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Thomas, A J et al 2016 No Child Left Alone: Moral Judgments about Parents Aect
Estimates of Risk to Children.
Collabra,
2(1): 10, pp. 1–14, DOI: http://dx.doi.org/10.1525/
collabra.33
* University of California Irvine, Department of
Cognitive Sciences, Irvine, California
† University of California Irvine, Department of
Logic and Philosophy of Science, Irvine, California
Corresponding author: Ashley J. Thomas (ashleyjt@uci.edu)
ORIGINAL RESEARCH REPORT
No Child Left Alone: Moral Judgments about Parents
Aect Estimates of Risk to Children
Ashley J. Thomas*
, P. Kyle Stanford† and Barbara W. Sarnecka*,†
In recent decades, Americans have adopted a parenting norm in which every child is expected to be under
constant direct adult supervision. Parents who violate this norm by allowing their children to be alone,
even for short periods of time, often face harsh criticism and even legal action. This is true despite the
fact that children are much more likely to be hurt, for example, in car accidents. Why then do bystanders
call 911 when they see children playing in parks, but not when they see children riding in cars? Here, we
present results from six studies indicating that moral judgments play a role: The less morally acceptable a
parent’s reason for leaving a child alone, the more danger people think the child is in. This suggests that
people’s estimates of danger to unsupervised children are aected by an intuition that parents who leave
their children alone have done something morally wrong.
Keywords: moral psychology; risk perception; moral norms
On December 20, 2014, Rafi Meitiv, age 10, and his sister
Dvora, age 6, were walking home from a park about a mile
from their home in Silver Spring, Maryland. A bystander
saw them walking and called 911 to report, quite literally,
a sighting of unaccompanied children [1]. Police picked
the children up and drove them home. When their father
told police that Rafi and Dvora had permission to walk
home from the park, the officer asked him, “Don’t you
realize how dangerous the world is? Don’t you watch
TV?” The police officer called in Child Protective Services,
who threatened to remove the children from their home
unless their father signed a ‘safety plan’ promising never
to leave the children unsupervised [2].
By letting their children walk home from the park, the
Meitivs violated a parenting norm specifying that every
child must be under direct adult supervision at all times.
As the officer’s comments suggest, this norm seems to
reflect a fear of horrific events such as children being
kidnapped by strangers. But the actual risk of a teen
or child being abducted by a stranger and killed or not
returned is estimated at around 0.00007%, or one in
1.4 million annually—a risk so small that experts call it
de minimis, meaning effectively zero [3]. Motor vehicle
accidents, by contrast, are the most common cause of pre-
ventable death among children [4]. Thus, by driving the
Meitiv children home (ostensibly to protect them from
the risk of kidnapping), police actually exposed them to
the much greater risk of being killed in a car accident.
The idea that unsupervised children are in constant
danger is relatively new. Just one generation ago, children
had much more freedom to explore their surroundings. In
the early 1970s, psychologist Roger Hart spent two years
making maps of the places that children in a rural New
England town were allowed to go by themselves. He found
that 4- and 5-year-olds were allowed to travel throughout
their neighborhoods alone, and 10-year-olds had free run
of the town [5]. Forty years later, Hart returned to the
same town and found that although the crime rate was
exactly the same, most children were now forbidden from
roaming past their own backyards [6]. Ironically, some of
the very same people who were children in the earlier
study grew up to become the parents in the later study.
How have parenting norms changed so dramatically in a
single generation?
This change is likely due in part to the availability
heuristic. That is, the easier it is for people to call to mind
examples of a phenomenon, the more frequently they
think it happens [7]. For example, heavy media coverage
of plane crashes causes many people to fear air travel far
out of proportion to its actual risks. Similarly, programs
like CNN’s Taken: Children Lost and Found feature heart-
wrenching vignettes of abducted and murdered children
along with tips on (for example) teaching three-year-olds
what to do when a kidnapper locks them in the trunk of a
car [8]. Such programs likely lead people to overestimate
the risk of child abduction to the point where they
believe that a child of any age, alone for any amount of
Thomas et al: No Child Left AloneArt. 10, page 2 of 14
time, is in grave danger. The same is true for intensive
media coverage of rare, but tragic accidents such as
children dying in house fires, or infants dying after being
forgotten in cars.
But note one key difference: The fact that many peo-
ple irrationally fear air travel does not result in air travel
being criminalized. Parents are not arrested for bringing
their children with them on airplanes. In contrast,
parents are arrested and prosecuted for allowing their
children to wait in cars, play in parks, or walk through
their neighborhoods without an adult (e.g., [9, 10, 11, 12,
13, 14]). As legal scholar David Pimentel observes, “In pre-
vious generations, parents who ‘let their kids run wild’
were viewed with some disdain by neighbors, perhaps,
but subjected to no greater sanction than head wagging
or disapproving gossip in the community. Today, such
situations are far more likely to result in a call to Child
Protective Services, with subsequent legal intervention”
[15]. A recent study in Britain found that fully one in ve
children in England born during the 2009-2010 fiscal
year were the subject of a call to Child Protective Services
before reaching the age of five [16].
The effective criminalization of parenting choices that
objectively pose little risk to children suggests that addi-
tional factors may be at work. We hypothesize that one
such factor is moral judgment. Specifically, we propose
that people overestimate the dangers facing children in
order to better rationalize their intuition that parents
have done something morally wrong in allowing their
children any unsupervised time.
Research in other domains has shown that moral
judgments do affect people’s estimates of harm. For
example, intentional actions that result in harm are
seen as more harmful than unintentional actions with
the same outcomes [17, 18]. Moral intuitions also affect
judgments about cause: A driver who gets into an accident
while speeding home to hide his cocaine is said to have
‘caused’ the accident more than a driver who was speed-
ing home to hide his parents’ anniversary gift [19]. People
have also been shown to seek what is called ‘moral coher-
ence’: they modify their factual beliefs to match their
moral intuitions. For example, after reading an argument
that capital punishment is morally wrong no matter the
consequences, people are less likely to believe that capital
punishment deters crime [20].
We hypothesize that a similar process may be at work
when people imagine the harm likely to befall unsuper-
vised children. That is, people may overestimate the danger
to unsupervised children in order to justify their moral
condemnation of the parents who allow the children to
be alone. Thus, exaggerated fears of harm and increas-
ing moral prohibitions form a sort of self-reinforcing
feedback loop. We will ultimately suggest that much of
the recent hysteria concerning danger to unsupervised
children is the product of this feedback loop, in which
inflated estimates of risk lead to a new moral norm against
leaving children alone, and then the need to justify moral
condemnation of parents who violate this norm leads in
turn to even more inflated estimates of risk, generating
even stronger moral condemnation of parents who violate
the norm, and so on.
This hypothesis is consistent with the literature on
moral dumbfounding. According to Haidt’s [21, 22]
Social Intuitionist Model, people make moral judgments
quickly and unconsciously, and then use facts and rea-
soning to rationalize those moral judgments. (This is
in opposition to the common assumption that moral
beliefs are based on reasoned fact.) In the present case,
we hypothesize that when they are free to do so, people
adopt and/or modify their factual beliefs (e.g., regard-
ing the amount of danger posed to a child by a given
situation) so as to better rationalize their intuitive moral
judgments (e.g., that this mother did something morally
wrong).
To test this hypothesis, we examined whether
participants’ judgments about danger to an unsupervised
child vary according to the moral acceptability of the
parent’s reason for leaving the child alone. If our hypothe-
sis is correct, then participants should judge that children
are in more danger when parents deliberately allow them
to be unsupervised, (as the Meitiv parents did) than when
children are left unsupervised by accident. Similarly,
when parents choose to leave children unsupervised,
participants should judge that children are in more danger
when a parent leaves for a morally unacceptable reason
(e.g., to meet an illicit lover) than for a morally neutral or
acceptable reason (e.g., to go to work).
