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The Psychological and Academic Costs of
School-Based Racial and Ethnic
Microaggressions
Micere Keels
University of Chicago
Myles Durkee
University of Michigan
Elan Hope
North Carolina State University
Research examining links between racial-ethnic microaggressions and edu-
cational and psychological outcomes can be improved with the development
of brief and reliable measurement tools. Our brief School-Based Racial and
Ethnic Microaggressions Scale addresses this gap. First, we examined the
prevalence of school-based microaggressions among an analytic sample of
462 Black and Latinx students attending five historically White universities
in the Midwest. Then, we examined the association between school-based
microaggressions and depressive symptoms and academic achievement.
An exploratory principal components analysis of Wave 1 data and a confir-
matory factor analysis of Wave 3 data validated a three-factor model: (a)
Academic Inferiority, (b) Expectations of Aggression, and (c) Stereotypical
Misrepresentations. Students’ exposure to microaggressions and its effects
were conditional on individual and school characteristics.
MICERE KEELS is an associate professor in the Department of Comparative Human
Development at the University of Chicago, Rosenwald Hall, 1101 58th St. Chicago,
IL 60637; e-mail: micere@uchicago.edu. Her principal research interests are in under-
standing how race-ethnicity and poverty structure children and youth exposures to
developmental inputs and contextual challenges and supports.
MYLES DURKEE is an assistant professor in the Department of Psychology at the
University of Michigan. Dr. Durkee’s research examines how youth and emerging
adults navigate racial experiences within educational contexts.
ELAN HOPE is an assistant professor in the Department of Psychology at North Carolina
State University and director of the Hope Lab. In the Hope Lab, Dr. Hope and her
team take an assets-based approach to investigate factors that promote well-being
for emerging adults who face racism and racial discrimination with an emphasis
on both individual differences and contextual variation.
American Educational Research Journal
Month XXXX, Vol. XX, No. X, pp. 1–29
DOI: 10.3102/0002831217722120
Ó2017 AERA. http://aerj.aera.net
KEYWORDS: academic achievement, mental health, microaggressions, school
climate
We are in a period of U.S. history where the vast majority of Americans
avoid overt acts of racism and exhibit politically correct behaviors
(Sue, 2010). The uncommonness of overt racism is more than political cor-
rectness as most people self-report that they are not racist and declare egal-
itarian values (Gaertner & Dovidio, 2005). However, as studies of implicit
bias indicate, people who appear nonprejudiced on self-report measures
display lingering negative biases when spontaneous response tasks are
used to measure attitudes (Dovidio, Kawakami, Smoak, & Gaertner, 2009).
The consensus is that thoughtful and deliberate behaviors are guided by
egalitarian beliefs but subconscious negative feelings toward groups emerge
when engaging in more spontaneous behaviors (Stanley, Sokol-Hessner,
Banaji, & Phelps, 2011).
Theories of aversive racism account for the persistence of prejudice
despite the ‘‘near universal endorsement of the principles of racial equality
as a core cultural value’’ (Pearson, Dovidio, & Gaertner, 2009, p. 314).
Aversive racists sympathize with minority groups and endorse principles
of racial equality while simultaneously holding often nonconscious feelings
of superiority, discomfort, anxiety, and/or fear. Aversive racism can manifest
as microaggressions—‘‘brief and commonplace daily verbal, behavioral, and
environmental indignities, whether intentional or unintentional, that com-
municate hostile, derogatory, or negative racial slights and insults to the tar-
get person or group’’ (Sue et al., 2007, p. 272). Despite numerous studies
substantiating the existence of implicit bias and aversive racism, recent dis-
putes among academics, policymakers, and in the popular press have raised
questions about whether minority college students are indiscriminately see-
ing microaggressions everywhere (McWhorter, 2014; Vega, 2014). This dis-
missal of people of color’s experiences of racial-ethnic discrimination is
one aspect of aversive racism and is evidenced in the gulf between
Whites’ and Blacks’ perceptions of the persistence of discrimination: 69%
of White Americans believe that Blacks are now treated the same as
Whites, but 59% of Black Americans believe that Blacks continue to be trea-
ted worse than Whites (Dovidio & Gaertner, 2004).
Although qualitative evidence detailing the negative effects of microag-
gressions on racial-ethnic minority students attending historically White col-
leges and universities is compelling, there is a need for longitudinal
quantitative studies to test and extend the generalizability of existing evidence
(R. S. Harris, 2008). First, quantitative studies allow us to test the strength of
the effect of exposure to microaggressions on educational and psychological
outcomes. Second, quantitative studies also allow us to estimate the unique
implications of microaggressions beyond the effect of the racial-ethnic compo-
sition of the school itself. The very experience of being in the demographic
Keels et al.
2
minority can have negative effects on one’s perception of the institution’s
racial-ethnic climate (Carter, 2007). Finally, because existing research does
not track students as they transition from one educational context to another,
it remains unclear whether students distinguish changes in racial-ethnic cli-
mate between educational contexts. The high level of K–12 school segrega-
tion means that most Black and Latinx students transitioning to historically
White colleges and universities attended high schools where their group
was in the majority (Massey & Fischer, 2002). Consequently, for many Black
and Latinx students, the transition to college is likely marked with an increase
in exposure to racial-ethnic microaggressions within educational contexts.
The present study extends existing research by first developing and val-
idating a scale that measures students’ experiences of school-based racial
and ethnic microaggressions (SB-REMA). We then examined stability and
change in Black and Latinx students’ reports of SB-REMA as they transitioned
from high school to college. Lastly, we examined the associations between
SB-REMA and educational and psychological outcomes, controlling for the
racial-ethnic composition of the student body. We focus on Black and
Latinx students to examine differences between and within two minority
groups that have a history of experiencing racial and ethnic discrimination
in educational contexts (Benner & Graham, 2011; Farkas, 2003).
Defining and Describing Microaggressions
The term microaggressions was first used by Pierce, Carew, Pierce-
Gonzalez, and Willsand (1977) but laid dormant until Sue and colleagues
(2007) completed the seminal work on which almost all contemporary
research on microaggressions is based. Sue and colleagues identified three
major classes of microaggressions. The first class of microaggressions is micro-
assaults, which are explicit racial derogations characterized primarily by ver-
bal and nonverbal behaviors meant to hurt the intended victim through name
calling, blatant isolation of the individual, or purposeful discriminatory
actions. The second class is microinsults, which are characterized by more
indirect verbal and nonverbal behaviors that convey stereotypical beliefs.
