AGE DISCRIMINATION
bandrade1981
DevelopmentandValidationoftheWorkplaceAgeDiscriminationScale.pdf
O R I G I N A L P A P E R
Development and Validation of the Workplace Age Discrimination Scale
Lisa A. Marchiondo1 • Ernest Gonzales2 • Shan Ran3
Published online: 9 December 2015
� Springer Science+Business Media New York 2015
Abstract
Purpose Workplace age discrimination research is prolifer-
ating, but researchers lack a valid measure with which to capture
targets’ discriminatory experiences. We developed a measure of
perceived workplace age discrimination that assesses overt and
covert forms of discrimination and then compared older, mid-
dle-aged, and younger workers’ experiences.
Design/Methodology In Study 1, we developed the
Workplace Age Discrimination Scale (WADS) based on
older workers’ experiences using a deductive approach, a
qualitative study, and two quantitative surveys. In Study 2,
we validated the measure among young employees using a
qualitative and two quantitative surveys. In Study 3, we
tested the WADS among middle-aged workers and tested
models of invariance between age groups.
Findings Participants frequently endorsed covert dis-
criminatory experiences, which the WADS reflects. The
WADS contains convergent and discriminant validity, high
reliability, and a unidimensional structure across age groups.
It demonstrates criterion-related validity among older and
younger workers but not middle-aged workers, given their
low experiences of age discrimination. Age discrimination
frequency follows a U-shaped pattern across age groups.
Implications Researchers can use the WADS to identify
long-term outcomes of age discrimination and to further
compare workers’ discriminatory experiences. Practition-
ers and policymakers can use the measure to develop
interventions to ameliorate workplace age discrimination
and inform policymaking.
Originality/Value The WADS is the first validated mea-
sure of targets’ perspectives of workplace age discrimina-
tion. Our results challenge assumptions that only older
workers experience age discrimination (younger workers’
means were highest) and that age discrimination is usually
overt in nature (it is often covert).
Keywords Ageism � Age discrimination � Measurement � Older workers � Middle-aged workers � Young workers � Modern discrimination
… I wonder whether employers, or whether the public generally realizes that age discrimination is
illegal… Stuart Ishimaru, Chairman, U.S. Equal Employment
Opportunity Commission (EEOC 2009)
The opening quote illustrates the degree to which age
discrimination is tacitly accepted within U.S. society. This
phenomenon is not unique to the U.S. though; reports of
age discrimination abound globally (e.g., Balch 2015;
Hock 2015; Medhora 2015). The number of workplace age
discrimination claims through federal human rights agen-
cies is growing, reflecting the negative climates that a
growing number of workers face (e.g., EEOC 2013).
& Lisa A. Marchiondo [email protected]
Ernest Gonzales
Shan Ran
1 Anderson School of Management, University of New
Mexico, MSC05 3090, 1 University of New Mexico,
Albuquerque, NM 87131-0001, USA
2 School of Social Work, Human Behavior Department, Boston
University, 264 Bay State Road, Boston, MA 02215, USA
3 Department of Psychology, Wayne State University, 5057
Woodward Avenue, Detroit, MI 48202, USA
123
J Bus Psychol (2016) 31:493–513
DOI 10.1007/s10869-015-9425-6
This prevalence may not be surprising, given that age-
ism is often not considered as offensive or unjust as other
highly researched forms of prejudice such as racism and
sexism (Deal et al. 2010; Levy and Banaji 2002).
Employees hold numerous, pervasive stereotypes about
older adults, including beliefs that they are less competent
and adaptable than other workers (Cuddy et al. 2005;
Posthuma and Campion 2009). Stereotypes such as these
ultimately manifest in discriminatory behavior, including
lower rates of hiring and promoting older workers (Post-
huma et al. 2012). Given estimates that by 2020 one in four
U.S. workers will be age 55 or older (Hayutin et al. 2013)
and one in three U.K. workers will be over age 50
(Department for Work and Pensions 2013), the prevalence
of and tolerance for ageism is concerning, as more workers
may become targets.
The ability to adequately study and address age dis-
crimination is hampered by several limitations in the lit-
erature. First, coworkers’ and supervisors’ (i.e.,
‘‘perpetrators’’’) negative age stereotypes and discrimina-
tory behavior are well documented, but the literature
speaks less to older employees’ (i.e., ‘‘targets’’’) experi-
ences of such discrimination. Targets’ perspectives are
important—often essential—to capture in order to under-
stand the impact of discrimination on targets’ productivity
and well-being. Second, the literature attends primarily to
overt forms of discrimination (e.g., refusal to hire) and
often ignores low-intensity, covert discriminatory behav-
iors (e.g., social exclusion). Consistent with modern dis-
crimination theories, such as interpersonal discrimination
(Hebl et al. 2002) and selective incivility (Cortina 2008),
manifestations of workplace discrimination are not as
explicit as in times past (Marchiondo et al. 2015). Covert
discrimination more closely reflects the experiences of
many stigmatized groups and occurs more frequently than
overt discrimination (Madera and Hebl 2013), warranting
its inclusion in age discrimination research. Finally,
although emerging scholarship has begun exploring older
workers’ experiences of discrimination, a validated mea-
sure of workplace age discrimination from the target’s
perspective does not exist. Proxy and convenience items
prevent accurate measurement of targets’ experiences.
Given these limitations, our initial objective was to develop
a reliable and valid measure that captures older employees’
experiences of workplace age discrimination in its many
forms.
Though sparse, increasing literature suggests that
younger workers also face discriminatory treatment (e.g.,
Snape and Redman 2003). Despite their seeming dissimi-
larities, younger and older workers alike possess less
influence and fewer resources than middle-aged workers
(North and Fiske 2012). Both groups are subject to more
negative stereotypes (Finkelstein et al. 2013), which may
give rise to discriminatory treatment. Further, the nature of
age stereotypes (and discrimination) might be changing,
with younger workers being judged more negatively than
older workers (Bertolino et al. 2012; Finkelstein et al.
2013; Weiss and Maurer 2004). As such, the second
objective was to apply our measure of age discrimination to
younger workers to capture their experiences.
We contend that ageism operates dynamically across the
working lifespan and that vulnerable out-groups include
older and younger workers, with middle-aged workers
constituting the in-group. To test this idea, the third
objective was to administer our age discrimination measure
to middle-aged workers and test a curvilinear pattern of
discrimination across age groups.
Negative Attitudes and Discrimination Toward Older Employees
Workforces worldwide are ‘‘graying.’’ Increasing longevity
is extending the average work life, as employees are
physically able to work longer and prefer to do so for social
and psychological benefits (Freedman 2008). Employees
also work longer as the financial landscape of retirement
evolves in many countries and more employees are finan-
cially unprepared for retirement (Munnell and Sass 2008).
These forces, among others, are contributing to the growth
of the older workforce, underscoring the importance of
understanding older workers’ experiences.
Ample research has attended to attitudes toward older
employees. Stereotypes of older adults in general are
ubiquitous, overwhelmingly negative, and resistant to
change (Cuddy et al. 2005). Stereotypes of older employ-
ees include perceptions that they have lower performance,
possess lower potential for training and development,
refrain from change (Posthuma and Campion 2009; Rosen
and Jerdee 1976), have poorer interpersonal skills (Bal
et al. 2011), and are less healthy and trusting of others (Ng
and Feldman 2012). Measures designed to capture ageist
attitudes toward older adults emerged at least 60 years ago
(e.g., the Old People Questionnaire by Tuckman and Lorge
1953) and have undergone numerous iterations since (e.g.,
the Aging Semantic Differential by Rosencranz and
McNevin 1969; the Fraboni Scale of Ageism by Fraboni
et al. 1990). Thus, scholars have long recognized the need
to validly capture attitudes toward older adults.
Many researchers, too, have devoted attention to
employers’ and coworkers’ discriminatory behavior. For
instance, employers assign lower ratings to older adults for
hiring (Avolio and Barrett 1987), advancement (Bal et al.
2011), promotion and training (Rosen and Jerdee 1976),
and economic worth to organizations (Finkelstein and
Burke 1998). They also provide harsher recommendations
494 J Bus Psychol (2016) 31:493–513
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following poor performance (Rupp et al. 2006). It is clear
that older adults often face unreceptive climates at work.
Significantly less is known about older workers’ per-
ceptions of discrimination—perspectives that may drive
their job attitudes and success, mental and physical well-
being, and retirement plans. The age discrimination liter-
ature exists in stark contrast to research on sex- and race-
based discrimination in this respect, the latter of which
have largely centered on targets’ experiences and subse-
quent well-being. For instance, targets of sexual harass-
ment experience greater job withdrawal and lower
psychological and physical health (Fitzgerald et al. 1997).
Racial discrimination, too, is associated with adverse
physical and mental health (Williams et al. 1997; Williams
et al. 2003). Most of these deleterious effects have not been
linked to age discrimination or have been done so using
invalidated measures. We echo calls by Ruggs et al. (2013)
and North and Fiske (2012) for more research on age
discrimination. We extend these calls by highlighting the
need for greater attention to targets’ perspectives, which we
address in this paper.
Negative Attitudes and Discrimination Toward Younger Employees
Researchers and policymakers primarily attend to the
treatment of older employees (Finkelstein et al. 2013),
given their increasing numbers and legal protection in the
U.S. However, the prevalence of age discrimination toward
older workers may be changing (Weiss and Maurer 2004),
as age stereotypes appear to favor them over younger
workers (Bertolino et al. 2012; Finkelstein et al. 2013).
