Contents lists available at ScienceDirectNeuroscience and

Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
Changing conceptualizations of regression: What prospective
studies reveal
about the onset of autism spectrum disorder
Sally Ozonoffa,⁎ , Ana-Maria Iosifb
a Department of Psychiatry and Behavioral Sciences, MIND
Institute, University of California – Davis, 2825 50th Street,
Sacramento CA, 95817, USA
b Department of Public Health Sciences, University of
California – Davis, Medical Sciences 1C, Davis CA, 95616,
USA
A R T I C L E I N F O
Keywords:
Autism spectrum disorder
Onset patterns
Regression
Prospective studies
A B S T R A C T
Until the last decade, studies of the timing of early symptom
emergence in autism spectrum disorder (ASD) relied
upon retrospective methods. Recent investigations, however, are

raising significant questions about the accuracy
and validity of such data. Questions about when and how
behavioral signs of autism emerge may be better
answered through prospective studies, in which infants are
enrolled near birth and followed longitudinally until
the age at which ASD can be confidently diagnosed or ruled out.
This review summarizes the results of recent
studies that utilized prospective methods to study infants at
high risk of developing ASD due to family history.
Collectively, prospective studies demonstrate that the onset of
ASD involves declines in the rates of key social
and communication behaviors during the first years of life for
most children. This corpus of literature suggests
that regressive onset patterns occur much more frequently than
previously recognized and may be the rule rather
than the exception.
1. Introduction
The onset of behavioral signs of autism spectrum disorder
(ASD) is
usually conceptualized as occurring in one of two ways: an
early onset
pattern, in which children demonstrate delays and deviances in
social
and communication development early in life, and a regressive
pattern,
in which children develop largely as expected for some period
and then
experience a substantial decline in or loss of previously
developed
skills. While it was long believed that the majority of children
with ASD
demonstrated an early onset pattern, more recent studies suggest
that
regressive onset occurs more frequently than previously

recognized
(Brignell et al., 2017; Hansen et al., 2008; Kern et al., 2015;
Pickles
et al., 2009; Shumway et al., 2011; Thurm et al., 2014; for a
review, see
meta-analysis by Barger et al., 2013). Studies occasional ly also
identify
a third onset pattern, that of developmental stagnation or
plateau
(Shumway et al., 2011), that is characterized by intact early
skills that
fail to progress or transform into more advanced developmental
achievements. This onset pattern is distinct from regression, in
that the
child does not lose acquired skills, but instead fails to make
expected
gains.
1.1. Methods for measuring onset patterns
The most common procedure for collecting information about
the
timing of early symptoms is retrospective parent report. A
number of
factors can influence report validity, including awareness of the
child’s
eventual diagnosis and knowledge of developmental milestones.
It has
long been understood that retrospective reports are subject to
problems
of memory and interpretation (Finney, 1981; Henry et al., 1994;
Pickles
et al., 1996), including in studies of ASD (Andrews et al.,
2002). Mul-
tiple studies have documented the ways in which recall

problems and
other biases can influence parent report. Changes in recall occur
over
time, with past events often reported to occur more recently,
closer to
the time of recollection, than they actually took place, an error
called
forward telescoping (Loftus and Marburger, 1983). Studies of
children
with ASD have demonstrated significant forward telescoping in
parent
report of milestones, resulting in parents being less likely to
report re-
gression and more likely to report early delays as their children
grow
older (Hus et al., 2011; Lord et al., 2004). A recent study from
our
research team (Ozonoff et al., 2018a) conducted longitudinal
inter-
views with parents about onset of ASD symptoms when their
child was
2–3 years old (Time 1) and approximately 6 years old (Time 2).
Sig-
nificant forward telescoping was found in both age of regression
and
age when milestones were achieved. The correspondence
between Time
1 and Time 2 parent report of onset was low (kappa = .38). One-
quarter
of the sample changed onset categories, most often due to
parents not
recalling a regression at Time 2 that they had reported at Time
1.
Analysis of home movies of children later diagnosed with ASD
is

https://doi.org/10.1016/j.neubiorev.2019.03.012
Received 24 September 2018; Received in revised form 12
February 2019; Accepted 14 March 2019
⁎ Corresponding author.
E-mail addresses: [email protected] (S. Ozonoff),
[email protected] (A.-M. Iosif).
Neuroscience and Biobehavioral Reviews 100 (2019) 296–304
Available online 15 March 2019
0149-7634/ © 2019 Elsevier Ltd. All rights reserved.
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another retrospective method used in research studies to study
symptom emergence (Goldberg et al., 2008; Palomo et al.,
2006). Video
analysis may be a more objective procedure for documenting
early
symptoms than parent recall (Werner and Dawson, 2005) but it
is labor-
intensive and subject to other limitations, such as selective
recording

(e.g., tendency of parents to film positive behaviors). In a study
from
our team that compared classification of onset based on coding
of fa-
mily movies to onset type as recalled by parents (Ozonoff et al.,
2011a),
less than half of children whose home video displayed clear
evidence of
a major decline in social and communication behavior were
reported to
have had a regression by parents. Similarly, only 40% of
participants
with clear evidence of early delays and little evidence of skill
decline on
video were reported by parents to show an early onset pattern.
1.2. Prospective studies of onset
Questions about when and how behavioral signs of autism
emerge
may be better answered through prospective investigations, in
which
infants are recruited and enrolled near birth, prior to the advent
of
parent concerns, and then followed longitudinally through the
window
of developmental risk, until the age at which ASD can be
confidently
diagnosed or ruled out, usually 36 months. A few large general
popu-
lation cohorts have been studied prospectively to examine onset
pat-
terns (Brignell et al., 2017; Havdahl et al., 2018) but this study
design is
inefficient, since fewer than 2 in 100 participants will develop
ASD

(Centers for Disease Control and Prevention, 2018), making it
difficult
to achieve an appropriate sample size. Additionally, large
prospective
cohort studies must, of necessity, rely upon parent
questionnaires and
rarely provide the opportunity for in-person clinical assessments
to
verify diagnosis or onset pattern.
For this reason, most prospective investigations utilize high-risk
samples in order to increase the number of ASD outcomes that
are in-
formative for study. The most widely used high-risk group has
been
later-born siblings of children with ASD, who are known to be
at higher
ASD risk than the general population (Constantino et al., 2010).
Most
investigations compare high-risk infants to lower-risk
participants with
no known family history of ASD in first-, second-, and
sometimes third-
degree relatives. This study design improves on retrospective
methods
in a number of important ways. Serial comprehensive
assessments, in
standardized testing contexts, are used to document the timing
of
symptom emergence, thus avoiding reliance on potentially
fallible
parent recall or non-representative home video. Assessments
can utilize
a wide range of tools, including eye tracking, EEG, and
imaging, al-
lowing broader investigations of symptom onset and testing of

