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American Journal of Health Promotion
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Population-Level Measures to Predict Obesity Burden in Public
Schools: Looking Upstream for Midstream Actions
Wasantha P. Jayawardene, MD, PhD, David K. Lohrmann, PhD,
Stephanie Dickinson, MAS, and Mohammad R. Torabi, PhD
American Journal of Health Promotion201632:3, 708-717
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Wasantha P. Jayawardene, MD, PhD, David K. Lohrmann, PhD,
Stephanie Dickinson, MAS, and Mohammad R. Torabi, PhD
American Journal of Health Promotion201632:3, 708-717
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Show all authors
Wasantha P. Jayawardene, MD, PhD1
Wasantha P. Jayawardene
1Applied Health Science, School of Public Health Bloomington,
Indiana University, Bloomington, IN, USA
See all articles by this author
Search Google Scholar for this author
, David K. Lohrmann, PhD1
David K. Lohrmann
, Stephanie Dickinson, MAS2
Stephanie Dickinson
2Epidemiology and Biostatistics, School of Public Health
Bloomington, Indiana University, Bloomington, IN, USA
,
Mohammad R. Torabi, PhD1
Mohammad R. Torabi
...
First Published October 5, 2016 Research Article
https://doi-
org.contentproxy.phoenix.edu/10.1177/0890117116670305

https://doi-
org.contentproxy.phoenix.edu/10.1177/0890117116670305
Article information
Article Information
Volume: 32 issue: 3, page(s): 708-717
Article first published online: October 5, 2016; Issue published:
March 1, 2018
Wasantha P. Jayawardene, MD, PhD1, David K. Lohrmann,
PhD1, Stephanie Dickinson, MAS2, Mohammad R. Torabi,
PhD1
2Epidemiology and Biostatistics, School of Public Health
Bloomington, Indiana University, Bloomington, IN, USA
Corresponding Author: Wasantha P. Jayawardene, Applied
Health Science, School of Public Health Bloomington, Indiana
University, PH 116, 1025 E 7th Street, Bloomington, IN 47405,
USA. Email: [email protected]
Abstract
Full Text
References
PDF
Abstract
Section:
Top of Form
Bottom of Form

Purpose:
To estimate school-level obesity burden, as reflected in
prevalence of obesity, based on the characteristics of students’
socioeconomic and geographic environments.
Design:
Secondary analysis of cross-sectional data.
Setting:
Public schools (N = 504) from 43 of 67 counties in
Pennsylvania.
Participants:
Kindergarten through grade 12 students (N = 255 949).
Measures:
School-level obesity prevalence for the year 2014 was
calculated from state-mandated student body mass index (BMI)
measurements. Eighteen aggregate variables, characterizing
schools and counties, were retrieved from federal data sources.
Analysis:
Three classification variables—excess weight (BMI ≥ 85th
percentile), obesity (BMI ≥ 95th percentile), and severe obesity
(BMI > 35% or 120% of 95th percentile)—each with 3 groups of
schools (low-, average-, and high-prevalence) were created for
discriminant function analysis, based on state mean and
standard deviation of school distribution. Analysis tested each
classification model to reveal school- and county-level
dimensions on which school groups differed from each other.
Results:
Discriminant functions for obesity, which contained school
enrollment, percentage of students receiving free/reduced-price
lunch, percentage of black/Hispanic students, school location
(suburban/other), percentage of county adults with
postsecondary education, and percentage of county adults with
obesity, yielded 67.86% correct classification (highest
accuracy), compared to 34.23% schools classified by chance
alone.
Conclusion:

In the absence of mandated student BMI screenings, the model
developed in this study can be used to identify schools most
likely to have high obesity burden and, thereafter, determine
dissemination of enhanced resources for the implementation of
proven prevention policies and programs.
Keywords demographic factors, socioeconomic factors,
geographic factors, classification, schools, obesity
Introduction
Section:
Top of Form
Bottom of Form
In 2012, 34.2% of US elementary school-aged children and
34.5% of secondary school-aged adolescents were overweight;
respectively, 17.7% and 20.5% were obese.1 Obesity burden is
experienced disproportionately across demographic subgroups
and differs across communities, schools, and regions.2 Since
African American race, Hispanic ethnicity, low household
income, and parental education plus living in inner city are
associated with overweight,3 schools in neighborhoods with
higher proportions of such families likely have higher rates of
child overweight and obesity.2 Ethnic disparities related to
childhood obesity are widening with rates increasing faster
among non-Hispanic black and Mexican American boys than
non-Hispanic white boys, and among non-Hispanic black girls
than non-Hispanic white girls.4 These social environment
characteristics (ie, upstream determinants) influence behaviors
and health status of children and families.5,6
Population-level obesity prevention includes multilevel school
and child health policies (ie, upstream interventions), school-
and community-based programs (ie, midstream interventions),
and need-based individualized behavioral approaches (ie,
downstream interventions).6 Schools are important settings for
multicomponent preventive programs that improve weight status
of children and adolescents by addressing attitudes and

behaviors related to physical inactivity, healthy dietary habits,
and mental health.7 Prevention policy and program
implementation varies,8 likely contributing to overweight and
obesity prevalence differences across schools.9-11 In
consideration of nationwide interventions, schools involved in
on-site Healthy Schools Program demonstrated a trend toward
decreased overweight (−0.48%) and obesity (−0.42%) with each
additional contact with the program and each additional year of
exposure.11 From a statewide perspective, students exposed to
strong, specific, competitive food laws in 2003, compared to
students in states with no such laws, gained 0.25 less body mass
index (BMI) units on average from fifth to eighth grade and
were less likely to remain overweight or obese; also, if laws
remained consistently strong over a 3-year period, students
gained 0.44 fewer BMI units compared to students in states with
no such laws.10 In consideration of school-based interventions,
healthier school environments led to 15.2% relative reduction in
obesity prevalence among elementary school students over 6
years, while the number of obesity prevention strategies
implemented was negatively associated with overweight and
obesity prevalence over time.9 Therefore, student overweight
and obesity variability among public schools are attributed to
differences in health disparities across social environments (ie,
upstream determinants) or school-based obesity prevention
outcomes (ie, midstream interventions) or both.
Contextually, BMI-based identification of children at greatest
health risk due to excess body fat is essential. Prior studies
found that the obesity prevalence is higher in states with
school-based BMI surveillance mandates; although causality
was not established, this is likely due to the circumstance that
such states consider the obesity problem as more serious and,
consequently, enact BMI-screening policies.12 Despite
limitations, estimation of childhood obesity burden through
school-based BMI measurement by trained personnel can
identify high-risk groups, monitor progress toward achieving
objectives, and foster policy and/or program changes.13 Such

initiatives provide specific obesity burden estimates used to
inform design and implementation of school-level efforts;
interventions should be responsive to the existing obesity
burden within schools.14 Twenty states require BMI
surveillance and 9 recommend periodic body composition or
fitness screenings.12 However, even within states with BMI
screening mandates, all schools do not consistently collect BMI
data.13 Thus, development of a clearly articulated model of
health disparities6 with rational links between social
environments (ie, upstream determinants) and school-level BMI
outcomes is warranted to allow better approximation of obesity
burden within schools when BMI data are unavailable.
Interventions that are funded and designed based on well-
informed obesity burden estimates, accounting for empirically
established links between health disparities and varying obesity
rates across schools, can ultimately improve outcomes, cost-
effectiveness, and community benefits.
Purpose
The objective of this study was to determine school-level
obesity burden, based on the characteristics of the social,
economic, and geographic environments of students. The
research question was: “Can publicly available school-level and
population-level variables predict school classification by
student obesity prevalence?”
Methods
Section:
Top of Form
Bottom of Form
Design
The study involved a secondary analysis of cross-sectional data.
Sample
Multisource data from 43 of 67 Pennsylvania counties were
accessed. Geographically, counties included 19 of the 20 most
populous as well as all of Pennsylvania’s 10 largest cities. The