Microinsults can also be rudeness and insensitivity regarding a person’s
racial-ethnic heritage or identity. The third class is microinvalidations, which
are characterized by communications that exclude, negate, or nullify the
thoughts, feelings, or experiential reality of a racial-ethnic minority individual.
Researchers have identified approximately 11 thematic categories that
describe the content of microaggressions: alien in one’s own land, ascription
of intelligence, color blindness, assumption of criminality, denial of individual
racism, myth of meritocracy, pathologizing cultural values and styles, second-
class status, environmental invalidation, simultaneous invisibility and hypervi-
sibility, and exoticization and objectification (Sue et al., 2007; Wong, Derthick,
David, Saw, & Okazaki, 2014).
School-Based Microaggressions
3
Though most of the research has been conducted with samples of Black
Americans (Donovan, Galban, Grace, Bennett, & Felicie
´, 2013; Go
´mez, 2015;
Solo
´rzano, Ceja, & Yosso, 2000; Sue et al., 2007; Wong et al., 2014), several
studies have shown that a range of racial-ethnic minority groups experience
microaggressions: Asian Americans (Noh, Kaspar, & Wickrama, 2007; Ong,
Burrow, Fuller-Rowell, Ja, & Sue, 2013), indigenous peoples (Hill, Suah, &
Chantea, 2010), Latinx Americans (Rivera, Forquer, & Rangel, 2010), and mul-
tiracial individuals (Johnston & Nadal, 2010; Nadal, Wong, et al., 2011).
Additionally, microaggressions appear to permeate a myriad of contextual set-
tings across the life span, including elementary schools (Allen, 2010; Henfield,
2011), high schools (Benner & Graham, 2011; Huynh, 2012), college cam-
puses (Sue, Lin, Torino, Capodilupo, & Rivera, 2009; Yosso, Smith, Ceja, &
Solo
´rzano, 2009), and work environments (Alabi, 2015; DeCuir-Gunby &
Gunby, 2016).
Published Racial-Ethnic Microaggressions Scales
Strong quantitative scales enable researchers to respond to the critique
that much of the racial microaggression literature has depended on small sam-
ple, qualitative studies (R. S. Harris, 2008). Rigorous scales are needed to
examine the associations of microaggressions with various outcomes because,
as noted previously, many researchers and many in the public question the
‘‘reality’’ and consequences of microaggressions. There is also a need for brief
scales that can be included in large representative surveys that have enough
racial-ethnic diversity to enable examination of subgroup differences. Brief
scales would also facilitate their inclusion in large studies examining a broad
range of outcomes. We focused on developing a microaggressions scale that
would be ideal for survey studies focused on educational contexts and poten-
tially provide a better understanding of how race-ethnicity is associated with
differential educational experiences and how those differential experiences
affect educational and psychological outcomes.
Mercer, Zeigler-Hill, Wallace, and Hayes (2011) developed the Inventory
of Microaggressions Against Black Individuals (IMABI). The IMABI is a 14-
item unidimensional measure of racial microaggressions that captures both
microinsults and microinvalidations. The IMABI was developed to measure
four types of microinsults (assumptions concerning the intellectual inferior-
ity of Black individuals, inferior status or second-class citizenship of Black
individuals, assumed criminality of Black individuals, and superiority of
White cultural values), and three types of microinvalidations (assumed uni-
versality of Black experiences, denial of individual racism or color-blindness,
and the myth of meritocracy). Their sample included 385 undergraduates
who identified as Black or African American from two universities, one
Southern institution and one Southwestern institution. Their findings sup-
port the IMABI as a reliable and valid measure of racial microaggressions,
Keels et al.
4
and it was associated with other stressors including racial rejection sensitiv-
ity, race-related stress, general distress, and perceived stress. Though the
IMABI offers a sound measurement tool for racial microaggressions, because
most of the items are nonspecific to school contexts, it does not provide con-
textual specificity regarding school-based microaggressions. Additionally,
the IMABI does not capture microaggressions that are likely to occur in class-
room environments for racial-ethnic minority students.
Nadal (2011) developed the Racial and Ethnic Microaggressions (REMA)
scale to measure the microaggressions that racial-ethnic minorities experience
in their everyday lives. His study included a racially ethnically and education-
ally diverse sample of individuals recruited from a university context and over
the Internet (443 participants for the exploratory factor analysis and 218 for the
confirmatory factor analysis). The REMA is a 45-item scale with six subscales:
assumptions of inferiority, second-class citizen and assumptions of criminality,
microinvalidations, exoticization/assumptions of similarity, environmental
microaggressions, and workplace and school microaggressions. It was found
to be a valid measure of racial microaggressions and reliable across four
racial-ethnic groups, specifically, Asian Americans, Latinx Americans, Black
Americans, and multiracial Americans. The REMA is a comprehensive scale
with a wide range of items, and though none of its subscales focus on educa-
tional settings, several items assessed school-based microaggressions. These
items were helpful in the development of our scale.
Torres-Harding, Andrade, and Romero Diaz (2012) developed the Racial
Microaggressions Scale (RMAS) to measure the themes and categories of
racial-ethnic microinsults and microinvalidations reported in the literature.
The sample was racially, ethnically, and educationally diverse and was
recruited from a university (N= 175) and a community (N=202)setting.
The RMAS is a 45-item scale with six subscales: invisibility, criminality, low
achieving/undesirable culture, sexualization, foreigner/not belonging, and
environmental invalidations. Their findings indicated that the subscales should
be examined separately, as opposed to an overall measure of racial-ethnic
microaggressions. The RMAS was found to be a reliable and valid measure
for individuals from diverse racial-ethnic backgrounds. However, they also
found different patterns of mean subscale scores indicating that some sub-
scales were more salient for particular racial-ethnic groups than others. The
RMAS offers a rigorous scale, but similar to the REMA, it does not focus spe-
cifically on racial microaggressions encountered in educational settings. We
utilized items from this scale that were particularly relevant for educational
contexts in the initial pool of items used to develop our scale.
People navigate numerous contexts over the course of a given period of
time, and although there is often a substantial amount of similarly across
contexts, exposure to racial-ethnic microaggressions is likely to differ sub-
stantially based on the racial-ethnic composition of residential contexts,
community contexts (e.g., churches), and institutional contexts (e.g.,
School-Based Microaggressions
5
schools). Therefore, it is important for research investigating the effects of
a specific context, such as ours focused on the effects of educational con-
texts, to utilize microaggression items that are situationally specific rather
than generalized items assessing aggregate exposure.