General Method
Overview
In each of six experiments, we invited participants on
Amazon Mechanical Turk to read brief vignettes in
which a child spends a brief period of time unsupervised.
The children’s ages, locations and duration of time
unsupervised in each vignette were kept constant across
participants (see Table 1).
Child’s name & age Location Minutes alone
Olivia, 10 months Asleep in the car in a gym’s cool underground parking garage 15
Cassidy, 2.5 Home, eating a snack, watching Frozen 20
Grace, 4 Playing on ipad in the car, in a shady spot in a library parking lot 30
Jenny, 6 A park about a mile from her house 25
Susie, 8 Starbucks, one block away from where her mother is 45
Table 1: Basic Five Vignette Types.
Note. Ages are in years unless otherwise noted.
Thomas et al: No Child Left Alone Art. 10, page 3 of 14
Experiment Design
1
(N = 166)
Basic Design. Participants read 5 vignettes, in which mothers ages 26–33 left their children alone for brief
periods. After each vignette participants were asked, “On a scale of 1 to 10, with 1 being SAFEST/LOWEST RISK,
and 10 being MOST DANGEROUS/HIGHEST RISK, what is the risk of some harm coming to the child during the
time that the parent is gone?”
2
(N = 158)
Fathers. The parents described in the vignettes were fathers instead of mothers. Otherwise identical to
Experiment 1.
3
(N = 164)
Younger mothers. Mothers described in the vignettes were 10 years younger (ranging in age from 16–23) and
held lower-paying jobs than in the standard vignettes (e.g., McDonalds cashier instead of accountant). Otherwise
identical to Experiment 1.
4
(N = 247)
Explicit Moral Judgments. This experiment used the same vignettes as Experiment 1, but added an explicit moral
question (“On a scale from 1 to 10, with 1 meaning the mother did NOTHING WRONG, and 10 meaning the
mother did something HIGHLY UNETHICAL/IMMORAL, did the mother do something morally/ethically wrong
by leaving her child alone?”) in addition to the standard risk question used in all experiments (i.e., “On a scale of
1 to 10, with 1 being SAFEST/LOWEST RISK, and 10 being MOST DANGEROUS/HIGHEST RISK, what is the risk
of some harm coming to the child during the time that the parent is gone?”) The order of the two questions was
counterbalanced across participants, so that half of the participants always answered the moral question first
and the other half always answered the risk question first.
5
(N = 149)
List the Dangers. This experiment used the same vignettes as Experiment 1, but after the standard risk question
(“On a scale of 1 to 10, with 1 being SAFEST/LOWEST RISK, and 10 being MOST DANGEROUS/HIGHEST RISK,
what is the risk of some harm coming to the child during the time that the parent is gone?”) participants were
asked an explicit rationale question: “If there is risk to the child, please explain what the risk is. (That is, what
harmful thing or things might happen to the child while the parent is gone?)”
6
(N = 611)
Basic Design vs. Moral Judgment vs. List the Dangers. The purpose of Experiment 6 was to directly compare
the effects of asking an explicit moral question, an explicit rationale question, or neither. Participants in this
experiment read the same vignettes as in Experiments 1, 4 and 5, but were randomly assigned to receive either
the standard risk question alone (as in Experiment 1); the standard question and the explicit moral question (as
in Experiment 4); or the standard question followed by the explicit rationale question (as in Experiment 5).
Table 2: Designs of Experiments 1–6.
The vignettes differed only in the reason for the
parent’s absence. In the ‘Unintentional’ version of each
vignette (‘Unintentional’ condition), the parent was
involuntarily separated from the child by an accident. In
the other four versions, the parent intentionally left the
child in order to work (‘Work’ condition), volunteer for
charity (‘Volunteer’ condition), relax (‘Relax’ condition),
or meet an illicit lover (‘Affair’ condition). After reading
each vignette, participants were asked to estimate (on
a scale of 1 to 10) how much danger the child was in
during the parent’s absence. (See Table 2 for a summary
of experimental designs; see Appendix A for the full texts
of all vignettes).
Participants
Participants for all experiments were recruited through
Amazon Mechanical Turk, and were paid $1.00 each to
complete the survey. Participants were excluded if they
met any of the following criteria: (a) They had already
participated in a previous experiment in this series;
(b) They failed to answer a question that checked whether
they were reading the vignettes (i.e., “Cindy P. (23) is a
stay at home mom and the mother of Dorothy, age 3. On
Tuesday evenings, Cindy takes Dorothy to the fair to eat
cotton candy. This is just a test question please answer ‘7’.
Cindy leaves Dorothy alone for four hours.”) (c) They spent
less than five minutes taking the survey. See Appendix B
for a full discussion of this exclusion and a separate
analysis that includes the data from participants who took
less than five minutes.
Procedure
Before reading the vignettes, participants answered a set
of demographic questions asking about their gender, age,
parental status (i.e., whether they had children), racial/
ethnic identity, level of education and political outlook
(i.e., “Do you consider yourself politically conservative or
liberal?”).
After answering the demographic questions each
participant read five vignettes. The vignettes were ran-
domly assigned, with the constraint that each participant
read one vignette featuring a child of each age (10 months,
2.5 years, 4 years, 6 years and 8 years; see Table 1) and
orthogonally, one vignette from each of the five moral
conditions (Unintentional, Work, Relax, Volunteer and
Affair). Vignettes were presented in a random order. As
mentioned above, participants also read one fake vignette
that instructed them to answer ‘7,’ the purpose of which was
to check whether they were paying attention. Responses
to this fake vignette were not included in the data analysis,
except to exclude participants who answered it wrong.
Data Analysis
For each experiment, we used R [23] and lme4 [24] to
perform a linear mixed effects analysis of the relationship
between moral condition (the reason why the parent left)
Thomas et al: No Child Left AloneArt. 10, page 4 of 14
and participants’ estimates of danger to the child in
each vignette. As fixed effects, we included participants’
gender, age, education level, race, political beliefs, and
parental status (i.e., whether they had children). As ran-
dom effects, we included intercepts for participants and
vignette type (see Table 1). Visual inspection of residual
plots did not reveal any obvious deviations from homo-
scedasticity or normality. P-values were obtained using a
likelihood ratio test of the full model, with and without
the effect of moral condition [25]. To obtain F statistics
and accompanying p-values, we used afex [26], which uses
Kenward-Roger approximations for degrees of freedom.
For all pairwise comparisons, we used lsmeans [27] with
Tukey adjustments. To calculate effect sizes we used meth-
ods outlined by Nakagawa & Schielzeth [28], using the
r.squaredGLMM function in the MumIn package in R [29].
We report the marginal R2 (R2
GLMMm), which represents the
variance explained by fixed factors and the conditional R2
(R2
GLMMc), which represents the variance explained by both
fixed and random factors. All of the data collected for
this entire set of experiments (including two pilot stud-
ies) is publicly available via the Open Science Framework,
https://osf.io/dr7hg/.
Experiment 1. Basic Design
Method
Participants. A total of 219 participants were recruited
through Amazon Mechanical Turk for this experiment. Of
those, 4 were excluded because they failed to answer the
attention-check question, and 49 were excluded because
they spent less than five minutes taking the survey (see
Appendix B for an alternative analysis that includes data
from these participants). The remaining 166 participants
contributed data to the analysis. These participants ranged
in age from 19 to 69 years (M = 34.07 SD = 10.07; 47.59%
were female and 52.41% were male; 59.6% said they
had children and 40.36% said they did not. In response
to the question “What is your race?” he most common
answer chosen was ‘Caucasian’ (79.1%); followed by
‘Black/African American’ (8.37%); ‘Asian/Pacific Islander’
(5.58%), ‘Hispanic’ (5.12%); ‘Other’ (0.93%); and ‘Decline
to Respond’ (0.93%). In response to the question, “What
is the highest level of education you have received?” the
most common answer was ‘Bachelor’s Degree’ (35.54%);
followed by ‘Some College’ (30.12%); ‘Associate’s Degree’
(16.87%); ‘High School or GED’ (8.96%); ‘Graduate
Degree’ (6.63%); In response to the question, “Do you
consider yourself politically conservative or liberal?” the
most common answer was ‘Liberal’ (34.94%); followed by
‘Moderate’ (25.90%); ‘Very Liberal’ (18.67%); ‘Conserva-
tive’ (16.87%) and ‘Very Conservative’ (3.61%).