sample consisted of 504 public elementary and secondary
schools that reported measured height and weight data of 255
949 students in grades K-12 for the year 2014.
Measures
Eighteen potential discriminating (predictor) variables were
identified—9 at school level and 9 at county level. Public
school characterization variables were total students, percentage
of students eligible for free lunch (≤130% of federal poverty
threshold), percentage of students eligible for reduced-price
lunch (≤185% of federal poverty threshold), percentage of black
students, percentage of Hispanic students, percentage of male,
school-level (eg, elementary, middle, high), student/teacher
ratio, and school location based on urban-centric locale codes (1
= city large, 2 = city midsize, 3 = city small, 4 = suburb large, 5
= suburb midsize, 6 = suburb small, 7 = town fringe, 8 = town
distant, 9 = town remote, 10 = rural fringe, 11 = rural distant,
and 12 = rural remote). County-related variables were county
population, percentage of African American (black), percentage
of Hispanic, percentage of adults with postsecondary education,
county median household income, percentage of population with
limited healthy foods access, percentage of single parent
households, percentage of low-birth-weight live births, and
percentage of obese adults. Table 1 indicates the data source for
each discriminant variable retained in the final model.15-17
Table 1. Publicly Available Data Sources for the Discriminant
Variables in the Final Model.
Table 1. Publicly Available Data Sources for the Discriminant
Variables in the Final Model.
View larger version
Excess weight, obesity, and severe obesity were considered
classification (dependent) variables. Centers for Disease
Control and Prevention (CDC)-developed growth charts for
children and adolescents (2000 as the growth reference year for

calculation of percentiles) were used to classify BMIs.18
Obesity burden variables—percentage of excess weight students
(≥85th percentile), percentage of obese students (≥95th
percentile), and percentage of severely obese students (≥120%
of the 95th percentile or BMI > 35 regardless of age)19,20—
were derived from 2014 student height and weight
measurements. State-mandated height and weight measurements
were taken by qualified personnel (eg, school nurses), using
established protocols,12 and entered into an electronic health
record.21 Data were compiled in 3 repositories maintained by
Population Health Innovations, LLC22 and supported by the
Highmark Foundation for Pittsburgh and most Pennsylvania
counties, Independent Blue Cross for Philadelphia and
surrounding counties, and Blue Cross of Northeast Pennsylvania
for northeastern counties. Nonpublicly available de-identified
BMIs were accessed under a data sharing agreement. Data were
collected and analyzed in 2015.
Analysis
Files were configured into a relational database and then
aggregated into 3 levels: student, school, and county. Data were
validated by eliminating unrealistic values for height (≥7 ft or
≤3 ft), weight (≥ 350 pounds or ≤ 50 pounds), and BMI (≥55 or
≤7). Outliers constituted 103 of 255 949 students (0.04%). For
each variable, normal distribution of data was required to meet
the assumptions of the main analysis, that is, discriminant
function analysis. Therefore, if the distribution was skewed,
square root transformation was initially performed. If skewness
still remained, log transformation was performed. Closely
related variables, that is, (1) percentage of free lunch eligible
students and percentage of reduced-price eligible students, (2)
percentage of black students and percentage of Hispanic
students, and (3) percentage of African American (black)
population and percentage of Hispanic population were
combined. As some of the 12 locale codes had very few schools
and little difference between urban and rural obesity rates was
found, school location was transformed into 2 binary variables:

(1) urban (locale categories 1-6) versus rural (other categories)
and (2) suburban (locale categories 4-6) versus not suburban
(other categories). Three Philadelphia schools constituted
multivariate outliers and were eliminated because their
Mahalanobis distance was 22.46 or greater at P < .001;
calculation of the Mahalanobis distance was based on the
multidimensional generalization of 6 independent variables.
Although the statistical method cannot identify the potential
reason case by case, a school can become a multivariate outlier
due to errors (eg, sampling, data collection, data entry) or
legitimately by random chance (eg, large enrollment plus
unusually high level of poverty and minority students).
Discriminant function analysis23 was performed using SPSS
version 22.024 and SAS version 9.425 statistical packages.
Discriminant function analysis assumptions required that groups
of schools be mutually exclusive and group sizes not grossly
different.23 To meet this assumption, in each classification
variable (mean − 0.5 standard deviation [SD]) and (mean + 0.5
SD)] were utilized as cutoffs to obtain 3 school groupings (ie,
low-, average-, and high-prevalence schools). Analysis revealed
the population-level dimensions (ie, school and county level) on
which 3 levels of each classification variable (excess weight,
obesity, and severe obesity) differed. Three classification
variables were examined separately with discriminant function
analysis. For each classification, the best combination of
discriminating variables was determined based on pooled
within-group correlations between discriminating variables,
functions at group centroids, and classification results.23 Then,
the best classification variable was determined based on
percentage of correctly classified schools.23
Results
Section:
Top of Form
Bottom of Form

For 43 counties, average prevalence of excess weight, obesity,
and severe obesity was 36.00%, 18.85%, and 6.42%,
respectively. For each retained school, percentage of excess
weight, obese, and severely obese students were calculated; SDs
of those distributions were 4.77%, 3.83%, and 1.74%,
respectively. Ranges were 18.42% to 52.04%, 6.25% to 32.43%,
and 0.00% to19.46%. Obesity-based classification, compared to
excess-weight- and severe-obesity-based classifications, yielded
the highest rate of correctly classified schools (67.86% correct
classification vs 34.23% classified by chance alone; 33.63%
improvement; Table 2). Cross validation was done by the leave-
one-out method, where each school was classified by the
functions derived from all schools other than that school. Cross
validation of obesity-based classification revealed a 66.18%
correct classification (31.95% improvement). Since
classification accuracy was established as the criterion for
selecting the classification variable, only results from the
obesity-based classification are presented and discussed below.
School- and county-level variable ranges proved wide (Table 3).
Table 2. Classification of Schools Based on Excess Weight,
Obesity, and Severe-Obesity Burdens.a,b
Table 2. Classification of Schools Based on Excess Weight,
Obesity, and Severe-Obesity Burdens.a,b
View larger version
Table 3. Population Characteristics (A) and Unadjusted Group
Means for Discriminant Variables in the Obesity-Based
Classification (B).
Table 3. Population Characteristics (A) and Unadjusted Group
Means for Discriminant Variables in the Obesity-Based
Classification (B).