Expanding the scope of the ageism literature to capture
discrimination toward younger employees is important
given contentions that we have an ‘‘ageist ageism litera-
ture’’ that centers on the experiences of older adults
(Rodham 2001).
Although nascent, research on younger employees has
clearly demonstrated that they, like older employees, are
subject to a host of negative stereotypes. These include
assumptions that they are disloyal, inexperienced, unmo-
tivated, immature, irresponsible, and selfish (cf. Finkelstein
et al. 2013; Snape and Redman 2003). Younger workers
are rated lower than older adults on desirable personality
traits, such as conscientiousness, emotional stability, and
agreeableness, as well as on performance-related variables,
such as organizational citizenship behavior (Bertolino et al.
2012). They are also perceived less favorably than older
workers in terms of initiative, stability, and work experi-
ence (Gibson et al. 1993). Negative attitudes toward
younger people are not new; Baby Boomers and subse-
quent generations were all targeted with stereotypes of
entitlement and laziness when they were young (Deal et al.
2010). Middle-aged employees are particularly likely to
hold these negative stereotypes, assigning significantly
more undesirable stereotypic traits to younger workers than
positive ones (Finkelstein et al. 2013).
These negative stereotypes lead to discriminatory prac-
tices. Younger workers face more denials of promotion,
fewer opportunities for training and development, dispro-
portionately lower pay and benefits, restricted freedom and
responsibility, and increased vulnerability to layoffs
(Duncan and Loretto 2004; Loretto et al. 2000; Snape and
Redman 2003). Thus, researchers are uncovering the sur-
prisingly prevalent negative attitudes and treatment of
younger workers. These findings broaden the call to cap-
ture targets’ perceptions of age discrimination by attending
to younger employees’ perspectives too.
Targets’ Perceptions of Workplace Age Discrimination
Although small in number, some studies have attended to
employees’ personal experiences and outcomes of age
discrimination at work. For instance, among workers age
50 and older in the Midlife in the United States II (MIDUS
II) dataset, 81 % reported at least one instance of age
discrimination in the past year (Chou and Choi 2011). For
older targets, age discrimination correlates negatively with
variables such as job satisfaction, organizational commit-
ment, life satisfaction, and job involvement, and correlates
positively with turnover intentions (Minnotte 2012; Orpen
1995; Redman and Snape 2006). This work has drawn
attention to the importance of studying employees’ per-
ceptions of age discrimination.
A small related body of work has addressed older
workers’ perceptions of organizational climates of ageism.
For example, Furines and Mykletun (2010) developed a
measure of Nordic employees’ perceptions of discrimina-
tory age climates with regard to formal aspects of the job
(e.g., training, promotion). Kunze et al.’s (2011) measure
of macro-level age discrimination aggregates employees’
perceptions of discrimination stemming from the organi-
zation (e.g., with regard to performance assessment). In
contrast to these measures of organizational climate, we
sought to capture employees’ personal experiences as tar-
gets of age discrimination. An individually-focused mea-
sure not only sheds light on discrete experiences of ageism,
but permits meaningful research on targets’ outcomes of
age discrimination.
Despite calls for greater research on age discrimination
(Ruggs et al. 2013), researchers lack a measure with which
they can validly capture employees’ experiences of it.
Numerous methodological issues exist. First, studies often
J Bus Psychol (2016) 31:493–513 495
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contain convenience items, not developed based on theory
(a deductive approach) or respondents’ experiences (an
inductive approach). Many contain single-item measures,
which do not capture the breadth of age discrimination
(i.e., content adequacy). Second, few measures have
undergone validation procedures (for exceptions, see
measures of discriminatory age climate by Furines and
Mykletun 2010 and Kunze et al. 2011). Third, some mea-
surement intended to capture targets’ personal experiences
is confounded by the inclusion of items that assess
respondents’ perceptions of discriminatory climates as well
as of coworkers’ discriminatory experiences (Arvey and
Cavanaugh 1995). For instance, items such as ‘‘Young/old
adults as a group have been victimized,’’ ‘‘The best jobs
here are reserved for younger workers,’’ and ‘‘[There is a]
lower chance of promotion for older workers’’ capture
respondents’ beliefs about the discriminatory experiences
of age group members as a whole. Combining these items
with ones about a respondent’s personal experience (e.g.,
‘‘I have been overlooked here because of my age’’) makes
it difficult to determine whether responses are based on
one’s personal experience as a target, observations of
coworkers as targets, or general rumors of discrimination
within a company. This imprecision precludes clear and
accurate operationalization of the construct. Fourth, most
surveys do not specify a timeframe in which respondents
recall discriminatory experiences (e.g., ‘‘How frequently in
the last year…’’). The absence of a timeframe allows some participants to focus on recent experiences and others to
draw on experiences from decades past, thereby introduc-
ing varying degrees of memory bias and measurement
error.
Fifth, some age discrimination items rely on participants
to define age discrimination (e.g., ‘‘I have experienced age
discrimination’’). This practice introduces significant
measurement error, as participants’ definitions of age dis-
crimination may vary widely (Hardy and Ford 2014) or
encompass only egregious or commonly discussed events
(Fuegen and Biernat 2000). If targets do not comprehend a
term in the same way as the researcher, lexical compre-
hension suffers, undermining the measure’s validity (Hardy
and Ford 2014). Indeed, targets can report experiencing the
same mistreatment behaviors as one another but differen-
tially define them (Fitzgerald and Shullman 1993). To
ensure consistency of interpretation and ultimately validity,
mistreatment items should concern specific behaviors and
avoid the inclusion of perceptual labels such as ‘‘age dis-
crimination’’ (Arvey and Cavanaugh 1995; Fitzgerald and
Shullman 1993).
Finally, most age discrimination items address only for-
mal aspects of the job, such as selection, promotion, and
training. While important, this lens ignores insidious forms
of discrimination that some workers may experience with
greater frequency. According to modern discrimination
theories, such as interpersonal discrimination (Hebl et al.
2002) and selective incivility (Cortina 2008), discriminatory
treatment has shifted toward more subtle manifestations.
Underhanded behaviors, such as social exclusion and gossip,
can be defended as non-discriminatory and as such, skirt the
radar of legal and organizational policies. For instance,
selective incivility theory states that lower-intensity, disre-
spectful acts serve to marginalize and undermine work-
ers belonging to stigmatized groups, constituting a less
explicit, ‘‘modern’’ method of discrimination (Cortina
2008). Although modern discrimination is less overt than
formal discrimination, its negative effects are of comparable
magnitude (Jones et al. 2013), highlighting the importance of
this perspective given that employers may not treat it as
seriously as formal discrimination.
Modern discrimination theories have primarily been
applied to uncovering the experiences of women and
people of color but should be extended to explain workers’
experiences based on age (Marchiondo et al. 2015). Older
workers might be especially vulnerable to covert discrim-
ination due to stereotypes that older adults generally are
warm but less competent (Cuddy et al. 2005). Members of
‘‘pitied’’ groups (those perceived as warm but incompetent)
are more likely to experience interpersonal isolation and
other covert discrimination (Cuddy et al. 2007). In the U.S.,
older workers might also experience heightened covert
discrimination given that they (but not younger workers)
are legally protected from age discrimination. Laws may
quell the prevalence of formal discrimination, yet negative
stereotypes can continue to manifest through covert dis-
crimination that operates outside legal boundaries. We
draw on modern discrimination theories during scale
development, ensuring that covert experiences of age dis-
crimination are captured.
Comparing Age Discrimination Between Age Groups
We contend that a validated measure of age discrimination
among those most likely to experience it (younger and
older workers) is needed. We recognize that middle-aged
workers could face age discrimination as well, albeit likely
with lower frequencies based on the age stereotype litera-
ture. Employees at either end of the age spectrum are most
vulnerable to age-based stereotypes and discrimination
(Duncan and Loretto 2004; Gee et al. 2007; North and
Fiske 2012). Snape and Redman (2003) found that younger
workers experience age discrimination with similar if not
greater frequency as older workers. This result is consistent
with evidence of the changing landscape of age stereotypes
in which younger workers are viewed less positively than
496 J Bus Psychol (2016) 31:493–513
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older workers (Bertolino et al. 2012; Finkelstein et al.
2013). In contrast, employees hold the most positive (and
fewest negative) stereotypes of middle-aged workers
(Finkelstein et al. 2013), possibly exposing them to the
least age discrimination. Middle-aged workers’ privilege in
this respect can be explained by their possession of greater
status, resources, and wealth than other age groups
(Garstka et al. 2004; North and Fiske 2012). Middle-aged
workers constitute an ‘‘idealized standard against which
other age groups are judged’’ (Finkelstein et al. 2013, p. 21)
and are posited to hold the highest social status with regard
to age (Garstka et al. 2004). Thus, a U-shaped pattern of
age discrimination likely exists, with younger and older
workers being prime targets. In Study 3, we administer our
age discrimination measure to middle-aged workers and
test the following hypothesis:
Hypothesis Age discrimination experiences are curvi-
linear across age groups, such that younger and older
workers experience the highest frequencies and middle-
aged workers experience the lowest frequency.