Design. This experiment followed the basic design
for the series. Each participant first answered the demo-
graphic questions and then read five vignettes in random
order. (See Appendix A for full text of vignettes.) After
each vignette, the participant was asked, “On a scale of 1
to 10, with 1 being SAFEST/LOWEST RISK, and 10 being
MOST DANGEROUS/HIGHEST RISK, what is the risk of
some harm coming to the child during the time that the
parent is gone?”
Results and Discussion
Estimates of risk were high overall. The mean estimate
of risk across all situations (on a scale of 1-10) was 6.99
(SD = 2.63), and the modal estimate was 10. As predicted,
respondents’ estimates of risk to children differed accord-
ing to why the parent left. A likelihood ratio test and
an ANOVA both revealed a significant effect of moral
condition on risk estimates: (χ2(4) = 42.34 p < .001),
(F(4,656) = 10.87, p < .001). R2
GLMMc = 61.09% and R2
GLMMm =
30.58%.
Specifically, a mother’s unintentional absence was seen
as safer for the child than a mother’s intentional absence
for any reason, and a mother’s work-related absence was
seen as more dangerous than an unintentional absence,
but less dangerous than if the mother left to pursue an
illicit sexual affair. Estimates of risk for each condition
were: Munintentional = 6.22; Mwork = 6.78; Mvolunteer = 7.00;
Mrelax = 7.19; Maffair = 7.29. See Table 3 for pairwise
comparisons.
Experiment 2: Fathers
Method
Participants. A total of 222 participants were recruited
through Amazon Mechanical Turk for this experiment. Of
those, 9 were excluded because they failed to answer the
attention-check question, and 55 were excluded because
they spent less than five minutes taking the survey (see
Appendix B for an alternative analysis that includes data
from these participants). The remaining 158 participants
contributed data to the analysis. These participants ranged
in age from 18 to 63 years (M = 33.51 SD = 10.37); 43.04%
were female and 56.96% were male; 42.41% said they
had children and 57.59% said they did not. In response
to the question “What is your race?” the most common
answer chosen was ‘Caucasian’ (83.54%), followed by
‘Black/African American’ (5.70%); ‘Asian/Pacific Islander’
(4.43%), ‘Hispanic’ (5.06%); ‘Other’ (1.27%) In response
Table 3: Pairwise Comparisons for Experiment 1 (Basic Design).
Note. *p < .05, **p < .01, ***p < .001.
Affair Relax Volunteer Work
Unintentional t(664) = 5.716*** t(664) = 4.27*** t(664) = 4.367*** t(664) = 2.82, p = .0397*
Work t(664) = 2.895, p = .0319* t(664) = 2.581 p = .0750 t(664) = 1.446 p = .598
Volunteer t(664) = 1.448 p = .59 t(664) = 1.1 3 p = .78
Relax t(664) = .315 p = .99
Thomas et al: No Child Left Alone Art. 10, page 5 of 14
to the question, “What is the highest level of educa-
tion you have received?” the most common answer was
‘Bachelor’s Degree’ (33.54%); followed by ‘Some College’
(30.38%); ‘Associate’s Degree’ (8.23%); ‘High School or
GED’ (9.49%); ‘Graduate Degree’ (13.29%); ‘Some Gradu-
ate School’ (3.19%); and ‘Less than 12th Grade’ (1.90%). In
response to the question, “Do you consider yourself politi-
cally conservative or liberal?” the most common answer
was ‘Liberal’ (36.54%); followed by ‘Moderate’ (23.08%);
‘Very Liberal’ (18.59%); ‘Conservative’ (19.87%) and ‘Very
Conservative’ (1.92%).
Design. In this experiment, the parent described in the
vignettes was a father instead of a mother. (See Appendix
A for full text of vignettes.) Otherwise, the design was
identical to Experiment 1.
Results and Discussion
The pattern of responses was similar to Experiment 1.
The mean estimate of risk across all situations was 7.07
(SD = 2.46), and the modal estimate was 10. A likelihood
ratio test revealed a significant effect of moral condition
on risk assessment (χ2(4) = 40.017 p < .001), as did an
ANOVA (F(4,616.5) = 10.26, p < .001). R2
GLMMc = 59.53%
and R2
GLMMm = 33.31%.
Specifically, children were seen as less at risk when
fathers left unintentionally than when fathers left to
pursue an affair, volunteer for charity or relax. However,
unlike in Experiment 1, fathers’ work-related absences
were not treated as significantly different from their unin-
tentional absences. In fact, going to work was seen as safer
(for one’s child) than going somewhere to relax, and as in
Experiment 1, going to work was also seen as safer (for
one’s child) than leaving to pursue an affair. Estimates of
risk for each condition were as follows: Munintentional = 6.48;
Mwork = 6.82; Mvolunteer = 7.19; Mrelax = 7.43; Maffair = 7.41.
See Table 4 for pairwise comparisons.
Experiment 3: Younger Mothers
Method
Participants. A total of 243 participants were recruited
through Amazon Mechanical Turk for this experiment. Of
those, 15 were excluded because they failed to answer the
attention-check question, and 64 were excluded because
they spent less than five minutes taking the survey (see
Appendix B for an alternative analysis that includes data
from these participants). The remaining 164 participants
contributed data to the analysis. These participants ranged
in age from 18 to 83 years (M = 33.71 SD = 11.78); 45.12%
were female and 54.88% were male; 54.04% said they had
children and 45.96% said they did not. In response to the
question “What is your race” the most common answer
chosen was ‘Caucasian’ (82.93%); followed by ‘Black/Afri-
can American’ (6.10%); ‘Asian/Pacific Islander’ (4.88%),
‘Hispanic’ (4.27%); ‘Native American’ (1.22%); and ‘Decline
to Respond’ (0.61%). In response to the question, “What
is the highest level of education you have received?” the
most common answer was ‘Some College’ (34.76%);; fol-
lowed by ‘Bachelor’s Degree’ (31.71%); ‘Associate’s Degree’
(12.80); ‘High School or GED’ (9.15%); ‘Graduate Degree’
(9.76%); ‘Some Graduate School’ (1.83%). In response to
the question, “Do you consider yourself politically con-
servative or liberal?” the most common answer was ‘Lib-
eral’ (39.63%); followed by ‘Moderate’ (22.36%); ‘Very
Liberal’ (17.07%); ‘Conservative’ (17.68%) and ‘Very Con-
servative’ (3.05%).
Design. In this experiment, the ages of the mothers
described in the vignettes was lower by 10 years, and
the mothers held lower-paying jobs (e.g., McDonalds
cashier instead of accountant; see Appendix A for full
text of vignettes). Otherwise the design was identical to
Experiment 1.
Results and Discussion
The pattern of responses was similar to Experiments
1 and 2. The mean estimate of risk across all situations
was 7.18 (SD = 2.53) and the mode was 10. The likelihood
ratio test revealed a significant effect of moral condition
on risk assessment (χ2(4) = 33.613 p < .001), as did an
ANOVA (F(4, 636.26) = 8.57, p < .001). R2
GLMMc = 55.56%
and R2
GLMMm = 35.24%.
