Childhood socioeconomic status and risk in 
early family environments: predictors of 
global sleep quality in college students
Authors: Cory J. Counts, Fiona C. Grubin, Neha A. 
John-Henderson
NOTICE: this is the author’s version of a work that was accepted for publication in Sleep Health. 
Changes resulting from the publishing process, such as peer review, editing, corrections, 
structural formatting, and other quality control mechanisms may not be reflected in this document. 
Changes may have been made to this work since it was submitted for publication. A definitive 
version was subsequently published in Sleep Health, vol. 4, issue 3, June 2018, DOI# 10.1016/
j.sleh.2018.02.003
Counts, Cory J. , Fiona C. Grubin, and Neha A. John-Henderson. "Childhood socioeconomic 
status and risk in early family environments: predictors of global sleep quality in college students." 
Sleep Health (March 2018). DOI: 10.1016/j.sleh.2018.02.003.
Made available through Montana State University’s ScholarWorks 
scholarworks.montana.edu 
Childhood socioeconomic status and risk in early family
environments: predictors of global sleep quality in college studentsCory J. Counts, Fiona C. Grubin, BS, Neha A. John-Henderson, PhD ⁎
Montana State University, Department of Psychology, 319 Traphagen Hall, Bozeman, MT 59717, USAduring childhood, and
We used linear regres
risk in childhood fami
Results: Lower childhoo
sleep quality. Importan
as a function of childho
reported high levels of
Conclusions: Findings h
childhood as informana b s t r a c t
Objective: Low socioeconomic status (SES) in childhood associates with poor sleep quality in adulthood.
Separately, childhood family environments shape health into adulthood. Here, we investigated whether
these early life factors independently or interactively inform global sleep quality in college students.
Design: Cross-sectional.
Participants: College students at a state university (N = 391).
Measurements: As a measure of childhood SES, we asked participants to consider their families’ socioeco-
nomic standing relative to the rest of the society during their childhood. We used the Risky Family ques-
tionnaire to measure adversity and the presence of warmth and affection in the family environmentthe Pittsburgh Sleep Quality Index as a measure of current global sleep quality.
sions adjusting for age and sex to examine relationships between childhood SES,
ly environments, and global sleep quality.
d SES andgreater risk in childhood family environments independently predictedpoor
tly, in low-risk family environments, therewas no significant difference in sleep quality
od SES. However, students who were from low childhood SES backgrounds who also
risk in their early family environments had the worst sleep quality.
ighlight the importance of considering socioeconomic and family environments in
ts of sleep quality across the lifespan. Compromised sleep quality in college students
erformance and health over time.could affect academic pA large body of research documents a graded relationship be-
tween socioeconomic status (SES) and health, such that individuals
from low-SES backgrounds are worse off compared to their high-
SES counterparts.1,2 This relationship takes shape early in the
lifespan, with economic disadvantage in childhood associating with
poorer physical health across the lifespan and reduced life
expectancy.3,4 These associations are largely independent of SES in
adulthood.3 Children raised in low-SES environments are exposed
tomore physical and psychosocial stress compared to children raised
in high-SES environments5,6 and are more likely to perceive ambigu-
ous situations as a threat to their well-being.7,8
Separate from socioeconomic exposures, a growing body of litera-
ture documents an association between early life adversity and trauma
with health across the lifespan.9,10 Importantly, recent investigations
suggest that it is not only the presence of adversity or trauma in earlyenvironments which can shape health and health behaviors in endur-
ing ways but also the dynamics of the early family environment.11
Family environments with high levels of conflict and low levels of
warmth and affection can negatively impact adult health by shaping
psychosocial functioning.12,13 These family environments are referred
to as risky and associate with increased risk for ill health.11
Greater exposure to stress during the early years of a child’s life
may negatively impact the health behaviors that develop during
this time, and these patterns may in turn persist into adulthood, par-
tially explaining the enduring effect of early life experiences on
health.14,15 For example, children raised in risky families are more
likely to exhibit health-threatening behaviors including smoking, al-
cohol abuse, and drug abuse.11 Children who are exposed to stress
may rely on these strategies to manage negative emotions or to
cope in the absence of adequate emotion coping strategies.11 Sleep
quality is one such behavioral pathway through which early life ad-
versity and trauma are believed to negatively impact health.16,17
During childhood, socioeconomic adversity associates with com-
promised sleep quality,18,19 and low childhood SES predicts worse
sleep quality in adulthood, independent of adult SES.20 Furthermore,
previous research suggests that the relationship between low income
and adverse health in adulthood is in part accounted for by compro-
mised sleep quality.21,22 In a similar manner, early life adversity and
trauma associate with compromised sleep quality across the
lifespan.23,24 Specific to family environments, a prior investigation
found that conflict in early life family environments associates with
insomnia in adulthood.25
Multiple sources of early life environmental stress, such as low
SES and a risky family environment, could have a cumulative nega-
tive effect on sleep. It remains unknown whether these independent
dimensions of early life experiencemay interact to inform sleep qual-
ity, which is closely related to both physical and mental health.26,27
Although the majority of research focuses on the impact of child-
hood environments on sleep quality in adulthood, previous investiga-
tions provide evidence of an association between childhood adversity
and compromised sleep quality in college students.24,28 More re-
search in this area is needed given that the sleep quality of college
students is particularly impaired.29,30 For many students, college is
the first experience of living independently from their caregivers.