specific
hypotheses. And while most retrospective studies recruit
samples
through clinics, which may influence the results by including
more
severely affected children, infant sibling studies avoid such
potential
biases by ascertaining participants via family history alone.
Several recent papers provide comprehensive reviews of the
infant
sibling literature (Bölte et al., 2013; Jones et al., 2014; Pearson
et al.,
2018; Szatmari et al., 2016). Here we focus on research reports
of
greatest relevance to symptom emergence, specifically those
that study
infants beginning in the first year of life on measures that are
appro-
priate for examining potential skill decline over time. Using a
variety of
different prospective methods, these studies have reported
largely in-
tact early development, followed by developmental declines and
onset
of symptoms around the first birthday and in the second year of
life. For
example, Zwaigenbaum et al. (2005), using the Autism
Observation
Scale for Infants (AOSI), reported no differences at 6 months
between
infants subsequently diagnosed with ASD and both high- and
low-risk
infants without ASD outcomes; significant group differences
emerged at
12 months and increased over time. This pattern on the AOSI

was later
replicated by an independent research team (Gammer et al.,
2015).
Wan et al. (2013) found that infant-parent interaction quality at
6–10
months did not predict which children would be diagnosed with
ASD at
age 3, but by 12–15 months, such variables were significantly
associated with diagnostic outcome. Similar findings of lack of
early
group differences (or lack of early predictive ability), followed
by later
divergence from typically developing infants, have been
reported by
Landa and Garrett-Mayer (2006) using the Mullen Scales of
Early
Learning, Rozga et al. (2011) in joint attention, Bedford et al.
(2012) on
a gaze-following eye-tracking task, Elsabbagh et al. (2013) on a
gap-
overlap attention task, and Wolff et al. (2014) studying
repetitive be-
havior. In an incisive recent review that attempts to reconcile
retro-
spective and prospective studies of regression and expl ore how
study
design affects the likelihood of capturing regression, Pearson et
al.
(2018) conclude that, among infants who later develop ASD,
“the ma-
jority show declining fixation of eyes, gaze to faces, and social
en-
gagement, from typical levels in early infancy (2–6 months) to
sig-
nificantly reduced levels by 24–36 months (p. 14).”

2. Findings from the University of California Davis infant
sibling
study
In our laboratory, we have taken the analytic approach of
growth
curve modeling to examine directly the evidence of longitudinal
de-
velopmental change in the first three years of life. Between
2003 and
2015, the UC Davis Infant Sibling Study recruited three cohorts
of later-
born siblings, each composed of 50 low-risk and 100 high-risk
infants.
Participants were tested as early as 6 months of age and then
seen every
3 to 6 months until their 3rd birthday (up to 7 in-person
evaluations).
They have since been followed into school age and tested at
approxi-
mately three-year intervals. The oldest children from Cohort 1
are now
16 years of age and the retention rate is over 80%. At each
infant and
preschool visit, a battery of age-appropriate standardized tests
and
experimental tasks was administered that measured language,
cogni-
tion, social, communication, motor, and many other domains.
Approximately 20% of the high-risk infants were later
diagnosed with
ASD (Ozonoff et al., 2011b). Diagnoses of ASD were made at
any point
that a child met criteria (mean age 24.2 months) but a full
diagnostic

assessment was completed on all children, regardless of
previous find-
ings, at 36 months by examiners unaware of family risk or prior
as-
sessment results. In the following sections, we summarize
several stu-
dies from these cohorts that consistently demonstrate declining
trajectories across a variety of different measures and
developmental
domains.
The phenomenon of regression is defined by loss or significant
de-
crease in already-acquired skills. Thus, a critical
methodological issue
in prospective studies that wish to examine onset patterns is
selection of
which behaviors to measure. They must be 1) developmentally
appro-
priate across the full age window of risk and 2) robustly
present, at high
frequency, in the first year of life. Such behaviors have the
capacity to
decrease and are therefore of highest relevance to the study of
onset
patterns. Measures that focus on socio-communicative behaviors
that
have not yet emerged in the first year of life, such as joint
attention,
imitation, and verbal communication, will be less useful for
testing
hypotheses about declining capacities. The behaviors our
laboratory
has focused on, including gaze to faces and eyes of others,
shared affect,
and social interest/engagement, are well developed in the first

year of
life (Inada et al., 2010) and therefore of highest relevance in the
pro-
spective measurement of regression.
2.1. Behavioral coding of social-communication rates
Our first exploration of longitudinal change in early social and
communicative behaviors used video recordings of participants
inter-
acting with examiners during structured developmental testing
(Ozonoff et al., 2010). Research assistants, unaware of family
risk group
or diagnostic outcome, were trained to 90% reliability to detect
three
behaviors: gaze to an adult’s face, smiles at an adult that were
paired
with eye contact, and vocalizations directed at an adult that
were ac-
companied by eye contact. Rate per minute of eye gaze, shared
affect,
S. Ozonoff and A.-M. Iosif Neuroscience and Biobehavioral
Reviews 100 (2019) 296–304
297
and directed vocalizations of the 25 children in Cohort 1 with
outcomes
of ASD were compared to those of 25 children who did not have
ASD
outcomes randomly selected from the low-risk group. The two
groups
behaved similarly at 6 months: frequencies of none of the three

beha-
viors differed between the groups and effect sizes were in the
small
range. Over time, the Low-Risk (LR) Non-ASD group had a
significant
increase in social smiles and directed vocalizations, while
maintaining
the same consistently high level of gaze to faces. In the ASD
group, in
contrast, the rates of all three behaviors dramatically decreased
over
time. Fig. 1A displays longitudinal trajectories of eye contact
rate per
minute, showing comparable values between groups at 6
months, fol-
lowed by group differences that became statistically significant
by 12
months and persisted and widened over time. These longitudinal
de-
creases in the rates of key social and communicative behaviors
provided
the first prospectively measured evidence consistent with a
regressive
onset pattern.
We have since replicated these findings (see Fig. 1B) using the
same
methods in an independent sample of 46 infants later diagnosed
with
ASD from Cohorts 2 and 3 of our longitudinal project. In this
analysis
(Gangi et al., in preparation), a third group, composed of high-
risk (HR)
infants who did not have ASD outcomes, was also included.
This group
was not different from the LR Non-ASD group in the frequency

of gaze
to adult faces at any age and did not show any evidence of
decline in
development, which was evident only in the participants
developing
ASD, replicating our 2010 study.
2.2. Observer ratings of social engagement
Coding behavior frequencies from video is time-consuming,
labor-
intensive, expensive, and not transferable to clinical contexts,
so our
research program has also sought to establish whether declining
tra-
jectories are evident using other methodological approaches. At
the end
of each visit, examiners rate the frequency of eye contact,
shared affect,
and overall social engagement (number of social initiations and
social
responses) made by the infant throughout the session, across all
tasks,
using a 3-point scale (1 = rare, 2 = occasional, 3 = frequent).
These
scores are summed to create a composite that ranges from 3 to
9. As
reported in Ozonoff et al. (2010) and depicted in Fig. 2A, these
ex-
aminer ratings of social engagement showed similar
longitudinal pat-
terns to the social behaviors coded from video. There w ere no
group
differences in the 6-month examiner ratings; however, while the
LR
Non-ASD group showed a significant increase in social

engagement
ratings over time, reaching close to the maximum score by 36
months,
the children in the ASD outcome group had a strong decline in
social
engagement ratings over the same time period.
This finding was recently replicated in an independent group of
32
infants with ASD outcomes from Cohorts 2 and 3 of our sample
(Ozonoff et al., 2018b). We used the same examiner rating
variable
(this time with an expanded 5-point scale) and compared the
ASD group
to both a low-risk and a high-risk group without ASD. Again,
all three
groups had comparable levels of social engagement based on
examiner
scores at 6 months of age. The ASD group then demonstrated a
decrease
in scores with age, while the HR Non-ASD group showed stable
high
scores over time and the LR Non-ASD group demonstrated
increasing
scores longitudinally. By 12 months, the two Non-ASD groups
demon-
strated significantly higher rates of social engagement, as
judged by
examiners, than the ASD group and these differences widened
over
time, as can be seen in Fig. 2B. Along with our 2010 paper,
these
findings demonstrate that the declines in the frequency of social
and
communication behaviors detected through more labor-intensive