Consequences of Microaggressions
The subtlety of microaggressions and the belief that they are a normative
aspect of cross race-ethnicity interactions has led to the popular mispercep-
tion that microaggressions may offend but cause no real harm to one’s well-
being (Campbell & Manning, 2015; Marcus, 2015; Thomas, 2008). Research
shows, however, that microaggressions affect individuals even when they
do not consciously recognize that it has occurred; research also suggests
that subtle microaggressions may have the strongest effects (Cheryan &
Monin, 2005; Nguyen & Ryan, 2008). As Yosso and colleagues (2009) noted,
The stress of one racial microaggression can last long after the assault
because the victim often continues to spend time with the microag-
gressor while considering whether the assailant intended harm, and
whether or how they must launch a sufficient response. (p. 670)
There is substantial evidence showing that microaggressions have negative
associations with many aspects of well-being: anxiety and depression (Huynh,
2012; Hwang & Goto, 2008; Lambert, Herman, Bynum, & Ialongo, 2009; Smith,
Allen, & Danley, 2007), substance abuse (Blume, Lovato, Thyken, & Denny,
2012; Wei, Alvarez, Ku, Russell, & Bonett, 2010), posttraumatic stress symptoms
(Flores, Tschann, Dimas, Pasch, & de Groat, 2010; Pieterse, Carter, Evans, &
Walter, 2010; Wei, Wang, Heppner, & Du, 2012), high blood pressure
(Harrell, Hall, & Taliaferro, 2003; Steffen & Bowden, 2006), and educational per-
formance (Reynolds, Sneva, & Beehler, 2010; Solo
´rzano et al., 2000; Yosso et al.,
2009). Because of the wide-ranging effects of microaggressions, it is important
to have brief scales that can be included in large survey studies.
Though there is a substantial body of research documenting the harmful
effects of overt racism on mental health across several racial-ethnic groups
(Paradies, 2006), fewer studies have tested the effects of more subtle micro-
aggressions. Four studies that explicitly tested the link found that microag-
gressions were positively associated with depressive symptoms (Huynh,
2012; Lambert et al., 2009; Nadal, Griffin, Wong, Hamit, & Rasmus, 2014;
Torres, Driscoll, & Burrow, 2010). The general finding across these studies
is that microaggressions evoke powerful emotional reactions and an increase
in perceived stress, which is detrimental to depressive symptoms and mental
health in general. Two proposed mechanisms that may link microaggres-
sions to depressive symptoms are perceptions of lack of control over
one’s outcomes and internalization of others’ negative opinion (Lambert
et al., 2009).
Keels et al.
6
Role of Racial-Ethnic Identity in Coping With Microaggressions
We expect that exposure to microaggressions will have direct and indi-
rect effects on depressive symptoms and will be mediated in part by racial-
ethnic identity. Racial-ethnic identity beliefs are cognitions and attitudes
regarding the importance and meanings of racial-ethnic group membership
(Sellers, Chavous, & Cooke, 1998). Racial-ethnic identity is a fundamental
aspect of human development that facilitates the process by which individ-
uals perceive, interpret, and cope with racial-ethnic experiences in their
daily lives (Spencer, 2006; Williams, Tolan, Durkee, Francois, & Anderson,
2012). Racial-ethnic regard represents the affective dimension of racial-
ethnic identity and captures positive and negative feelings about one’s
racial-ethnic group. Private regard represents an individual’s attitudes toward
their own racial-ethnic group and feelings about their racial-ethnic group
membership. Public regard represents an individual’s perceptions of how
others view their racial-ethnic group. Research suggests that positive feelings
about one’s racial-ethnic group (high private regard) and recognition of neg-
ative societal perceptions of one’s racial-ethnic group (low public regard)
may protect Black and Latinx college students from the negative mental
health repercussions of experiencing racial-ethnic discrimination (Sellers et
al., 1998). Empirical evidence suggests that public regard captures a great
deal of variability in one’s sensitivity to detect racial-ethnic discrimination,
particularly subtle or ambiguous events, and explains affective and physio-
logical responses to racial-ethnic discrimination (Hoggard, Jones, & Sellers,
2017; Neblett & Roberts, 2013). Private regard is more closely associated
with the internalization of discrimination and is linked to outcomes such
as depressive symptoms (Neblett, Cooper, Banks, & Smalls-Glover, 2013;
Seaton, Yip, & Sellers, 2009). The present study is one of the first to examine
the mediating roles of public and private regard across multiple contexts,
specifically, Black and Latinx students transitioning from high school to col-
lege, many of whom transitioned from segregated, minority-serving high
schools to predominantly White universities.
We hypothesized that microaggressions experienced during high school
would be associated with greater depressive symptoms at the start of college
and have a longitudinal influence on depressive symptoms at the end of
their first year. We also predicted that prior academic achievement (high
school GPA) and racial-ethnic identity (public and private racial-ethnic
regard) would mediate the relationship between microaggressions and
depressive symptoms. Based on previous evidence, we expected that micro-
aggressions experienced during high school would have a negative associa-
tion with high school GPA and racial-ethnic identity, which would then be
associated with greater depressive symptoms at the start of college and carry
a lasting effect through the end of first-year (Rivas-Drake et al., 2014; Sellers
& Shelton, 2003). Greater exposure to microaggressions during high school
School-Based Microaggressions
7
may stunt students’ academic, emotional, and identity development, leading
them to enter college less prepared for both academic and social adjust-
ments. It is important to note an alternative hypothesis: Greater exposure
to microaggressions during high school may be positively associated with
first-year GPA and negatively associated with depressive symptoms. This
could occur if experiencing microaggressions in high school prepares minor-
ity youth for navigating college contexts that are not always welcoming and
supportive of racial-ethnic minority students.