As they work to adapt to their new surroundings, experiences,
stresses, and challenges, their sleep quality is at a greater risk for dis-
turbance. It is possible that early life experiences related to SES and
family environments may inform quality of sleep during this time
of adaptation, with college students who were raised in low-SES en-
vironments who also experienced a risky family life environment
reporting the worst sleep quality. At the same time, the association
between low childhood SES and compromised sleep quality in col-
lege could beweakened by a less risky family life environment during
childhood. Prior research supports this line of reasoning, with find-
ings indicating that maternal warmth moderates the association be-
tween low SES and an immune system inflammatory phenotype (a
marker of disease risk).31 A separate investigation found that individ-
uals reporting low levels of love and affection coupled with high
levels of abuse in childhood had physiological profiles associated
with high risk for disease in adulthood.32
The present study examineswhether early life socioeconomic and
family environments independently or interactively inform global
sleep quality in college students. We predict that college students
from low socioeconomic backgrounds who were also raised in risky
family environments will report the lowest quality of sleep. This
study extends prior research examining the relationships between
adversity, SES, family environment, and sleep in important ways.
First, prior investigations have largely focused on adversity, trauma,
and conflict as shapers of sleep quality later in life. Here, we use a
measure of early life environments which considers not only the
presence of adversity or trauma but also the presence or absence of
warmth and affection in the family environment. Furthermore, we
consider the interaction between risk in early family environments
and early SES. This is important because we are able to test whether
a less risky family environment is capable of offsetting the previously
observed relationship between low SES in childhood and low sleep
quality later in life.20Methods
Participants were students enrolled in introductory psychology
courses at a large state university (N = 391). As part of their
coursework, students were asked to participate in research. Students
who elected to participate in this research completed several health-
and behavior-related questionnaires, and course credit was awarded
upon completion of the questionnaire. No exclusionary criteria were
used, and the study was made available to all students in the partici-
pant pool. All measures were approved by the university’sinstitutional review board, and informed consent was obtained
from all participants.
Measures
Subjective childhood SES
The MacArthur scale of subjective childhood SES is used to cap-
ture SES during childhood across objective SES indicators. Partici-
pants are presented with a 10-rung ladder and are asked to indicate
where they feel their family stood during their childhood relative to
other families in the United States.33,34 Scores ranged from 1 (lowest
SES) to 9 (highest SES) (M [SD] = 6.42 [1.75]). The question explains
that the top of the ladder represents those familieswithmoremoney,
education, and better jobs, whereas the bottom of the ladder repre-
sents families who were worse off, had the least amount of money
or education, and had jobs that are poorly respected or were
unemployed.
Subjective current SES
The MacArthur scale of subjective SES is used to capture an indi-
vidual’s current SES across objective SES indicators. The same l0-
rung ladder described above is used, and participants were asked to
place an “X” to indicate where they feel they currently stand relative
to others in society with regard to occupation, money, and education
(M [SD] = 6.10 [1.54]).33
Sleep quality
The Pittsburgh Global Sleep Quality Index (PSQI) was used as a
measure of participants’ global sleep quality.35 The 19-item PSQI as-
sesses sleep quality disturbances during the previous month. The
scale consists of 19 itemswhichwere used to derive a total of 7 com-
ponent scores: sleep quality, sleep latency, sleep duration, habitual
sleep efficiency, sleep disturbances, sleep medication, and daytime
dysfunction. The 7 component scores were summed to produce a
global PSQI score. Global PSQI scores (with a possible range of 0-21;
higher scores represent more severe sleep complaints) were com-
puted for each participant (M [SD] = 6.58 [3.21]). Internal consis-
tency in this sample between the 7 component scores was α= .75.