View larger version
The final model for obesity-based classification, providing the
highest discriminatory power with the least number of variables,
contained 6 independent variables (5 continuous and 1 binary;
Table 1): square root of total students (sq-#students), square
root of percentage of free and reduced-price lunch eligible
students (sq-poverty), logarithm of percentage of black and
Hispanic students (log-minority), school’s location as suburban
according to urban-centric locale codes (locale-suburb; 1 =
suburban or 0 = urban or rural), percentage of county adults
with some postsecondary education (adult-educ), and percentage
of county adults who were obese (adult-obese). Sq-#students,
sq-poverty, log-minority, locale-suburb, adult-educ, and adult-
obese separated low-, average-, and high-obesity schools (P =
.001 for sq-#students and P < .001 for other variables). At
school level, log-minority positively correlated with sq-poverty
(r = 0.598); other correlations were very weak (r < 0.25). At the
county level, adult-educ negatively correlated with adult-obese
(r = −0.742).
Two discriminant functions were identified: sq-poverty (r =
0.922) alone was loaded on the first, whereas all other
variables, that is log-minority (r = −0.690), adult-obese (r =
0.623), adult-educ (r = −0.536), locale-suburb (r = −0.474), and
sq-#students (r = 0.332), were loaded on the second. The first
function (rc = 0.709), based on the percentage of free and
reduced-price lunch eligible students, maximally separated low-
obesity schools from the rest, while also separating high-obesity
schools from average-obesity schools to some extent (Figure 1).
The second function (rc = 0.207), based on black/Hispanic
percentage, adult obesity, adult education level, school location
(ie, suburban or not), and school size, maximally separated
average-obesity schools from the rest.
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Figure 1. Group centroids for unstandardized canonical
discriminant functions: Function 1 = student poverty. Function
2 = adult education level, and school location (suburban or not),
percentage of black/Hispanic, school size, and adult obesity.
The canonical score plot demonstrates how the function 1 (x
axis) and function 2 (y axis) classify schools between low-,
average-, and high-obesity groups by plotting the observation
score, computed via unstandardized canonical discriminant
functions. Theoretically, each school is represented by a dot on
the plot (476 dots in total); included in the graph are only the
group centroid for low-, average-, and high-obesity groups. The
first function (rc = 0.709), based on student poverty, maximally
separated low-obesity schools from the rest, while it also
separated high-obesity schools from average-obesity schools to
some extent. The second function (rc = 0.207), based on
black/Hispanic percentage, adult obesity, adult education level,
school location (ie, suburban or not), and school size,
maximally separated average-obesity schools from the rest.
Standardized canonical discriminant function coefficients were
estimated to standardize the distribution of scores from each
function, utilizing a mean of 0 and SD of 1. The absolute value
of these coefficients indicated the relative contribution of
discriminating variables to function 1 and function 2. In
function 1, greatest contribution was from sq-poverty (1.066),
followed by adult-educ (−0.269), log-minority (−0.259), adult-
obese (−0.156), and locale-suburb (−0.116), while the least
important predictor was sq-#students (−0.004). In function 2,
greatest contribution was from log-minority (−0.684), followed
by sq-#students (0.549), adult-obese (0.499), locale-suburb
(−0.347), and adult-educ (0.182), while the least important
predictor was sq-poverty (0.122).
Based on unadjusted group means (Table 3), low-obesity
schools had highest student enrollment, least poverty, least
percentage of black/Hispanic students, and were more likely to
be in suburban counties with high rates of postsecondary

education and lower rates of adult obesity. The antithetical
situation was observed for high-obesity schools; that is, the
lowest student enrollment, highest poverty, highest percentage
of black/Hispanic students, lowest percentage of suburban
schools, lowest adult postsecondary education rate, and highest
adult obesity rate.
Using classification function coefficients, an equation was
developed for each of the low- average-, and high-obesity
groups to determine school assignment to group, based on the
scores for 6 variables (Table 4). To improve correct
classification, utilizing school- and county-level discriminant
variables, a school should be assigned to the group for which it
obtained the highest classification score; for example, if a rural
school (locale-suburb = 0) has 225 students (sq-students = 15),
with 49% free and reduced-price lunch eligibility (sq-poverty =
0.7), and 11% black and Hispanic students (log-minority =
−0.9586), and is located in a county that has 23% of adults with
postsecondary education (adult-educ = 0.23) and 38% of adults
who are obese (adult-obese = 0.38), the school will score 298
for low obesity, 309 for average obesity, and 317 for high
obesity; hence, the school will be assigned to the high-obesity
group.
Table 4. School Assignment to Obesity Burden Group, Based on
Classification Function Coefficients.a
Table 4. School Assignment to Obesity Burden Group, Based on
Classification Function Coefficients.a
View larger version
Discussion
Section:
Top of Form
Bottom of Form

Study results clearly demonstrate that publicly available school-
level and county-level variables can be used to predict school
classification by student obesity prevalence; resultant models
included a rational combination of publicly available,
population-level variables for classification of schools based on
obesity burden, which, in comparison with classification by
chance alone, almost doubled the percentage of correctly
classified schools. Critical variables for determining high-
obesity schools were common: lower student numbers, higher
student poverty, higher minority student enrollment,
nonsuburban location plus location in communities with lower
adult education, and higher adult obesity levels. Results
demonstrate that reliance on a single variable (eg, percentage of
free and reduced-price lunch) is unlikely to provide the best
approach to identifying schools in need of more robust
interventions, because obesity burden clearly depends on
multiple upstream health determinants (eg, race/ethnicity,
poverty, and urban/rural divide), along with interactions that
may also contribute to this variation (eg, rural poverty).
Nevertheless, free or reduced-price lunch eligibility played a
substantial role in classifying schools. Differentiation of low-,
average-, and high-obesity schools involved a relatively
stronger effect of poverty, compared to the effect of all other
variables combined. This finding was unsurprising since
previous studies confirmed that poverty leads to inequities in
multiple factors (eg, quality housing, access to healthy food,
access to quality education, and safe places to be physically
active) that contribute to community obesity rates.26 According
to 1999 to 2004 National Health and Nutrition Examination
Survey (NHANES), 23% of 15- to 17-year-olds living in
poverty were obese, compared to 14% obesity rate among
adolescents not living in poverty.27 However, national data
from 1971 to 2002 revealed a weakening association between
poverty and childhood obesity over decades, especially among
adolescents.28
This study’s finding that the percentage of black and Hispanic

students significantly predicted school obesity rates is
supported by research which found that overweight and obesity
rates are higher among black than white children.29
Furthermore, minority children’s obesity rates are increasing
faster at earlier ages; by age 6 to 11, 26.1% of Hispanic
children and 23.8% of African American children were obese
compared with 13.1% of white children.29 Almost three-
quarters of the difference in rates between Hispanic and white
children occurs by third grade; three-quarters of the difference
between African American and white children occurs between
grades 3 and 8.30
Regarding upstream race/ethnicity-related determinants, almost
one-fourth of Hispanic and black families had limited healthy
food access due to lack of financial or other resources, versus
11% of white families.31 Fewer black (11.3%) and Hispanic
(9.3%) than white adolescents (4.5%) eat vegetables during the
prior week.32 Compared to predominantly white neighborhoods,
outdoor advertising for unhealthy foods was 13 times greater in
predominantly black neighborhoods and 9 times greater in
predominantly Hispanic neighborhoods.33,34 Access to safe and
quality public parks, green space, and sidewalks for physical
activities was much lower in predominantly black and Hispanic
neighborhoods.35 Substandard neighborhood safety had a strong
negative impact on the amount of outdoor play by black girls36;
Hispanic children engaged in less after-school physical activity
due to cost and language barriers.37
Although poverty and obesity rates were higher among black
and Hispanic families than white families, black race and
Hispanic ethnicity are unlikely to be the sole reasons that track
poverty; for example, 2005 to 2008 NHANES data revealed a
significant inverse association of family income with obesity
for white children but an inconsistent relationship for minority
children.38 Contrary to expectations, a 1999 to 2004 NHANES
study found higher obesity rates with higher family income
among school-aged black children, especially girls.39 In 2005
to 2008, a similar but nonsignificant association was observed