The Present Studies
We conducted three studies based on participant age group
(older, younger, middle-aged), each of which contained an
iterative series of qualitative and quantitative phases, to
create and validate the Workplace Age Discrimination
Scale (WADS). Study 1 addressed older workers’ experi-
ences of age discrimination. Through four phases, we
deductively generated a pool of items, inductively devel-
oped items using a qualitative survey, reduced the item list
several times, and tested confirmatory factor structure, as
well as multiple types of validity. Study 1 also included an
experimental design to determine an appropriate timeframe
for the measure’s stem. In Study 2, we extended the WADS
to younger workers. Through three phases, we used
inductive item generation and exploratory factor analysis to
determine if the items adequately captured younger work-
ers’ experiences. We also tested the measure’s confirma-
tory factor structure and validity among this group. In
Study 3, we administered the WADS to middle-aged
workers and tested confirmatory factor structure and mul-
tiple types of validity. We then conducted measurement
invariance tests to compare the WADS between the three
age groups.
The use of self-report was appropriate—indeed, neces-
sary—given our emphasis on employees’ perceptions of
age discrimination targeted at them. We followed four
recommendations for reducing common method bias
(Conway and Lance 2010; Podsakoff et al. 2003, 2012),
which can strengthen or weaken relationships between
variables (Chan 2009). First, we selected established
measures with high reliability and validity. Next, all sur-
veys were anonymous, which reduces pressure to respond
in a consistent and socially desirable manner. Third,
mistreatment measures were placed later in the survey than
non-mistreatment measures so that recollection of
mistreatment did not influence responses to other con-
structs. Finally, scale formats (e.g., scale type, anchor
labels, polarity) varied between measures, reducing
anchor- and end-point biases.
A Note About Study Samples
We collected samples via crowdsourcing (Amazon
Mechanical Turk; MTurk) for several important reasons.
First, the diversity of an organization affects employees’
reports of discrimination (Avery et al. 2008) and emotional
conflict (Pelled 1996). Organizational age distributions,
specifically, can influence communication (Zenger and
Lawrence 1989) and emotional conflict (Pelled et al. 1999)
between employees. As a result, we avoided sampling
single organizations due to their unique organizational
demographies that could shape participants’ experiences of
age discrimination. Crowdsourcing addresses these issues
by providing samples of workers across hundreds of
organizations (and age distributions, industries, etc.),
thereby increasing the generalizability of the measure
(Aguinis and Lawal 2012). This decision was reinforced by
literature demonstrating that organizational samples are
vulnerable to similar convenience sampling limitations as
crowdsourcing (Landers and Behrend 2015) and that
MTurk is a valid and reliable source of data collection
(Barger et al. 2011; Buhrmester et al. 2011). Large and
diverse samples are also recommended for scale develop-
ment (Clark and Watson 1995). Finally, organizational
leaders are often unwilling to allow researchers to conduct
discrimination-related studies within their companies,
fearing reputational harm and lawsuits.
Study 1: Older Workers
Phase 1: Deductive Item Generation
Drawing on Hinkin’s (1998) deductive approach to item
generation, the Phase 1 goals were to define our concep-
tualization of age discrimination and to generate items by
evaluating the literature. First, each author independently
defined age discrimination and identified its essential fea-
tures. The authors discussed their definitions until they
agreed on the following operationalization of workplace
age discrimination: (a) it is a behavioral manifestation of
J Bus Psychol (2016) 31:493–513 497
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prejudice and negative stereotypes; (b) the behaviors are
unjust, disrespectful, and/or unfavorable; (c) one or mul-
tiple parties (targets, observers, perpetrators) can define the
behavior as unjust, disrespectful, or unfavorable (consistent
with definitions of other mistreatment constructs, such as
workplace incivility; Andersson and Pearson 1999); (d) the
behaviors occur in the work context (for pay or volunteer)
and may stem from supervisors, coworkers, customers, or
any other member of one’s work environment; and (e) the
behaviors can be overt but, consistent with modern dis-
crimination theories, may also be covert. Although
numerous age ranges have been used to define ‘‘older
employees,’’ we selected age 50 and older, consistent with
cutoffs that aging experts have used in nationally repre-
sentative and federally funded studies (e.g., the Health and
Retirement Study). Further, studies of age stereotyping
have defined older adults as being over ages 50–52 (e.g.,
Finkelstein et al. 2013; Fritzsche and Marcus 2013).
Next, each author generated survey items by reviewing
the literatures on ageism and workplace discrimination, as
well as by drawing on his/her expertise to produce new
items. We compared our lists, removing items that were
redundant or did not meet the facets of the construct’s
definition. This approach yielded 55 items.
Phase 2: Inductive Item Generation and Initial Item
Reduction
Because limited research was available on age discrimi-
nation from targets’ perspectives, the next goal was to
inductively generate items (Hinkin 1998) by collecting and
examining qualitative data from older employees. This
approach ensured that items encompassed the breadth of
their experiences, particularly with regard to covert age
discrimination.
Participants and Procedure
We recruited N = 96 U.S. employees age 50 and older (all
currently working for pay) from MTurk. We prescreened
participants by asking them to select ranges for their age,
hours of work per week, and country of employment. At
the end of the survey, participants provided their specific
ages and work hours per week, providing another eligibility
check. 1
The average age was 56.5 years (SD = 4.5 years),
average organizational tenure was 15 years (SD =
7.3 years), 75 % worked 35 or more hours per week,
44.8 % were male, 88.5 % were White, 11.5 % were
African American, and 8.3 % were Latino (in all studies in
this paper, participants could select more than one race).
Participants read a definition of age discrimination:
‘‘Differential workplace treatment based on age, which
impairs fairness of treatment or opportunity.’’ Based on this
definition, participants described in as much detail as
possible a time they personally experienced age discrimi-
nation at work. Fourteen participants left the question
blank.
Analytic Method and Results
The authors reviewed each participant’s story and dis-
cussed whether items from Phase 1 accurately captured it.
When they did not, a new item was developed. The authors
also drew from participant stories to clarify and expand
Phase 1 items. Consistent with modern discrimination
theory, many narratives addressed covert forms of dis-
crimination, such as being ignored or excluded and
receiving less support or respect than workers of other
ages. The revised list contained 67 items.
Adopting an iterative approach, the research team (in-
cluding four research assistants) met four times to discuss
the items, re-evaluate participant stories and items from the
literature, and identify opportunities for item consolidation.
At the end of this process, the list was pared down to 26
items (see Table 1) using the following criteria: (a) item
redundancy: similar items were deleted or combined. If an
item captured a broader content domain than another, the
broader item was retained; (b) item clarity and complexity:
double-barreled or vaguely worded items were edited or
deleted. Items were assessed for ease of comprehension
across reading levels; and (c) item length: if an item was
substantially longer than others, it was edited to the extent
possible to minimize participant burden. Although some of
the 26 items pertained to overt experiences traditionally
associated with age discrimination (e.g., unfairly given
lower salary/benefits), many items tapped into covert
behaviors not typically discussed in the ageism literature.
These items included being treated with less respect, hav-
ing work requests delayed or ignored, receiving less social
support, and being excluded from events—all due to one’s
age.
Phase 3: Exploratory Factor Analysis and Further
Item Reduction
The first goal of Phase 3 was to evaluate targets’ fre-
quencies and appraisals of the age discrimination items.
Assessing appraisal (i.e., how bothersome) in addition to
frequency determined the extent to which participants
assessed each item as a stressor, as opposed to an unim-
portant or irrelevant event (Fitzgerald and Shullman 1993;
Lazarus and Folkman 1984). Mistreatment targets can
experience certain incidents but find them inconsequential,
1 This information was assessed at the beginning and end of every
survey.
498 J Bus Psychol (2016) 31:493–513
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in which case the events may have little bearing on targets’
outcomes. Appraisal is increasingly being assessed in
mistreatment surveys in order to uncover targets’ percep-
tions of the nature of events (e.g., sexual harassment:
Langhout et al. 2005; workplace incivility: Cortina and
Magley 2009).
Additional goals of Phase 3 were to (a) empirically
determine a timeframe for the measurement stem, (b) test
the preliminary measure’s exploratory factor structure, and
(c) further reduce the number of items using guidelines
from Stanton et al. (2002), given that the initial item pool
was over inclusive (per Clark and Watson 1995).
Participants and Procedure
U.S. workers from MTurk were recruited if they were age
50 or older and working 20 or more hours per week. Three
hundred seventy workers took the survey. We removed 25
responses due to unreasonably short completion times,
resulting in N = 345. Nearly 48 % of participants were
male, 85.8 % were White, 6.4 % were African American,
and 5.8 % were Latino. The average age was 55.7
(SD = 4.7). Participants worked M = 42.1 hours per week
(SD = 4.0) and had M = 9.7 years (SD = 7.0) of tenure.
Participants rated the frequencies with which they
experienced the 26 workplace age discrimination items on
a 5-point scale from never to very often, anchors adopted
from the Interpersonal Conflict at Work Scale (Spector and
Jex 1998). For each item, participants also answered,
‘‘How much does this experience typically bother you?’’
from 1 (not at all) to 5 (a lot).