video
coding methods are also detectable through much simpler
methods that
would be feasible for broader use, such as brief observational
ratings of
social engagement by clinical professionals.
2.3. Longitudinal parent ratings of social behavior
The question remained, however, whether such findings could
be an
artifact or byproduct of the assessment context with an
unfamiliar ex-
aminer. For both clinical use and future development of
screening
methods, it is critical to also establish whether parent ratings
are sen-
sitive to the developmental decline phenomenon we have
reported. In a
recent study (Ozonoff et al., 2018b), we examined parent
prospective
ratings of the same early-appearing socio-communicative
behaviors
measured in the video and examiner ratings. Parents in our
study
completed the Early Development Questionnaire (EDQ; Ozonoff
et al.,
2005) prior to each visit. The EDQ consists of 45 questions
about the
child’s current functioning in social, communication, and
repetitive
behavior domains. Each item is rated on a 4-point frequency
scale
(0=behavior never occurs, 3=behavior often occurs). Three
items,
comparable in content to the video codes and examiner ratings,

were
summed: item 1 (“my child looks at me during social
interactions”),
item 4 (“my child smiles back at me when I smile at him/her”),
and
item 13 (“when I call my child’s name, he/she looks at me right
away”).
In addition to being parallel to the behaviors rated by
examiners, these
items were selected because they represent early-appearing
behaviors
that are relevant and developmentally appropriate across all
ages of the
Fig. 1. Declining trajectories of gaze to eyes in children
developing ASD, coded from a videotaped interaction with an
examiner. Panel A: Cohort 1, Panel B: Cohorts 2
and 3.
Reviews 100 (2019) 296–304
298
study (6–36 months). In contrast to other EDQ items that
measure later-
developing skills (e.g., joint attention, language), the items
selected for
the composite measure behaviors present in the first year of life
(Inada
et al., 2010). As with the behaviors we selected for coding and
examiner
observational ratings, it was critical that the behaviors rated by
parents

have the potential to demonstrate decreases over time as ASD
signs
emerge. The composite variable, quantifying parent report of
the fre-
quency of key early social behaviors, had a potential range of 0
– 9. On
the parent-rated EDQ, there were again no group differences at
6
months. As with the other measures, the ASD group showed a
decline in
levels of social engagement with age, while both the high-risk
and low-
risk Non-ASD groups demonstrated gains in social engagement
over
time. The ASD group’s scores were significantly lower than
both Non-
ASD groups by 12 months and the differences increased with
age, de-
monstrating the same declining trajectory as evident in the
coded be-
havior and examiner ratings (see Fig. 3A).
Employing a similar approach, we replicated the ability of
parent
report to capture the decline in social and communication
development
using a standardized, normed measure (Parikh et al., 2018), the
Infant-
Toddler Checklist (ITC), a 24-item parent questionnaire from
the
Communication and Social Behavior Scales (CSBS; Wetherby
and
Prizant, 2002). This instrument is normed from 6 to 24 months
and
includes questions that span this developmental range, from

early-ap-
pearing behaviors like social smiling to those that emerge at
older ages,
such as spoken language and pretend play. We created a
composite of
three items that represent behaviors typically present in the first
year of
life (item 2: “when your child plays with toys, does he/she look
at you
to see if you are watching?”; item 3: “does your child smile or
laugh
while looking at you?”; item 19: “when you call your child’s
name, does
he/she respond by looking or turning toward you?”). We then
com-
pared growth trajectories in infants subsequently diagnosed with
ASD
(n = 46) to the HR Non-ASD (n = 139) and LR Non-ASD groups
(n = 96). There were no group differences on the 3-item ITC
composite
at 6 months of age; however, over time, the ASD group showed
a de-
cline in scores, while the two Non-ASD groups demonstrated
gains (see
Fig. 3B). This resulted in the ASD group having significantly
lower
scores at 24 months than both comparison groups.
These studies from our lab show that children with ASD, as a
group,
evidence declines in development from 6 to 36 months. Such
declines
are seen only in the ASD group and not in comparison samples,
even
those with elevated genetic risk or other developmental
concerns.

Findings of declining trajectories have since been replicated by
other
Fig. 2. Declining trajectories of social engagement in children
developing ASD, as rated by examiners unaware of risk group
or outcome. Panel A: Cohort 1, Panel B:
Cohorts 2 and 3.
Fig. 3. Declining trajectories of social engagement in children
developing ASD, as rated by parents, Cohorts 2 and 3. Panel A:
Early Development Questionnaire,
Panel B: Infant Toddler Checklist.
Reviews 100 (2019) 296–304
299
independent research teams. Landa et al. (2013) examined
growth
trajectories in infants later diagnosed with ASD and Non-ASD
partici-
pants. Approximately half of the children with ASD, labeled the
Early-
ASD group, demonstrated differences from the Non-ASD cases
at 14
months but the other half (Later-ASD group) did not diverge
from ty-
pical infants until 24 months. The Later-ASD group
demonstrated a
steep decline in shared positive affect, as measured by the
CSBS
(Wetherby and Prizant, 2002), between 14 and 24 months. Jones
and

Klin (2013) conducted a prospective eye-tracking study with
high- and
low-risk infants to assess attention to eyes. The authors reported
that
very early in development (i.e., first two months of life), both
low-risk
and high-risk infants displayed high levels of attention to eyes,
with no
group differences. However, high-risk infants who were later
diagnosed
with ASD began to demonstrate a steady decline in looking at
eyes at
four months, reaching a level that was approximately half that
of low-
risk infants by 24 months. What was most predictive of a later
ASD
outcome was not the amount of visual fixation on eyes
displayed at any
particular age, but the overall declining trajectory over time.
This study
found that the majority of infants developing ASD demonstrated
this
declining pattern.
2.4. Growth curve modeling approaches to determining onset
classifications
In aggregate, the studies reviewed up to this point converge on
the
conclusion that longitudinal decreases in key social behaviors
are a
signature of the early emergence of ASD. But these data do not
clarify
how widespread such phenomena are within ASD and whether
the
group-level findings are driven by extreme outliers or

characterize a
majority of young children developing ASD. In our lab, we have
ap-
proached this issue analytically using multivariate Latent Class
Analysis
(LCA; Muthen, 2004), permitting us to identify distinct
subgroups of
children based on their longitudinal patterns on multiple
measures of
social communication. This technique does not rely on
preconceived
notions or poorly defined definitions of onset phenomena, but
instead
uses statistical modeling to empirically derive the optimum
number of
classes described by the patterns of performance demonstrated
in the
measures.
Data from a recent paper (Ozonoff et al., 2018b) address the
ques-
tion of how widespread the declining trajectories pattern is
within a
group of 32 infants subsequently diagnosed with ASD. We
employed
latent class growth models to examine potential within-group
variation
in onset patterns, using both examiner ratings of social
engagement and
parent ratings from the EDQ (see Sections 2.2 and 2.3 for
instrument
descriptions). Best model fit for examiner ratings was a two-
group so-
lution. Using their highest posterior group probability, the 32
partici-
pants were classified into two trajectories (see Fig. 4A, which