Risky family environments
The Risky Families questionnaire was used to assess participants’
exposure to physical, mental, and emotional neglect or abuse during
their adolescent and childhood years in addition to the presence or
absence of warmth and affection.36 Participants indicate how fre-
quently certain events or situations occurred in their homes during
the ages of 5-15 years using a 5-point Likert scale (1 = not at all
and 5 = very often). Example questions from this measure include
“How often did a parent or other adult in the household make you
feel that you were loved, supported, and cared for?” and “How
often would you say there was quarreling, arguing, or shouting be-
tween your parents?” Itemsmeasuring the presence of positive qual-
ities in the family environment are reverse scored, and all 10 items
are summed to capture the overall level risk in the early family
environment.
Depressive symptoms
Weused Beck’s Depression Inventory (BDI-II) as ameasure of cur-
rent depressive symptoms. The BDI-II is a 21-item questionnaire
widely used to assess subclinical and clinical depression.37 Each
item includes 4 response options. As an example, participants are
Table 2
Bivariate correlations of SES measures with variables of interest
Subjective Subjective
Childhood SES Current SES
Age −0.62 −.04
Sex (1 = male, 2 = female) −0.13⁎ −0.11⁎
Subjective childhood SES 1.0 0.52⁎⁎
Subjective current SES 0.52⁎⁎ 1.0
Objective current SES 0.55⁎ 0.27⁎⁎
Global sleep quality −0.23⁎⁎ −0.21⁎⁎
Risk in family environment −0.003 −.04
Current depressive symptoms (BDI) −.08 −.02
Positive affect (PANAS) .22⁎⁎ .20⁎⁎
Negative affect (PANAS) −.17⁎⁎ −.08
Alcohol use (1 = regular user) .05 .01
Tobacco use (1 = current user) −.01 −.01
⁎ P b .05.
⁎⁎ P b .001.asked to select which of the following 4 statements most accurately
reflects how they are have been feeling in the last 2 weeks, including
today: (0) I am not particularly discouraged about the future, (1) I
feel discouraged about the future, (2) I feel I have nothing to look for-
ward to, and (3) I feel the future is hopeless and that things cannot
improve. A total score is calculated by summing all of the responses,
and higher scores reflect more depressive symptoms.
Positive and negative affect
Participants reported how much they had experienced negative
and positive affect over the course of the past week, including
today, using 20 items (eg, enthusiastic, hostile, irritable, inspired)
on the Positive and Negative Affect Schedule (PANAS) ranging from
1 (never) to 5 (always).38
Alcohol use and smoking status
As measures of alcohol use and current smoking status, partici-
pants responded to the following questions with a yes or no: Do
you drink alcoholic beverages regularly? Do you smoke any tobacco
products regularly?
Data analysis
Analyses were conducted using SPSS (version 24; IBM, Armonk,
NY). Linear regression models were used to conduct the main analy-
ses. Continuous covariates were centered prior to use in statistical
models. In all reported linear regression models, age and sex are in-
cluded as covariates. We first examined the relationships between
subjective childhood SES andglobal sleep quality and the relationship
between risky family environments and global sleep quality. Next, to
test our hypothesis, we created an interaction term between subjec-
tive childhood SES and risk in early family environments, and exam-
ined whether this interaction term predicted global sleep quality. In
subsequent analyses, we expand our model to include current sub-
jective SES, depressive symptoms, positive and negative affect, alco-
hol use, and current smoking status.
Results
Descriptive statistics are listed in Table 1, and bivariate correla-
tions between measures of SES and main variables of interest are
listed in Table 2. The sample was 87.5% White, 4.6% mixed race,
2.8% American Indian/Alaska Native, 2.1% Hispanic/Latino/Chicano,
1.5% Asian, 2.0% other, and .3% African American. Of note, the mean
sleep quality in this sample of college students was slightly above
the threshold for poor sleep quality according to the PSQI cut-off
point N5.27Table 1
Descriptive statistics
n M (SD) % (n) Range
Race (% White) 392 87.1 (344)
Sex (% female) 392 65.1 (257)
Age 376 19.57 (3.22) 18-52
Subjective childhood SES 384 6.42 (1.75) 1-10
Subjective current SES 376 6.10 (1.54) 2-10
Global sleep quality 380 6.58 (3.21) 0-18
Depressive symptoms (BDI) 380 15.34 (5.04) 0-27
Positive affect (PANAS) 379 32.83 (7.95) 10-50
Negative affect (PANAS) 380 22.91 (7.56) 10-50
Regular alcohol users 167 43%
Current tobacco users 26 6.75%Childhood SES and global sleep quality
Controlling for age and sex, subjective childhood SES was asso-
ciated with poorer global sleep quality (β=−0.30 t[365] =−5.75,
P b .001, R2 change= 0.08).