Specifically, children were seen as being in less danger
when their mother left unintentionally (because of an
accident) than when she left intentionally, no matter what
the reason. In this experiment, there was no evidence that
any particular reason for leaving was seen as riskier to
children than any of the others. Estimates of risk for each
condition were as follows: Munintentional = 6.55; Mwork = 7.18;
Mvolunteer = 7.25; Mrelax = 7.57; Maffair = 7.35. See Table 5 for
pairwise comparisons.
Experiment 4: Explicit Moral Judgments
Method
Participants. A total of 354 participants were recruited
through Amazon Mechanical Turk for this experiment. Of
those, 47 were excluded because they failed to answer the
attention-check question, and 60 were excluded because
they spent less than five minutes taking the survey (see
Appendix B for an alternative analysis that includes data
Table 4: Pairwise Comparisons for Experiment 2 (Fathers).
Note. *p < .05, **p < .01, ***p < .001.
Affair Relax Volunteer Work
Unintentional t(624) = 5.156*** t(624) = 4.904*** t(624) = 4.582*** t(624) = 1.89 p = .321
Work t(624) = 3.26 p = .010** t(624) = 3.01 p = .023* t(624) = 2.69 p = .057
Volunteer t(624) = 0.574 p = .98 t(624) = 0.322 p = .99
Relax t(624) = .252, p = .99
Thomas et al: No Child Left AloneArt. 10, page 6 of 14
from these participants). The remaining 247 participants
contributed data to the analysis. These participants ranged
in age from 18 to 68 years (M = 33.41; SD = 10.80; 44.53%
were female and 55.47% were male; 57.89% said they
had children and 42.11% said they did not. In response
to the question “What is your race?” the most common
answer chosen was ‘Caucasian’ (77.73%); followed by
‘Asian/Pacific Islander’ (6.88%); ‘Black/African Ameri-
can’ (6.07%); ‘Hispanic’ (5.26%); ‘Other’ (3.24%); ‘Native
American’ (.81%). In response to the question, “What is
the highest level of education you have received?” the
most common answer was ‘Bachelor’s Degree’ (34.41%);
followed by ‘Some College’ (32.39%); ‘Associate’s Degree’
(9.49%); ‘High School or GED’ (10.93%); ‘Graduate Degree’
(8.91%); ‘Some Graduate School’ (2.43%); and ‘Less than
12th Grade’ (1.21%). In response to the question, “Do you
consider yourself politically conservative or liberal?” the
most common answer was ‘Liberal’ (32.93%); followed by
‘Moderate’ (29.27%); ‘Very Liberal’ (18.29%); ‘Conserva-
tive’ (16.26%) and ‘Very Conservative’ (3.25%).
Design. In this experiment we added a second ques-
tion, asking about the morality of the mother’s actions:
“On a scale from 1 to 10, with 1 meaning the mother did
NOTHING WRONG, and 10 meaning the mother did some-
thing HIGHLY UNETHICAL/IMMORAL, did the mother do
something morally/ethically wrong by leaving her child
alone?” Participants also answered the same risk question
as in Experiments 1–3 (““On a scale of 1 to 10, with 1 being
SAFEST/LOWEST RISK, and 10 being MOST DANGEROUS/
HIGHEST RISK, what is the risk of some harm coming to
the child during the time that the parent is gone?”) The
order of the questions was counterbalanced across partici-
pants, but kept constant for each participant across the
vignettes.
We had two reasons for adding this moral question. One
was as a manipulation check, to test whether people’s
moral intuitions followed the presumed pattern (e.g., that
respondents viewed an extramarital affair as immoral). A
second reason was to allow respondents to separate their
moral judgments from their risk judgments. We reasoned
that respondents might think something like “I realize
that the child is not actually in more danger just because
the parent left to have an affair, but I still feel morally
offended and I want to express that.” Adding a separate
moral question would allow participants to express their
moral judgment directly, and therefore also (at least in
theory) to express a risk judgment that was actually an
assessment of risk, separate from the morality of the
situation.
Results and Discussion
Analysis of Responses To Moral Question (i.e., “Did the
mother do something morally/ethically wrong by leav-
ing her child alone?”) Participants’ moral judgments
followed a very similar pattern to their risk judgments (see
Figure 1). The mean moral judgment on a scale of 1–10
(with 1 meaning the mother did nothing wrong) was 6.89;
the modal judgment was 10. As predicted, the likelihood
ratio test revealed a significant effect of moral condition
on moral judgment, (χ2 (4) = 796.14 p < .001) as did an
ANOVA (F(4,776.66) = 306.85 p < .001). R2
GLMMc = 63.10%
and R2
GLMMm = 41.60%. These tests confirmed that the
different moral conditions (e.g., ‘Unintentional,’ ‘Affair,’
etc.) successfully elicited different moral judgments from
participants.
Interestingly, although the ‘Unintentional’ condition
was rated as the most morally acceptable, respondents
still judged that mothers in this condition had done
something somewhat morally wrong (mean judgment of
3.05 out of 10) by leaving their children alone even for
a moment. (In these ‘Unintentional’ conditions, the par-
ents stepped away from their children for a moment to
do something like return a library book or retrieve the
mail.) Estimates of risk for each condition were as follows:
Munintentional = 3.05; Mwork = 7.27; Mrelax = 7.48; Maffair = 8.86;
Mvolunteer = 7.32.
Pairwise comparisons of moral judgments among the
five different moral conditions showed a similar pattern to
the risk estimates for those conditions. Participants rated
the ‘Unintentional’ condition as more morally accept-
able than the other four conditions: (Work t(984.24) =
23.87, p < .001; Volunteer t(984.17) = 24.31, p < .001;
Relax t(984.24) = 24.93, p < .001; Lover t(984.17) = 32.65,
p < .001). Participants also rated the ‘Affair’ condition
as less morally acceptable than the ‘Work’, ‘Relax’ and
‘Volunteer’ conditions (Affair/Work t(984.17) = 8.772,
p < .001; Affair/Relax t(984.24) = 7.71, p < .001; Affair/
Volunteer t(984.24) = 8.329, p < .001) All other pairwise
comparisons were non-significant, Work/Relax t(984.17) =
1.06, p = .8254; Work/Volunteer t(984.24) = 0.44,
p = .99; Relax/Volunteer t(984.17) = 0.622, p = .971).
Analysis Of Responses To Risk Question. (“What is the
risk of some harm coming to the child during the time
that the parent is gone?”) Although we thought that add-
ing a moral question might free participants to lower
their risk estimates, the opposite occurred. The effect of
moral condition on risk estimates was actually larger in
this experiment than in Experiments 1-3. It seems that
when people are encouraged to make explicit moral
Affair Relax Volunteer Work
Unintentional t(644) = 4.402*** t(644) = 5.47*** t(644) = 3.785, p = .002** t(644) = 3.87, p = .001**
Work t(644) = .556 t(644) = 1.601, p = .497 t(644) = .127, p = .99
Volunteer t(644) = .618, p = .972 t(644) = 1.678, p = .448
Relax t(644) = .541, p = .983
Table 5: Pairwise Comparisons for Experiment 3 (Younger Mothers).
Note. *p < .05, **p < .01, ***p < .001.
Thomas et al: No Child Left Alone Art. 10, page 7 of 14
judgments, their risk estimates become more skewed by
moral intuitions, not less so. The mean risk estimate in
this experiment was 6.95 (SD = 2.66) and the mode was
10. The likelihood ratio test revealed a significant effect
of moral condition on risk assessment (χ2(4) = 155.42
p < .001), as did an ANOVA (F (4,976) = 42.09 p < .001)
R2
GLMMc = 56.87% and R2
GLMMm = 28.7%. Specifically, the
‘Unintentional’ condition was seen as posing significantly
less risk to children than any of the other conditions;
and the ‘Affair’ condition was seen as posing significantly
more risk to children than any of the others. Estimates of
risk for each condition were as follows: Munintentional = 5.70;
Mwork = 7.18; Mrelax = 7.17; Maffair = 7.66; Mvolunteer = 7.06. See
Table 6 for pairwise comparisons.