also
presents the Low- and High-Risk Non-ASD groups as
contrasts). Only a
small proportion of the ASD cases (n = 4; 13%) were assigned
to an
Early Onset/No Regression group by the latent class analyses,
based on
examiners prospectively reporting low levels of social behavior
at all
ages. The vast majority (n = 28; 88%) were classified by these
analyses
into a Regression group, in which examiners prospectively rated
in-
itially high levels of social engagement that dropped
significantly over
time.
The best fit for the parent EDQ 3-item composite in the latent
class
models was a three-group solution: Group 1, an Early Onset
trajectory,
Group 2, a Declining trajectory, and Group 3, an Improving
trajectory
(see Fig. 4B). Parents prospectively reported low levels of
social en-
gagement at all ages for Group 1, which again made up a small
minority
of the sample (n = 4; 13% of the sample). The majority of the
sample
(n = 22; 69%) was classified in Group 2; these children were
pro-
spectively reported by parents to show high rates of social
engagement
early in life, which significantly declined over time. Parents of
children
in Group 3 (n = 6; 19%) prospectively reported low levels of

skills at
early ages that then significantly increased over time.
2.5. Concordance between retrospective and prospective onset
classifications
In several of our studies, we have examined the correspondence
between prospectively- and retrospectively-defined onset
patterns and
in each case have found them to be quite poor. For example, in
our
initial paper (Ozonoff et al., 2010), we compared onset
classifications
based upon coded frequencies of social and communicative
behaviors
to onset classifications employing retrospective parent report on
the
Autism Diagnostic Interview-Revised (ADI-R; Le Couteur et
al., 2003).
Using prospective observational data, 86% of the ASD sample
showed
decreasing rates of eye contact, social smiles, and vocalizations
over
time, but by parental recall using the ADI-R, only 17% of the
children
were classified as having regressive onset. In a more recent
study
(Ozonoff et al., 2018b), 69% of parents rated their child in a
manner
consistent with regression on a prospective questionnaire (the
EDQ,
described in section 2.3), but only 29% rated that same child as
losing
skills using a retrospective measure (the ADI-R). Parents were
able to
implicitly identify the changes in their child’s development over

time
when making ratings of the frequencies of current behaviors,
but often
did not explicitly label these changes as skill loss or regression
when
asked in a more categorical way. These results were particularly
striking since both relied upon parental observations of the
child. Si-
milar findings of low concordance between retrospecti ve and
pro-
spective methods of defining onset were reported by Landa et
al.
(2013). And a recent large general population study (Havdahl et
al.,
2018) found similar under-reporting of losses based on a
retrospective
parent interview. Of parents who prospectively reported a loss,
defined
as rating certain social behaviors as present at 18 months but
absent at
36 months, only a striking 2% of them recalled such a decline,
or la-
beled it as a loss, when asked at age 3.
3. Conclusions and theoretical implications
A number of conclusions can be drawn from the collective body
of
work reviewed in this paper.
3.1. Onset involves declining social development
ASD emerges over the first two years of life and is not present
“from
the beginning of life” as stated by Kanner (1943, p. 242) in his
seminal

paper. For many years, it was presumed that ASD signs were
present,
but were just challenging to measure, from birth. Diagnostic
criteria for
ASD were developed at a time when children with autism were
rarely, if
ever, identified in infancy and thus many symptoms in the DSM
and ICD
criteria, such as delays or deficits in gestures, language,
imitation, and
pretense, are less relevant to the first year of life. As we have
empha-
sized in this review, one key to understanding early symptom
emer-
gence is to focus on very early-appearing social behaviors,
those that
are robustly present in early infancy, such as social interest,
shared
affect, gaze to faces and eyes, and response to name. When such
a
methodological approach is taken, there is clear evidence,
across mul-
tiple methods, replicated by independent research teams, of
declining
social behavior over time, after a period of relatively typical
develop-
ment. There is convergence across studies of a lack of group
differences
from comparison samples without ASD before 9 months of age,
fol-
lowed by statistically significant differences starting at 12
months that
widen over time. Logically, if certain skills are evident at
typical rates at
an early point in development and then those same skills,
defined and

measured the same way later in development, have substantially
di-
minished, resulting in statistically significant differences from
typical
infants, a loss or regression of some magnitude must have
occurred.
This paper advocates for taking a dimensional approach and
using
trajectories to identify patterns of onset. We are not arguing,
however,
for a fundamental reconsideration of the use of the word
“regression.”
The Merriam-Webster definition of the word regression is “a
trend or
Reviews 100 (2019) 296–304
300
shift toward a lower or less perfect state, such as (a) progressive
decline
of a manifestation of disease or (b) gradual loss of acquired
skills.” This
aptly describes the loss of established skills (e.g., eye contact,
response
to name, social interest) that occurs during the declines in
social de-
velopment described in this paper.
3.2. Regression in ASD is the Rule, not the exception
The data summarized in this review suggest that a regressive

pattern
of onset is much more common than previously thought, the rule
rather
than the exception. While retrospective studies yielded
regression es-
timates of 20–30%, prospective data put them much higher, in
some
studies well over 80%. We and others (Jones et al., 2014;
Ozonoff et al.,
2018b; Pearson et al., 2018) have suggested that the regressions
re-
ported by parents retrospectively on measures like the ADI-R
represent
just “the tip of the iceberg,” while prospective studies are able
to cap-
ture earlier, more gradual, subtle changes that may be less
noticeable in
real-time observation. A hypothesis deriving from this
supposition is
that concordance between retrospective and prospective
methods
should be most frequent when the regression occurs later, is
more
drastic or severe, and involves loss of clearly defined skills like
lan-
guage. No published studies have yet examined this question
and it
would be a fruitful avenue for future investigation.
We propose that the way ASD starts, for all children, is through
declines in early social and communication abilities. This
presents a
testable hypothesis: that all infants developing ASD lose some
skills, but
at different ages, some of which may be harder to detect with
current

measurement approaches than others. It may be difficult for
parents to
perceive and describe changing patterns of development that
occur over
many months during infancy, particularly when the period of
normalcy
is fairly brief. Our team (Ozonoff et al., 2010, 2011a) and
others
(Pearson et al., 2018; Rogers, 2009; Szatmari et al., 2016;
Thurm et al.,
2014) have suggested that onset is better thought of
dimensionally, as a
continuum of age when social and communication behaviors
begin to
diverge or decline, rather than a dichotomy (regression v. early
onset).
In a dimensional conceptualization of onset, at one end of the
con-
tinuum lie children who display declines so early that they are
difficult
to measure and symptoms appear to have always been present.
At the
other end of the continuum are children who experience losses
so late,
when more skills have been acquired and thus there are more
skills to
lose, that the regression appears quite overt and dramatic. We
propose
that variable timing of these processes across children leads to
symp-
toms exceeding the threshold for diagnosis at different points in
the first
3 years of life, resulting in a distributed curve of onset timing.
3.3. Simplex v. multiplex samples