We experimentally manipulated (between-subjects) the
timeframe during which participants reported their age
discrimination experiences. The stem, ‘‘Please indicate
how often you have experienced the following AT WORK
in the past…’’ contained one of four timeframes: 6 months,
Table 1 Principal axis factor for the original 26 WADS items
Factor
1 2
I have been recommended less frequently for promotion due to my age .922
I have been passed over for a work role/task due to my age .839
My contributions are not valued as much due to my age .805
I have unfairly been given lower salary/benefits due to my age .797
My supervisor has ignored my strengths due to my age .783
I have worried about being laid off due to my age .761
I have been given fewer opportunities to express my ideas due to my age .733
I have unfairly been evaluated less favorably due to my age .714
I receive less social support due to my age .702
I have been excluded from/during work events due to my age .701
I was not asked to take part in decision-making as often due to my age .688
I have been provided with fewer resources due to my age .655
I have been treated as though I am less capable due to my age .640
I have been excluded from/during social interactions at work due to my age .627
I have been treated with less respect due to my age .588
I was not encouraged to take advantage of relevant training/education opportunities due to my age .551
Someone has delayed or ignored my requests due to my age .500
Someone has not provided me with help/information due to my age .458
Responsibilities have been taken away from me due to my age .439 .408
Someone at work has unnecessarily talked slowly or loudly to me due to my age .766
Someone has insensitively brought up my retiring .761
I have heard rude or insensitive comments at work about me due to my age .727
I have heard jokes at work that poke fun at me due to my age .651
Someone has blamed me for failures or problems due to my age .556
Someone at work has assumed I have health problems due to my age .521
At work, someone has spread rumors about me due to my age .516
Items in the final WADS measure are in bold
J Bus Psychol (2016) 31:493–513 499
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1, 3, or 5 years. Popular mistreatment measures, such as
the Chronic Work Discrimination and Harassment scale
(Williams et al. 1997) and Workplace Incivility Scale
(WIS; Cortina and Magley 2009; Cortina et al. 2001),
contain 1 and 5 year stems, from which we drew. These
periods allow for assessment of experiences that may not
occur multiple times per year (e.g., discrimination during
annual evaluations). Yet, scholars have questioned whether
reporting periods such as these are too long, introducing
memory bias (Arvey and Cavanaugh 1995). As such, we
included a shorter, 6 months stem too. Although the
study’s experimental design could not assess memory bias,
it did uncover the timeframe during which targets’ most
common and salient discriminatory age experiences
occurred, which are likely to pose the greatest threat to
targets’ performance, retention, and well-being.
We then reduced the number of survey items based on
criteria by Stanton et al. (2002) for shortening self-report
measures: (1) internal criteria: assess the items’ relation-
ships with one another and with the overarching measure;
(2) external criteria: examine relationships between the
construct of interest and other variables; and (3) judg-
mental: judge the quality of items (e.g., construct repre-
sentativeness, clarity, length, redundancy). To evaluate
external criteria, we selected two measures of occupational
well-being, given their theoretical and empirically
demonstrated relationships with workplace discrimination
(Jones et al. 2013): job satisfaction (a = .92; three items from the Michigan Organizational Assessment Question-
naire; Cammann et al. 1983) and turnover intentions
(a = .80; two items from Balfour and Wechsler 1996; Porter et al. 1976).
Results
The left side of Table 2 presents the ranges of means and
standard deviations across the 26 WADS items (for fre-
quency assessment). Most items captured adequate vari-
ance (SD [ 1). Item means were positively skewed, similar to the majority of mistreatment measures (e.g., Cortina
et al. 2001; Spector and Jex 1998; Williams et al. 1997).
The measure demonstrated high reliability (a = .97), although this could reflect the relatively large number of
items (Cortina 1993). Corrected item-total correlations
were high (r [ .50; Stanton et al. 2002), yet inter-item correlations fell across a wide range (.33 to .75), suggesting
potential to improve internal consistency through item
reduction (Clark and Watson 1995).
Descriptive statistics were similar for appraisals of the
WADS. Appraisal items also captured adequate variance,
were positively skewed, and demonstrated high reliability
(a = .98). Corrected item-total correlations were high and
inter-item correlations spanned a similar, large range. At
the scale level (i.e., items averaged), the frequency and
appraisal versions of the WADS correlated highly
(r = .83). Given the nearly identical results when assessing
frequency and appraisal of the WADS, we retained the
frequency measurement in subsequent studies.
To examine the effect of recall length on discrimination
frequency, we first drew on data from the Phase 2 quali-
tative study. Of participants’ age discrimination narratives,
26.6 % occurred within the past six months, 49.1 %
occurred within the past year, and 74.7 % occurred within
the past three years. Although these participants high-
lighted only one discriminatory experience (likely the most
impactful), these results suggest that longer time frames
may be necessary to fully capture the discriminatory events
that resonate with older workers.
Using Phase 3 data, we then conducted one-way ANOVAs
to compare mean frequencies between the four timeframe
conditions. At the scale level, frequency did not significantly
differ between them, F(3,336) = .90, p = .439. However,
examination of trends between the means (at both scale and
item levels) suggested that the 6-month stem led to the lowest
reports (M = 1.57, SD = 0.70), compared to the other three
stems (M = 1.74, SD = 0.72 for 1 year; M = 1.71,
SD = 0.77 for 3 years; M = 1.71, SD = 0.80 for 5 years).
Small practical significance (Cohen’s d = .24) existed
between the 6-month and 1-year stems.
We also examined whether age discrimination fre-
quency differentially related to work-related variables as a
function of the recall window. The 1- and 5-year stems
most strongly correlated with job satisfaction (r = -.33
for both stems, as opposed to r = -.14 for the 6-month and
3-year stems) and turnover intentions (r = .40 and .39,
respectively, versus r = .18 for 6 months and r = .11 for
3 years). Further, the 6-month and 5-year timeframes most
strongly related to negative affect (r = .37 and .52,
respectively, compared to r = .29 for 1 year and r = .19
for 3 years). Together, these analyses demonstrate that the
5-year stem most strongly related to the most variables.
However, participants high in negative affect were sub-
stantially more likely to endorse age discrimination during
this long timeframe, suggesting that this timeframe could
also be capturing pessimistic worldviews. The 6-month
timeframe did not relate to work-related outcomes and
contained the second highest correlation with negative
affect. The 1-year timeframe best balanced predicting
work-related variables with having a moderate relationship
with negative affect. Based on these results, we adopted the
1-year stem in the replication studies. The 1-year stem was
short enough to capture meaningful and salient events
(likely to affect targets’ outcomes), while being long
enough to capture discriminatory experiences that cannot
500 J Bus Psychol (2016) 31:493–513
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occur with great frequency (e.g., biased annual
evaluations).
Next, we adopted principal axis factoring (PAF) with
oblique rotation to explore the factor structure of the
26-item WADS (see Table 1). Two factors emerged,
explaining 60.0 % of variance. The first factor, con-
taining 19 items, explained the majority of this variance
(56.7 %). Several aspects of the results led us to ques-
tion whether the two factors were meaningfully unique
from one another. First, the factors were highly corre-
lated (r = .78), suggesting low discriminant validity.
Next, the second factor explained only about three per-
cent of variance. Third, the scree plot supported a one
factor solution; the scree test has received support as a
more accurate method for determining factors, compared
to the eigenvalue-greater-than-one test, which has been
critiqued for producing incorrect numbers of factors
(Floyd and Widaman 1995; Ford et al. 1986). Fourth, the
factors did not resemble theoretically meaningful con-
structs, such as overt versus covert, formal versus
informal, or supervisor- versus coworker-instigated
manifestations. Rather, the second factor appeared to
capture verbal manifestations of discrimination (e.g.,
‘‘Someone at work has unnecessarily talked slowly or
loudly to me due to my age’’), while the first factor
encompassed a broad range of experiences, from overt to
covert and supervisor- to coworker-instigated. In addi-
tion, an item in the second factor (‘‘Someone at work
has assumed I have health problems due to my age’’)
was not verbal in nature, and at least one item in the first
factor (‘‘I have been given fewer opportunities to express
my ideas due to my age’’) pertained to verbal interac-
tions, complicating this delineation between factors.
Fifth, Clark and Watson (1995) advocate unidimension-
ality in scale development. They recommend using the
results of EFAs to determine item removal; items that
load strongly on the first factor should be given greater
consideration for retention. Therefore, we tested a single
factor PAF, which explained 56.5 % of variance. Factor
loadings were higher, ranging from .58 to .84. 2
The descriptive and PAF results for the 26-item WADS
were promising, but items with low means and/or low
variances may not be as valuable as others, because these
experiences are infrequent or largely invariant between
employees. Also, highly correlated items may be redun-
dant. Therefore, we used Stanton et al.’s (2002) three cri-
teria for reducing scale length. Items that performed poorly
Table 2 Study 1 Phase 3 results for the original and final
WADS for older employees
Response Scale Original 26 items Final 9 items
Univariate descriptives N = 332–340 N = 334–338
Scale M 1.68 1.68
Scale SD 0.75 0.78
Item Ms 1.40–2.13 1.47–1.80
Item SDs 0.82–1.25 0.82–1.02
Reliability N = 305 N = 321
Cronbach’s a .97 .93
Corrected ITCs .57–.83 .72–.80
Inter-item correlations .33–.75 .48–.74
Exploratory factor structure N = 305 N = 321
Number of factors 2 1
%variance explained by 1st factor 56.7 % 60.8 %
Factor loadings on 1st factor .44–.92 .71–.84
Item-criterion correlations N = 332–338 N = 334–337
Job satisfaction -.03 to -.25 -.13 to -.25
Turnover intentions .06–.28 .18–.28
Results are based on the 1 year timeframe. Pairwise deletion was used for obtaining sample sizes
ITC item-total correlation
2 We also conducted a PAF within each timeframe. In the 6-month
and 1-year timeframes, three and four factors, respectively, with
eigenvalues greater than one emerged although many items contained
substantial dual loadings. In the 3-year timeframe, four factors
emerged, although the last three factors each contained only several
items, many of which had dual loadings. In the 5-year timeframe, two
factors emerged, but the second factor contained two items, one of
which had a relatively high dual loading. Importantly, only one factor
emerged in every timeframe based on scree plots, which have been
shown to be more accurate tests for determining numbers of factors,
compared to eigenvalues (Floyd and Widaman 1995; Ford et al.