for Mexican American girls. Put differently, most obese
children do not live in poverty; 62% of 12 million US obese
children are not impoverished. Just 27% of 6 million obese
white children are impoverished.38
Although the urban-to-rural gradient and urban–rural binary
variable did not improve classification of schools in the current
study, use of a binary variable that categorized suburban
schools versus all other schools significantly improved
classification. Previous studies representing 8 states revealed
that childhood obesity rates were highest in rural areas.40 Rural
areas have the lowest food location availability, least nutrition
education resources, and worst exercise facilities, while rural
residents, on average, have lower incomes, lower education
levels, and limited prevention and treatment options, all
contributing to obesity.41 Meanwhile, many urban children also
lack access to safe parks, playgrounds, and healthy foods and
are more exposed to unhealthy foods; income modifies the
relationship between food environment and BMI.42
The current study also demonstrated that the percentage of
adults with some postsecondary education, even if measured at
county level, can contribute to obesity-based school
classifications. Empirical evidence suggested that the household
head’s education level had a significant negative relationship to
obesity prevalence, although not consistent across genders and
race/ethnicity groups.38 Compared to 21.1% and 20.4% obesity
prevalence among boys and girls, respectively, living in
households where the head had no high school diploma, 11.8%
and 8.3% obesity rates were found among boys and girls,
respectively, who lived in households where the head had a
college degree. The relationship between household head’s
education and children’s obesity was statistically significant for
both white and black girls. From 1988 to 2008, with the
exception of girls living in homes where the head had a college
degree, obesity prevalence of girls increased significantly at all
levels of household head’s education.38 Finally, several family-
and community-characteristics associated with poverty and

adult education potentially contribute to childhood obesity, for
example dietary practices, screen time, parental behaviors and
attitudes, home environment, and neighborhood physical
activity opportunities.43 These may be addressed through
specific obesity prevention interventions within a multilayered
ecological context,43-45 although poverty reduction and adult
education require broader and more upstream interventions.
In addition to upstream determinants, school characteristics may
contribute to obesity prevalence. The current study revealed
that, although school type (ie, elementary, middle, or high) did
not improve classification of schools for obesity, school
enrollment size did. Number of students in school constitutes a
proxy measure of both school type and urban–rural divide
combined. Elementary schools usually have the least number of
students, while high schools have the greatest numbers46;
accordingly, elementary schools are more likely to be classified
as high obesity; this supports the need for interventions during
the earliest formative years. Also, rural schools are more likely
to be classified as high obesity because they generally have
fewer students than urban and suburban schools. Compared to
all US schools in 2010, average student enrollment in
Pennsylvania elementary schools was lower, while the averages
for middle, high, and other (eg, junior high) schools were much
higher, creating a wide range of within-state school sizes.46
Small schools in rural areas face unique challenges, including
smaller food service programs, teacher shortages, and limited
financial resources,47 although, compared to large schools,
availability and purchase of competitive food and beverages by
students in small schools may be favorable.48 While school
program variables were not considered in this study, previous
studies suggest that such midstream actions somewhat correlate
with upstream determinants included in this study.49 For
example, schools with higher poverty rates, compared to others,
allowed students to purchase unhealthful competitive foods
significantly more often.50
Finally, the current study demonstrated that, in the absence of

up-to-date county-level childhood obesity prevalence data, adult
obesity prevalence data can be used to help classify schools.
Obesity, once established in childhood, tends to persist through
adult life51 and prevailing environmental conditions and
behaviors conducive to weight gain in childhood usually operate
in adult life. Thus, states and counties with relatively higher
rates of adult obesity also tend to have higher rates of childhood
obesity.
Limitations
This study has several limitations. Prior research revealed that
children with excess body fat can be identified reasonably well
using BMI criteria.52 However, interpretation of overweight
(≥85th percentile but <95th percentile) in children utilizing age-
sex-based BMI alone may not be fully accurate because some
individuals, especially male adolescents, can be categorized as
overweight due to high lean body mass rather than having
excess fat.53 A similar misclassification is highly unlikely at
≥95th percentile, because assessment of body fat using standard
methods (eg, dual-energy X-ray absorptiometry) revealed that
almost all children identified as obese via BMI have excess
body fat.54 From a study design perspective, not all public
schools from each county provided student BMI, data were not
available for all students in selected schools, and public schools
were excluded from the analysis if demographic data were
unavailable. Therefore, the possibility that systematic bias that
occurred in school selection cannot be excluded. Height and
weight data were collected for state-mandated health
screenings, not for research purposes. Although height and
weight measured by trained professionals, such as school
nurses, are likely to be accurate, full-time nurses are
unavailable in many schools.55 Additionally, many nurses
believe that BMI surveillance is an added burden to their
workload56; personnel assigned to assist nurses with BMI
surveillance need technical training for measuring height and
weight accurately.57,58 Electronic and beam balance scales
used to accurately measure weight (spring balance scales are not

suitable) should be calibrated properly and regularly to the
nearest one-fourth pound, following manufacturer’s directions,
and stadiometers should measure height to the nearest one-
eighth inch.58-60 Adherence to auxiliary personnel training and
equipment quality-control standards for measuring student
weight and height in all involved Pennsylvania schools cannot
be fully guaranteed. Finally, although overall discriminant
function classification accuracy was high, functions tended to
overclassify low-obesity schools.
Conclusions
Section:
Top of Form
Bottom of Form
Estimation of the obesity burden in schools utilizing publicly
available school-level and community-level aggregate variables
identified both the magnitude of the problem in a given school
and related associations or childhood obesity risk factors. Thus,
the models from this study can be used to (1) identify schools
most likely to have high obesity levels, in the absence of
routine student BMI screenings and (2) inform dissemination of
enhanced resources for implementation of proven and robust
prevention policies and programs. Public schools, clearly
identified and supported by CDC as an integral part of the
public health system,61,62 are expected to provide
opportunities for students to adopt healthy lifestyles,63,64
regardless of socioeconomic status or ethnicity, even when
communities and families are unable to do so. As mentioned in
the introduction section, limited research have proven that
school-based interventions (conducted at national, state, and
local levels) were beneficial in reducing obesity rates. While
reduction of student obesity rates is an added challenge for
schools, especially high-poverty schools that struggle with
meeting current academic standards, doing so is in schools’ best
interest since children who were physically fit and ate healthily

had lower absenteeism, concentrated better on academic tasks,
and received better grades, while childhood obesity was
associated with several health and social problems that can
negatively affect the academic performance.65
So What? Implications for Health Promotion Practitioners and
Researchers
What is already known on this topic?
Empirical studies and body mass index (BMI) surveillance
programs have confirmed the magnitude and health determinants
of childhood obesity plus variations in childhood obesity burden
across communities.
What does this article add?
This is the first study to estimate school-level obesity burden
based on the characteristics of the socioeconomic and
geographic environments in which students live and learn.
What are the implications for health promotion practice and
research?
Even within those 20 states having BMI screening mandates, all
schools do not consistently collect BMI data. Estimation of
schools’ obesity burden utilizing publicly available school- and
county-level aggregate variables identified both the magnitude
of the problem in a given school and related associations and/or
obesity determinants. Health promotion practitioners can utilize
this unique information to allocate greater physical and human
resources for implementation of prevention policies and
programs in schools that likely have high obesity rates.
Acknowledgments
We thank Christina L. Wilds, DrPH, of Highmark Foundation
and Robert G. Gillio, MD, of Population Health Innovations,
LLC, for providing data from the Health eTools for Schools
database and their support in the preparation of this article. We
also thank Godfred Antwi for his contribution to data extraction
from public-use databases.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this

article.
Funding
The author(s) received no financial support for the research,
authorship, and/or publication of this article.
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[Abstract: Type the abstract of the HR Plan here. The abstract is
typically a short summary of the contents of the document.]
Table of Contents
I.Executive Summary1
II.Business Strategy Report 1
III.HR Organization Design 1
V.Compensation Plan1
VI.Training and Development Plan1
VII.HR Forecast Plan2
VIII.Conclusions2
IX.Appendix2
X.References2
XI.Document Work Log2

MBA 533 – Human Resource in Management - HR Project
Template
Page 0 of 3I. Executive Summary
You will complete this portion when the Comprehensive HR
Project is submitted in Module 7.
II. Business Strategy Report - 100 Points (Module 1)
As a team, review and discuss the company strategy, including
industry realities, competitors’ analysis, and differentiation in
the market. Be sure to focus on the role of strategy in the
competitive position of Motors and More. Your report should
address the items below.
A. Using Porter’s Five Factors Analysis of competitive position,
analyze the business strategy and describe your findings.
B. Review the role of strategy in company performance. What
will be needed in the human resource strategy to support the
company strategy?
III. HR Organization Design - 100 Points (Module 2)
A. Design a typical HR department and identify each HR unit.
For each HR unit, provide roles/ responsibilities and job titles.
Develop an organization chart of a typical HR department.
B. Given the size of Motors and More, indicate which positions
identified in your typical HR department should be combined or
eliminated to reduce the number of HR employees. Provide new
job titles and develop an organizational chart specifically for
Motors and More’s new HR department. Provide the total
number of staff for each HR unit.
IV. Recruiting and Retention Plan - 100 Points (Module 3)
A. Given the increase in product demand, how many people will
you need to hire and in what functional areas (manufacturing,
operations, customer service, marketing and sales, finance/
accounting, and HR)? Provide your rationale for the proposed
hiring in each unit. Consider the turnover rate. Identify the
factors that could be causing turnover. Identify the costs of
turnover.