Method
Participants and Procedures
Data come from the Minority College Cohort Study, a longitudinal inves-
tigation of Black (N= 221) and Latinx (N= 312) students who began college
in fall 2013. Students were recruited from five historically White universities
in the Midwest: 24% recruited from two urban private institutions that were
8% and 4% Black and 17% and 13% Hispanic, 35% recruited from an urban
public institution that was 8% Black and 26% Hispanic, 28% recruited from
a rural public institution that was 16% Black and 14% Hispanic, and 13%
recruited from a suburban public institution that was 5% Black and 9%
Hispanic. Administrators at each of the five universities distributed an e-
mail containing a description of the research study and a link to the online
survey during September of the 2013–2014 academic year. Students then
went to the online survey, provided informed consent, and completed
a screening questionnaire. To qualify, students had to be enrolled as
a full-time and first-time first-year student and primarily identify as African
American/Black or Hispanic/Latinx (including multiracial students who pri-
marily identify as either Black or Latinx). Across each institution, we
recruited approximately 11% to 35% of all eligible students.
Participants graduated from 255 different high schools, including 203
public high schools (86% of the sample). Approximately 75% of Black and
57% of Latinx participants were women; this is reflective of the gender
imbalance in college enrollment in the United States (Snyder & Dillow,
2015). Only 8% of the sample was foreign-born: 6% of Black and 10% of
Latinx students. However, 57% of the sample had at least one foreign-born
parent: 25% of Black and 81% of Latinx students. The mean age of the sam-
ple at recruitment was 18.2 years old (SD = 0.45). Forty-eight percent of
Black students and 69% of Latinx students were first-generation college
students.
Six waves of data collection took place during the first two years after
enrollment: Waves 1 and 4 occurred during the initial months of the fall
term, Waves 2 and 5 occurred shortly after winter break, and Waves 3 and
Keels et al.
8
6 occurred at the close of each academic year. For each wave of data collec-
tion, participants were e-mailed an individualized link to the online survey.
The fall and spring waves of data collection took approximately 45 minutes
to complete, and participants were compensated with a $25 electronic gift
card. The winter waves of data collection took approximately 15 minutes
to complete, and participants were compensated with a $15 electronic gift
card. Data collection was managed using REDCap software tools hosted at
the University of Chicago (P. A. Harris et al., 2009). Participant retention
for each wave of data collection was above 90%. Participants remained in
the study and were surveyed regardless of changes in college enrollment.
The host institution’s Institutional Review Board approved all study proce-
dures. This article focuses on data from Wave 1 (fall of first year) and
Wave 3 (just after the end of first year, 92% retention rate).
Measures
School-Based Racial and Ethnic Microaggressions
The SB-REMA scale was developed to capture racial-ethnic microaggres-
sions within educational settings, including microinvalidations (‘‘People on
campus acted as if all of the people of my race/ethnicity are alike’’), micro-
insults (‘‘I have been made to feel like the way I speak is inferior in the class-
room because of my race/ethnicity’’), and microassaults (‘‘I was singled out
by police or security people because of my race/ethnicity’’). The initial pool
of items was based on the RMAS (Torres-Harding et al., 2012) and an unpub-
lished measure used as part of a qualitative and quantitative examination of
racial microaggressions at a large historically White university (Harwood,
Huntt, Mendenhall, & Lewis, 2012). Harwood and colleagues’ (2012) survey
items were developed from a qualitative examination of a diverse sample of
81 racial-ethnic minority students using a semistructured interview protocol
adapted from Sue et al. (2007). We selected items from these two scales
based on high factor loadings and strong face validity. When the two scales
had overlapping items, we retained the item with the strongest face validity.
Items from these scales that did not focus on the school context, such as ‘‘I
receive poorer treatment in restaurants and stores because of my race,’’ were
dropped. For Black students, ‘‘because of your race’’ was used, and for
Latinx students, ‘‘because of your ethnicity’’ was used. The final list of 15
items was randomized and included, as a block, in the larger survey of
over 150 items.
At Wave 1, participants were asked to indicate ‘‘how often the following
things occurred during high school,’’ and at Wave 3, they were asked to report
‘‘how often the following things occurred over first-year.’’ A 4-point Likert
scale was utilized (1 = never,2=rarely,3=sometimes/a moderate amount,
4=often/frequently, and ‘‘refuse’’). More than 60% of all participants reported
that they never experienced 10 of the 15 items, and a very low proportion
School-Based Microaggressions
9
experienced the items often/frequently. Therefore, responses were recoded
and dichotomized to indicate whether each microaggression was experienced
at all (1) or never experienced (0),during the specified period. Previous racial-
ethnic microaggression scales have similarly found that participants reported
low frequencies, necessitating the creation of ordered categorical variables
or other transformations to best reflect the observed response distribution
(Mercer et al., 2011; Torres-Harding et al., 2012).
Race-Ethnicity
Participants were asked to select ‘‘all that apply’’ from a list of over 20
racial-ethnic groups, including the option to write in their own response. Of
the participants who self-identified as Black in the screening questionnaire,
we excluded 14 who selected a response such as ‘‘Filipino’’ or ‘‘Native
Hawaiian’’ and did not select a Black racial-ethnic group/country of origin
(African American, Caribbean, etc.) or refused to provide this information.
Of the participants who self-identified as Latinx in the screening question-
naire, we excluded 57 who selected a response such as ‘‘Japanese’’ or
‘‘European American’’ and did not select a Latinx racial-ethnic group/country
of origin (Mexican, Puerto Rican, etc.) or refused to provide this information.
Due to the limited sample size within each reported ethnic group, participants
from similar racial-ethnic origins were collapsed together and grouped as
either Black or Latinx in all analyses.
High School GPA
Cumulative high school GPA was self-reported at Wave 1. Ten students
reported a GPA that was slightly higher than 4.0, and their responses were
recoded to 4.0. The range of high school GPA was 2.0 to 4.0 with a mean
of 3.55 (SD = .39).
First-Year GPA
Academic performance was collected at Waves 2 and 3, and a small por-
tion of the sample with missing data provided this information at Wave 4
(n= 36). At each time point, participants were asked, ‘‘Please list all of the
courses that you were enrolled in this last academic term and your final
grade, even if you dropped a course.’’ The mean and median number of
completed courses after each academic term was approximately 4. For
each course that was listed, participants indicated whether they dropped
the course, received an incomplete grade, received a pass or fail grade, or
received a final letter grade ranging from 1 (A1) to 13 (F). Responses
from each course with a final letter grade were reverse coded and trans-
formed to a GPA scale ranging from 0 to 4; all course grades were averaged
Keels et al.
10
together to create a cumulative first-year GPA that ranged from .31 to 4.0
with a mean of 3.12 (SD = .62).