Risky family environments and sleep quality
As predicted, adjusting for age and sex, growing up in a risky
family was associated with poor sleep quality (β= 0.41, t[360] =
8.63, P b .001, R2 change= 0.17).
Subjective childhood SES by risky family environment predicting sleep
quality
To test our prediction that childhood SES would interact with risk
in early family environments to predict global sleep quality, we
entered the subjective childhood SES measure, the risky family
measure, their interaction term, and the covariates of age and sex
into a linear regression model. In line with our hypothesis, there
was a significant interaction between risky family environments
and childhood SES in predicting global sleep quality (β=−0.12,
t[356] =−2.58, P= .01, R2 change=0.02). The pattern of this inter-
action is displayed in Figure 1. To further probe the reported interac-
tion, we conducted simple slopes analyses. Greater risk in family
environment was associated with lower sleep quality for individuals
fromboth low (1 SD belowmean) andhigh (1 SD abovemean) child-
hood SES (b = 1.65, t[359] = 6.42, P b .001 and b= 0.79, t[359] =
2 .83, P= .01, respectively). In less risky family environments (1 SD
below mean), childhood SES was not associated with global sleep
quality (b = −0.17, t[359] = −0.59, P = .56). However in high-
risk family environments (1 SD above mean), childhood SES was
associated with sleep quality such that individuals who were also
from low childhood SES backgrounds had significantly worse sleep
compared to those individuals from high childhood SES back-
grounds (b = −1.03, t[359] = −4.07, P b .001).
In a separate linear regression model, we added covariates to ex-
amine whether the observed relationships exist above and beyond
the effects of current SES, depressive symptoms, current mood, and
health behaviors, all of which are factors that are known to associate
with sleep quality.39–42 In this model, in addition to age and sex, we
added the covariates of current subjective SES, depressive symptoms
(BDI total score excluding sleep item), positive andnegative affect, al-
cohol use, and smoking status to see if the previously observed inter-
action persisted.With the addition of these covariates, the interaction
between childhood SES and early family environments remained
Fig. 1. PSQI global sleep quality as a function of degree of risk in early family environment and subjective childhood SES. Results from a linear regression analysis controlling for age
and sex.significant as a predictor of global sleep quality in this sample of col-
lege students, (β= −0.10, t[337] = −2.09, P = .04, R2 change =
0.01).Discussion
Our findings revealed an inverse relationship between sleep qual-
ity and subjective childhood SES. Lower SES during childhood associ-
ated with worse sleep quality among college students. In addition,
the data revealed a relationship between early life risk in family envi-
ronments, with greater risk associating with worse sleep quality.
However, as predicted, the association between childhood SES and
sleep quality was moderated by early life family dynamics. Partici-
pants who reported low childhood SES in addition to a risky family
environment reported the worst sleep quality as college students,
whereas students who grew up in a low-SES environmentwho expe-
rienced a less risky, more nurturing family environment reported sig-
nificantly better sleep quality as college students. Students who
reported high childhood SES and a risky family environment also
had significantly worse sleep quality compared to their peers from
similar childhood SES backgroundswho experienced a less risky fam-
ily environment. The pattern of these findings highlights the impor-
tant role of the childhood family environment as a moderator of the
relationship between childhood SES and sleep quality later in life.
Furthermore, our findings persisted with the inclusion of several
covariates that are associated with sleep. The PSQI measure of global
sleep quality associates with depressive symptoms.43 As such, we
considered the possibility that childhood SES and early family envi-
ronments were predicting depressive symptoms and not necessarily
sleep problems. In this sample, although the PSQI sleep quality mea-
sure was significantly associated with depressive symptoms, inclu-
sion of depressive symptoms as a covariate did not affect our
findings. In addition, we included current subjective SES, affect (pos-
itive and negative), alcohol use, and smoking status as covariates
given their documented associationwith sleep,39–42 and the reported
findings were not affected.
