BUS308 Week 4 Lecture 1 Examining Relationships Expect.docx

BUS308 Week 4 Lecture 1
Examining Relationships
Expected Outcomes
After reading this lecture, the student should be familiar with:
1. Issues around correlation
2. The basics of Correlation analysis
3. The basics of Linear Regression
4. The basics of the Multiple Regression
Overview
Often in our detective shows when the clues are not providing a
clear answer – such as
we are seeing with the apparent continuing contradiction
between the compa-ratio and salary
related results – we hear the line “maybe we need to look at this
from a different viewpoint.”
That is what we will be doing this week.
Our investigation changes focus a bit this week. We started the
class by finding ways to
describe and summarize data sets – finding measures of the
center and dispersion of the data with
means, medians, standard deviations, ranges, etc. As interesting
as these clues were, they did not
tell us all we needed to know to solve our question about equal
work for equal pay. In fact, the
evidence was somewhat contradictory depending upon what

measure we focused on. In Weeks 2
and 3, we changed our focus to asking questions about
differences and how important different
sample outcomes were. We found that all differences were not
important, and that for many
relatively small result differences we could safely ignore them
for decision making purposes –
they were due to simple sampling (or chance) errors. We found
that this idea of sampling error
could extend into work and individual performance outcomes
observed over time; and that over-
reacting to such differences did not make much sense.
Now, in our continuing efforts to detect and uncover what the
data is hiding from us, we
change focus again as we start to find out why something
happened, what caused the data to act
as it did; rather than merely what happened (describing the data
as we have been doing). This
week we move from examining differences to looking at
relationships; that is, if some measure
changes does another measure change as well? And, if so, can
we use this information to make
predictions and/or understand what underlies this common
movement?
Our tools in doing this involve correlation, the measurement of
how closely two
variables move together; and regression, an equation showing
the impact of inputs on a final
output. A regression is similar to a recipe for a cake or other
food dish; take a bit of this and
some of that, put them together, and we get our result.
Correlation

We have seen correlations a lot, and probably have even used
them (formally or
informally). We know, for example, that all other things being
equal; the more we eat. the more
we weigh. Kids, up to the early teens, grow taller the older they
get. If we consistently speed,
we will get more speeding tickets than those who obey the
speed limit. The more efforts we put
into studying, the better grades we get. All of these are
examples of correlations.
Correlations exist in many forms. A somewhat specialized
correlation was the Chi
Square contingency test (for multi-row, multi-column tables) we
looked at last week, if we find
the distributions differ, then we say that the variables are
related/correlated. This correlation
would run from 0 (no correlation) thru positive values (the
larger the value the stronger the
relationship).
Probably the most commonly used correlation is the Pearson
Correlation Coefficient,
symbolized by r. It measures the strength of the association –
the extent to which measures change
together – between interval or ratio level measures. Excel’s Fx
Correl, and the Data Analysis
Correlation both produce Pearson Correlations.
Most correlations that we are familiar with show both the
direction (direct or inverse) as
well as the strength of the relationship, and run from -1.0 (a
strong and perfect inverse

correlation) through 0 (a weak and non-existent correlation) to
+1.0 (a strong an perfect direct
correlation). A direct correlation is positive; that is, both
variables move in the same direction,
such as weight and height for kids. An inverse, or negative,
correlation has variables moving in
different directions. For example, the number of hours you sleep
and how tired you feel; the
more hours, the less tired while the fewer hours, the more tired.
The strength of a correlation is shown by the value (regardless
of the sign). For example,
a correlation of +.78 is just as strong as a correlation of -.78;
the only difference is the direction
of the change. If we graphed a +.78 correlation the data points
would run from the lower left to
the upper right and somewhat cluster around a line we could
draw thru the middle of the data
points. A graph of a -.78 correlation would have the data points
starting in the upper left and run
down to the lower right. They would also cluster around a line.
Correlations below an absolute value (when we ignore the plus
or minus sign) of around
.70 are generally not considered to be very strong. The reason
for this is due to the coefficient of
determination(CD). This equals the square of the correlation
and shows the amount of shared
variation between the two variables. Shared variation can be
roughly considered the reason that
both variables move as they do when one changes. The more
the shared variation, the more one
variable can be used to predict the other. If we square .70 we
get .49, or about 50% of the
variation being shared. Anything less is too weak of a
relationship to be of much help.