Depressive Symptoms
Depressive symptoms were measured using the Harvard Department of
Psychiatry/National Depression Screening Day Scale (HANDS; Baer et al.,
2000). The HANDS was developed as a brief 10-item screening scale for
depression. Participants indicated frequency of depressive symptoms over
the past 2 weeks using 9 items of the HANDS scale. A question regarding sui-
cidality was omitted given the sensitive nature of the question. Items used
a 4-point scale that ranged from none or a little bit of the time to all of the
time. Sample items include ‘‘had poor appetite’’ and ‘‘been feeling hopeless
about the future.’’ Items indicated high internal reliability at both Wave 1
(Black students a= .92; Latinx students a= .91) and Wave 3 (Black students
a= .93; Latinx students a= .94).
Racial-Ethnic Identity
Public and private regard were measured using two subscales from the
Multidimensional Inventory of Black Identity–Short (MIBI-S; Martin, Wout,
Nguyen, Gonzalez, & Sellers, 2013). Items used a 7-point scale that ranged
from strongly disagree to strongly agree. The private regard subscale consists
of three items measuring the extent to which respondents assess their own
racial-ethnic group positively or negatively, such as ‘‘I am happy that I am
Black/Latino.’’ Items indicated high internal reliability at both Wave 1
(Black students a= .81; Latinx students a= .88) and Wave 3 (Black students
a= .85; Latinx students a= .90). The public regard subscale consisted of four
items measuring the extent to which respondents believed that society
viewed their racial-ethnic group positively or negatively, such as ‘‘in general,
others respect Black/Latino people.’’ Items indicated high internal reliability
at both Wave 1 (Black students a= .92; Latinx students a= .91) and Wave 3
(Black students a= .92; Latinx students a= .92).
High School Percent White
At Wave 1, participants provided the name and location of the high
school from which they graduated. This information was used to obtain
high school composition data from the National Center for Education
Statistics Common Core of Data. These data contain detailed demographic
information on public and private secondary schools within the United
States. Demographic data were obtained from the 2012–2013 school year
(when participants were high school seniors). A total of 255 high schools
were represented in the sample, and demographic data were available
and obtained for 250 high schools.
School-Based Microaggressions
11
Gender
Participants self-reported their gender as male, female, or transgender.
Two students who identified as transgender were excluded from analysis.
First-Generation Student Status
Students with non–college educated parents were identified as first-
generation college students (1), and students with at least one college-
educated parent were identified as not first-generation college students (0).
Financial Distress
Previous analyses of these data revealed the importance of controlling
for financial distress when examining GPA and depressive symptoms
(Keels, 2015). Three questions were used to measure students’ level of finan-
cial distress: (1) ‘‘How much difficulty, if any, are you having paying your
bills,’’ (2) ‘‘how upset or worried are you because you do not have enough
money to pay for things,’’ and (3) ‘‘how concerned do your current financial
conditions make you about the chances you can afford to complete your col-
lege degree.’’ Items used a 5-point scale that ranged from no difficulty at all/
not upset or worried at all/not at all concerned at one end of the scale to tre-
mendous amount of difficulty/extremely upset or worried/extremely con-
cerned at the other end of the scale. Items indicated good internal
reliability at both Wave 1 (Black students a= .78; Latinx students a= .80)
and Wave 3 (Black students a= .82; Latinx students a= .82).
Results
Scale Development
Exploratory Factor Analysis
Exploratory factor analysis (EFA) using principal component analysis
with a promax rotation was used with the Wave 1 data. The percentage of
students that reported experiencing each microaggression is shown in
Supplementary Table S1 in the online version of the journal. Because items
were bivariate and not normally distributed, the EFA was estimated in STATA
14 using a polychoric correlation matrix and promax rotation (Holgado-
Tello, Chaco
´n-Moscoso, Barbero-Garcı
´a, & Vila-Abad, 2009). Items with fac-
tor loadings of .45 or greater were included (Tabachnick & Fidell, 2007), and
items with cross-loadings of .50 or above on more than one component were
removed (Osborne & Costello, 2005). These analyses resulted in a three-
factor solution (we tested up to a five-factor solution, and the strongest factor
loadings with no cross-loadings occurred with the three-factor solution).
One item (‘‘People asked where I am from suggesting that I don’t belong’’)
Keels et al.
12
was removed from the three-factor solution because its highest factor load-
ing was only .31. The factor loadings are shown in Table 1.
The three-factor solution explained 24.1% of the scale’s variance, and
each of the three factors explained 9.2%, 7.7%, and 7.2% of the total vari-
ance, respectively. The first factor, named Academic Inferiority, measured
experiences of discouragement at school, being made to feel intellectually
inferior, feeling that one’s classroom contributions were minimized, and feel-
ing isolated at school because of one’s race-ethnicity. The second factor,
named Expectations of Aggression, measured others acting as if scared,
assumptions that they would behave aggressively, and being singled out
by police or security because of one’s race-ethnicity. The third factor, named
Stereotypical Misrepresentations, measured denial of individuality, denial of
racial obstacles, and being exoticized because of one’s race-ethnicity. At
each wave, all factors were significantly positively correlated with each
other, and the intercorrelations increased from Waves 1 to 3 (Wave 1: r=
.54–.64; Wave 3: r= .64–.70).
Confirmatory factor analysis. The three-factor solution resulting from
the EFA with Wave 1 data was entered into a confirmatory factor analysis
(CFA) using Wave 3 data. Model fit statistics indicated that the three-factor
solution fit the data well at Wave 3, x2(68) = 137, p\.001, root mean square
Table 1
Factor Loadings From Exploratory Factor Analysis
Wave 1
Variable
Academic
Inferiority
Expectations
of Aggression
Stereotypical
Misrepresentations
Discouragement .918 .060 –.003
Intellectually .900 .092 –.022
Excluded .889 .173 –.111
Minimized .874 .124 .038
Speak .838 .057 .142
Isolation .892 –.009 .081
Segregated .758 –.161 .282
Scared .030 .908 .051
Aggressive .093 .839 .097
Police/security .125 .592 .233
Alike .047 .402 .541
Obstacles .128 .289 .561
Exotic .059 .023 .794
Sexual .037 .074 .830
Note. Bold numbers indicate the items that comprise each subscale.