B. Include the types of interviews you would conduct and why.
C. Develop strategies to recruit the appropriate applicants and
include sources and tools used for recruiting and selection.
Identify the possible areas and types of discrimination that
could occur.
D. What can you do to retain current employees? What are the
benefits of retention?
E. How will you assess the effectiveness of your recruiting
efforts?
V. Compensation Plan - 100 Points (Module 4)
Develop a progressive yet competitive compensation plan that
will support recruiting and retention efforts and lower the
employee turnover rate. Traditionally, Motors and More has
provided employees minimum wage and statutory benefits.
A. How does Motors and More’s employee compensation
compare relative to other organizations in the area? Will the
organization meet, lead, or lag the local market? Explain your
rationale.
B. Identify alternative pay methods and discuss the advantages
and disadvantages of each.
C. What benefits will you offer? Include statutory benefits.
What are the costs of those benefits? What is the rationale for
offering those benefits?
D. Develop a communications plan regarding how employees
will be informed about the compensation plan. Define the
sequence of communications considering the who, what, and
when of the message.VI. Training and Development Plan - 100
Points (Module 5)
Develop a human resource development (HRD) plan. Given the
fact that Motors and More has no formal training program and
promotions have been based on seniority, your plan should
address:
A. New-employee training.
B. Current-employee training for current and future jobs
according to a career path.
C. Manager and supervisory training.

In your training plan, address the following:
A. How will you conduct a needs assessment for each group
(include methods and instruments)?
B. How will training content be developed or obtained?
C. How will training be delivered (e.g., classroom, intranet,
blended, self-study, etc.) and by whom internal employee or
external consultant/trainer)? Provide a rationale for your
decision.
VII. HR Forecast Plan - 100 Points (Module 6)
Develop a three-year HR forecast (prediction of the future)
using the following assumptions:
A. Labor supply/demand will become more rigorous (demand
for workers will increase, but the labor pool will remain the
same or shrink). Labor costs will increase.
B. Demand for Motors and More products will continue to
increase. Production defects will also continue to increase.
C. Motors and More’s workforce will become more diverse as
the company hires more Hispanics, Kurds and persons from
alternative workforces.
D. The president will start another company and will hire
someone to manage the daily operations of Motors and More
while he takes on more of an overseeing role.
E. Motors and More will decide to develop an additional
product to broaden its portfolio. There is no existing capacity
for the product, nor do the existing production lines meet the
manufacturing requirements for the new product.
VIII. Conclusions
You will complete this portion when the Comprehensive HR
Project is submitted in Module 7.
IX. Appendix
Add any supplemental information, diagrams, pictures, or forms
that support your project work.
X. References
Include references to support your conclusions.
XI. Document Work Log

Complete the table to assist in assessing the contributions made
by the individual team members.
Section
Team Member - Primary
Team Member - Secondary
I. Executive Summary
II. Business Strategy Report
III. HR Organization Design
IV. Recruiting and Retention Plan
V. Compensation Plan
VI. Training and Development Plan
VII. HR Forecast Plan
Page 2
© 2007 SHRM. Don McCain, Ed.D.
1

Society for Human Resource Management
MOTORS AND MORE, INC. – A PROGRESSIVE HR CASE
STUDY
By Don McCain, Ed.D.
Learning Objective(s)
Upper-level undergraduate students will work through issues
associated with developing and sustaining
an HR department to support an organization facing labor
shortages and high product demand. At the
end of the study, students learn how to:
1. Align HR initiatives with corporate strategy.
2. Develop a complete HR organization structure, including
roles and responsibilities, and then
adjust the structure to support the organization.
3. Develop a basic staffing plan.
4. Develop a basic training plan.
5. Determine and support a pay and benefits plan.
6. Determine future HR requirements.
CASE OVERVIEW
You are hired as the HR director for the fictitious Motors and
More, Inc. Motors and More, a business-to-
business sales company, manufactures small motors and
accessories for industrial and home products.
The industry is highly competitive, and the company follows a

prospector strategy.
A prospector strategy takes advantage of new markets and
products (Gomez-Mejia, Galkin and Cardy,
2001). Organizational emphasis is on growth, innovation and
new product development. A prospector
wants to be first to market. To respond to competitive and
rapidly changing markets, prospectors have
flexible, flat and more decentralized organizational structures.
Motors and More is headquartered in a small southern town of
28,000 people, with a low unemployment
rate of 3.1 percent. This means that demand for workers exceeds
the labor supply. There is a technical
school and a community college within 50 miles of Motors and
More. Motors and More’s president is
former military and is highly patriotic. He is committed to
staying in the community. Recently, several
other local companies have experienced labor organizing
activities.
Motors and More employs 116 people. Until you were hired,
there was no HR department. Recently, the
organization’s employee turnover rate has been higher than
normal. The marketing and sales department
continues to sell products to an expanding market. Because of
this increased product demand, output
must be increased by 96 percent.
Eighty-eight percent of Motors and More employees are
Caucasian. With the exception of one female
supervisor in the customer service department, the president and
all other managers are Caucasian men.
Management promotions have been based on seniority. The
local labor market population is
approximately 48 percent minority. There is a growing Hispanic

and Kurdish population that have not
been accepted into the community.
All the employees in manufacturing (including quality control),
customer service and operations
(responsible for shipping and receiving; distribution of raw
materials, components parts and finished
goods inventory; and maintenance and cleaning) have at least a
high school degree or GED. The
organization provides some skills training courses. Please refer
to the organizational chart in Figure 1 for
more details.
2
Figure 1: Motors and More Organization Chart

President
HR Director
Staff (TBD)
Finance and
Accounting
1 manager, 5
professionals
and 1 hourly
(includes
payroll)
Manufacturing
1 manager and
69 employees
Quality Control
3 employees
Operations
1 manager
14 employees
Customer
Service –
inbound only
1 female
supervisor

5 CRS
Marketing/
Sales
1 manager
9 salesmen
Maintenance
and Cleaning
1 leader
3 employees
3
DESIGNING THE HR DEPARTMENT
A. Design a typical HR department and identify each HR unit.
For each HR unit, provide roles/
responsibilities and job titles. Develop an organization chart of
a typical HR department.
B. Given the size of Motors and More, indicate which positions
identified in your typical HR
department should be combined or eliminated to reduce the
number of HR employees. Provide
new job titles and develop an organizational chart specifically
for Motors and More’s new HR
department. Provide the total number of staff for each HR unit.