Students often feel that a correlation shows a “cause-and-effect”
relationship; that is,
changes in one thing “cause” changes in the other variable. In
some cases, this is true – height
and weight for pre-teens, weight and food consumption, etc. are
all examples of possible cause-
and- effect relationships; but we can argue that even with these
there are other variables that
might interfere with the outcomes. And, in research, we cannot
say that one thing causes or
explains another without having a strong correlation present.
However, just as our favorite detectives find what they think is
a cause for someone to
have committed the crime, only to find that the motive did not
actually cause that person to
commit the crime; a correlation does not prove cause-and-
effect. An example of this is the
example the author heard in a statistics class of a perfect +1.00
correlation found between the
barrels of rum imported into the New England region of the
United States between the years of
1790 and 1820 and the number of churches built each year. If
this correlation showed a cause-
and-effect relationship, what does it mean? Does rum drinking
(the assumed result of importing
rum) cause churches to be built? Does the building of churches
cause the population to drink
more rum?
As tempting as each of these explanations is, neither is
reasonable – there is no theory or
justification to assume either is true. This is a spurious

correlation – one caused by some other,
often unknown, factor. In this case, the culprit is population
growth. During these years – many
years before Carrie Nation’s crusade against Demon Rum – rum
was the common drink for
everyone. It was even served on the naval ships of most
nations. And, as the population grew,
so did the need for more rum. At the same time, churches in the
region could only hold so many
bodies (this was before mega-churches that held multiple
services each Sunday); so, as the
population got too large to fit into the existing churches, new
ones were needed.
At times, when a correlation makes no sense we can find an
underlying variable fairly
easily with some thought. At other times, it is harder to figure
out, and some experimentation is
needed. The site http://www.tylervigen.com/spurious-
correlations is an interesting website
devoted to spurious correlations, take a look and see if you can
explain them. ��
Regression
Linear. Even if the correlation is spurious, we can often use the
data in making
predictions until we understand what the correlation is really
showing us. This is what
regression is all about. Earlier correlations between age,
height, and even weight were
mentioned. In pediatrician offices, doctors will often have
charts showing typical weights and
heights for children of different ages. These are the results of
regressions, equations showing
relationships. For example (and these values are made up for

this example), a child’s height
might be his/her initial height at birth plus and average growth
of 3.5 inches per year. If the
average height of a newborn child is about 19 inches, then the
linear regression would be:
Height = 19 inches plus 3.5 inches * age in years, or in math
symbols:
Y = a + b*x, where y stands for height, a is the intercept or
initial value at age 0
(immediate birth), b is the rate of growth per year, and x is the
age in years.
In both cases, we would read and interpret it the same way: the
expected height of a child is 19
inches plus 3.5 inches times its age. For a 12-year old, this
would be 19 + 3.5*12 = 19 + 42 = 61
inches or 5 feet 1 inch (assuming the made-up numbers are
accurate).
Multiple. That was an example of a linear regression having
one output and a single,
independent variable as an input. A multiple regression
equation is quite similar but has several
independent input variables. It could be considered to be
similar to a recipe for a cake:
http://www.tylervigen.com/spurious-correlations
Cake = cake mix + 2* eggs + 1½ * cup milk + ½ * teaspoon
vanilla + 2 tablespoons* butter.
A regression equation, either linear or multiple, shows us how
“much” each factor is used in or

influences the outcome. The math format of the multiple
regression equation is quite similar to
that of the linear regression, it just includes more variables:
Y = a + b1*X1 + b2*X2 + b3*X3 + …; where a is the intercept
value when all the inputs
are 0, the various b’s are the coefficients that are multiplied by
each variable value, and
the x’s are the values of each input.
A note on how to read the math symbols in the equations. The
Y is considered the output or
result, and is often called the dependent variable as its value
depends on the other factors. The
different b’s (b1, b2, etc.) are coefficients and read b-sub-1, b-
sub-2, etc. The subscripts 1, 2, etc.
are used to indicate the different coefficient values that are
related to each of the input variables.
The X-sub-1, X-sub-2, etc., are the different variables used to
influence the output, and are called
independent variables. In the recipe example, Y would be the
quality of the cake, a would be the
cake mix (a constant as we use all of what is in the box), the
other ingredients would relate to the
b*X terms. The 2*eggs would relate to b1*X1, where b1 would
equal 2 and X1 stands for eggs,
the second input relates to the milk, etc.
Summary
This week we changed our focus from examining differences to
looking for relationships
– do variables change in predictable ways. Correlation lets us
see both the strength and the
direction of change for two variables. Regression allows us to
see how some variables “drive” or