Correlation Between Risk and Moral Question. To
investigate whether participants’ risk judgments were
correlated with their moral judgments, we ran a simple
regression analysis. As predicted, participants’ answers
were significantly positively correlated. That is, the more
immoral a mother’s reason for leaving, the more danger
participants thought her child was in while she was away
(R = .656, p < .001).
Mediation Analysis. We also performed a mediation
analysis to investigate whether people’s moral judgments
in Experiment 4 mediated their risk assessments. We used
the lavaan package in R [30] for this analysis, and collapsed
the moral conditions into two factors (‘Unintentional’
versus all other conditions). We found evidence of a full
mediation: The direct effects were not significantly differ-
ent than zero (Estimate = −.096, std. err, z-value = .658,
p = .510), and the indirect effects were significantly differ-
ent than zero (Estimate = 1.666, std. err = .166, z-value =
10.054, p < .001) [31].
Comparing Experiment 1 and Experiment 4
The results of Experiment 4 suggested that when we
asked respondents an explicit moral question, their
risk estimates actually became more biased by moral
judgment. To investigate this in a more formal way, we
compared the impact that moral condition had on risk esti-
mates in Experiment 1 to that in Experiment 4. To do this,
we averaged each participant’s risk estimates across all
‘intentional’ conditions (affair, relax, work and volunteer)
and subtracted their risk estimate for the Unintentional
condition. We controlled for vignette type in order to
account for the fact that some items (e.g., those involving
younger children) might be judged to have a different
baseline level of risk. The mean difference score in Experi-
ment 4 (M = 1.48) was significantly higher than the mean
difference score in Experiment 1 (M = 0.89), (χ2(4) = 8.94
p = .003), ANOVA (F(1, 516 = 8.99 p = .005). This result indi-
cates that participants’ risk estimates were more biased by
moral context in Experiment 4 (where they were asked an
explicit moral question alongside the risk question) than in
Experiment 1 (where they were only asked about risk). This
effect was explored more fully in Experiment 6 (see below).
Figure 1: Participants’ responses by moral condition. Left panel: Mean estimate of risk to child by moral condition.
Right panel: Mean moral judgment by moral condition. Error bars indicate standard error. ***p < .001.
Thomas et al: No Child Left AloneArt. 10, page 8 of 14
Experiment 5. List the Dangers
Method
Participants. A total of 159 participants were recruited
through Amazon Mechanical Turk for this experiment. Of
those, 4 were excluded because they failed to answer the
attention-check question, and none were excluded because
they spent less than five minutes taking the survey (see
Appendix B for an alternative analysis that includes data
from these participants). The remaining 155 participants
contributed data to the analysis. These participants ranged
in age from 18 to 67 years (M = 33.21 SD = 9.29); 41.29%
were female and 58.71% were male; 45.16% said they had
children and 54.84% said they did not. In response to the
question “What is your race,” the most common answer
chosen was ‘Caucasian’(80.00%); followed by ‘Asian/
Pacific Islander’ (7.10%), ‘Hispanic’ (6.45%); ‘Black/African
American’ (3.23%);; ‘Other’ (2.58%); ‘Native American’
(.65%). In response to the question, “What is the high-
est level of education you have received?” the most com-
mon answer was ‘Bachelor’s Degree’ (39.35%); followed
by ‘Some College’ (27.10%); ‘Associate’s Degree’ (10.32%);
‘High School or GED’ (13.55%); ‘Graduate Degree’ (7.74%);
‘Some Graduate School’ (1.29%). In response to the ques-
tion, “Do you consider yourself politically conservative or
liberal?” the most common answer was ‘Liberal’ (35.48%);
followed by ‘Moderate’ (26.45%); ‘Very Liberal’ (18.71%);
‘Conservative’ (16.77%) and ‘Very Conservative’ (2.58%).
Design. In this experiment after participants estimated
the risk to the child in each vignette, we asked them for
an explicit rationale for that judgment: “If there is risk to
the child, please explain what the risk is. (That is, what
harmful thing or things might happen to the child while
the parent is gone?)” We added this instruction in order
to check whether respondents imagined different dan-
gers facing the children in the different conditions. For
example, participants might think that children were in
more danger in the ‘Affair’ condition because the moth-
er’s husband might discover the affair and react in some
way that endangered the child. Our intention in writing
the vignettes was to make the risks equal across moral
conditions. But if participants thought that the differ-
ent conditions actually posed different risks to children,
then it would be rational for them to estimate the danger
differently.
Results and Discussion
Analysis Of Responses To Risk Question. This was the
standard question used in all experiments (i.e., “What is
the risk of some harm coming to the child during the time
that the parent is gone?”) Results were similar to those
in Experiments 1–4, but the effect of moral condition on
risk estimates was less pronounced. The mean response
was 6.47 (SD = 2.82) and the modal response was 10. The
likelihood ratio test revealed a significant effect of moral
condition on risk assessment (χ2(4) = 28.10 p < .001), as
did an ANOVA (F(4, 588) = 7.17 p < .001). R2
GLMMc = 54.68%
and R2
GLMMm = 30.8%. Specifically, as in Experiments 1-4,
children whose mothers were unintentionally absent were
seen as safer than those whose mothers chose to leave
them alone. Estimates of risk for each condition were
as follows: Munintentional = 5.89; Mwork = 6.38; Mrelax = 6.66;
Maffair = 6.92; Mvolunteer = 6.54. See Table 7 for pairwise com-
parisons.
Analysis of Responses to List-the-Dangers Question.
(“What harmful thing or things might happen to the child
while the parent is gone?”). We found no evidence that
participants imagined different dangers to children in the
different conditions. Participants listed the same dangers
in all conditions, with the most common being that a
stranger would harm the child (60.38% of responses) or
that an accident would occur (55.51% of responses). Less
than 1% of responses mentioned anything specific to the
condition. This suggests that when respondents estimate
different levels of risk to children based on what parents
are doing elsewhere, those estimates do indeed reflect a
moral judgment about the parents, rather than a percep-
tion that the children actually face different risks.
Experiment 6: Basic Design, Moral Judgment
and List-the-Dangers Conditions
This experiment directly tested the hypothesis that
answering the moral question increased the effect of
moral condition on risk assessment. We also were inter-
ested in whether participants would make lower risk
estimates when they were asked to list the dangers to
children. In this experiment participants were randomly
assigned to one of three conditions. In Condition 6A, (as
in Experiment 1) participants were asked only to judge
the risk to the child. In Condition 6B (as in Experiment 4)
participants were asked how dangerous the situation was
and whether the mother did something morally/ethically
wrong. In Condition 6C (as in Experiment 5) participants
were asked to rate the risk, and to list the dangers that
might occur.
Participants. A total of 701 participants were recruited
through Amazon Mechanical Turk for this experiment.
Of those, 23 were excluded because they failed to answer
the attention-check question, and 74 were excluded
Affair Relax Volunteer Work
Unintentional t(984) = 12.15*** t(984) = 9.01*** t(984) = 8.62*** t(984) = 9.20***
Work t(984) = 2.95 p = .03* t(984) = .190 p = 1.00 t(984) = .579 p = .98
Volunteer t(984) = 3.53 p = .004** t(984) = .389 p = .995
Relax t(984) = 3.214 p = .015*
Table 6: Pairwise Comparisons of Risk Estimates for Experiment 4 (Explicit Moral Judgments).
Note. *p < .05, **p < .01, ***p < .001.