Organization Design
Organizing is a basic managerial function. Organizing is the
process of designing jobs, grouping jobs into
manageable units, and establishing patterns of authority among
jobs and groups of jobs (Griffin and
Moorhead, 2006). “Organization design refers to the framework
of jobs, positions, groups of positions,
and reporting relationships among positions that are used to
construct an organization” (DeNisi and
Griffin, p. 50). Organizing combines with organization design
to form an organizational structure.
Using Anthony, Kacmar and Perreewe (2006) as a source,
Figure 2 represents a comprehensive HR
structure. Some HR professionals may argue against including
organization development as a part a
human resource development strategy. Nonetheless, this figure
is helpful because it depicts the
organizational functions that must be included in a
comprehensive HR department.
Figure 2: An HR Organization Chart

Roles/Responsibilities and Job Titles
All HR managers, regardless of their functional areas of
expertise, must be able to hire, train, coach,
recognize and reward performance (performance management),
plan, organize, set goals, develop and
implement strategies, lead employees, create and administer
budgets, etc. These are responsibilities
common to all managers. In addition, a decision needs to be
made regarding administrative support—
should it be centralized or should it be dispersed among the
functional or operational areas?
HR Director or Manager
Roles/responsibilities
goals and strategies with
those of the organization.
HR.
HR (VP, Director, or Manager)
Organization
Development

Staffing or
Employment
Employee or
Industrial
Relations
HRD or
Training and
Development
Compensation Employee or
Industrial
Relations
Health and
Safety
Job and Salary
Administration
Benefits
Administration
4
Organization Development (OD)
Possible job titles: OD specialist, OD consultant

Role/responsibilities
internal contract(s).
identify organizational issues
with the goal of performance improvement. Issues may include
the reward system or
performance management, management style, structure,
processes, tools and
equipment, goal setting, etc.
ltants to
develop interventions) to address
issues or problems that can be solved by collecting survey data,
coaching, training;
provides feedback to management and employees.
achievement.
Staffing or Employment
Possible job titles: Employment manager, staffing manager,
recruiting manager, staffing or
recruiting specialist or coordinator

Ensures they are consistent with
performance management requirements.
tions, résumés
and references.
instruments such as tests or pre-
employment processes, procedures or protocols.
tes.
complete.
.
Human Resource Development (HRD) or Training and
Development
Possible job titles: Training specialist, training coordinator or
administrator, facilitator, learning
specialist, designer, developer, evaluator, training or
performance consultant
HRD is responsible for the development of the organization’s

intellectual capital. HRD
professionals are involved in needs assessment; design,
development, delivery and evaluation of
learning experiences; development of career path models;
employee orientation; etc. HRD
supports the performance management process by training
managers and employees on
performance management. Specific roles and responsibilities are
discussed below. Robinson and
Robinson (1996) provide some of the roles/responsibilities by
job.
experiences; manages group work
and processes; maintains the agenda; provides feedback to
learners, designers and
developers.
t; writes goals and
objectives; defines and outlines
content in conjunction with the evaluator; develops evaluation
plans in conjunction with
the internal client; determines instructional strategies.
5
oper: Develops full content and instructional strategies;
develops or secures
instruments, cases, assessments, etc.; develops leaders’ and

participants’ guides and
materials; develops media; may conduct train-the-trainer
sessions.
nction with designer and client, develops
and implements evaluation
plans; conducts all levels of evaluation; reports evaluation
findings to appropriate
persons; may assess facilitator skills.
ery
of learning experiences;
coordinates participant materials and media; enrolls participants
and sends pre-course
materials; secures facilities; coordinates facilities, including
hotels, training rooms and
breakout rooms; tracks attendance and maintains records;
promotes the course or
learning experiences; ships materials; tracks expenses.
recipient of the services)
liaison: Conducts organizational analyses for internal client
organization; contracts for
performance improvement; consults with internal clients on
performance issues;
prioritizes needs; secures support (including funding; access to
subject matter experts;
collects audience profiles; supports learners’ participation;
supports transfer of new
knowledge and skills to the job; has access to data necessary to
carry out these
responsibilities); with client input, selects facilitators; provides
feedback to internal clients;
manages the interface with the HRD staff.

Compensation has two primary areas—benefits and salary
administration. In many organizations,
payroll is a function of the accounting department. In other
organizations, payroll is placed in
compensation.
Benefits
Job titles: Benefits analyst, benefits specialist, benefits
administrator
to the internal requirements
of staff versus the competition and to retain employees.
Administers the health plan (including HMO or PPO plans).
contribution or defined benefit
plans.
Salary Administration
Job titles: Job analyst, job and salary analyst or specialist
merit, incentives, bonuses,
gain sharing, profit sharing, stock options and other rewards.

competition with the goal to
lead, lag or meet the competition.
responsibilities, job
specifications and pay grades.
goals and standards;
supports the performance management process.
adjustments to pay grades
based on survey results.
6
Employee Relations or Industrial Relations
Job title: Employee relations specialist

relations issues.
es employee
survey results. Develops and
implements plans to address identified issues.
-free
environment.
cipline procedures.
issues (EAP role).
maintains “ethics” hotline.
n with staffing, supports relocation and
outplacement services.
employees.
Health and Safety
Job titles: Safety specialist, safety coordinator, safety

administrator, industrial nurse
environment.
aining.
OSHA requirements.
on the job.
provided for the facility.
Prospector Strategy
A strategy is a plan for interacting with the competitive
environment to achieve organizational goals (Daft,
2003). According to Gomez-Mejia, Balkin and Cardy (2004),
the objective of a prospector strategy is to
find and exploit new products and market opportunities.
Organizations that use a prospector strategy are aggressive in
the marketplace, highly competitive and
quick to produce new products and services to be the first to
market. Their key objective is to find and
exploit new products and market opportunities. They operate in
an environment of uncertainty and
instability.

Organizational practices inherent in a prospector strategy
include (Gomez-Mejia, Balkin and Cardy,
2004):
on faster innovation, flexibility and creativity.
making the decision.
development purposes.
-based and cross-functional training.
To be flexible, organizational structures in a prospector strategy
are flat in organization design,
decentralized and/or team-based.
7
DEVELOPING THE RECRUITMENT AND RETENTION PLAN

A. Given the increase in product demand, how many people will
you need to hire and in what
functional areas (manufacturing, operations, customer service,
marketing and sales, finance/
accounting, and HR)? Provide your rationale for the proposed
hiring in each unit. Consider the
turnover rate. Identify the factors that could be causing
turnover. Identify the costs of turnover.
B. Include the types of interviews you would conduct and why.
C. Develop strategies to recruit the appropriate applicants and
include sources and tools used for
recruiting and selection. Identify the possible areas and types of
discrimination that could occur.
D. What can you do to retain current employees? What are the
benefits of retention?
E. How will you assess the effectiveness of your recruiting
efforts?
The Selection Process
This is a good time to review the selection process. According
to Noe, Hollenbeck, Gerhart and Wright
(2007), the steps in a selection process include screening
applications and résumés; reviewing and
testing work samples; interviewing candidates; checking
references and background; and making a
selection. Internal candidates would not require all of these
steps.
Turnover
The Department of Labor uses the following formula to measure
turnover:

(
Number of separations during the month
Total number of employees at midmonth
) x 100
Separations = those leaving the organization
Motors and More is not experiencing involuntary turnover,
however. The organization is expanding its
workforce, and the community is experiencing a labor shortage.
The turnover, then, is voluntary. Some
reasons for voluntary turnover include:
—work overload, issues with the manager
or other employees, little flexibility in
work scheduling, lack of challenge.
—employees can easily find alternative
employment because of the high
demand for employees in the area.
—competitive pay and benefits, no career path,
perceived unfairness in rewards
distribution.
nal or family reasons.