explain the change in another.
Pearson’s (for interval and ratio data variables) and Spearman’s
(for rank ordered or
ordinal data variables) are the two most commonly used
correlation coefficients. Each looks at
how a pair of variables moves in predictable patterns – either
both increasing together or one
increasing as the other decreases. The correlation ranges from -
1.00 (moving in opposite
directions) to +1.00 (moving in the same direction). These are
both examples of linear
correlation – how closely the variables move in a straight line
(if graphed). Curvilinear
corrections exist but are not covered in this class.
Regression equations show the relationship between
independent (input) variables and a
dependent (output variables). Linear regression involves a pair
of variables as seen in the linear
correlations. Multiple regression uses several input
(independent) variables for a single output
(dependent) variable.
The basic form of the regression equation is the same for both
linear and multiple
regression equations. The only difference is in the number of
inputs used. The multiple
regression equation general form is:
Y = Intercept + coefficient1 * variable1 + coefficient2 *
variable2 + etc. or
Y = A + b1*X1 + b2*X2 + …; where A is the intercept value, b
is a coefficient value, and
X is the name of a variable, and the subscripts identify different

variables.
Summary
This week we changed focus from examining differences to
examining relationships –
how variables might move in predictable patterns. This, we
found, can be done with either
correlations or regression equations.
Correlations measure both the strength (the value of the
correlation) and the direction (the
sign) of the relationship. We looked at the Pearson Correlation
(for interval and ratio level data)
and the Spearman’s Rank Order Correlation (for ordinal level
data). Both range from -1.00 (a
perfect inverse correlation where as one value increases the
other decreases) to +1.00 (a perfect
direct correlation where both value increase together). A
perfect correlation means the data
points would fall on a straight line if graphed. One interesting
characteristic of these correlations
occurs when you square the values. This produces the
Coefficient of Determination (CD), which
gives us an estimate of how much variation is in common
between the two variables. CD values
of less than .50 are not particularly useful for practical
purposes.
Regression equations provide a formula that shows us how
much influence an input
variable has on the output; that is, how much the output changes
for a given change in an input.
Regression equations are behind such commonly used

information such as the relationship
between height and weight for children that doctors use to
assess our children’s development.
That would be a linear regression, Weight = constant +
coefficient*height in inches or Y = A +
b*X, where Y stands for weight, A is the constant, b is the
coefficient, and X is the height. A
multiple regression is conceptually the same but has several
inputs impacting a single output.
If you have any questions on this material, please ask your
instructor.
After finishing with this lecture, please go to the first
discussion for the week, and engage
in a discussion with others in the class over the first couple of
days before reading the second
lecture.
ORIGINAL ARTICLE
Emotion Regulation Mediates the Relationship between a
History
of Child Abuse and Current PTSD/Depression Severity
in Adolescent Females
Sufna G. John1 & Josh M. Cisler2 & Benjamin A. Sigel1
Published online: 20 April 2017
# Springer Science+Business Media New York 2017

Abstract Although experiencing child abuse (i.e., physical
abuse, sexual abuse, exposure to violence) is associated with
a variety of mental health difficulties, simple exposure to abuse
does not produce symptoms in every individual. The current
study explored emotion regulation as a mediator in the relation-
ship between a history of child abuse and symptoms of post-
traumatic stress and depression. Adolescent females (ages 11–
17 years) were asked to retrospectively report on their exposure
to child abuse, current symptoms of PTSD/depression, and
emotion regulation abilities. Caregiver report of adolescent
emotional difficulties was also obtained. Analyses revealed that
child abuse-exposed females, when compared to females with-
out a trauma history, had worse emotion regulation abilities and
increased mental health difficulties. Moreover, emotion regula-
tion significantly mediated the relationship between child abuse
and all assessed mental health symptoms. These findings ex-
tend previous work from adult samples, underscoring the im-
portance of assessing emotion regulation abilities in abuse-
exposed youth.
Keywords Emotion regulation . Child abuse . PTSD .
Depression . Mediation . Adolescents
Introduction
Child Abuse
Child abuse (in this study defined as physical abuse, sexual
abuse, and exposure to violence) represents a widespread pub-
lic health concern. In 2013, the National Child Abuse and
Neglect Data System reported 122, 159 counts of physical
abuse (representing 14.1% of all maltreatment reports) and
60,956 counts of sexual abuse (representing 7.0% of all mal-
treatment reports) for children ages birth through 18 years.
Additionally, caregiver domestic violence was reported for