Thomas et al: No Child Left Alone Art. 10, page 9 of 14
because they spent less than five minutes taking the
survey (see Appendix B for an alternative analysis that
includes data from these participants). The remaining 611
participants contributed data to the analysis. These par-
ticipants ranged in age from 18 to 75 years (M = 36.21
SD = 11.56); 51.55% were female and 48.45% were male;
62.03% said they had children and 37.97% said they did
not. In response to the question “What is your race,” the
most common answer chosen was ‘Caucasian’(79.21%);
followed by ‘Black/African American’ (6.22%); ‘Asian/
Pacific Islander’ (7.20%), ‘Hispanic’ (4.58%); ‘Other’
(1.96%); ‘Native American’ (0.49%); ‘Declined to Respond’
(0.33%). In response to the question, “What is the high-
est level of education you have received?” the most com-
mon answer was ‘Bachelor’s Degree’ (35.19%); followed
by ‘Some College’ (29.13%); ‘Associate’s Degree’ (10.47%);
‘Graduate High School or Equivalent’ (9.33%); ‘Graduate
Degree’ (11.78%); ‘Some Graduate School’ (3.60%), ‘Less
than 12th grade’ (0.49%). In response to the question,
“Do you consider yourself politically conservative or
liberal?” the most common answer was ‘Liberal’ (33.06%);
followed by ‘Moderate’ (23.57%); ‘Very Liberal’ (18.00%);
‘Conservative’ (19.80%) and ‘Very Conservative’ (5.56%).
Results and Discussion
The likelihood ratio test revealed a significant effect
of moral condition on risk assessment (χ2(4) = 270.15
p < .001), as did an ANOVA (F(4, 2428.10 = 73.08 p < .001).
R2
GLMMc = 57.68% and R2
GLMMm = 13.62%. We also found an
interaction between moral condition and ‘Experiment
Condition’ (i.e. whether they were assigned to Condition
A,B, or C) χ2 (8) = 19.821 p = .011, F(8, 1800.18) = 2.47
p = .01. This interaction confirms our hypothesis that
answering the moral question increased the effect of
moral condition on risk assessment.
The overall pattern of responses in each condition
matched those from Studies 1–5 (see Table 8). Means
for Condition 6A were: MA unintentional = 6.09; MA work = 7.31
MA volunteer = 6.70; MA relax = 6.93; MA affair = 7.51. Means for
Condition 6B were: MB unintentional = 5.70; MB work = 7.49
MB volunteer = 7.89; MB relax = 7.62; MB affair = 8.00. Means for
Condition 6C were: MC unintentional = 6.42; MC work = 6.92;
MC volunteer = 7.16; MC relax = 7.46; MC affair = 7.52.
Dierence Scores
The purpose of this experiment was to find out whether
the effect of moral condition on risk estimates could
either be increased by asking respondents an explicit
moral question, or decreased by asking them to list the
actual dangers that children face. To investigate this, we
averaged each participant’s risk estimates across all ‘inten-
tional’ conditions (affair, relax, work and volunteer) and
subtracted their risk estimate for the Unintentional condi-
tion. We controlled for vignette type in order to account
for the fact that some items (e.g. those involving younger
children) might be judged to have a different baseline
level of risk. We found a significant effect of experimen-
tal condition on these difference scores: (χ2(3) = 10.75
p = .005), ANOVA (F(2, 604.2 = 5.26 p = .005). Pairwise
comparisons revealed significant differences between 6A
Affair Relax Volunteer Work
Unintentional t(620) = 5.18*** t(620) = 3.91, p = .001** t(620) = 3.38 p = .007** t(620) = 2.62 p = .067
Work t(620) = 2.53 p = .079 t(620) = 1.08 p = .82 t(620) = .762 p = .94
Volunteer t(620) = 1.80 p = .37 t(620) = .522 p = .98
Relax t(620) = 1.28 p = .703
Table 7: Pairwise Comparisons of Risk Estimates for Experiment 5 (List the Dangers).
Note. *p < .05, **p < .01, ***p = or < .001.
Affair Relax Volunteer Work
Unintentional 6A: t(2452) = 7.63***
6B: t(2452) = 11.48***
6C: t(2452) = 8.01***
6A: t(2452) = 5.39***
6B: t(2452) = 9.63***
6C: t(2452) = 6.08***
6A: t(2452) = 4.88***
6B: t(2452) = 9.61***
6C: t(2452) = 5.75***
6A: t(2452) = 6.00***
6B: t(2452) = 8.72***
6C: t(2452) = 6.0***
Volunteer 6A: t(2452) = 2.73 p = .284
6B: t(2452) = 1.87 p = .867
6C: t(2452) = 2.27 p = .62
6A: t(2452) = .503 p = 1.00
6B: t(2452) = .26 p = 1.00
6C: t(2452) = 1.41 p = .97
6A: t(2452) = 1.17 p = 1.00
6B: t(2452) = .90 p = 1.00
6C: t(2452) = .25 p = 1.00
Work 6A:t(2452) = 1.61 p = .96
6B: t(2452) = 2.76 p = .27
6C: t(2452) = 2.02 p = .79
6A: t(2452) = .614 p = 1.00
6B: t(2452) = .632 p = 1.00
6C: t(2452) = 1.16 p = 1.00
Relax 6A: t(2452) = 2.23 p = .642
6B: t(2452) = 2.13 p = .716
6C: t(2452) = .86 p = 1.00
Table 8: Pairwise Comparisons of Risk Estimates for Experiment 6A, 6B, 6C.
Note. *p < .05, **p < .01, ***p = or < .001.
Thomas et al: No Child Left AloneArt. 10, page 10 of 14
and 6B (t(608.47) = 3.05 p = .007) and between 6B and
6C (t(608.47) = 3.56 p = .03); but not between 6A and
6C (t(608.47) = .53 p = .86)). These results indicate that
asking people to answer an explicit moral question does
increase the moral bias in their risk estimates, and that
asking people to list the dangers facing children does not
decrease this bias.
Summary of Experiments 1-6
Method
Summary of Participants. A total of 1898 participants
were recruited through Amazon Mechanical Turk across
all six experiments. 89 were excluded because they failed
to answer the question that checked whether they were
reading the vignettes; and 375 were excluded because
they spent less than five minutes taking the survey. (See
Appendix B for a separate analysis that includes these par-
ticipants’ data.) The remaining 1328 participants contrib-
uted data to this analysis. Of these, 52% were female and
48% were male, 56.43% of the participants indicated they
had children, 43.57% did not. Participants ranged in age
from 18 to 75 years (M = 34.55, SD = 11.10).
In response to a question about their racial/ethnic iden-
tity, the most common answer chosen was ‘Caucasian’
(80.32%); followed by ‘Black/African American’ (6.11%);
‘Asian/Pacific Islander’ (5.88%), ‘Hispanic’ (5.20%);
‘Other’ (1.58%); ‘Native American’ (0.57%); and ‘Decline
to Respond’ (0.34%). In response to the question, “What
is the highest level of education you have received?”, the
most common answer was ‘Bachelor’s Degree’ (34.62%);
followed by ‘Some College’ (31.22%); ‘Associate’s Degree’
(11.43%); ‘High School or GED’ (10.86%); ‘Graduate
Degree’ (9.28%); ‘Some Graduate School’ (1.81%); and
‘Less than 12th Grade’ (0.79%). In response to the ques-
tion, “Do you consider yourself politically conservative or
liberal?” the most common answer was ‘Liberal’ (35.64%);
followed by ‘Moderate’ (25.88%); ‘Very Liberal’ (18.39%);
‘Conservative’ (17.14%) and ‘Very Conservative’ (2.95%).
See Table 9 for a summary of the pairwise comparisons
that were significant across the six experiments.
The pattern of moral judgments that participants gave
for the explicit moral question in Experiments 4 and 6B
was qualitatively similar to the pattern of risk estimates
seen across studies: For example, the ‘Unintentional’ con-
dition was seen as both most moral and safest. As might
be expected, moral judgments differed more across condi-
tions than risk estimates. For example, the ‘Unintentional’
condition was seen as much more moral than the ‘Affair’
condition, but only somewhat safer for the child.