School-Based Microaggressions
13
error of approximation (RMSEA) = .050, 90% CI [.038, .062]. A RMSEA value
less than .08 suggests that the model is a close fit to the data (Hu & Bentler,
1999). Three fit indices were also examined and demonstrated good model
fit: Comparative Fit Index (CFI) = .981, Tucker-Lewis Index (TLI) = .974, and
standardized root mean square residual (SRMR) = .027. Generally, CFI and
TLI values closer to 1.00 and SRMR values closer to zero suggest good model
fit (Hu & Bentler, 1999; Steiger, 2007).
Factor structure across demographic subgroups. The final step in sub-
scale development was to test the factor structure separately for Black and
Latinx students and separately for men and women to assess whether the
factors were comparable for each group. The goodness of fit for the three-
factor model for Black students was good with Wave 3 data, x2(68) = 96,
p= .014, RMSEA = .047, 90% CI [.022, .068], and the fit indices also indicated
a good level of fit (CFI = .982, TLI = .976, SRMR = .039). Model fit statistics for
Latinx students were also good with Wave 3 data, x2(68) = 142, p\.001,
RMSEA = .070, 90% CI [.053, .086], and the fit indices indicated a good level
of fit as well (CFI = .962, TLI = .949, SRMR = .036).
The goodness of fit for the three-factor model for women was good with
Wave 3 data, x2(68) = 116, p\.001, RMSEA = .052, 90% CI [.035, .068], and
the fit indices indicated a good level of fit (CFI = .978, TLI = .970, SRMR =
.033). Model fit statistics for men were adequate with Wave 3 data,
x2(68) = 144, p\.001, RMSEA = .089, 90% CI [.069, .109], and the fit indices
indicated an acceptable level of fit (CFI = .951, TLI = .935, SRMR = .035). Due
to sample size limitations, we did not test the factor structure separately for
each race-ethnicity by gender subgroup; Black men were our smallest group
(n= 56).
Internal consistency. Academic Inferiority items indicated high internal
reliability at both Wave 1 (Black students a= .92; Latinx students a= .92)
and Wave 3 (Black students a= .92; Latinx students a= .92). Expectations
of Aggression items indicated strong internal reliability at both Wave 1
(Black students a= .80; Latinx students a= .82) and Wave 3 (Black students
a= .78; Latinx students a= .88). Stereotypical Misrepresentations items indi-
cated moderate internal reliability at both Wave 1 (Black students a= .78;
Latinx students a= .77) and Wave 3 (Black students a= .71; Latinx students
a= .74). Descriptive statistics for each subscale are shown in Table 2.
Differential Exposure to SB-REMA
Hypothesis 1: Reports of exposure to microaggressions would differ by students’
race-ethnicity, high school racial-ethnic composition, and the interaction
between these two factors.
Keels et al.
14
Racial-ethnic group and school-level differences for Academic Inferiority
and Expectations of Aggression microaggressions are shown in the figures
and discussed in the text; Stereotypical Misrepresentation microaggressions
are shown in the supplementary figures in the online version of the journal.
Academic Inferiority and Expectations of Aggression had the strongest reli-
ability, validity, and associations with racial-ethnic attitudes, GPA, and
depressive symptoms. As shown in Figure 1 (Panels A and C), there was
a main effect of race-ethnicity in high school. Black students reported signif-
icantly higher levels of microaggressions than Latinx students (Academic
Inferiority: 2.4 vs. 1.7, p=.002; Expectations of Aggression: 1.7 vs. 0.8,
p\.001). There was an interaction between student race-ethnicity and
high school percent White. Black students reported significantly different
levels of microaggressions based on school percent White, whereas Latinx
students’ reports of microaggressions did not differ by school percent
White. The mean level of Academic Inferiority microaggressions for Black
students in predominantly White, diverse, and predominantly non-White
schools was 4.1, 3.0, and 1.9, respectively (p= .002); the mean level for
Latinx students in predominantly White, diverse, and predominantly non-
White schools was 1.9, 1.7, and 1.5, respectively (p= .689).
Black students reported significantly higher levels of Academic
Inferiority microaggressions than Latinx students in mostly White and
diverse high schools. However, in mostly non-White high schools, there
was no significant difference between Black and Latinx students. In contrast,
the Black-Latinx gap was always significant for Expectations of Aggression
microaggressions, regardless of school composition. The Black and Latinx
averages for Academic Inferiority microaggressions in mostly non-White
high schools was 1.9 versus 1.5 (p= .412); the averages for Expectations
of Aggression was 1.5 versus 0.9 (p= .001).
Table 2
Descriptive Statistics for Each Subscale
Black Latinx
Subscale Mean SD Mean SD Range
Wave 1
Academic Inferiority 2.41 2.72 1.65 2.39 0–7
Expectations of Aggression 1.72 1.23 0.81 1.14 0–3
Stereotypical Misrepresentations 2.41 1.42 1.96 1.48 0–4
Wave 3
Academic Inferiority 2.95 2.79 2.05 2.61 0–7
Expectations of Aggression 1.49 1.22 0.75 1.16 0–3
Stereotypical Misrepresentations 2.39 1.34 2.06 1.43 0–4
School-Based Microaggressions
15
Figure 1. Exposure to microaggressions, by race-ethnicity and school percent White.
Note. Error bars represent 95% confidence interval.
16
Figure 1 (Panels B and D) shows that students’ reports of microaggres-
sions over the first year were context dependent. For both Black and Latinx
students, the highest levels of Academic Inferiority and Expectations of
Aggression microaggressions were reported by those attending a predomi-
nantly White, medium-sized university in a predominantly White suburban
city. Reports from students at all other universities were not significantly dif-
ferent from each other. There was also a main effect of race-ethnicity, with
higher levels of microaggressions reported by Black students compared to
Latinx students (Academic Inferiority: 3.0 vs. 2.1, p=.001; Expectations of
Aggression: 1.5 vs. 0.8, p\.001).
Figure 2 (Panel A) shows that students discriminated changes in their
exposure to SB-REMA as they transitioned from high school to college, in
accordance with the racial-ethnic composition of their schools. Students
who attended predominantly non-White high schools reported a significant
increase in academic inferiority microaggressions when they transitioned to
college (Black students: 2.0 vs. 3.0, p\.001; Latinx students: 1.6 vs. 2.3, p=
.019). Interestingly, Black students who attended predominantly White high
schools reported an insignificant decrease in academic inferiority microag-
gressions when they transitioned to college (4.0 vs. 3.5, p=.491). More
data are needed to accurately estimate this difference; only 14 Black students
attended predominantly White high schools. Latinx students who attended
predominantly White high schools reported no change in microaggressions
when they transitioned to college. Both Black and Latinx students who
attended diverse high schools reported no change in microaggressions
when they transitioned to college.