1986). Further, most of the final nine WADS items stemmed from the
first factors across timeframes (and these final nine items produced
one factor). Taken together, these results best support a unidimen-
sional structure. More detailed information about the exploratory
factor structures by timeframe can be obtained by contacting the first
author.
J Bus Psychol (2016) 31:493–513 501
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on multiple indicators of internal criteria (item means,
standard deviations, corrected item-total correlations, inter-
item correlations, and factor loadings) were deleted. To
assess external criteria, we flagged item-criterion correla-
tions that were weaker than the average correlation
between all items and each criterion (i.e., job satisfaction,
turnover intentions; left side of Table 2). We then evalu-
ated the items based on judgmental criteria, reassessing
clarity, redundancy, and representativeness.
Drawing on these criteria, we reduced the number of
items to nine. Eight of these items stemmed from the first
factor of the first PAF using the original set of items.
Univariate descriptives, reliability analyses, exploratory
factor structure, and item-criterion correlations were rerun
for the nine-item version of the WADS (right side of
Table 2). These results were somewhat better than those
for the original items. Ranges for inter-item, item-total, and
item-criterion correlations were shorter and these correla-
tions were higher. Based on PAF, the nine-item WADS
was unidimensional and explained 60.8 % of variance.
Phase 4: Confirmatory Factor Analysis and Tests
of Validity
The next goals were to (a) conduct a confirmatory factor
analysis (CFA) of the nine-item WADS, and (b) assess the
measure’s convergent, discriminant, and criterion-related
validity.
Participants and Procedure
New U.S. workers from MTurk were recruited if they were
age 50 or older and employed 20 or more hours per week.
Of 415 respondents, we excluded 13 due to short response
times and substantial missing data. The survey also inclu-
ded six items (e.g., ‘‘I have never used a computer’’) to
assess insufficient effort responding (IER: Huang et al.
2012, 2015). Participants who answered more than 50 % of
the IER items incorrectly were excluded (n = 12; 2.9 %),
resulting in N = 390. Of the final sample, 49 % was male,
86.7 % was White, 7.4 % was African American, and
3.3 % was Latino. The average age was 55.5
(SD = 4.3 years), 76 % of respondents worked 35 or more
hours per week, and average organizational tenure was
10.4 years (SD = 7.6 years).
The survey was nearly identical to the previous phase
but included the nine-item WADS with a 1-year timeframe
in the stem. In addition to measuring job satisfaction and
turnover intentions, we included the Nordic Age Discrim-
ination Scale (NADS; 6 items; Furunes and Mykletun
2010), the WIS (7 items; Cortina et al. 2001), extraversion
(two items from the Ten Item Personality Inventory;
Gosling et al. 2003), and negative affect (NA; 10 items
from the PANAS; Watson et al. 1988). The NADS and the
WIS, measures of age and generic workplace mistreatment,
respectively, were used to test convergent validity. To test
discriminant validity, we selected a construct that is theo-
retically irrelevant to the WADS—extraversion—expect-
ing an insignificant relationship. Although some
personality traits relate to targets’ reports of workplace
mistreatment, extraversion is not one (Milam et al. 2009).
For criterion validity, we examined whether the WADS
contained incremental validity in predicting work-related
variables beyond individual differences (i.e., NA) and a
related age discrimination measure (i.e., the NADS). We
used NA to determine whether reports of age discrimina-
tion relate to theoretical outcomes independent of one’s
disposition. Respondents with higher NA may report
greater discrimination or worse well-being, given their
tendencies to hold negative attitudes (Judge and Hulin
1993; Levin and Stokes 1989). Further, we determined
whether the WADS explains additional variance in
respondent well-being beyond the NADS.
Results
Descriptive statistics for and correlations between the
variables appear in Table 3. In the CFA, the WADS items
were modeled as indicators of a single latent variable. The
model fit the data adequately: v2(27) = 102.29, p \ .001, RMSEA = .086, 90 % CI [.068, .103], CFI = .97,
SRMR = .026. Consistent with Kline (2010), we consid-
ered these goodness of fit statistics holistically, rather than
using any single statistic to determine acceptable fit.
Completely standardized factor loadings were high, rang-
ing from .73 to .87.
We then examined convergent and discriminant validity.
A CFA was conducted that included the WADS, NADS,
WIS, and extraversion. The first loading for each factor
was set equal to one to identify the model. The error
variances for the two extraversion items were initially
negative and so were also set equal to one. Model fit was
acceptable: v2(226) = 518.31, p \ .001, RMSEA = .058, 90 % CI [.052, .065], CFI = .95, SRMR = .042. Sup-
porting convergent validity, the WADS significantly cor-
related (based on completely standardized factor
correlations) with both the NADS (U = .66) and the WIS (U = .61), yet these correlations were not so high as to suggest that the WADS captures the same underlying
constructs as other mistreatment measures. Supporting
discriminant validity, the WADS did not significantly
relate to extraversion (U = -.02).
502 J Bus Psychol (2016) 31:493–513
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Next, we used hierarchical regressions to examine the
incremental validity of the WADS beyond NA and the
NADS. The WADS explained significant variance in job
satisfaction (b = -.31, DR2 = 1.9 %, p \ .01) after including both NA and the NADS in the first block of the
model. The WADS predicted turnover intentions beyond
NA and the NADS when they were considered separately
(beyond NA: b = .19, DR2 = 3.3 %, p \ .001; beyond the NADS: b = .16, DR2 = 1.7 %, p \ .01) but not when they were simultaneously included in the first block of the
model (b = .05, DR2 = 0.2 %, p = .38).3
Because the WADS shares theoretical and measurement
overlap with the other predictor variables in these models
(and shared variance is automatically assigned to the first
set of variables in incremental validity models), we sup-
plemented the hierarchical regressions with relative weight
analysis to better understand the relative importance of
each predictor (LeBreton et al. 2007; Tonidandel and
LeBreton 2011). We used RWA Web (Tonidandel and
LeBreton 2015), specifying bootstrapping with 10,000
replications and a .05 alpha level. Together, the three
predictors (NA, NADS, WADS) explained 21.7 % of the
variance in job satisfaction. Each predictor explained sig-
nificant variance, and their relative weights did not sig-
nificantly differ from one another (RW = .070 for the
WADS; RW = .057 for the NADS; RW = .090 for NA).
The same predictors explained 18.4 % of the variance in
turnover intentions. Unlike the regression analysis, all
predictors, including the WADS, explained significant
variance in turnover intent. Their relative weights did not
significantly differ from one another (RW = .033 for the
WADS; RW = .059 for the NADS; RW = .092 for NA).
These results demonstrate that the WADS significantly
relates to and explains meaningful variance in two popular
work-related criterion variables.
Study 2: Younger Workers
Phase 1: Inductive Item Generation
The first goal of Study 2 was to qualitatively determine
whether the WADS adequately captures younger workers’
perspectives. Younger workers’ discriminatory experiences
may be more overt in nature than older workers’, given that
they are not legally protected in the U.S. Stereotypes of
older versus younger adults also differ (Bertolino et al.
2012; Cuddy et al. 2005; Finkelstein et al. 2013) which
may drive different manifestations of discrimination.
Participants and Procedure
In this phase, we strove to understand the experiences of all
adult employees who are not legally protected from age
discrimination in the U.S. (ages 18–39) and so assessed the
experiences of all workers under age 40. However, in
subsequent phases, we focused specifically on workers
18–30, as this age range has been used to differentiate
young from middle-aged workers (Finkelstein et al. 2013).
One hundred six U.S. workers were recruited from
MTurk who were ages 18–39 and employed at least
20 hours per week. They were 49 % men, 74.5 % White,
11.3 % African American, 8.5 % Asian, and 4.1 % Latino,
and 71.7 % worked 35 hours or more per week. The
average age was 28.9 (SD = 5.4 years), and average
organizational tenure was 4.2 years (SD = 3.7). We
replicated the qualitative study from Study 1, providing the
same definition of age discrimination and asking partici-
pants to describe a time they had such an experience at
work. Ten participants left the question blank.
Results
Like Study 1, the research team assessed whether the
WADS items captured each younger worker’s story of
Table 3 Study 1 Phase 4 descriptive statistics and zero-
order correlations between
variables for older employees
M SD 1 2 3 4 5 6 7
1. WADS 1.69 0.80 (.95)
2. NADS 2.57 1.03 .62** (.90)
3. WIS 1.76 0.72 .58** .46** (.90)
4. Negative affect 1.46 0.61 .34** .16** .39** (.95)
5. Extraversion 2.95 0.98 -0.02 -0.02 -.07 -.17** (.65)
6. Job satisfaction 5.29 1.34 -.37** -.33** -.42** -.35** .21** (.92)
7. Turnover intentions 2.96 1.72 .29** .31** .38** .34** -.20** -.76** (.83)
Cronbach’s a coefficients are presented in parentheses on the diagonal. N = 388–390 using pairwise deletion
NADS Nordic Age Discrimination Scale, WIS Workplace Incivility Scale
** p \ .01
3 More detailed information about the hierarchical regressions can be
obtained by contacting the first author.