Effect of participants’ gender and parental status.
Although all groups of respondents showed the patterns
described above, there were small but significant differ-
ences in the absolute estimates of risk made by different
groups in some studies. Most notably, women tended to
rate situations as more dangerous than men, and par-
ents tended to rate situations as more dangerous than
non-parents. The result of these tendencies was that the
highest overall estimates of risk were made by mothers;
followed by fathers, childless women, and finally (with
the lowest estimates), childless men. There was no evi-
dence that other demographic factors had a noticeable
effect on responses. For all demographic analyses, see
Appendix C.
General Discussion
In the present studies, we examined how moral intuitions
affect risk estimates. We found that when people make a
negative moral judgment about a parent who leaves her
child alone, their estimate of the danger facing that child
is higher than for a situation that objectively poses equal
risk to the child, but does not elicit the same moral dis-
approval. Specifically, participants judged that children
whose parents left them alone on purpose were in greater
danger than those whose parents left them by accident,
despite identical descriptions of the circumstances in
which children were alone (i.e., asleep in a car, parked
in the cool underground parking garage of a gym, for 15
minutes). This finding is consistent with earlier studies
showing that intentional violations of a norm are judged
as morally worse than unintentional violations (e.g. [32,
33, 34, 35]).
The general reasoning behind our experimental design
was that whatever might be going on elsewhere, as long
as the immediate circumstances surrounding the child
Affair Relax Volunteer Work
Unintentional All experiments:
p’s < .001***
All experiments:
p’s < or = .001***
Experiments 1, 2, 4, 6:
p’s < .001***.
Experiment 2: p = .002**
Experiment 5: p = .011*
Experiment 1: p < .05*
Experiments 3, 4, 6:
p’s < or = .001***
Work Experiments 1 and 4:
p’s < .05*
Experiment 2: p = .010 **
Experiment 2:
p = .023*
Relax Experiment 4: p = .015*
Volunteer Experiment 4:
p = .004**
Table 9: Significant Pairwise Comparisons Among Risk Estimates for Different Experiments.
Note. Table lists the experiments in which each pairwise comparison was statistically significant.
*p < .05, **p < .01, ***p < .001.
Thomas et al: No Child Left Alone Art. 10, page 11 of 14
were the same (same child, same location, same duration
of time), any differences in the estimates of risk to the
child in that situation must reflect bias on the part of
participants. However, we did consider the possibility that
respondents might see parents’ moral shortcomings as
posing a general, rather than specific risk to their children.
For example, participants might reason that a parent who
would intentionally leave her child alone for 30 minutes
would probably make a lot of other ‘risky’ parenting deci-
sions as well, which could cumulatively place the child
in danger. Although we cannot rule out this possibility
completely, we did try to address it in the piloting phase
by modifying the test question from its original wording
in the pilot studies (“On a scale of 1 to 10, with 1 being
SAFEST/LOWEST RISK, and 10 being MOST DANGEROUS/
HIGHEST RISK, what is the risk of some harm coming to
the child?”) to the wording used in Experiments 1-6, (“On
a scale of 1 to 10, with 1 being SAFEST/LOWEST RISK, and
10 being MOST DANGEROUS/HIGHEST RISK, what is the
risk of some harm coming to the child during the time that
the parent is gone?”).
In reality of course, children who are left alone in cir-
cumstances approved by their parents are likely to be
safer than children who find themselves alone by acci-
dent, because parents can take steps to ensure their child’s
well-being in their absence (e.g., making sure the baby is
securely buckled into a car seat; that the car is parked in
a shady spot; that an older child has a cell phone, knows
when to expect the parent back, etc.) The fact that par-
ticipants considered children left alone by accident safer
than those left alone on purpose strongly suggests that
participants’ moral condemnation of parents skewed their
risk estimates.
Experiments 2–6 represent extensions and variations
on the original design. In Experiment 2, the vignettes
described fathers. The only noticeable difference
between risk estimates in this experiment and the
others (which featured mothers) was in how partici-
pants treated the ‘Work’ condition. In six out of seven
experimental conditions featuring vignettes with moth-
ers (i.e., Experiments 1, 3, 4, 6A, 6B and 6C), a mother’s
choosing to leave for work was considered significantly
more dangerous to her child than if she left by acci-
dent. However, in Experiment 2 (featuring fathers),
work-related absences and unintentional absences were
treated similarly, presumably reflecting a more positive
moral evaluation of fathers who work than of mothers
who do so. We hesitate to conclude too much from the
results of one experiment, but it does suggest an inter-
esting direction for future research, namely the question
of how moral judgments and thus risk estimates differ
for fathers vs. mothers.
Interestingly, the difference between the Unintentional
and Work conditions also was not significant in
Experiment 5, where participants had to list the dangers
facing children. We wondered if asking people to list the
dangers might have suppressed the moral bias in much
the same way that asking them a moral question increased
it. However, Experiment 6 failed to provide confirmation
for this hypothesis.
In Experiment 3, we lowered the ages of the mothers
by 10 years and gave them lower-paying jobs (e.g.,
McDonald’s cashier instead of accountant). The pattern of
responses was the same as in Experiment 1, suggesting
that mothers’ age and occupation did not make a big dif-
ference to responses. However, it is worth noting that the
data in all of these experiments showed some evidence of
ceiling effects (e.g., the modal risk estimate in every exper-
iment was 10). So if participants had been less judgmen-
tal of younger mothers, that difference could have shown
up in the form of lower risk estimates. But if participants
were more judgmental of younger mothers, it might be
hard to detect that difference, because many participants
were already making the highest possible estimation of
risk. This suggests another direction for future research: If
researchers can identify a set of vignettes that elicit more
moderate estimates of risk, it might be worth revisiting
the question of whether participants make different moral
judgments about mothers who differ in age, occupation
or other attributes.
In Experiment 4, we asked participants to make explicit
moral judgments about the behavior of the mothers in
the vignettes. This served as a manipulation check, con-
firming that subjects did consider leaving a child alone on
purpose to be less morally acceptable than leaving a child
alone by accident. Leaving to meet one’s lover was also
considered less acceptable than leaving to work or relax.
A second reason for including the moral question was to
allow participants to make separate evaluations of the
risk to the child and the morality of the mother’s actions.
We thought that by giving participants a way to express
their moral disapproval separately from their estimates of
risk, they might produce less biased estimates of risk. In
fact, the opposite turned out to be true: Risk estimates in
Experiment 4 were more affected by moral judgments than
in Experiments 1–3. It seems that the explicit moral ques-
tion simply primed respondents to pay more attention to
morality, producing even more exaggerated estimates of
risk. The effect was particularly noticeable in the ratings
for the ‘Affair’ condition. In every experiment, the ‘Affair’
condition was rated more dangerous to children than the
‘Unintentional’ condition. But in Experiment 4, the ‘Affair’
condition was also rated as significantly riskier than any of
the other three intentional conditions (‘Work,’ ‘Volunteer’
or ‘Relax’). Similar findings were seen in Studies 1 and 2,
where a parent’s affair-related absence was seen as more
dangerous to the child than the same parent’s work-
related absence; but the effect in Experiment 4 was more
pronounced than in the other experiments.
In Experiment 5, we asked respondents to provide a
rationale for their risk estimates by listing the risks that
children actually face when left alone. The practical rea-
son for doing this was to check whether respondents actu-
ally imagined different risks to children whose parents left
for different reasons. For example, if respondents thought
that a mother meeting her lover was placing her child in
danger of harm from a jealous husband, then estimates
of risk might reasonably be higher for the ‘Affair’ condi-
tion than for other conditions. But in fact we found no
evidence of such reasoning. The most common risks listed
Thomas et al: No Child Left AloneArt. 10, page 12 of 14
by respondents were stranger abduction and accidents,
and this did not differ across moral conditions.