27.4% of all victims of maltreatment (ages birth through
18 years), equivalent to 127,519 children nationally (U.S.
Department of Health and Human Services 2015). Of note,
these statistics likely underestimate the true prevalence, as
these data only reflect instances of abuse that were reported
to the authorities.
Child Abuse and Associated Difficulties
Experiencing child abuse is associated with an increased
risk for developing mood and anxiety disorders within
adulthood (Briere and Jordan 2009; Greif Green et al. 2010),
as well as greater engagement in problematic behavior such as
substance abuse and risky sexual behavior (Arata et al. 2005;
Blumenthal et al. 2008). Adolescents who have been exposed
to abuse often suffer from co-morbid conditions (i.e.,
depression or substance abuse), complicating the diagnostic
picture, treatment considerations, and degree of functional im-
pairment (Donnelly and Amaya-Jackson 2004). Given the
high rate of comorbid conditions in those exposed to trauma,
* Sufna G. John
[email protected]
1 Department of Psychiatry, University of Arkansas for Medical
Sciences, Little Rock, AR, USA
2 Department of Psychiatry, University of Wisconsin, Madison,
WI,
USA
J Fam Viol (2017) 32:565–575
DOI 10.1007/s10896-017-9914-7
http://crossmark.crossref.org/dialog/?doi=10.1007/s10896-017-
9914-7&domain=pdf

it is important to examine transdiagnostic difficulties as poten-
tial mediators.
Emotion Regulation
One transdiagnostic feature receiving increased attention is
emotion regulation, a term which incorporates emotion under-
standing, awareness, acceptance, identification, and behavioral
regulation/decision-making during periods of emotional dis-
tress (Gratz and Roemer 2004). Early and middle adolescence
(11–14 years of age referring to early adolescence, 15–17 years
of age referring to middle adolescence) represent especially
important developmental periods in which to study emotion
regulation, and are the focus of the present study, as they in-
clude a multitude of changes in autonomy and social relation-
ships that require these youth, perhaps for the first time, to
develop and use emotion regulation strategies largely indepen-
dent of parental guidance (American Academy of Pediatrics
2015; Larson and Richards 1991; Steinberg and Avenevoli
2000). They also represent important time periods in which to
study mental health disorders, due to significant cognitive and
neurodevelopmental changes (Blakemore and Choudhury
2006; Blakemore 2008), the high prevalence of mental health
symptoms, and the emergence of several adult disorders within
these age groups (Patton et al. 2014; Paus et al. 2008).
Poor emotion regulation skills are linked to a variety of
mental health symptoms and appear to represent a strong
transdiagnostic correlate of mental health symptoms in adults
and adolescents (Aldao et al. 2010). Good emotion regulation
skills during childhood and adolescence are linked to greater
peer acceptance (Kim and Cicchetti 2010), concurrent and
future social competence (Spinrad et al. 2006), and lower
internalizing/ externalizing pathology (Kim and Cicchetti
2010). Conversely, poor emotion regulation is documented

in individuals diagnosed with anxiety and mood disorders,
eating disorders, substance abuse, and those who display ag-
gression or experience peer rejection and social withdrawal
(Herts et al. 2012; McLaughlin et al. 2011).
The Relationship between Emotion Regulation and Child
Abuse
Poor emotion regulation also has been heavily examined as
both a maladaptive outcome of child abuse and a risk factor
for developing other mental health difficulties after abusive
incidents (Kring and Werner 2004). Indeed, those who have
experienced childhood abuse demonstrate difficulties in recog-
nition, understanding, and acceptance of emotions, as well as
overall difficulties with emotion regulation (Gratz et al. 2007;
Pollak and Sinha 2002; Shipman et al. 2000). Moreover, sev-
eral aspects of emotion regulation have been correlated with
posttraumatic stress symptoms, including low emotional ac-
ceptance and clarity and impulsive decision-making during
periods of distress (Ehring and Quack 2010; Lilly and Lim
2013; Tull et al. 2007; Weiss et al. 2012). Sundermann and
DePrince (2015) also found that both maltreatment character-
istics (e.g., types of trauma) and difficulties with emotion reg-
ulation significantly predicted posttraumatic symptoms in a
community sample of adolescent females with a history of
maltreatment.
Despite substantive research on the relationship between
child abuse and mental health symptoms, much less is known
about the potential mediating role of emotion regulation in the
relationship between abuse exposure and mental health symp-
toms, particularly in adolescents. Results from adult samples
indeed demonstrate that poor emotion regulation partially me-
diates the relationship between child abuse and subsequent
posttraumatic and depressive symptoms (Crow et al. 2014;