J Bus Psychol (2016) 31:493–513 503
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discrimination. The WADS reflected most experiences
well. Like older workers’ stories, younger workers often
described covert forms of discrimination. However, eight
stories suggested one theme unique to younger workers that
never emerged among older workers: superiors treating
them as inexperienced and naive. For example, a respon-
dent reported, ‘‘My supervisor equated my age with inex-
perience, even though I actually had dealt with more
appeals than the other associates…’’ Thus, the following item was added to the nine-item version of the WADS:
‘‘Supervisors treat me as inexperienced due to my age.’’
Phase 2: Exploratory Factor Analysis and Item
Reduction
Next, we (a) tested the descriptive statistics, reliability, and
exploratory factor structure of the updated 10-item WADS
among younger employees, and (b) compared the nine-item
WADS to the 10-item version to determine whether the
additional item contributed meaningfully to explaining
younger workers’ experiences of age discrimination.
Participants and Procedure
Participants aged 18–30 were recruited from MTurk if they
worked 20 or more hours per week. Three hundred forty-
three participants completed the survey; 49 were removed
for completing the survey in an unreasonably short period
of time and/or skipping numerous measures. Within the
final sample (N = 294), 59.9 % of participants were male,
78.9 % were White, 7.5 % were African American, and
7.5 % were Latino. The average age was 25.2
(SD = 2.7 years). Participants worked M = 39.2 hours per
week (SD = 8.4) and had been employed in their organi-
zations M = 2.9 years (SD = 2.5).
The survey contained 10 WADS items, including the
item specific to young workers. Respondents rated the
frequencies with which they experienced each item from 1
(never) to 5 (very often). Identical measures as in Study 1
were administered for job satisfaction (a = .94) and turn- over intentions (a = .83).
Results
The same analyses for preliminary measure evaluation
were performed as in Study 1. Univariate statistics were
similar for the nine- and 10-item WADS (Table 4).
Exploratory factor structure, variance explained, and item
loadings remained stable regardless of the additional item.
Item-criterion correlations were also similar. Given these
results, we did not retain the ‘‘young-specific’’ discrimi-
nation item because the shorter version performed similarly
internally and externally. Parsimony is also preferable.
From a practical standpoint, age-specific items complicate
administration and comparative analyses of the measure
across age groups. Hence, we recommend using the nine-
item WADS for younger employees.
Phase 3: Confirmatory Factor Analysis and Tests
of Validity
The goals of this phase were to (a) test the factor structure
of the WADS among younger employees, and (b) validate
the nine-item WADS among younger employees by
assessing convergent and discriminant as well as criterion-
related validity.
Participants and Procedure
A new sample of 428 participants age 18–30 who were
employed at least 20 hours per week was drawn from
MTurk. Nineteen participants were excluded due to unrea-
sonably short response times or substantial missing data. Six
IER items were also included, and the six participants who
answered more than 50 % of the items incorrectly were
excluded (Huang et al. 2012), resulting in a final dataset of
N = 403. The sample was 60.5 % men, 73 % White, 10.7 %
Asian, 10.2 % Latino, and 8.2 % African American. The
average age was 25.2 years (SD = 2.9), 70 % of partici-
pants worked at least 35 hours per week, and average
organizational tenure was 2.7 years (SD = 2.5).
Similar measures as in Study 1 were administered. The
NADS is less applicable to younger workers because its
items pertain to climates of discrimination toward ‘‘elderly
workers,’’ so in this study, we used the NADS (in addition
to extraversion) to test discriminant validity. We then
included a different mistreatment measure to test conver-
gent and incremental validity: the Interpersonal Conflict at
Work Scale (ICAWS; Spector and Jex 1998).
Results
Descriptive statistics and zero-order correlations appear in
Table 5. We tested a unidimensional measurement model
for the nine-item WADS. Confirmatory factor analysis
revealed adequate fit: v2(27) = 69.31, p \ .001, RMSEA = .063, 90 % CI [.045, .082], CFI = .98,
SRMR = .024. Completely standardized factor loadings
were high, ranging from .69 to .83.
To test convergent and discriminant validity, we con-
ducted a CFA that included the WADS, NADS, extraver-
sion, and ICAWS. The first loading of each factor was set
equal to one. The error variances for the two extraversion
items were initially negative and so were also set equal to
one. Model fit was good: v2(166) = 354.71, p \ .001, RMSEA = .054, 90 % CI [.046, .062], CFI = .95,
504 J Bus Psychol (2016) 31:493–513
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SRMR = .043. Supporting convergent validity, the WADS
positively related to the ICAWS based on the completely
standardized factor correlation (U = .43). We also exam- ined the factor correlation between the WADS and
extraversion, a theoretically unrelated construct. This cor-
relation was small and non-significant (U = .05), sup- porting discriminant validity. Moreover, we expected the
WADS to relate to the NADS more weakly for younger
compared to older workers, given its lower relevance to
younger workers. Indeed, the correlation was only U = .31 (compared to U = .66 among older workers), further sup- porting discriminant validity.
To test criterion-related validity, we examined rela-
tionships between the WADS and work-related variables,
beyond the influence of NA and other mistreatment expe-
riences (i.e., the ICAWS). Based on hierarchical
regressions, the WADS accounted for incremental variance
beyond both NA and the ICAWS in predicting job satis-
faction (b = -.16, DR2 = 2.2 %, p \ .001) and turnover intentions (b = .16, DR2 = 2.2 %, p \ .001). Like Study 1, we supplemented the hierarchical regressions with rel-
ative weight analyses using RWA Web and the same
specifications. Together, the three predictors (NA, ICAWS,
WADS) explained 19.1 % of the variance in job satisfac-
tion and 16.8 % of the variance in turnover intentions.
Each predictor explained significant variance in the
dependent variables. The relative weight of the WADS did
not significantly differ from those of the other predictors. 4
Overall, these results support the criterion-related validity
Table 4 Study 2 Phase 2 results for the 9- and 10-item
WADS for younger employees
Response scale 9-item WADS 10th item only
Univariate descriptives N = 291–294 N = 291–294
Scale M 1.75 1.78 a
Scale SD 0.78 0.79 a
Item M(s) 1.56–1.80 2.05
Item SD(s) 0.93–1.15 1.24
Reliability N = 286 N = 284
Cronbach’s a .91 .91a
Corrected ITC(s) .59–.72 .67
Inter-item correlations .40–.64 .38–.63
Exploratory factor structure N = 286 N = 284
Number of factors 1 1 a
%variance explained by 1st factor 51.9 % 51.6 % a
Factor loadings on 1st factor .62–.76 .70
Item-criterion correlations N = 291–294 N = 291
Job satisfaction -.18 to -.30 -.26
Turnover intentions .16 - .23 .23
Pairwise deletion was used to obtain sample sizes
ITC item-total correlation a
Scale properties with the 10th item included
Table 5 Study 2 Phase 3 descriptive statistics and zero-
order correlations between
variables for younger
employees
M SD 1 2 3 4 5 6 7
1. WADS 1.92 0.89 (.93)
2. ICAWS 1.72 0.64 .36** (.74)
3. NADS 2.40 .96 .26** .20** (.88)
4. Negative affect 1.69 0.68 .22** .34** .11* (.90)
5. Extraversion 2.74 0.99 0.02 0.03 -.01 -.15** (.66)
6. Job satisfaction 5.01 1.47 -.28** -.38** -.14** -.29** .14** (.92)
7. Turnover intentions 3.79 1.82 .28** .33** .15** .29** -.09 -.72** (.82)
Cronbach’s a coefficients are presented in parentheses on the diagonal. N = 399–403 using pairwise deletion
ICAWS Interpersonal conflict at work scale, NADS Nordic Age Discrimination Scale
* p \ .05; ** p \ .01
4 More information about the relative weight analyses can be
obtained by contacting the first author.
J Bus Psychol (2016) 31:493–513 505
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of the WADS, demonstrating that it explains significant
variance in work-related variables beyond the effects of
trait affect and other mistreatment measures.
Study 3: Middle-Aged Workers
Phase 1: Confirmatory Factor Analysis and Tests
of Validity
The goals of the first phase were to test (a) the factor
structure, and (b) the convergent, discriminant, and crite-
rion-related validity of the WADS among middle-aged
employees.
Participants and Procedure
A sample of 444 participants aged 31–49 who were
employed at least 20 hours per week was drawn from
MTurk. Twenty-nine participants were excluded due to
unreasonably short response times or substantial missing
data. Eight participants who answered more than 50 % of
the IER questions incorrectly were excluded (Huang et al.
2012), resulting in N = 407. The sample was 53.6 % men,
84.5 % White, 7.1 % African American, 6.4 % Asian, and
3.7 % Latino. The average age was 36.7 years (SD = 5.5),
88.7 % of participants worked 35 or more hours per week,
and average organizational tenure was 6.6 years
(SD = 4.8). Measures were identical to Study 2.
Results
Descriptive statistics and zero-order correlations appear in
Table 6. We tested a unidimensional measurement model
for the WADS, which revealed adequate fit:
v2(27) = 101.43 p \ .001, RMSEA = .083, 90 % CI [.066, .101], CFI = .97, SRMR = .029. Completely stan-
dardized factor loadings ranged from .66 to .84.
To test convergent and discriminant validity, we con-
ducted a CFA that included the WADS, NADS, ICAWS,
and extraversion. The first loading of each factor, as well as
the error variances of the extraversion items were set equal
to one. Model fit was acceptable: v2(166) = 403.90, p \ .001, RMSEA = .060, 90 % CI [.053, .068], CFI = .94, SRMR = .046. Supporting convergent valid-
ity, the WADS significantly correlated with the ICAWS
(U = .38). Supporting discriminant validity, the WADS had a near-zero correlation with extraversion (U = -.001). The factor correlation between the WADS and the NADS
(U = .33) was also smaller than this correlation within the dataset of older workers (U = .66), as predicted.
