios most commonly implicated in perceived
discriminatory experiences. Aware-
ness of the need for this type of work is increasing, as
highlighted by the Physicians’ Charter
which states that physicians must “work actively to eliminate
discrimination in health care”
[49]. Though previous studies of the effectiveness of culturally
competent care education have
shown mixed results [50], there is some evidence that
completion of such interventions can
mediate greater patient satisfaction and greater follow-up
appointment attendance [51]. Simi-
larly, medical schools have shown some success in reducing
medical student implicit bias
through health disparities and culture competence curricula
[52]. Strategies to specifically

address physician implicit bias have been proposed [53], but
there is minimal implementation
of this type of training in practice [54].
Directions for future research
Larger and national studies of perceived discrimination in
medical settings are needed to
assess the prevalence and consequences of this issue in the
broader U.S. health care system
[10]. Future studies, following patient perceptions, health
behaviors, and outcomes over time,
would help clarify which health care outcomes are most
vulnerable to this type of perceived
discrimination. Learning health care systems working to reduce
provider-based discrimina-
tion through professional training (such as implicit bias
training) should carefully evaluate
and report on the efficacy of such systemic implementations.
Ideally, objective health out-
comes from EMR data should be assessed to better understand
the impact of health care dis-
crimination perceptions on health, as well as potential
improvements resulting from system

April 25, 2019 11 / 15
interventions. Ultimately, however, we must recognize that the
factors contributing to health
disparities are vast, and extend far beyond the direct
interactions between patients and health
professionals [2]. Further research into the influence of
institutional racism and other social
determinants of health is needed to guide broader policy-making
that might work synergisti-
cally with improvements in interpersonal health care
experiences, with the ultimate goal of
improving health equity [10, 55].
Conclusions
Levels of perceived discrimination in medical settings over a
lifetime are high for Blacks, Mexi-
cans, and Puerto Ricans in this sample from ten Chicago
communities and this type of per-
ceived discrimination was strongly related to perceptions of the
patient experience. While
racism is widely acknowledged (and considered a determinant

of existing health disparities)
for Black populations, perceived discrimination in the medical
setting represents an emerging
issue to be addressed for the growing Hispanic populations and
those who serve them. Our
findings further reveal that perceived discriminatory events are
not limited to one type of
health care setting or role. More work is needed to identify,
address, and, hopefully, prevent,
this type of interaction within our health care systems in order
to improve patient experiences
and ultimately outcomes.
Acknowledgments
We are grateful to the Chicago residents who contributed to the
Sinai Community Health Sur-
vey 2.0. We also thank our colleagues at the Sinai Urban Health
Institute for their valuable
feedback on earlier drafts.
Author Contributions
Conceptualization: Maureen R. Benjamins.
Formal analysis: Maureen R. Benjamins.
Methodology: Maureen R. Benjamins.

Project administration: Maureen R. Benjamins.
Writing – original draft: Maureen R. Benjamins, Megan
Middleton.
Writing – review & editing: Maureen R. Benjamins.
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Practical Management Science
Wayne L. Winston
Kelley School of Business, Indiana University

S. Christian Albright
Kelley School of Business, Indiana University
6th
Edition
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Practical Management Science,
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Wayne L. Winston,
S. Christian Albright
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Printed in the United States of America
Print Number: 01 Print Year: 2017
To Mary, my wonderful wife, best friend, and constant
companion
And to our Welsh Corgi, Bryn, who still just wants to play
ball S.C.A.
To my wonderful family
Vivian, Jennifer, and Gregory W.L.W.

S. Christian Albright got his B.S. degree in Mathematics from
Stanford in 1968 and his Ph.D. degree in Operations Research
from Stanford in 1972. Until his retirement in 2011, he taught in
the Operations & Decision Technologies Department in the
Kelley
School of Business at Indiana University. His teaching included
courses in management science, computer simulation, and
statis-
tics to all levels of business students: undergraduates, MBAs,
and
doctoral students. He has published over 20 articles in leading
operations research journals in the area of applied probability,
and he has authored several books, including Practical Manage-
ment Science, Data Analysis and Decision Making, Data