Experiment 6 was designed to test whether the moral
bias in risk estimates could be manipulated either by ask-
ing participants to make an explicit moral judgment (as
in Experiment 4), or by asking them to think about the
actual dangers facing children (as in Experiment 5). Based
on the results of Experiment 4, we hypothesized that
asking participants to make an explicit moral judgment
would increase the bias. Based somewhat on the results of
Experiment 5 (specifically on the relative similarity of risk
estimates for the Unintentional and Work conditions) and
perhaps on our own wistful hopes for human rationality,
we hypothesized that asking people to list the dangers
might suppress the moral bias, by encouraging them to
realize that the dangers were (a) actually low-probability
events, and (b) in no way dependent on what the parent
was doing elsewhere.
Participants in Experiment 6 were randomly assigned to
one of three conditions. Those assigned to Condition 6A
were given Experiment 1: They read the vignettes and esti-
mated how much danger each child was in. Those assigned
to Condition 6B were given Experiment 4: They read the
same vignettes, and provided both a risk estimate (of how
much danger the child was in) and an explicit moral judg-
ment (of how wrong the mother’s actions were), in coun-
terbalanced order. Those assigned to Condition 6C were
given Experiment 5: They read the vignettes, estimated
how much danger the child was in, and then listed what
harmful things might happen to the child in the parent’s
absence. Risk estimates in Condition 6B were higher than
in 6A and 6C, confirming that the effect of moral condi-
tion on risk estimates was indeed increased when partici-
pants were invited to make explicit moral judgments. Risk
estimates in Conditions 6A and 6C were similar, indicating
that when participants are asked to list the dangers to chil-
dren, the moral bias affecting their risk estimates does not
change. Another way of interpreting this finding might
be that participants were already thinking about the dan-
gers to children, and so asking them to list the dangers
had no effect. An interesting direction for future research
might be to explore what manipulations, if any, decrease
this moral bias. For example, if participants were told that
twenty times as many children die in car accidents than in
parked cars every year, would their estimates of the risk to
children in parked cars be lower?
The most important conclusion we draw from this set
of experiments is the following: People don’t only think
that leaving children alone is dangerous and therefore
immoral. They also think it is immoral and therefore dan-
gerous. That is, people overestimate the actual danger to
children who are left alone by their parents, in order to
better support or justify their moral condemnation of par-
ents who do so.
This brings us back to our opening question: How can we
explain the recent hysteria about unsupervised children,
often wildly out of proportion to the actual risks posed by
the situation? Our findings suggest that once a moralized
norm of ‘No child left alone’ was generated, people began
to feel morally outraged by parents who violated that
norm. The need (or opportunity) to better support or justify
this outrage then elevated people’s estimates of the actual
dangers faced by children. These elevated risk estimates, in
turn, may have led to even stronger moral condemnation
of parents and so on, in a self-reinforcing feedback loop.
Some readers may wonder whether subjects in these
experiments simply confounded the notion of ‘putting
a child in danger’ with that of ‘irresponsible parenting’,
in effect answering a different question than the one we
asked. Of course, this hypothesis is only a competitor to
our own if subjects’ judgments of increased parental irre-
sponsibility are not themselves a consequence of increased
judgments of risk to the child. That is, if subjects judge
parents to be increasingly irresponsible because they
judge the children to be at greater risk, this is not an alter-
native to our hypothesis but an elaboration of it.
Moreover, even if subjects’ increasing judgments of
parental responsibility are not consequences of similarly
increasing judgments of risk, the larger point we hope to
make still stands. That is, even if we assume that onlook-
ers, police officers, district attorneys, social workers and
judges are answering an implicit question about parental
irresponsibility when they are asked an explicit question
about risk, these are still the answers they give when asked
to make judgments about risk. Indeed, if people are ‘really’
judging parental responsibility when they are asked about
risk, then the effect is even more extreme than we have
described here: People’s estimates of dangers are not
merely inuenced but instead simply replaced by norma-
tive evaluations of the acceptability or propriety of the
parent’s conduct.
Either way, this particular case can be seen as an instance
of a more general phenomenon we might call the moral-
ized reinforcement of factual beliefs (here, the belief that
children left alone are in grave danger). This is a process
whereby one forms or revises one’s factual beliefs about
the world so that they better support one’s moral convic-
tions. This general phenomenon is consistent with other
models of moral cognition, including the idea of moral
coherence [20] and the Social Intuitionist Model [22]. This
phenomenon provides a ready explanation for the moral-
ized hysteria currently seen in the criminalization of par-
ents who leave their children alone, even in objectively
low-risk situations. Further experiments could explore
whether this phenomenon extends to risk estimates in
other domains.
Although it was not the focus of our studies, looking
at these data it is hard not to be struck by how exagger-
ated participants’ estimates of risk were overall. We asked
participants to rate the danger to children on a scale of 1
to 10, and in every single experiment, the most common
answer given was 10. We tried to address this problem in
the piloting stage: In the first version of the survey we
piloted, children in the vignettes were left alone for 45
minutes to 2 hours. With those times, the modal response
was 10 and the mean response was 7.52. We shortened
the times (down to 10 minutes for a baby and 45 minutes
for an 8-year-old), hoping to get more estimates near the
middle of the scale. But even with the shorter times, the
modal response was still 10 and the mean barely dropped
Thomas et al: No Child Left Alone Art. 10, page 13 of 14
(to 7.32). An obvious direction for future studies would be
to design vignettes describing even less risky situations
in order to eliminate ceiling effects. However, it may well
be that the moral panic about this issue has reached the
point where people believe that any child not under an
adult’s direct gaze is in imminent, grave danger. By the
same token, researchers may find it difficult to concoct
any fictional situation in which a child is somehow
alone, but participants hold the child’s mother morally
blameless.
As a practical matter, these findings have important
implications for public policy. Currently, intuitive esti-
mates of the risk to children left alone—estimates made
by onlookers, police officers, social workers, judges, and
so on—serve as the rationale for prosecuting parents
who allow their children to play in parks, walk to school,
wait in cars, etc., without an adult present. For example,
mothers are now frequently arrested, charged with abuse
or neglect and even jailed for allowing their children to
wait in a car for just a few minutes (e.g., [9, 10, 36]). As
Pimentel [15] observed, “If criminal child neglect stand-
ards are sufficiently vague, applied in the discretion of
prosecutors and in the judgment of juries steeped in the
media’s fearmongering, parents will have little choice but
to . . . buy into the Intensive Parenting culture.” Our find-
ings suggest that those estimates of risk are consistently
and systematically biased by people’s moral disapproval
of parents who violate a recently-imposed (and empiri-
cally unsupported) norm against ever leaving children
unsupervised. These findings should caution those who
make and enforce the law to distinguish factually-based,
rational assessments of risk to children from intuitive
moral judgments about parents, and to avoid investing
the latter with the force of law.
Supplementary Files
The supplementary files for this article can be found as
follows:
• Supplementary File 1: http://dx.doi.org/10.1525/
collabra.33.s1 Appendix A.
• Supplementary File 2: http://dx.doi.org/10.1525/
collabra.33.s2 Appendix B.
• Supplementary File 3: http://dx.doi.org/10.1525/
collabra.33.s3 Appendix C.
• Supplementary File 4: http://dx.doi.org/10.1525/
collabra.33.s4 Appendix D.
Competing Interests
The authors declare that they have no competing interests.
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How to cite this article: Thomas, A J, Stanford, P K and Sarnecka, B W 2016 No Child Left Alone: Moral Judgments about
Parents Aect Estimates of Risk to Children.
Collabra,
2(1): 10, pp. 1–14, DOI: http://dx.doi.org/10.1525/collabra.33
Submitted: 10 October 2015 Accepted: 17 July 2016 Published: 23 August 2016
Copyright: © 2016 The Author(s). This is an open-access article distributed under the terms of the Creative Commons
Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
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