Figure 2 (Panel B) shows that for Expectations of Aggression microaggres-
sions, only Black students who attended diverse high schools reported a signif-
icant change when they transitioned to college. These students reported the
highest level of these microaggressions in high school and a significant
decrease in exposure over the first year of college (2.1 vs. 1.5, p\.001).
Path Analysis
Hypothesis 2: Academic Inferiority microaggressions experienced during high
school would be associated with greater depressive symptoms at the start of
college and have a longitudinal influence on depressive symptoms at the
end of the first year of college.
Hypothesis 3: Racial-ethnic identity (public and private racial-ethnic regard) would
mediate the relationship between microaggressions and depressive symptoms.
We used structural equation models (SEM) in Stata 14 to test our study
hypothesis regarding the direct and indirect effects of exposure to microag-
gressions on academic achievement and mental health. We conducted all
analyses using maximum likelihood with missing values, also known as
School-Based Microaggressions
17
full information maximum likelihood estimations, to avoid listwise deletion
and retain all possible data points. Analysis used robust standard error
adjustments to account for the clustering of students at the five universities.
We then examined goodness-of-fit statistics to determine acceptability of
model fit, including chi-square (x2), RMSEA, CFI, and TLI (Kline, 2015).
We focused on Academic Inferiority microaggressions for modeling the
effects of microaggressions because it measures issues that are most ger-
mane to educational settings. Additionally, Expectations of Aggression
microaggressions did not yield an effect above and beyond Academic
Inferiority microaggressions and were thus excluded from final models.
The lack of significant correlations between microaggressions and GPA
meant that only the mediated effects of microaggressions on depressive
Figure 2. High school to first-year of college change in exposure to microaggres-
sions, by race-ethnicity.
Note. Bold lines indicate significant difference at p\.05.
Keels et al.
18
symptoms were modeled. Intercorrelations between all study variables are
shown in Supplementary Table S2 in the online version of the journal. We
used path analysis to examine mediating factors and longitudinal implica-
tions (Kline, 2015). Models controlled for high school percent White, gender,
first-generation college status, financial distress, and on-campus residence.
Figure 3 shows the model for Black students. The model fit the data
well, x2(39) = 42.66, p=.317, RMSEA = .021, 90% CI [.000, .054], CFI =
.988, TLI = .980. This model accounted for 31% and 34% of the variance in
depressive symptoms at the start of college and at the end of first-year,
respectively. Academic Inferiority microaggressions during high school
were linked to greater depressive symptoms at the start of college (b=
.22, p= .001), which resulted in a significant indirect effect on depressive
symptoms at the end of first-year (b= .17, p\.001). High school GPA
and racial-ethnic private regard partially mediated the relationship between
Academic Inferiority microaggressions and depressive symptoms at the start
of college, and this indirect effect was significant (b= .04, p= .005).
Academic Inferiority microaggressions during high school were associated
with lower high school GPA (b= –.18, p\.001) and less racial-ethnic private
regard (b= –.10, p\.001). In return, high school GPA and racial-ethnic pri-
vate regard were associated with less depressive symptoms at the start of col-
lege (b= –.12, p= .047 and b= –.23, p\.001, respectively). Racial-ethnic
private regard at the start of college also yielded an indirect effect on
Figure 3. Path model showing direct and indirect effects, Black Students.
Note. Standardized coefficients are shown, and standard errors are in parentheses.
*p\.05. **p\.01. ***p\.001.
School-Based Microaggressions
19
depressive symptoms at the end of first-year (b= –.16, p\.001). Academic
Inferiority microaggressions experienced during first-year were not associ-
ated with depressive symptoms at the end of first-year.
Figure 4 shows the model for Latinx students. The model fit the data well,
x2(42) = 53.49, p= .110, RMSEA = .033, 90% CI [.000, .057], CFI = .966, TLI =
.948. This model accounted for 18% and 32% of the variance in depressive
symptoms at the start of college and at the end of first-year, respectively.
Academic Inferiority microaggressions during high school were linked to
greater depressive symptoms at the start of college (b=.11,p=.014),which
influenced depressive symptoms at the end of first-year through a significant
indirect effect (b=.12,p= .001). Racial-ethnic public regard partially mediated
the relationship between Academic Inferiority microaggressions and depres-
sive symptoms at the start of college. Academic Inferiority microaggressions
during high school were associated with lower racial-ethnic public regard
(b=–.21,p\.001), which was in turn associated with less depressive symp-
toms at the start of college (b=–.19,p= .019). Racial-ethnic public regard at
the start of college also yielded an indirect effect on depressive symptoms at
the end of first-year (b=–.15,p\.001). Academic Inferiority microaggres-
sions experienced during first-year were associated with depressive symptoms
at the end of first-year (b=.07,p\.001); however, mediation effects through
public regard and GPA did not occur during first-year.
Figure 4. Path model showing direct and indirect effects, Latinx students.
Note. Standardized coefficients are shown, and standard errors are in parentheses.
*p\.05. **p\.01. ***p\.001.
Keels et al.
20
Discussion
This study provides longitudinal evidence that racial-ethnic minority stu-
dents are reliable reporters of their experiences of racial-ethnic microaggres-
sions and detect shifts in climate as they transition from one educational
context to another. The SB-REMA was found to be a reliable and valid mea-
sure of students’ experiences of racial and ethnic microaggressions. The
items and subscales cover a broad range of thematic categories of microag-
gressions identified in the literature (Sue et al., 2007; Wong et al., 2014) and
relate these categories to educational contexts. The items used were worded
to assess microaggressions that may occur across many racial-ethnic groups
due to similarities that arise from being a stigmatized and devalued racial-
ethnic group in the United States (Sue, 2010). Exploratory and confirmatory
factor analyses were used to identify a scale that included 14 microaggres-
sions, categorized into three subscales: Academic Inferiority, Expectations
of Aggression, and Stereotypical Misrepresentations. The intercorrelations
among the subscales show that they are related yet distinct enough to be
examined separately.