Analysis for Managers, Spread-
sheet Modeling and Applications, and VBA for Modelers. He
jointly developed StatTools,
a statistical add-in for Excel, with the Palisade Corporation. In
“retirement,” he continues
to revise his books, and he has developed a commercial product,
ExcelNow!, an extension
of the Excel tutorial that accompanies this book.
On the personal side, Chris has been married to his wonderful
wife Mary for
46 years. They have a special family in Philadelphia: their son
Sam, his wife Lindsay,
and their two sons, Teddy and Archer. Chris has many interests
outside the academic
area. They include activities with his family (especially
traveling with Mary), going to
cultural events, power walking, and reading. And although he
earns his livelihood from
statistics and management science, his real passion is for
playing classical music on the
piano.
Wayne L. Winston is Professor Emeritus of Decision
Sciences at the Kelley School of Business at Indiana University
and is now a Professor of Decision and Information Sciences

at the Bauer College at the University of Houston. Winston
received his B.S. degree in Mathematics from MIT and his
Ph.D. degree in Operations Research from Yale. He has written
the successful textbooks Operations Research: Applications
and Algorithms, Mathematical Programming: Applications
and Algorithms, Simulation Modeling with @RISK, Practical
Management Science, Data Analysis for Managers, Spreadsheet
Modeling and Applications, Mathletics, Data Analysis and
Business Modeling with
Excel 2013, Marketing Analytics, and Financial Models Using
Simulation and
Optimization. Winston has published over 20 articles in leading
journals and has won
more than 45 teaching awards, including the school-wide MBA
award six times. His
current interest is in showing how spreadsheet models can be
used to solve business
problems in all disciplines, particularly in finance, sports, and
marketing.
Wayne enjoys swimming and basketball, and his passion for
trivia won him an
appearance several years ago on the television game show
Jeopardy, where he won two
games. He is married to the lovely and talented Vivian. They

have two children, Gregory
and Jennifer.
About the Authors
vii
Preface xiii
1 Introduction to Modeling 1
2 Introduction to Spreadsheet Modeling 19
3 Introduction to Optimization Modeling 71
4 Linear Programming Models 135
5 Network Models 219
6 Optimization Models with Integer Variables 277

7 Nonlinear Optimization Models 339
8 Evolutionary Solver: An Alternative Optimization Procedure
407
9 Decision Making under Uncertainty 457
10 Introduction to Simulation Modeling 515
11 Simulation Models 589
12 Queueing Models 667
13 Regression and Forecasting Models 715
14 Data Mining 771
References 809
Index 815
MindTap Chapters
15 Project Management 15-1
16 Multiobjective Decision Making 16-1
17 Inventory and Supply Chain Models 17-1
Brief Contents

ix
Preface xiii
CHAPTER 1 Introduction to Modeling 1
1.1 Introduction 3
1.2 A Capital Budgeting Example 3
1.3 Modeling versus Models 6
1.4 A Seven-Step Modeling Process 7

1.5 A Great Source for Management Science
Applications: Interfaces 13
1.6 Why Study Management Science? 13
1.7 Software Included with This Book 15
1.8 Conclusion 17
CHAPTER 2 Introduction to Spreadsheet
Modeling 19
2.1 Introduction 20
2.2 Basic Spreadsheet Modeling:
Concepts and Best Practices 21
2.3 Cost Projections 25
2.4 Breakeven Analysis 31
2.5 Ordering with Quantity Discounts
and Demand Uncertainty 39
2.6 Estimating the Relationship between
Price and Demand 44
2.7 Decisions Involving the Time Value of
Money 54

2.8 Conclusion 59
Appendix Tips for Editing and
Documenting Spreadsheets 64
CASE 2.1 Project Selection at Ewing Natural
Gas 66
CASE 2.2 New Product Introduction at eTech 68
CHAPTER 3 Introduction to Optimization
Modeling 71
3.1 Introduction 72
3.2 Introduction to Optimization 73
3.3 A Two-Variable Product Mix Model 75
Contents
3.4 Sensitivity Analysis 87
3.5 Properties of Linear Models 97
3.6 Infeasibility and Unboundedness 100
3.7 A Larger Product Mix Model 103
3.8 A Multiperiod Production Model 111
3.9 A Comparison of Algebraic

and Spreadsheet Models 120
3.10 A Decision Support System 121
3.11 Conclusion 123
Appendix Information on Optimization Software 130
CASE 3.1 Shelby Shelving 132
CHAPTER 4 Linear Programming Models 135
4.1 Introduction 136
4.2 Advertising Models 137
4.3 Employee Scheduling Models 147
4.4 Aggregate Planning Models 155
4.5 Blending Models 166
4.6 Production Process Models 174
4.7 Financial Models 179
4.8 Data Envelopment Analysis (DEA) 191
4.9 Conclusion 198
CASE 4.1 Blending Aviation Gasoline at Jansen
Gas 214
CASE 4.2 Delinquent Accounts at GE Capital 216
CASE 4.3 Foreign Currency Trading 217
CHAPTER 5 Network Models 219
5.2 Transportation Models 221