To test criterion-related validity, we examined rela-
tionships between the WADS and two work attitudes,
beyond the influence of NA and the ICAWS. Based on
hierarchical regressions, the WADS did not account for
incremental variance beyond NA and the ICAWS in pre-
dicting job satisfaction (b = -.04, DR2 = 0.1 %, p = .478) or turnover intentions (b = .09, DR2 = 0.6 %, p = .086). We used RWA Web with the same specifica-
tions as Studies 1 and 2 to supplement these analyses. NA,
the ICAWS, and the WADS explained 15 % of the vari-
ance in job satisfaction. NA and the ICAWS, but not the
WADS, explained significant variance in job satisfaction.
These predictors explained 13.9 % of the variance in
turnover intentions. All predictors except the WADS
explained significant variance in turnover intent. The rel-
ative weight of the ICAWS (but not NA) was significantly
higher than that of the WADS in predicting both variables.
The criterion-related validity results demonstrate that
general workplace mistreatment better explains variance in
middle-aged employees’ work-related attitudes, compared
to the WADS. This finding is not surprising, given our
hypothesis (and support below) that middle-aged workers
experience lower frequencies of age discrimination.
Because middle-aged employees constitute the workplace
age ‘‘in-group’’ and are targeted with mostly positive age-
related stereotypes (Finkelstein et al. 2013), the WADS
does not relate as strongly (or at all) to their occupational
variables, compared to mistreatment measures that better
resonate with them. Aside from the non-significant tests of
criterion-related validity, the WADS contained high relia-
bility and convergent and discriminant validity among
middle-aged workers.
Phase 2: Invariance of the WADS Between Age
Groups
Having tested the WADS within each age group, we then
conducted a series of measurement equivalence tests (per
Vandenberg and Lance 2001) to compare the WADS
between age groups (Table 7). All tests were based on the
‘‘confirmatory’’ datasets (i.e., Study 1 Phase 4, Study 2
Phase 3, and Study 3 Phase 1). In Model 1 (M1), we
imposed the same unidimensional factor structure across
groups (setting the first factor loading to one) to test con-
figural invariance. Model fit was acceptable:
v2(81) = 265.87, p \ .001, RMSEA = .078, 90 % CI [.067, .088], CFI = .99. Given configural invariance, we
proceeded to test metric invariance in M2, fixing factor
loadings to be equivalent between groups. M2 fit was
acceptable: v2(97) = 297.86, p \ .001, RMSEA = .074,
506 J Bus Psychol (2016) 31:493–513
123
90 % CI [.064, .083], CFI = .99. M1 and M2 demonstrated
comparable fit: Dv2(16) = 31.99, p [ .05; DCFI = -.001.
5 We built on M2 to test scalar invariance, con-
straining the vector of items’ intercepts to be equal (M3).
We did not expect the groups to have invariant item
intercepts however, given our hypothesis that middle-aged
workers experience significantly lower rates of age dis-
crimination than older and younger workers. In cases of
hypothesized group differences, scalar variance does not
reflect undesirable biases but rather demonstrates under-
lying differences between groups (Vandenberg and Lance
2001). Indeed, this model did not fit the data very well:
v2(115) = 544.90, p \ .001, RMSEA = .099, 90 % CI [.091, .108], CFI = .97. M3 also had significantly worse
model fit than M2: Dv2(18) = 247.04, p \ .05; DCFI = -.015. All items’ intercepts significantly differed between the groups. We next compared the latent means
between age groups. M4 built on M2 by constraining the
factor means to be equivalent. Model fit was poor:
v2(123) = 956.31, p \ .001, RMSEA = .133, 90 % CI [.126, .141], CFI = .95. M4 fit significantly worse than
M2: Dv2(26) = 658.45, p \ .05; DCFI = -.04. Thus, the age groups are not invariant in their latent means.
We supplemented these analyses by comparing the
latent means between two sets of age groups at a time
(younger vs. older; younger vs. middle-aged; middle-aged
vs. older). Fit for all three models was poor, 6
demonstrating
that the means significantly differed between each set of
age groups. Specifically, younger workers experienced the
highest rates of age discrimination, followed by older
workers, and then middle-aged workers. Additional anal-
yses revealed that only 22 % of younger workers and 30 %
of older workers reported not having experienced any of
the WADS items in the past year, but nearly half (48 %) of
middle-aged workers reported ‘‘never’’ to all WADS items.
We plotted age discrimination frequency across ages
(Fig. 1). Taken together, these results support our hypoth-
esis that a U-shaped pattern exists between age groups with
regard to frequency of age discrimination.
Discussion
The ‘‘graying’’ workforce and the well-documented nega-
tive stereotypes of both older and younger workers
underscore the need to better understand employees’
Table 6 Study 3 descriptive statistics and zero-order
correlations between variables
for middle-aged employees
M SD 1 2 3 4 5 6
1. WADS 1.42 0.61 (.93)
2. ICAWS 1.62 0.59 .31** (.78)
3. Negative affect 1.50 0.64 .33** .44** (.92)
4. Extraversion 2.88 1.11 -.06 -.11* -.25** (.81)
5. Job satisfaction 5.03 1.59 -.17** -.36** -.29** .28** (.95)
6. Turnover intentions 3.39 1.87 .21** .34** .28** -.20** -.80** (.90)
Cronbach’s a coefficients are presented in parentheses on the diagonal. N = 406–407 using pairwise deletion
* p \ .05; ** p \ .01
Table 7 Invariance of the WADS between age groups
Model df v2 CFI Ddf Dv2 DCFI
1. Equivalent factor structures 81 265.87* .988
2. Equivalent factor loadings 97 297.86* .987
1 versus 2 16 31.99 -.001
3. Equivalent intercepts 115 544.90* .972
2 versus 3 18 247.04* -.015
4. Equivalent factor means 123 956.31* .947
2 versus 4 26 658.45* -.040
CFI comparative fit index
* p \ .001
5 We supplemented all model comparisons with differences in CFI.
Invariance hypotheses should typically not be rejected when CFI
change values are -.01 or less, they should be viewed with caution
when values are -.01 to -.02, and they should be rejected when
values are greater than -.02 (Vandenberg and Lance 2001).
6 Goodness of fit indices can be obtained by contacting the first
author.
J Bus Psychol (2016) 31:493–513 507
123
perspectives of age discrimination. Age discrimination has
become a central topic in international conversations, such
as the 2015 White House Conference on Aging and the
World Health Organization’s (WHO) initiatives on global
aging. Yet, researchers lack a validated measure with
which to capture workers’ experiences of age discrimina-
tion. Our objective was to develop and validate a measure,
the Workplace Age Discrimination Scale, that researchers,
practitioners, and policymakers can use to better under-
stand employees’ perspectives and outcomes of age dis-
crimination. The present study provides four contributions
to the literature.
First, the WADS turns attention to individuals’ percep-
tions of age discrimination. Although measures of age
stereotypes and discriminatory age climates exist, no val-
idated measure captures targets’ personal experiences of
age discrimination, which are essential for conducting
future research on targeted employees’ organizational,
mental, and physical outcomes. This absence of attention to
personal experiences of age discrimination differs from
literatures on race- and sex-based workplace discrimination
that highlight the importance of targets’ perspectives in
uncovering and addressing organizational mistreatment
(e.g., Fitzgerald et al. 1997; Williams et al. 1997, 2003).
The WADS is particularly useful in that its items capture
workers’ experiences across age groups; thus, it is possible
to administer one measure to all employees.
Second, the WADS addresses many methodological
issues in the ageism literature (e.g., lack of validation,
reliance on participants to define ‘‘age discrimination,’’
absence of a referent time period in instructional stems).
We conducted a series of qualitative and quantitative
studies, employed numerous analytic approaches, and drew
on methodological best practices to develop, refine, and
validate the WADS. The measure contains high reliability
among older, younger, and middle-aged workers.
Exploratory and confirmatory factor analyses revealed a
unidimensional structure that explains significant variance
in workers’ experiences of age discrimination. Further, we
used an experimental manipulation to determine that 1 year
is an appropriate timeframe for the stem of the WADS,
capturing meaningful target experiences.
The paper also challenges two normative assumptions
that scholars and policymakers make about age discrimi-
nation: (a) that discriminatory behaviors are only overt and
explicit and (b) that only older employees are targeted with
it. Speaking to the first assumption, overt discriminatory
actions based on age do indeed occur, and they certainly
fall within our conceptualization and definition of ‘‘dif-
ferential workplace treatment based on age, which impairs
fairness of treatment or opportunity.’’ Yet our inductive
method of analyzing over two hundred workers’ stories
revealed that age discrimination is often subtle, elusive,
and understated—for older and younger workers alike.
Employees described being excluded from work-related
and social events, experiencing comments insinuating that
their education and training are inadequate (outdated for
older workers, insufficient for younger workers), learning
about age-related gossip (e.g., when one will ‘‘finally’’
retire), and hearing age-related ‘‘jokes.’’ These covert
Fig. 1 Mean of the WADS across age groups. Error bars
contain 95 % confidence
intervals
508 J Bus Psychol (2016) 31:493–513
123
manifestations highlight the need to apply modern dis-
crimination theories to the ageism literature with greater
frequency. As discrimination becomes less socially
acceptable and as targets gain greater legal protection,
discrimination is less likely to occur in overt forms, instead
emerging in insidious ways that skirt legal and public
attention. The WADS includes ‘‘modern’’ manifestations
of age discrimination, thereby better capturing the breadth
of employees’ experiences.