An important question to consider is whether regression in
infant
sibling samples is representative of regression in children with
ASD who
are the first in their families to be diagnosed with the condition.
If
symptoms emerge differently in multiplex and simplex families,
then
the insights about onset afforded by prospective research may
not be
applicable to the general population of children with ASD. For
example,
perhaps children in multiplex families are more likely to
experience a
regression than children from simplex families, accounting for
the
higher rates of decline apparent in prospective studies. We have
no
reason to believe this is the case. In fact, the rate of
retrospectively-
reported regression in multiplex families has been reported to
be similar
or lower than in simplex families (Boterberg et al., 2019b; Parr
et al.,
2011), failing to account for the high rates apparent in infant
sibling
studies, whose participants are, by definition, from multiplex
families.
A related issue is that parents participating in infant si bling
investiga-
tions have an older child with ASD. It is possible that these
parents may
be different reporters than other parents, given their previous
experi-
ence with ASD. This may make them more astute observers of

devel-
opment than parents in the general population and therefore
more
likely to recognize skill decline. This hypothesis, however, is
not sup-
ported by the data presented earlier in this review in which
parents in
multiplex families also under-report skill loss (Landa et al.,
2013;
Ozonoff et al., 2010, 2018a, 2018b). Nevertheless, it is
important to
keep these cautions in mind in interpreting the extant data on
onset
patterns. Validating these results in different kinds of samples,
such as
community-based epidemiological cohorts or other high-risk
groups
like very preterm infants, will be critical.
3.4. Improving the measurement of onset
Collectively, the studies reviewed in this paper present
significant
concerns about the accuracy of the most widely used methods of
measuring regression, that is, retrospective parent report, and
argue
against their widespread use. The challenge currently faced by
the field
is that there are no practical alternative strategies to parent
report for
characterizing onset status. The time-intensive process and cost
of home
videotape analysis is prohibitive for large samples. Future
studies will
continue to rely on retrospective data, of necessity, since
inclusion

criteria for most samples require a confirmed ASD diagnosis
(i.e., not
Fig. 4. Latent classes of social engagement, demonstrating
declining trajectories in the majority of children developing
ASD, Cohorts 2 and 3. Panel A: Examiner
ratings, Panel B: Parent ratings.
Reviews 100 (2019) 296–304
301
prospective data).
Several strategies have been proposed to improve reporting
(Ayhan
and Isiksal, 2005). To minimize comprehension or interpretation
pro-
blems, it is recommended that further specific information about
the
behavior in question be provided. ASD screening instruments
have
begun to incorporate video to improve accuracy (Marrus et al.,
2018;
Smith et al., 2017) and this strategy could be adapted to
improve re-
porting of onset patterns. For example, longitudinal video of a
child
experiencing skill loss could be shown to parents to illustrate
the kinds
of changes in behavior that define regression. To minimize
recall pro-
blems, the simplest approach, and the one shown to have the

best va-
lidity, is to ask respondents to consult relevant records prior to
com-
pleting the interview (Ayhan and Isiksal, 2005). Parents could,
for
example, review entries in baby books or journals or watch
home video
of the child prior to the interview. Another approach is to link
reporting
to key events in the respondent’s life by creating a detailed
timeline and
context that assist recall of specific details (Loftus and
Marburger,
1983). This method has already been used by Werner et al.
(2005) to
improve recall of early development in ASD and it could be
further
adapted for reporting about onset patterns. Whether these
methods will
enhance the validity of parent report of onset remains to be seen
and
would be a fruitful area of future study. For further discussion,
see also
Boterberg et al. (2019a).
3.5. Validity of previous studies of regression
The studies reviewed in this paper call into serious question the
validity of previous studies of regression, which have, of
necessity and
the lack of alternatives, relied upon retrospective measures.
Refining
methods of studying the onset of ASD has the potential to
transform
research programs on etiological factors that contribute to the
devel-

opment of ASD by providing more precise and accurate
measurements
of an important phenotype (Barbaresi, 2016; Thurm et al .,
2018). A
better understanding of the inflection points at which
development
diverges from a typical trajectory to an autism trajectory could
be
highly informative to the search for risk factors. Better
measures of
onset are urgently needed for etiologic studies, which have been
hin-
dered already by the tremendous heterogeneity of the autism
pheno-
type (Constantino and Charman, 2016). Many recent studies
have ex-
amined whether onset types are associated with potential
etiologic
factors and biological correlates, such as brain growth (Nordahl
et al.,
2011; Valvo et al., 2016), seizures (Barger et al., 2017),
vaccinations
(Goin-Kochel et al., 2016), gastrointestinal problems (Downs et
al.,
2014; Richler et al., 2006), immunological function (Scott et
al., 2017;
Wasilewska et al., 2012), and genetic and genomic variations
(Goin-
Kochel et al., 2017; Gupta et al., 2017; Parr et al., 2011),
including
mitochondrial and MeCP2 mutations (Shoffner et al., 2010;
Veeraragavan et al., 2016; Xi et al., 2007). So far, none of these
factors
has been firmly associated with onset patterns. This may be due
to the
errors that are likely to have occurred in the classifications of

onset
done in these studies. Clearly, examining the biological
underpinnings
of an imprecise measure is problematic.
In a review of autism genetics, one of the major priorities
identified
for future research is the characterization of ASD subtypes to
relate to
genetic variations (Geschwind, 2011). As more and more risk
genes for
ASD are identified, the common molecular pathways that these
genes
share are becoming understood, with some expressed early in
neuro-
biological development and others later (Konopka et al., 2012).
A twin
study (Hallmayer et al., 2011) suggested a greater role for
environ-
mental factors in ASD than previously appreciated. A more
precise
timing of first symptom emergence would enhance
identification of
etiological factors and when they might operate, with potential
im-
plications for intervention and prevention.
4. Clinical implications
Finally, the studies reviewed here provide hope and promise for
improvements in screening, early diagnosis, and treatment.
Many pro-
spective studies (e.g., Bosl et al., 2018; Jones and Klin, 2013;
Ozonoff
et al., 2010) used measures, such as behavioral coding, eye
tracking,

and EEG, that are expensive, labor intensive, and not practical
for
routine use. Studies reviewed in this paper, however, have
demon-
strated that prospective parent report can identify declining
trajectories
of development (Ozonoff et al., 2018b; Parikh et al., 2018), as
long as
the instruments focus on early-appearing social behaviors,
present in
the first year of life, that have the potential to demonstrate
decreases
over time as ASD signs emerge. Brief rating scales of this type,
ad-
ministered longitudinally at regular well-child health care
visits, could
provide a clinically feasible and cost-effective screening tool
capable of
detecting declines over time. We hypothesize that dynamic
screening,
which utilizes longitudi nal screenings over time and comparison
of
scores across ages to identify declining trajectories, will
improve
identification over static, cross-sectional screenings examining
whether
a single score at a single age exceeds a cutoff. This approach
has been
successfully used in identifying Rett syndrome (RTT), where
head cir-
cumference is normal at birth, followed by deceleration of head
growth
between 5 months and 4 years (Hagberg et al., 2001; Tarquinio
et al.,
2012). Through the development of RTT-specific growth
references