5.3 Assignment Models 233
5.4 Other Logistics Models 240
5.5 Shortest Path Models 249
5.6 Network Models in the Airline Industry 258
5.7 Conclusion 267
CASE 5.1 Optimized Motor Carrier Selection at
Westvaco 274
CHAPTER 9 Decision Making under
Uncertainty 457
9.2 Elements of Decision Analysis 460

9.3 Single-Stage Decision Problems 467
9.4 The PrecisionTree Add-In 471
9.5 Multistage Decision Problems 474
9.6 The Role of Risk Aversion 492
9.7 Conclusion 499
CASE 9.1 Jogger Shoe Company 510
CASE 9.2 Westhouser Paper Company 511
CASE 9.3 Electronic Timing System for
Olympics 512
CASE 9.4 Developing a Helicopter Component
for the Army 513
CHAPTER 10 Introduction to Simulation
Modeling 515
10.2 Probability Distributions for Input
Variables 518
10.3 Simulation and the Flaw of Averages 537
10.4 Simulation with Built-in Excel Tools 540
10.5 Introduction to @RISK 551
10.6 The Effects of Input Distributions on

Results 568
10.7 Conclusion 577
Appendix Learning More About @RISK 583
CASE 10.1 Ski Iacket Production 584
CASE 10.2 Ebony Bath Soap 585
CASE 10.3 Advertising Effectiveness 586
CASE 10.4 New Project Introduction at eTech 588
CHAPTER 11 Simulation Models 589
11.2 Operations Models 591
11.3 Financial Models 607
11.4 Marketing Models 631
11.5 Simulating Games of Chance 646
11.6 Conclusion 652
Appendix Other Palisade Tools for Simulation 662
x Contents
CHAPTER 6 Optimization Models with Integer
Variables 277
6.2 Overview of Optimization with Integer

Variables 279
6.3 Capital Budgeting Models 283
6.4 Fixed-Cost Models 290
6.5 Set-Covering and Location-Assignment
Models 303
6.6 Cutting Stock Models 320
6.7 Conclusion 324
CASE 6.1 Giant Motor Company 334
CASE 6.2 Selecting Telecommunication Carriers to
Obtain Volume Discounts 336
CASE 6.3 Project Selection at Ewing Natural Gas 337
CHAPTER 7 Nonlinear Optimization Models 339
7.2 Basic Ideas of Nonlinear Optimization 341
7.3 Pricing Models 347
7.4 Advertising Response and Selection Models 365
7.5 Facility Location Models 374
7.6 Models for Rating Sports Teams 378
7.7 Portfolio Optimization Models 384
7.8 Estimating the Beta of a Stock 394
7.9 Conclusion 398

CASE 7.1 GMS Stock Hedging 405
CHAPTER 8 Evolutionary Solver: An Alternative
Optimization Procedure 407
8.2 Introduction to Genetic Algorithms 411
8.3 Introduction to Evolutionary Solver 412
8.4 Nonlinear Pricing Models 417
8.5 Combinatorial Models 424
8.6 Fitting an S-Shaped Curve 435
8.7 Portfolio Optimization 439
8.8 Optimal Permutation Models 442
8.9 Conclusion 449
CASE 8.1 Assigning MBA Students to Teams 454
CASE 8.2 Project Selection at Ewing Natural Gas 455

Contents xi
CASE 11.1 College Fund Investment 664
CASE 11.2 Bond Investment Strategy 665
CASE 11.3 Project Selection Ewing Natural Gas 666
CHAPTER 12 Queueing Models 667
12.2 Elements of Queueing Models 670
12.3 The Exponential Distribution 673
12.4 Important Queueing Relationships 678
12.5 Analytic Steady-State Queueing Models 680
12.6 Queueing Simulation Models 699
12.7 Conclusion 709
CASE 12.1 Catalog Company Phone Orders 713
CHAPTER 13 Regression and Forecasting Models 715
13.2 Overview of Regression Models 717
13.3 Simple Regression Models 721
13.4 Multiple Regression Models 734
13.5 Overview of Time Series Models 745

13.6 Moving Averages Models 746
13.7 Exponential Smoothing Models 751
13.8 Conclusion 762
CASE 13.1 Demand for French Bread at Howie’s
Bakery 768
CASE 13.2 Forecasting Overhead at Wagner
Printers 769
CASE 13.3 Arrivals at the Credit Union 770
CHAPTER 14 Data Mining 771
14.2 Classification Methods 774
14.3 Clustering Methods 795
14.4 Conclusion 806
CASE 14.1 Houston Area Survey 808
References 809
Index 815
MindTap Chapters
CHAPTER 15 Project Management 15-1