Interestingly, workers—particularly younger workers—
often introduced their stories of covert age discrimination
by saying, ‘‘I haven’t really experienced age discrimina-
tion, but…’’ and then proceeded to describe insidious manifestations of age discrimination. From a method-
ological standpoint, this reinforces the need to avoid using
terms such as ‘‘age discrimination’’ in measurement items.
Although some participants were hesitant to define their
experiences as age discrimination—likely due to their
schemas of age discrimination as high-intensity, overt
behaviors that can result in legal proceedings—their stories
clearly exemplify age-based mistreatment at work. Devel-
oping items that pertain to particular discriminatory
behaviors rather than to participants’ definitions of ‘‘age
discrimination’’ more accurately captures age discrimina-
tion frequency, because the behaviors align with
researchers’ construct definitions and contain less concep-
tual variance between participants (Hardy and Ford 2014).
This finding reveals the need for greater research on inter-
individual appraisals of and coping strategies for age dis-
crimination. Do employees only label their experiences as
‘‘age discrimination’’ when the events are egregious or
pertain to formal aspects of the job? When targets do not
explicitly label their experiences as age discrimination, are
their appraisals less negative and their well-being less
affected? These questions await future research.
In our fourth contribution, we challenge assumptions
about who experiences age discrimination. Our findings
indicate that older workers are not alone in their reports of
mistreatment; younger workers report similar experiences,
often at higher frequencies. This substantiates emerging
evidence that the nature of ageism might be changing, such
that younger employees are denigrated even more often
than older employees (Bertolino et al. 2012; Weiss and
Maurer 2004). Our hope is that the WADS will spur greater
research on younger workers’ experiences of age discrim-
ination. Early onset discrimination could pose important
implications for workers’ long-term organizational success,
labor force attachment, economic and social participation,
self-perception, and health. Meanwhile, middle-aged
workers experience significantly less age discrimination,
creating a U-shaped pattern of age discrimination across
the lifespan. This finding builds on research on age
stereotypes in which the fewest negative stereotypes exist
toward middle-aged, compared to younger or older,
employees (Finkelstein et al. 2013). It reinforces the notion
that middle-aged workers are favored and demonstrates
that they can be used as an appropriate comparison (‘‘in-
group’’) in future research on aging.
Limitations and Future Directions
Although we employed numerous methods (e.g., deductive,
qualitative, experimental) to develop and validate the
WADS, the quantitative studies were cross-sectional, pre-
venting us from drawing conclusions about causal rela-
tionships between age discrimination and theoretical target
outcomes (e.g., job satisfaction, turnover intentions). Our
intent was to develop a measurement tool that scholars can
use to pursue a host of future studies, including those that
document longitudinal relationships between perceived age
discrimination and target outcomes. The WADS can be
used to address numerous other research topics, including
target meaning-making of and coping mechanisms for age
discrimination.
Another important future direction is the potential for
the WADS to spur intersectional and comparative dis-
crimination research with regard to age. The WADS
enables scholars to compare targets’ experiences of age
discrimination with other discriminatory experiences,
addressing questions such as: Do employees cope similarly
with different types of discrimination? Do employees’
outcomes differ based on discrimination type? Scholars can
also better study discrimination based on intersections of
employees’ identities. For instance, how do measures of
age-, race-, and sex-based discrimination contribute to
predicting the outcomes of older women of color? Ques-
tions such as these deserve greater empirical attention as
Western societies become increasingly diverse with respect
to age, race, and ethnicity.
Results were similar between frequencies and appraisals
of the WADS items, leading to our recommendation that
scholars use the WADS to measure respondents’ frequen-
cies of age discrimination. However, we used only one
item (‘‘How much does this experience typically bother
you?’’) to assess appraisal of each WADS statement.
Respondents’ appraisals might be better captured using a
holistic measure of appraisal that follows the WADS (see
Cortina and Magley 2009). Although age discrimination is
fundamentally negative, employees likely vary in their
appraisals of it, as they do for other types of workplace
mistreatment (Cortina and Magley 2009; Langhout et al.
2005). Better understanding of targets’ appraisals of age
discrimination could spur research on coping mechanisms
J Bus Psychol (2016) 31:493–513 509
123
and outcomes (Lazarus and Folkman 1984), as well as
interventions for reducing the impact of age discrimination.
We used crowdsourcing samples in order to enhance
generalizability during measure development, but in future
research, the WADS should be administered in specific
organizational settings to determine how tacit definitions of
older, middle-aged, and younger age groups—and their
respective experiences of age discrimination—differ
between professions. For instance, employees at technol-
ogy companies might be considered older (and targeted
with more discrimination) when they reach their thirties.
Age discrimination may not follow a U-shaped curve in
industries such as this that favor youth but rather may
develop along an upward linear trajectory. The opposite
pattern might exist in industries (e.g., academia) that tend
to value older employees with extensive experience. Future
research should test the impact of career timetables and job
norms on organizational definitions of ‘‘young,’’ ‘‘middle,’’
and ‘‘old’’ and on employees’ subsequent discriminatory
experiences.
Practical Implications
Equal opportunity is a virtue of American society. By
better understanding age discrimination, practitioners can
generate multi-level strategies to reduce age discrimination
and minimize its effects. Because we demonstrate that
many age discriminatory acts are covert, practitioners
could draw on interventions for addressing other types of
low-intensity mistreatment. For instance, the Civility,
Respect, and Engagement in the Workplace (CREW)
program successfully reduces workplace incivility (Osa-
tuke et al. 2009). Another strategy is to address perpetra-
tors’ implicit attitudes and stereotypes toward particular
age groups. Covert discrimination often stems from sub-
conscious perpetrator attitudes, allowing stereotypes to
manifest in discriminatory behavior without perpetrators’
awareness (Dovidio et al. 1997; Gaertner and Dovidio
2005). The Common Ingroup Identity Model (Gaertner and
Dovidio 2000) has been used to broaden individuals’ per-
ceptions of ingroup membership, thereby reducing preju-
dice. Interventions such as these could be tailored to
address age discrimination.
The WADS is also a valuable tool for informing social
policy. Employment laws in many countries (e.g., the
ADEA in the U.S.) only protect individuals from blatant
acts of workplace age discrimination in formal domains,
such as recruitment and promotion. Yet, respondents in our
studies repeatedly indicated that age discrimination is often
covert and occurs in informal domains—acts falling out-
side legislative protection. Thus, although age-based
claims of discrimination have swelled in many countries,
the prevalence of age discrimination is likely underesti-
mated given that lower-intensity forms of discrimination
are typically not legally covered. King et al. (2011) dis-
cussed misalignment between U.S. law and targets’ per-
spectives of other types of discrimination (e.g., race-
based), given that most laws cover only egregious mani-
festations of discrimination. Should the ADEA and similar
laws be expanded to include modern forms of age dis-
crimination? Greater discussion is warranted.
In the U.S., national legislation and organizational
policies also fail to protect younger workers, a population
that our paper demonstrates is subject to greater age dis-
crimination than older workers. Age discrimination does
not start when U.S. workers become legally protected but
rather, as our data show, affects individuals as young as 18.
This work, in conjunction with research documenting
numerous negative stereotypes of younger workers (e.g.,
Finkelstein et al. 2013), provides support for expanding the
ADEA to protect workers under age 40. Without more
comprehensive coverage through the ADEA and organi-
zational policies, large segments of society will continue to
have little recourse for age discrimination.
Acknowledgments We are grateful to participants of the Age in the Workplace conference—particularly Lisa Finkelstein and Donald
Truxillo—for their feedback on early stages of this project. We also
thank Alyssa McGonagle and Boris Baltes for their methodological
support. In addition, we greatly appreciate the thoughtful feedback
from the Associate Editor, Scott Tonidandel, and two anonymous
reviewers that allowed us to strengthen the manuscript. Finally, we
thank Kathi Marchiondo, who provided insight and valuable
feedback.
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- c.10869_2015_Article_9425.pdf
- Development and Validation of the Workplace Age Discrimination Scale
- Abstract
- Purpose
- Design/Methodology
- Findings
- Implications
- Originality/Value
- Negative Attitudes and Discrimination Toward Older Employees
- Negative Attitudes and Discrimination Toward Younger Employees
- Targets’ Perceptions of Workplace Age Discrimination
- Comparing Age Discrimination Between Age Groups
- The Present Studies
- A Note About Study Samples
- Study 1: Older Workers
- Phase 1: Deductive Item Generation
- Phase 2: Inductive Item Generation and Initial Item Reduction
- Participants and Procedure
- Analytic Method and Results
- Phase 3: Exploratory Factor Analysis and Further Item Reduction
- Participants and Procedure
- Results
- Phase 4: Confirmatory Factor Analysis and Tests of Validity
- Participants and Procedure
- Results
- Study 2: Younger Workers
- Phase 1: Inductive Item Generation
- Participants and Procedure
- Results
- Phase 2: Exploratory Factor Analysis and Item Reduction
- Participants and Procedure
- Results
- Phase 3: Confirmatory Factor Analysis and Tests of Validity
- Participants and Procedure
- Results
- Study 3: Middle-Aged Workers
- Phase 1: Confirmatory Factor Analysis and Tests of Validity
- Participants and Procedure
- Results
- Phase 2: Invariance of the WADS Between Age Groups
- Discussion
- Limitations and Future Directions
- Practical Implications
- Acknowledgments
- References