throughout early childhood, based on mapping head
circumference
trajectories, diagnosis of RTT has been possible at earlier ages
(Schultz
et al., 1993; Tarquinio et al., 2012). We (Ozonoff et al., 2010,
2018b)
and others (Landa et al., 2013; Pearson et al., 2018; Thomas et
al.,
2009) have suggested that this kind of dimensional, trajectory-
based
methodological approach, percentiling social and
communication
milestones as we percentile other growth parameters, could be
applied
to detect ASD early.
Prospective studies have repeatedly demonstrated that develop-
mental declines follow a period in the first year of life when
socio-
communicative skills are largely intact. Such early intact skills
can be
capitalized upon in treatment, presenting opportunities for
preventive
intervention when the brain is rapidly developing and
maximally
malleable. For many years, the holy grail has been finding a
marker
present prior to symptom emergence, thus affording the
possibility of
earlier, possibly preventative, treatment during the prodromal
period. If
the prospective methods described in this paper can be
harnessed to
identify infants at risk for ASD, during the decline of skills,
rather than
after the decline was over, it might be possible to disrupt these

trajec-
tories prior to the full onset of symptoms (Dawson, 2011).
Children
could be provided immediate access to infant interventions
(Fein et al.,
2016; Rogers et al., 2014), capitalizing on still-preserved skills
and
harnessing the brain plasticity of early infancy to improve
outcomes,
lessen disability, and perhaps, prevent the full disorder from
devel-
oping.
Conflict of interest
The authors have no competing interests to declare.
Acknowledgments
This work was supported by NIH grants R01 MH068398
(Ozonoff)
and R01 MH099046 (Ozonoff). Thank you to Sofie Boterberg
for her
reading of an earlier version of this manuscript. We are deeply
grateful
to all the children and parents who participated in and showed
sus-
tained commitment to our longitudinal program of research.
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Reviews 100 (2019) 296–304
304

http://refhub.elsevier.com/S0149-7634(18)30721-
8/sbref0405Changing conceptualizations of regression: What
prospective studies reveal about the onset of autism spectrum
disorderIntroductionMethods for measuring onset
patternsProspective studies of onsetFindings from the
University of California Davis infant sibling studyBehavioral
coding of social-communication ratesObserver ratings of social
engagementLongitudinal parent ratings of social
behaviorGrowth curve modeling approaches to determining
onset classificationsConcordance between retrospective and
prospective onset classificationsConclusions and theoretical
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exceptionSimplex v. multiplex samplesImproving the
measurement of onsetValidity of previous studies of
regressionClinical implicationsConflict of
interestAcknowledgmentsReferences

Human Resource ManagementHuman Resource Management
Human Resource ManagementHuman Resource Management
[AUTHOR REMOVED AT REQUEST OF ORIGINAL
PUBLISHER]
U N I V E R S I T Y O F M I N N E S O T A L I B R A R I E S
P U B L I S H I N G E D I T I O N , 2 0 1 6 . T H I S E D I T I
O N A D A P T E D F R O M A
W O R K O R I G I N A L L Y P R O D U C E D I N 2 0 1 1 B
Y A P U B L I S H E R W H O H A S R E Q U E S T E D T H A
T I T N O T R E C E I V E
A T T R I B U T I O N .
M I N N E A P O L I S , M N
Human Resource Management by University of Minnesota is
licensed under a Creative Commons Attribution-
NonCommercial-ShareAlike
4.0 International License, except where otherwise noted.
http://creativecommons.org/licenses/by-nc-sa/4.0/
http://open.lib.umn.edu/humanresourcemanagement

Contents
Publisher Information viii
Chapter 1: The Role of Human Resources
1.1 What Is Human Resources? 2
1.2 Skills Needed for HRM 11
1.3 Today’s HRM Challenges 16
1.4 Cases and Problems 27
Chapter 2: Developing and Implementing Strategic HRM Plans
2.1 Strategic Planning 31
2.2 Writing the HRM Plan 41
2.3 Tips in HRM Planning 49
Chapter 3: Diversity and Multiculturalism
3.1 Diversity and Multiculturalism 56
3.2 Diversity Plans 62
3.3 Multiculturalism and the Law 70
Chapter 4: Recruitment
4.1 The Recruitment Process 82
4.2 The Law and Recruitment 91
4.3 Recruitment Strategies 97
Chapter 5: Selection

5.1 The Selection Process 113
5.2 Criteria Development and Résumé Review 118
5.3 Interviewing 124
5.4 Testing and Selecting 132
5.5 Making the Offer 139
Chapter 6: Compensation and Benefits
6.1 Goals of a Compensation Plan 147
6.2 Developing a Compensation Package 151
6.3 Types of Pay Systems 155
6.4 Other Types of Compensation 170
Chapter 7: Retention and Motivation
7.1 The Costs of Turnover 187
7.2 Retention Plans 193
7.3 Implementing Retention Strategies 207
Chapter 8: Training and Development
8.1 Steps to Take in Training an Employee 224
8.2 Types of Training 230
8.3 Training Delivery Methods 237
8.4 Designing a Training Program 244
Chapter 9: Successful Employee Communication
9.1 Communication Strategies 267
9.2 Management Styles 279

Chapter 10: Managing Employee Performance
10.1 Handling Performance 291
10.2 Employee Rights 308
Chapter 11: Employee Assessment
11.1 Performance Evaluation Systems 325
11.2 Appraisal Methods 332
11.3 Completing and Conducting the Appraisal 345
Chapter 12: Working with Labor Unions
12.1 The Nature of Unions 361
12.2 Collective Bargaining 373
12.3 Administration of the Collective Bargaining Agreement
380
Chapter 13: Safety and Health at Work
13.1 Workplace Safety and Health Laws 390
13.2 Health Hazards at Work 399
Chapter 14: International HRM
14.1 Offshoring, Outsourcing 424
14.2 Staffing Internationally 437
14.3 International HRM Considerations 442

Please share your supplementary material! 462
Publisher Information
Human Resource Management is adapted from a work produced
and
distributed under a Creative Commons license (CC BY-NC-SA)
in 2011
by a publisher who has requested that they and the original
author not
receive attribution. This adapted edition is produced by the
University
of Minnesota Libraries Publishing through the eLearning
Support Initiative.
This adaptation has reformatted the original text, and replaced
some images and figures to make the resulting
whole more shareable. This adaptation has not significantly
altered or updated the original 2011 text. This work
is made available under the terms of a Creative Commons
Attribution-NonCommercial-ShareAlike license.
viii
http://www.lib.umn.edu/publishing
http://www.lib.umn.edu/publishing
https://www.lib.umn.edu/elearning

Chapter 1: The Role of Human Resources
Human Resource Management Day to DayHuman Resource
Management Day to Day
You have just been hired to work in the human resource
department of a small company. You heard about the job
through a conference you attended, put on by the Society for
Human Resource Management (SHRM). Previously,
the owner of the company, Jennifer, had been doing everything
related to human resource management (HRM).
You can tell she is a bit critical about paying a good salary for
something she was able to juggle all on her own.
On your first day, you meet the ten employees and spend several
hours with the company owner, hoping to get a
handle on which human resource processes are already set up.
Shortly after the meeting begins, you see she has a completely
different perspective of what HRM is, and you
realize it will be your job to educate her on the value of a
human resource manager. You look at it as a personal
challenge—both to educate her and also to show her the value
of this role in the organization.
First, you tell her that HRM is a strategic process having to do
with the staffing, compensation, retention, training,

and employment law and policies side of the business. In other
words, your job as human resources (HR) manager
will be not only to write policy and procedures and to hire
people (the administrative role) but also to use strategic
plans to ensure the right people are hired and trained for the
right job at the right time. For example, you ask her
if she knows what the revenue will be in six months, and
Jennifer answers, “Of course. We expect it to increase
by 20 percent.” You ask, “Have you thought about how many
people you will need due to this increase?” Jennifer
looks a bit sheepish and says, “No, I guess I haven’t gotten that
far.” Then you ask her about the training programs
the company offers, the software used to allow employees to
access pay information online, and the compensation
policies. She responds, “It looks like we have some work to do.
I didn’t know that human resources involved all
of that.” You smile at her and start discussing some of the
specifics of the business, so you can get started right
away writing the strategic human resource management plan.
The Role of Human ResourcesThe Role of Human Resources
(click to see video)
The author introduces the chapter defining the role of human
resource management.