The factor structure of the SB-REMA was consistent across Blacks and
Latinxs and across men and women, suggesting that the subscales are reli-
able across several demographic subgroups within the sample. As with other
scales measuring racism and discrimination, the pattern of mean differences
varied across race-ethnicity, indicating that exposure to microaggressions
and specific themes of microaggressions are more salient for some racial-
ethnic groups than for others (Torres-Harding et al., 2012). Unexamined in
the existing literature is the extent to which exposure to microaggressions
is associated with contextual demographics. We found that minority stu-
dents’ exposure to microaggressions is contingent on both race-ethnicity
and demographic context. Overall, Black students reported a higher likeli-
hood of experiencing microaggressions, across each subscale, compared
to Latinx students. However, the main effect of race-ethnicity was condi-
tioned by the demographic characteristics of the student body and the sub-
scale examined. For example, Black but not Latinx students reported higher
levels of microaggressions as the percent of the student body that was White
increased.
Through our longitudinal analyses, we were able to show that minority
students’ exposure to microaggressions changes as they move from one
demographic context to another. For example, students transitioning to col-
lege from mostly non-White high schools reported an increase in exposure
to Academic Inferiority microaggressions; correspondingly, students transi-
tioning from mostly White high schools reported no change in exposure.
This indicates that students are able to discriminate changes in microaggres-
sions across contexts. This longitudinal examination provides an important
addition to the literature because existing research has either examined
School-Based Microaggressions
21
youth in high school or college; we are unaware of microaggression litera-
ture that has followed students as they transitioned from one educational
context to another (Huynh, 2012; Hwang & Goto, 2008).
Most important, the path analyses showed that racially ethnically hostile
educational contexts are detrimental for students’ academic achievement
and mental health. Though much of the current attention is on microaggres-
sions in college contexts, our findings suggest that more attention should be
focused on racial stressors in primary and secondary schools (Benner &
Graham, 2011; Farkas, 2003; Hope, Skoog, & Jagers, 2015). Perhaps college
students’ greater autonomy in structuring their exposure to campus and
increased efficacy to call out microaggressive experiences blunt its negative
effects. This is in contrast to the argument that minority students’ increasing
public reporting of microaggressions is an unnecessarily oversensitive
response to incidents that should be overlooked, silently pardoned, and
quickly forgiven (Campbell & Manning, 2015). Research suggests that minority
students’ insistence on calling out each act of microaggression may be exactly
what American society needs to move past the current post–Civil Rights stale-
mate (Kawakami, Dunn, Karmali, & Dovidio, 2009). For microaggressions to
decrease, minority students need to self-disclose their experiences of microag-
gressions in ways that elicit individuation and build empathy—in effect, per-
sonalize majority individuals’ understanding of microaggressions (Ensari,
Christian, Kuriyama, & Miller, 2012; Zembylas, 2012). This must be accompa-
nied by support from allied bystanders: ‘‘Although people anticipate feeling
upset and taking action upon witnessing a racist act against an out-group,
they actually respond with indifference’’ (Kawakami et al., 2009, p. 278).
Racial-ethnic private and public regard at the beginning of college par-
tially mediated the detrimental consequences of academic inferiority micro-
aggressions during high school and were associated with less depressive
symptoms. We were surprised to find that these aspects of racial-ethnic iden-
tity functioned differently for Black and Latinx students. This is contrary to
meta-analytic research that has found no racial-ethnic differences in the
association between positive racial-ethnic affect and psychological adjust-
ment, including depressive symptoms (see Rivas-Drake et al., 2014).
Consistent with previous research examining the mediating role of private
regard among Black college students (Neblett et al., 2013), we found private
regard, an internally sponsored affective response, to be an important iden-
tity asset that helped blunt the negative effects of microaggressions on
depressive symptoms. For Latinx students, it was public regard, an externally
sponsored affective response, that helped blunt the negative effects of
microaggressions on depressive symptoms. It is important to note that
Latinx students reported higher public regard than Black students, indicating
that they were more optimistic about how others viewed their racial-ethnic
group.
Keels et al.
22
Limitations and Future Research
Some limitations should be considered when interpreting our findings.
Because the study only assessed self-reported experiences with microaggres-
sions, we have no externally validated measure of the frequency of micro-
versus macroaggressive insults within school contexts (Nadal, 2011). There
is no easy solution to this problem; however, future validation efforts with
this scale can assess its correlation with other scales measuring racial-ethnic
discrimination. The regional nature of our sample limits the generalizability
of the findings. The Midwest has its own history of racial-ethnic discrimina-
tion and segregation that may not generalize to the experiences of students
in other regions (Torres-Harding et al., 2012).
Additionally, though the Academic Inferiority subscale was the strongest
factor and aligns with the issues that are most germane to educational set-
tings, there is an open question regarding the applicability of this subscale
to Asian Americans, who are more likely to experience educational stereo-
types associated with ascriptions of intelligence (Sue et al., 2009). In this
regard, it is important to highlight that non-racial-ethnic–based microaggres-
sions are also prevalent in educational settings, such as those based on gen-
der (Capodilupo et al., 2010; Nadal, 2010), sexual orientation (Mccabe,
Dragowski, & Rubinson, 2013; Nadal, Issa, et al., 2011; Platt & Lenzen,
2013; Shelton & Delgado-Romero, 2011), immigration status (Keels &
Rusin, 2016; Nadal, Mazzula, Rivera, & Fujii-Doe, 2014), disability (Keller
& Galgay, 2010), and religious affiliation (Nadal, Issa, Griffin, Hamit, &
Lyons, 2010), to name a few. This indicates the need for the development
of a diverse set of survey instruments.
Despite these limitations, our finding that students differentiate between
more versus less racially ethnically hostile contexts has implications for prac-
tice. Educators can use the SB-REMA scale to assess the extent to which stu-
dents of various racial-ethnic groups experience the school climate as
hostile. This could be useful for producing actionable data at both the class-
room and school levels. The SB-REMA could also be a tool to assist school
counselors in facilitating discussions of distressing racial-ethnic experiences
with minority students (Pieterse et al., 2010).
Note
The research reported in this article was supported by a Scholar’s award from the
William T. Grant Foundation to the first author (Grant 180804). We gratefully acknowledge
the support of several graduate and undergraduate students at the University of Chicago
who engaged with us on this work. We would also like to thank the anonymous reviewers
of this manuscript, whose feedback strengthened the clarity of our arguments.
School-Based Microaggressions
23
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Manuscript received August 26, 2016
Final revision received May 26, 2017
Accepted June 18, 2017
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