15.1 Introduction 15-2
15.2 The Basic CPM Model 15-4
15.3 Modeling Allocation of Resources 15-14
15.4 Models with Uncertain Activity Times 15-30
15.5 A Brief Look at Microsoft Project 15-35
15.6 Conclusion 15-39
CHAPTER 16 Multiobjective Decision Making 16-1
16.2 Goal Programming 16-3
16.3 Pareto Optimality and Trade-Off Curves 16-12
16.4 The Analytic Hierarchy Process (AHP) 16-20
CHAPTER 17 Inventory and Supply Chain Models 17-1
17.2 Categories of Inventory and Supply Chain
Models 17-3
17.3 Types of Costs in Inventory and Supply Chain
Models 17-5
17.4 Economic Order Quantity (EOQ) Models 17-6
17.5 Probabilistic Inventory Models 17-21
17.6 Ordering Simulation Models 17-34

17.7 Supply Chain Models 17-40
CASE 17.1 Subway Token Hoarding 17-57

xiii
Practical Management Science provides a spreadsheet-
based, example-driven approach to management
science. Our initial objective in writing the book was to
reverse negative attitudes about the course by making
the subject relevant to students. We intended to do this
by imparting valuable modeling skills that students can
appreciate and take with them into their careers. We are
very gratified by the success of previous editions.
The book has exceeded our initial objectives. We are
especially pleased to hear about the success of the book
at many other colleges and universities around the
world. The acceptance and excitement that has been
generated has motivated us to revise the book and make
the current edition even better.
When we wrote the first edition, management
science courses were regarded as irrelevant or
uninteresting to many business students, and the use of
spreadsheets in management science was in its early
stages of development. Much has changed since the
first edition was published in 1996, and we believe that
these changes are for the better. We have learned a lot
about the best practices of spreadsheet modeling for

clarity and communication. We have also developed
better ways of teaching the materials, and we
understand more about where students tend to
have difficulty with the concepts. Finally, we
have had the opportunity to teach this material at
several Fortune 500 companies (including Eli Lilly,
PricewaterhouseCoopers, General Motors, Tomkins,
Microsoft, and Intel). These companies, through their
enthusiastic support, have further enhanced the
realism of the examples included in this book.
Our objective in writing the first edition was very
simple—we wanted to make management science
relevant and practical to students and professionals.
This book continues to distinguish itself in the market
in four fundamental ways:
■ Teach by Example. The best way to learn
modeling concepts is by working through
examples and solving an abundance of problems.
This active learning approach is not new, but our
text has more fully developed this approach than
any book in the field. The feedback we have
received from many of you has confirmed the
success of this pedagogical approach for
management science.

■ Integrate Modeling with Finance, Marketing,
and Operations Management. We integrate
modeling into all functional areas of business.
This is an important feature because the majority
of business students major in finance and
marketing. Almost all competing textbooks
emphasize operations management–related
examples. Although these examples are
important, and many are included in the book,
the application of modeling to problems in
finance and marketing is too important to ignore.
Throughout the book, we use real examples from
all functional areas of business to illustrate the
power of spreadsheet modeling to all of these
areas. At Indiana University, this led to the
development of two advanced MBA electives
in finance and marketing that built upon the
content in this book.
■ Teach Modeling, Not Just Models. Poor attitudes
among students in past management science
courses can be attributed to the way in which they
were taught: emphasis on algebraic formulations
and memorization of models. Students gain more

insight into the power of management science by
developing skills in modeling. Throughout the
book, we stress the logic associated with model
development, and we discuss solutions in this
context. Because real problems and real models
often include limitations or alternatives, we
include several “Modeling Issues” sections to
discuss these important matters. Finally, we
include “Modeling Problems” in most chapters to
help develop these skills.
■ Provide Numerous Problems and Cases.
Whereas all textbooks contain problem sets for
students to practice, we have carefully and
judiciously crafted the problems and cases
contained in this book. Each chapter contains
four types of problems: easier Level A Problems,
more difficult Level B Problems, Modeling
Problems, and Cases. Most of the problems
following sections of chapters ask students to
extend the examples in the preceding section.
The end-of-chapter problems then ask students
to explore new models. Selected solutions are
available to students through MindTap and are

Preface
xiv Preface
denoted by the second-color numbering of the
problem.

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