1
http://app.wistia.com/embed/medias/f325acb504
1.1 What Is Human Resources?
Learning Objectives
1. Explain the role of HRM in organizations.
2. Define and discuss some of the major HRM activities.
Every organization, large or small, uses a variety of capital to
make the business work. Capital includes cash,
valuables, or goods used to generate income for a business. For
example, a retail store uses registers and inventory,
while a consulting firm may have proprietary software or
buildings. No matter the industry, all companies have
one thing in common: they must have people to make their
capital work for them. This will be our focus
throughout the text: generation of revenue through the use of
people’s skills and abilities.
What Is HRM?What Is HRM?
Human resource management (HRM) is the process of
employing people, training them, compensating them,
developing policies relating to them, and developing strategies
to retain them. As a field, HRM has undergone

many changes over the last twenty years, giving it an even more
important role in today’s organizations. In
the past, HRM meant processing payroll, sending birthday gifts
to employees, arranging company outings, and
making sure forms were filled out correctly—in other words,
more of an administrative role rather than a strategic
role crucial to the success of the organization. Jack Welch,
former CEO of General Electric and management guru,
sums up the new role of HRM: “Get out of the parties and
birthdays and enrollment forms.… Remember, HR is
important in good times, HR is defined in hard times” (Frasch,
et. al., 2010).
It’s necessary to point out here, at the very beginning of this
text, that every manager has some role relating
to human resource management. Just because we do not have the
title of HR manager doesn’t mean we won’t
perform all or at least some of the HRM tasks. For example,
most managers deal with compensation, motivation,
and retention of employees—making these aspects not only part
of HRM but also part of management. As a result,
this book is equally important to someone who wants to be an
HR manager and to someone who will manage a
business.

2
Human Resource RecallHuman Resource Recall
Have you ever had to work with a human resource department at
your job? What was the interaction like?
What was the department’s role in that specific organization?
The Role of HRMThe Role of HRM
Keep in mind that many functions of HRM are also tasks other
department managers perform, which is what
makes this information important, despite the career path taken.
Most experts agree on seven main roles that HRM
plays in organizations. These are described in the following
sections.
StaffingStaffing
You need people to perform tasks and get work done in the
organization. Even with the most sophisticated
machines, humans are still needed. Because of this, one of the
major tasks in HRM is staffing. Staffing involves
the entire hiring process from posting a job to negotiating a
salary package. Within the staffing function, there are
four main steps:
1. Development of a staffing plan. This plan allows HRM to see
how many people they should hire based

on revenue expectations.
2. Development of policies to encourage multiculturalism at
work. Multiculturalism in the workplace is
becoming more and more important, as we have many more
people from a variety of backgrounds in the
workforce.
3. Recruitment. This involves finding people to fill the open
positions.
4. Selection. In this stage, people will be interviewed and
selected, and a proper compensation package will
be negotiated. This step is followed by training, retention, and
motivation.
Development of Workplace PoliciesDevelopment of Workplace
Policies
Every organization has policies to ensure fairness and
continuity within the organization. One of the jobs of HRM
is to develop the verbiage surrounding these policies. In the
development of policies, HRM, management, and
executives are involved in the process. For example, the HRM
professional will likely recognize the need for a
policy or a change of policy, seek opinions on the policy, write
the policy, and then communicate that policy to
employees. It is key to note here that HR departments do not

and cannot work alone. Everything they do needs to
involve all other departments in the organization. Some
examples of workplace policies might be the following:
• Discipline process policy
• Vacation time policy
• Dress code
1 . 1 W H A T I S H U M A N R E S O U R C E S ? • 3
• Ethics policy
• Internet usage policy
These topics are addressed further in Chapter 6 “Compensation
and Benefits”, Chapter 7 “Retention and
Motivation”, Chapter 8 “Training and Development”, and
Chapter 9 “Successful Employee Communication”.
Compensation and Benefits AdministrationCompensation and
Benefits Administration
HRM professionals need to determine that compensation is fair,
meets industry standards, and is high enough to
entice people to work for the organization. Compensation
includes anything the employee receives for his or her
work. In addition, HRM professionals need to make sure the pay
is comparable to what other people performing

similar jobs are being paid. This involves setting up pay
systems that take into consideration the number of years
with the organization, years of experience, education, and
similar aspects. Examples of employee compensation
include the following:
• Pay
• Health benefits
• 401(k) (retirement plans)
• Stock purchase plans
• Vacation time
• Sick leave
• Bonuses
• Tuition reimbursement
Since this is not an exhaustive list, compensation is discussed
further in Chapter 6 “Compensation and Benefits”.
RetentionRetention
Retention involves keeping and motivating employees to stay
with the organization. Compensation is a major
factor in employee retention, but there are other factors as well.
Ninety percent of employees leave a company for

the following reasons:
1. Issues around the job they are performing
2. Challenges with their manager
3. Poor fit with organizational culture
4. Poor workplace environment
Despite this, 90 percent of managers think employees leave as a
result of pay (Rivenbark, 2010). As a result,
managers often try to change their compensation packages to
keep people from leaving, when compensation isn’t
4 • H U M A N R E S O U R C E M A N A G E M E N T
the reason they are leaving at all. Chapter 7 “Retention and
Motivation” and Chapter 11 “Employee Assessment”
discuss some strategies to retain the best employees based on
these four factors.
Training and DevelopmentTraining and Development
Once we have spent the time to hire new employees, we want to
make sure they not only are trained to do the
job but also continue to grow and develop new skills in their
job. This results in higher productivity for the
organization. Training is also a key component in employee
motivation. Employees who feel they are developing

their skills tend to be happier in their jobs, which results in
increased employee retention. Examples of training
programs might include the following:
• Job skills training, such as how to run a particular computer
program
• Training on communication
• Team-building activities
• Policy and legal training, such as sexual harassment training
and ethics training
We address each of these types of training and more in detail in
Chapter 8 “Training and Development”.
Dealing with Laws Affecting EmploymentDealing with Laws
Affecting Employment
Human resource people must be aware of all the laws that affect
the workplace. An HRM professional might work
with some of these laws:
• Discrimination laws
• Health-care requirements
• Compensation requirements such as the minimum wage
• Worker safety laws
• Labor laws