Turnover is expensive for organizations:
Employee
Separation Costs
Recruiting Costs
Selection/
Interviewing Costs
Training Costs Less Direct Costs
• Severance pay
• Benefits
• Unemployment
insurance costs
• Exit interview
• Outplacement
• Legal fees
• Advertising
• Recruiter’s and
manager’s time
• Travel (applicant
and/or recruiter)
• Search firm
• Employee referral
fees

• Campus visits
• Interviewing: cost
of employees’ time
• Cost of travel: cost
of travel for
applicant to the
interview
• Instrument
development
(questions,
• Training new
employee
(orientation, job,
team)
• Travel for training
• Trainer’s time
• Lost productivity
during training
• Training materials
• Lost productivity
due to new
employee’s
productivity curve
or the existing staff
taking on more
work while being
less efficient/
effective while the

8
criteria, tests)
• Reference checks
• Background
checks
• Relocation
• Pre-employment
medical
• expenses
• Coaching time vacancy exists.
• Overtime for
current employees
• Loss of business
due to poor
customer service
• Not being able to
take on new
business due to
lack of resources
• Lost knowledge

Equal Pay
There is one supervisor who is a woman and four managers, all
of whom are men. Motors and More
should examine the jobs to determine if they are equal in terms
of skill (experience or training), effort
(mental or physical effort), responsibility (degree of
accountability) and similar working conditions
(physical surroundings or hazards).
Is there enough difference between the positions to warrant a
“supervisor” job classification and not
“manager” job classification? If job responsibilities (such as
outbound sales) expand, will the job
classification difference still be warranted?
Sex Discrimination
Why is there only woman in a lower management position
supervising the call center? It may appear that
Motors and More believes there are certain jobs better suited for
women. Was gender a factor in the
hiring decision? Is stereotyping occurring?
Racial/Ethnic Discrimination
Motors and More is 88 percent Caucasian, and all managers are
men. The local population is 48 percent
minority, indicating possible discrimination. Since promotions
are based on seniority, it appears that
Caucasian men have been there the longest and received the
promotions. Motors and More must
aggressively recruit, staff and promote qualified minorities. It
should also promote based on performance
and job specifications, not tenure.
Because of Motors and More’s racial demographics, we can
assume that the local Hispanic and Kurd

population has not been accepted at Motors and More. This lack
of inclusion may lead to discrimination
allegations.
Benefits of Retention
Organizations with good retention:
s, which can provide a
competitive advantage.
become available, thereby reducing
recruiting and training costs.
because employees know their jobs,
peers, products and customers.
performance.
goals.
Can become an employer of choice, reducing recruiting and
turnover costs.
Assessing the effectiveness of recruiting efforts
There are a number of ways to assess effectiveness of recruiting
efforts. One way is to calculate costs
per hire. To calculate costs per hire, add total recruiting costs
and divide by the number of candidates
hired.

9
A second method is to determine the yield ratio. According to
Mathis and Jackson (2006), the yield ratio is
the comparison of the number of candidates at one stage in the
recruiting process to the number of
candidates at another stage. To calculate a job offer yield ratio,
divide the number of applicants by the
number of applicants offered a job.
You can also use a selection rate to assess your recruiting
efforts. A selection rate is the percentage
hired from a given group of candidates. It equals the number of
employees hired divided by the number of
applicants (Mathis and Jackson, 2006). Mathis and Jackson
(2006) also discuss acceptance rate (the
percentage of job offers rejected) and success rate (comparing
the number of past candidates who were
good performers to current employees). You can also identify
effective recruiting sources by determining
where you found candidates who can be defined as good
employees.
You may also try to determine which recruiting sources provide
higher-quality applicants. For example,
many organizations regularly recruit from certain universities
because, over time, those universities have
provided the organization with higher-quality candidates than
other schools.

10
DEVELOPING A COMPENSATION PLAN
Develop a progressive yet competitive compensation plan that
will support recruiting and retention efforts
and lower the employee turnover rate. Traditionally, Motors and
More has provided employees minimum
wage and statutory benefits.
A. How does Motors and More’s employee compensation
compare relative to other organizations in
the area? Will the organization meet, lead or lag the local
market? Explain your rationale.
B. Identify alternative pay methods and discuss the
advantages/disadvantages of each.
C. What benefits will you offer? Include statutory benefits.
What are the costs of those benefits?
What is the rationale for offering those benefits?
D. Develop a communications plan regarding how employees
will be informed about the
compensation plan. Define the sequence of communications
considering the who, what, and
when of the message.

Merit Pay
Merit pay rewards employees for work already done, usually
over the last performance year. Employees
are familiar with merit pay, and there is little resistance
offering it. Over time, merit pay increases
employees’ base pay, thereby increasing organizational costs. It
is also difficult to really link performance
to merit pay. Further, merit pay may not result in increased
performance. Employees may not see the link
between merit pay and their performance.
Incentives and Bonuses
Incentives and bonuses reward individual performance without
adding to base salary. Incentives and
bonuses also link pay with performance. For example, in
manufacturing, a straight piecework plan pays
employees a certain amount for each unit produced. In a team
environment, a group incentive could
increase group productivity. A disadvantage is that it may cause
unhealthy competition among
employees.
Commissions
Typically, commissions are pay based on product sales. On a
straight commission pay plan, pay is based
on employee performance; the more sales, the more commission
the employee receives. A combined
commission pay plan pays a base salary plus commission.
Commission pay plans directly link pay to
performance. Commissions can result in increased sales.
Commission pay plans, however, may cause
employees to behave unethically in order to make the sale.
Generally, commissions reward individual, not
team performance.

On-the-Spot Awards
On-the-spot awards instantly reward employees for good
performance or behavior. The recipient is
usually nominated by a supervisor or peers. Awards may include
gifts, gift certificates or cash. On-the-
spot awards focus employee attention on organizational goals
and objectives. In addition, employees can
quickly see the link between performance or behavior and the
reward. On-the-spot awards can lead to
feelings of resentment or unfairness, however, if managers use
their own criteria for giving awards.
Gainsharing
In a gainsharing incentive compensation plan, employees are
rewarded for cost reduction or improved
productivity. Savings are usually shared equally between the
employees and the company. Organizations
using gainsharing incentives usually have participative
management styles. Gainsharing plans can help
employees understand what is important to the organization,
such as decreasing labor costs. Gainsharing
can also increase employee cooperation because employees are
rewarded equally, reducing competition.
Gainsharing also reinforces continuous improvement.
Gainsharing, however, can make it difficult to
determine a productivity baseline. It can also be difficult to
determine who will participate in the program.
In a gainsharing plan, an organization could pay for cost
reductions even when profits are declining.
Lastly, since all employees are rewarded equally, the payment
may not seem fair to all employees.

11
Profit Sharing
In profit sharing, the organization pays employees above and
beyond their base pay when the
organization earns a profit. Profit sharing allows the
organization to reward employees when the
organization can most afford it. Profit sharing also provides
flexibility in the distribution of the payments.
Employees may not see the link between individual performance
and profits, however, minimizing the link
to performance.
These pay methods add value to the company while providing
flexibility in pay. When suggesting possible
solutions, students may offer, as an option, issuing stock.
However, the instructor should advise students
that Motors and More is not a public company.
Deciding What Benefits to Offer
Currently, Motors and More employees receive only statutory
employment benefits. Statutory benefits are
Social Security, workers’ compensation, unemployment
compensation and FMLA leave.
Social Security provides retirement and disability income and
can supplement unemployment insurance.
It is funded by equal contributions between the employer and
the employee.
Workers’ compensation covers medical costs and employee pay
if the employee is unable to work due to

a job-related illness, injury or disability. It is paid for by the
organization.
Unemployment compensation provides income to employees
who lose their jobs. The employer pays for
unemployment compensation based on the organization’s
location and history of terminations and layoffs.
The Family Medical Leave Act requires employers with 50 or
more employees to allow employees to take
up to 12 weeks of unpaid leave during a 12-month period for
narrowly defined personal and family health
issues. The Act ensures that the employee can return to the
same position or one of equal status and
pay. In addition, the Act requires employers to maintain
employee’s health coverage (if offered).
17
Communication plan
Developing a communication plan ensures that everyone
understands benefits offerings. Figure 3
provides a sample communication plan structure targeting only
the employee level.
Figure 3: Communication Plan
Audience Message Medium Desired Result Timing Frequency
Person
Responsible
Employees Explanation
of benefits
Large group
meeting