Growing up in a low-SES environment may be stressful for many
reasons. Low SES associates with greater exposure to both physical
and psychological stressors.44,45 For example, low-SES environments
are likely to havemore noise, pollution, crime, and physical crowding
than high-SES environments.45,46 In addition, low-SES environments
are linked tomore psychosocial stressors (eg, less control, unpredict-
ability, lack of financial stability) and fewer resources to manage
these stressors.44 This greater load of both physical and psychosocialstress is believed to be an important explanatory factor in the rela-
tionship between low SES and poor health outcomes.45
Previous research indicates that stress exposures early in the
lifespan can sensitize individuals to stress experienced later in
life.47–50 As such, the college experience, which presents new stressors
and challenges formost students, may be particularly stressful for indi-
viduals raised in low-SES environments. Our findings indicate that the
relationship between childhood SES and an important health behavior,
sleep, ismoderated by early life family dynamics and, furthermore, that
early family environments affect sleep quality for college students from
both high and low childhood SES backgrounds. Thismakes sense given
the robust literature documenting the enduring negative effects of
being raised in a risky family environment on social relationships, hos-
tility, and reactivity in response to conflict.11,12 As noted previously, the
observed interaction between childhood SES and childhood family en-
vironments is in line with previous research indicating that family dy-
namics can moderate the association between childhood SES and
health-relevant outcomes.31,32
There are important limitations to note. First, this work is cross-
sectional, and thus, causality cannot be inferred. Second, we did not
have an objective measure of childhood SES. The MacArthur ladder
measure of childhood SES used in this research asks individuals to
consider where their family stood during childhood compared to
other families with regard to parental occupation, income, and edu-
cation. Accumulating evidence suggests that subjective measures of
childhood SES are more closely associated with outcomes later in
life than objectivemeasures of childhood SES.51,52 Advantages of sub-
jective measures of childhood SES include allowance for consider-
ation of several indicators of SES and their ability to capture
important subtleties such as prestige of college their parents
attended or family wealth which may not be apparent in reports of
parental income. Finally, with regard to limitations regarding the
childhood SESmeasure, it is important to note that themeans for cur-
rent SES and childhood SES are fairly similar. This raises the possibil-
ity that one’s current SES biases one’s view of their past SES. In an
effort to address this limitation, we included current SES as a covari-
ate in our analyses.
Second, although the PSQI is weakly associated with objective
measures of sleep (eg, polysomnography and actigraphy),53,54 it is re-
lated to unique psychological and biological correlates55–57 and per-
haps is more closely related to the psychological correlates of sleep
disturbances than objective measures of sleep.58 Future research
should examinewhether childhood SES and early life family environ-
mentswould similarly informobjectivemeasures of sleep quality (eg,
polysomnography).
In addition, it is important to note that the samplewas 87%White,
which limits generalizability of these findings to all college students.
It remains possible that the pattern of results could differ by ethnicity
or race, and this possibility should be examined in future research.
Furthermore, although our lack of exclusionary criteria can be viewed
as a strength in that it allowed for a broad sample, it is possible that
participantswere takingmedications that could affect their sleep. Im-
portantly the PSQImeasure of global sleep quality includes a question
to assess how often individuals are taking medication to help them
sleep; however, it remains possible that medications taken for other
purposes could affect their sleep. Lastly, variability in SES in a college
sample is less compared to the general population.
Finally, given our reliance on retrospective self-report for our pre-
dictors, it is possible that the accuracy of individuals’memory of their
early life environments may be negatively affected by time, and this
effect may be particularly strong for older participants. Furthermore,
an individual’s self-esteem may influence their report of these vari-
ables. We are unable to account for this possibility because this re-
search did not include a measure of self-esteem, and this factor
should be considered in future research.
In summary, our findings highlight the potential for a positive
family environment in childhood to offset the previously observed
association between low childhood SES and poor sleep quality later
in life. They also draw attention to the increased vulnerability to
poor sleep quality for those individuals who have both low childhood
SES and a risky family environment in childhood. In general, college
students report low sleep quality, but our work suggests that early
life experiences (both socioeconomic and familial) are important pre-
dictors of sleep quality for this population. Sleep quality has both di-
rect and indirect effects on college students' academic performance
and physical and psychological health.55 The compromised sleep
quality observed for college students who experienced risky family
environments in childhood may negatively affect their social rela-
tionships and academic performance, and these effects may be most
pronounced for those students whowere also raised in a low-SES en-
vironment. If these patterns persist, the psychological and physical
health of these students may suffer. Future research should extend
upon thesefindings by examining differences in stress exposure, cop-
ing, and health behaviors that may explain the association between
early life factors and sleep quality observed here.Conflicts of interest
The authors have no conflicts of interest to disclose.References
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