The legal environment of HRM is always changing, so HRM
must always be aware of changes taking place and
then communicate those changes to the entire management
organization. Rather than presenting a chapter focused
on HRM laws, we will address these laws in each relevant
chapter.
Worker ProtectionWorker Protection
Safety is a major consideration in all organizations. Oftentimes
new laws are created with the goal of setting
federal or state standards to ensure worker safety. Unions and
union contracts can also impact the requirements
for worker safety in a workplace. It is up to the human resource
manager to be aware of worker protection
requirements and ensure the workplace is meeting both federal
and union standards. Worker protection issues
might include the following:
• Chemical hazards
• Heating and ventilation requirements
• Use of “no fragrance” zones

• Protection of private employee information
We take a closer look at these issues in Chapter 12 “Working
with Labor Unions” and Chapter 13 “Safety and
Health at Work”.
Figure 1.1
Caption: Knowing the law regarding worker protection is
generally the job of human resources. In some
industries it is extremely important; in fact, it can mean life or
death.
ReSurge International – Tom Davenport Operating On A Patient
– CC BY-NC-ND 2.0.
CommunicationCommunication
Besides these major roles, good communication skills and
excellent management skills are key to successful
human resource management as well as general management.
We discuss these issues in Chapter 9 “Successful
Employee Communication”.
Awareness of External FactorsAwareness of External Factors
In addition to managing internal factors, the HR manager needs
to consider the outside forces at play that may
affect the organization. Outside forces, or external factors, are
those things the company has no direct control

http://open.lib.umn.edu/humanresourcemanagement/wp-
content/uploads/sites/7/2015/10/1.1.0.jpg
content/uploads/sites/7/2015/10/1.1.0.jpg
https://www.flickr.com/photos/interplast/415854485/
over; however, they may be things that could positively or
negatively impact human resources. External factors
might include the following:
1. Globalization and offshoring
2. Changes to employment law
3. Health-care costs
4. Employee expectations
5. Diversity of the workforce
6. Changing demographics of the workforce
7. A more highly educated workforce
8. Layoffs and downsizing
9. Technology used, such as HR databases
10. Increased use of social networking to distribute information
to employees
For example, the recent trend in flexible work schedules

(allowing employees to set their own schedules) and
telecommuting (allowing employees to work from home or a
remote location for a specified period of time, such
as one day per week) are external factors that have affected HR.
HRM has to be aware of these outside issues,
so they can develop policies that meet not only the needs of the
company but also the needs of the individuals.
Another example is the Patient Protection and Affordable Care
Act, signed into law in 2010. Compliance with
this bill has huge implications for HR. For example, a company
with more than fifty employees must provide
health-care coverage or pay a penalty. Currently, it is estimated
that 60 percent of employers offer health-care
insurance to their employees (Cappelli, 2010). Because health-
care insurance will be mandatory, cost concerns
as well as using health benefits as a recruitment strategy are big
external challenges. Any manager operating
without considering outside forces will likely alienate
employees, resulting in unmotivated, unhappy workers. Not
understanding the external factors can also mean breaking the
law, which has a concerning set of implications as
well.
Figure 1.2

An understanding of key external factors is important to the
successful HR professional. This allows him
or her to be able to make strategic decisions based on changes
in the external environment. To develop this
understanding, reading various publications is necessary.
One way managers can be aware of the outside forces is to
attend conferences and read various articles on the
web. For example, the website of the Society for Human
Resource Management, SHRM Online1, not only has job
postings in the field but discusses many contemporary human
resource issues that may help the manager make
better decisions when it comes to people management. In
Section 1.3 “Today’s HRM Challenges”, we go into
more depth about some recent external issues that are affecting
human resource management roles. In Section
1.1.2 “The Role of HRM”, we discuss some of the skills needed
to be successful in HRM.
Figure 1.3
content/uploads/sites/7/2015/03/38f44368950c5a67d7b2c29c536

4b0da.jpg
content/uploads/sites/7/2015/03/38f44368950c5a67d7b2c29c536
4b0da.jpg
Most professionals agree that there are seven main tasks HRM
professionals perform. All these need to be
considered in relation to external and outside forces.
Key Takeaways
• Capital includes all resources a company uses to generate
revenue. Human resources or the people
working in the organization are the most important resource.
• Human resource management is the process of employing
people, training them, compensating
them, developing policies relating to the workplace, and
developing strategies to retain employees.
• There are seven main responsibilities of HRM managers:
staffing, setting policies, compensation and
benefits, retention, training, employment laws, and worker
protection. In this book, each of these
major areas will be included in a chapter or two.
• In addition to being concerned with the seven internal aspects,
HRM managers must keep up to date
with changes in the external environment that may impact their
employees. The trends toward
flexible schedules and telecommuting are examples of external
aspects.
• To effectively understand how the external forces might affect

human resources, it is important for
content/uploads/sites/7/2015/03/0006ad93fc4da0b31ba152a320b
f82ee.jpg
content/uploads/sites/7/2015/03/0006ad93fc4da0b31ba152a320b
f82ee.jpg
the HR manager to read the HR literature, attend conferences,
and utilize other ways to stay up to
date with new laws, trends, and policies.
Exercises
1. State arguments for and against the following statement:
there are other things more valuable in an
organization besides the people who work there.
2. Of the seven tasks an HR manager does, which do you think
is the most challenging? Why?
1Society for Human Resource Management, accessed August
18, 2011, http://www.shrm.org/Pages/default.aspx.
ReferencesReferences
Cappelli, P., “HR Implications of Healthcare Reform,” Human
Resource Executive Online, March 29, 2010,
accessed August 18, 2011,
http://www.hreonline.com/HRE/story.jsp?storyId=379096509.

Frasch, K. B., David Shadovitz, and Jared Shelly, “There’s No
Whining in HR,” Human Resource Executive
Online, June 30, 2009, accessed September 24, 2010,
http://www.hreonline.com/HRE/
story.jsp?storyId=227738167.
Rivenbark, L., “The 7 Hidden Reasons Why Employees Leave,”
HR Magazine, May 2005, accessed October 10,
2010,
http://findarticles.com/p/articles/mi_m3495/is_5_50/ai_n137214
06.
1 0 • H U M A N R E S O U R C E M A N A G E M E N T
http://www.shrm.org/Pages/default.aspx
http://www.hreonline.com/HRE/story.jsp?storyId=379096509
http://findarticles.com/p/articles/mi_m3495/is_5_50/ai_n137214
06
1.2 Skills Needed for HRM
Learning Objectives
1. Explain the professional and personal skills needed to be
successful in HRM.
1. Be able to define human resource management and the
certifications that can be achieved in this
profession.

One of the major factors of a successful manager or human
resource (HR) manager is an array of skills to deal
with a variety of situations. It simply isn’t enough to have
knowledge of HR, such as knowing which forms need
to be filled out. It takes multiple skills to create and manage
people, as well as a cutting-edge human resource
department.
The first skill needed is organization. The need for this skill
makes sense, given that you are managing people’s
pay, benefits, and careers. Having organized files on your
computer and good time-management skills are crucial
for success in any job, but especially if you take on a role in
human resources.
Like most jobs, being able to multitask—that is, work on more
than one task at a time—is important in managing
human resources. A typical person managing human resources
may have to deal with an employee issue one
minute, then switch and deal with recruiting. Unlike many
management positions, which only focus on one task
or one part of the business, human resources focuses on all
areas of the business, where multitasking is a must.
As trite as it may sound, people skills are necessary in any type
of management and perhaps might be the most
important skills for achieving success at any job. Being able to

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