Employees will understand
the new benefits so they can
make an informed decision
Employee enrollment
At
enrollment
time
Quarterly HR Director
Benefits
Consultant
Website Understanding the new
benefits
Continuous Continuous Webmaster
Benefits
Consultant
Understanding the new
benefits
Based on
publication
dates
Semi-
annual
Editor

Benefits
Consultant
One-on-one
consultation
Employees will understand
the new benefits so they can
make an informed decision
Employee enrollment
By
appointment
Ongoing Benefits
Consultant
© 1998 Performance Advantage Group
12
Different communication mediums (or methods) should be used
for different audiences. The management
team may require group and/or one-on-one meetings. Employees
will want to see the details in a
document they can read and revisit.
Desired results are also audience-specific. For example, you

will want management to understand the
new benefits, enroll in the plans and also be supportive of the
plans. You will want employees to
understand the new plans, with enrollment as the desired result.
A communication plan considers timing (when the message will
be communicated) and frequency (how
often the message or a version of the message will be presented
to the audience). For example, for
benefits enrollment, timing may be one month before the
enrollment period begins. The frequency may be
once a week to start, increasing to twice a week as the
enrollment period draws to a close.
For a communication plan to succeed, each person assigned to
execute each phase of the plan must be
held responsible to complete it within the time specified.
13
DEVELOPING A HUMAN RESOURCE DEVELOPMENT
PLAN
Develop a human resource development (HRD) plan. Given the
fact that Motors and More has no formal
training program and promotions have been based on seniority,
your plan should address:
A. New-employee training.

B. Current-employee training for current and future jobs
according to a career path.
C. Manager and supervisory training.
In your training plan, address the following:
A. How will you conduct a needs assessment for each group
(include methods and instruments)?
B. How will training content be developed or obtained?
C. How will training be delivered (e.g., classroom, intranet,
blended, self-study, etc.) and by whom
internal employee or external consultant/trainer)? Provide a
rationale for your decision.
New-employee Training
New employees should undergo an orientation program.
Comprehensive orientation should include
information about the industry, organization, job unit and
position. To retain employees, orientation to the
team (job unit) and position are important. New-employee
orientation should address individual employee
goals. New employees may also need specific job training to
enhance their skills, to safely use equipment
and to follow procedures.
Current-employee Training
Current employees may need refresher skills training and
diversity training. Evaluate whether English-as-
a-second language (ESL) training would support the work
environment. If functional career paths are
developed, training should align to the functional career path.
Because Motors and More will likely add to
team leaders, basic supervisory and team leadership training
may be needed.

Manager and supervisory Training
Managers and supervisors were promoted by seniority, and there
is no indication they have had any
management level training. Promotion by seniority also resulted
in a lack of women and minorities in top-
level positions. Basic management training in areas such as
budgeting and forecasting, employment
laws, performance management, goal setting, and giving and
receiving feedback would improve their
capabilities. Diversity training is also recommended for this
group.
Needs Assessments
Needs assessments should be conducted at the organizational,
job and individual levels. At the
organizational level, a needs assessment should assess current
and future training needs, identify
existing gaps and recommend specific training and development
required to close the gaps. An
organizational needs assessment is client-based and considers
organizational objectives and strategies.
A job (or task) needs analysis identifies the specific skills,
knowledge and abilities (KSAs) needed to
perform the tasks in a current or future position (Jackson and
Schuler, 2006). A job analysis identifies the
training requirements for the job. Additional sources include
interviewing the incumbent and the
incumbent’s manager to identify specific skills, knowledge and
behavior required to successfully do the
job. The goal of a job needs analysis is to define what
successful performance looks like and identify the
KSAs required.
An individual needs assessment identifies the KSA gaps

between existing and required performance. A
good method to assess individual needs is to measure
performance against objectives. This is discussed
in the performance review where strengths and weaknesses can
also be identified. Observation is
another way to conduct this needs assessment.
Needs assessments can be conducted by examining the existing
(or extant) data, conducting individual
interviews or focus groups, implementing surveys, using multi-
rater assessments, assessing employees
through various tests, and reviewing individual, functional unit
and organizational performance records.
14
How will training content be developed or obtained?
Training content can be developed or purchased. Some training
may need to be developed internally. If
so, given the limited HR staff, how will this be done? Using
SMEs and/or an outside trainer is a possibility.
Buying training programs off-the-shelf is also an option.
However, off-the-shelf training products are
generally not customized to the individual organization.
Consequently, customization costs must be
considered. Cost, time to delivery and size of target audience
are also factors to consider.
How will training be delivered (e.g., classroom, intranet,

blended, self-study, etc.) and by whom
(internal employee or external consultant/trainer)?
It is important to match delivery methods to content and
audience. Some training (such as new employee
orientation) can be self-study; employees can receive individual
training material as such as workbooks or
CDs. Some of the new-employee orientation, though, may need
to be classroom-based. If the target
audience has computer access, training can be delivered by
Intranet, internet or CD. Classroom training
is always an option, but timing and space may be issues. When
planning training, keep in mind that
managers and professionals usually have more flexibility in
their schedules than hourly employees do.
This will affect classroom delivery. Consider using job
assignments and job rotation to provide depth and
breadth of training.
Some criteria or decision factors in training delivery include:
designers and facilitators.
—it takes longer to develop an online course
than a classroom exercise.
organization needs to be trained in remote

locations and quickly, you may need outside resources.
What processes and instruments will you use to evaluate the
program’s effectiveness for each
group?
Kirkpatrick’s four levels of evaluation is the model generally
used for training evaluation. These levels are
discussed extensively in Evaluating Training Programs: The
Four Levels and are summarized here for
your reference.
Level 1: Reaction
Level 1 assesses participants’ reaction to the training.
Participants are asked to complete a
survey at the end of the training. The survey asks participants to
indicate if training objectives
were met, rate facilitator skills, assess the relevancy of content
and rate the appropriateness of
instructional/learning strategies, materials, group interaction,
etc.
Level 2: Measure learning
Was there a change in knowledge, skills or attitudes? Learning
can be measured through tests
for knowledge, assessments of performance during role-plays,
demonstrations, case studies,
projects, work products, etc. These instruments, developed
specifically for the training, are used
during the learning and feedback is provided during the learning
activities. To accurately assess a
shift in learning, there a baseline must be established by
administering a pre-test and post-test.
Level 3: Assess behavior change on the job

This is also referred to as transfer. Are participants using the
new knowledge and skills on the
job? To what extent? To assess this, HRD professionals can
follow up on performance contracts,
action plans, work products and similar transfer strategies to
determine the extent to which these
are being completed. HRD professionals can also conduct
individual interviews of training
participants, their managers and/or peers or conduct focus
groups to determine transfer. Surveys
can also be used.
15
Another consideration in assessing learning transfer involves
the environment. To what extent
does the environment enable or hinder the use of the new KSA?
The same assessment methods
can be used.
Level 4: Measure results
Measure any changes in the initial business metric addressed by
the training. Did the metric
(sales, defects, turnover, etc.) change? The return on investment
(ROI) can be calculated if the
dollar value of the change in the metric (benefit) and total
program costs are known. When
measuring results, it is important that only results due to
training are considered. To do this,

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