Ullman et al. 2014). Research on young adults also suggests
that emotion dysregulation mediates the relationship between
trauma exposure and symptoms of PTSD (Goldsmith et al.
2013) and depression (Goldsmith et al. 2013; Tull et al.
2007). Examining school-aged children, Choi and Oh (2014)
found that caregiver-reported emotion regulation fully medi-
ated the relationship between childhood trauma, including
abuse, and emotional/behavioral symptoms. Therefore, there
is foundational literature to suggest that emotion regulation
mediates the relationship between child abuse exposure and
emotional/behavioral difficulties in several developmental pe-
riods. However, the current literature does not include a con-
current examination of emotion regulation and symptoms of
depression and posttraumatic stress in adolescent samples
with a strong history of child abuse, nor does it consistently
include information from caregivers. This latter point is espe-
cially crucial, as emotion regulation difficulties may bias the
way that individuals report their own symptoms, underscoring
the need for collateral information.
The Current Study
The current study explored emotion regulation as a mediator
for the relationship between child abuse severity (i.e., physical
abuse, sexual abuse, and witnessing violence) and mental
health symptoms (PTSD and depression) in a sample of
abuse-exposed adolescent females and a healthy comparison
sample of adolescent females. This study addresses several
limitations in the current literature by examining important
developmental periods (early and middle adolescence), utiliz-
ing a sample with a high degree of trauma exposure, and
including self- and caregiver-reported measures of posttrau-
matic and depressive symptoms. We hypothesized that emo-
tion regulation would significantly mediate the relationship
between child abuse and symptoms of PTSD and depression.
We further hypothesized that this relationship would also exist

for caregiver-reported mental health symptoms in their chil-
dren, thus avoiding potential reporter bias.
566 J Fam Viol (2017) 32:565–575
Method
Participants
Participants consisted of 81 early and middle adolescent girls,
aged 11–16 years, who were recruited as part of two separate
neuroimaging studies (Cisler et al. 2016; Lenow et al. 2014).
The rationale for this age range was to focus on the important
periods of early and middle adolescence while also allowing
feasibility in recruitment. Participant recruitment consisted of
both community-wide general advertising (e.g., newspaper
ads, flyering) as well as through networking with specific
trauma-focused mental health clinics and clinicians.
Interested participants first underwent a phone screening to
establish probable group membership (control or abuse expo-
sure), which was later confirmed through the assessment mea-
sures detailed below. Inclusion criteria for control girls were as
follows: age between 11 and 16 years, female sex, the absence
of exposure to any measured traumatic event (both abusive
and non-abusive trauma – such as experiencing a natural di-
saster), and current mental health disorders. Inclusion criteria
for girls with a history of child abuse was as follows: age
between 11 and 16 years; female sex; and a positive
history of sexual abuse, physical abuse, or witnessed
violence. Psychotic disorders, developmental disorders,
and MRI contraindications (e.g., internal metal objects,
claustrophobia) were exclusionary for all participants.
Table 1 lists demographic and clinical characteristics of the
sample. All procedures performed in this study were in

accordance with the ethical standards of the Institutional
Review Board (IRB) and with the 1964 Helsinki decla-
ration and its later amendments or comparable ethical stan-
dards. All participants and caregivers provided informed con-
sent into the study.
Assessments
Current Mental Health Diagnoses Participants whose data
are analyzed in the current study were recruited as part of two
separate brain imaging research studies, and as such the
structured clinical interview differed between partici-
pants. Participants current mental health diagnoses were
assessed with either the Mini International Neuropsychiatric
Interview-Kid (MINI-Kid; n = 48) or the Kiddie
Schedule for Affective Disorders and Schizophrenia (K-
SADS; n = 33) (Kaufman et al. 1997; Sheehan et al. 2010).
Both the MINI-Kid and K-SADS are semi-structured clinical
interviews that assess most mental health disorders in
childhood and adolescence and have established reliabil-
ity and validity (Kaufman et al. 1997; Sheehan et al. 2010),
depending on the study in which they participated. Only the
adolescents, and not the caregivers, completed these struc-
tured interviews.
Trauma Histories Participant trauma histories were gathered
with the trauma assessment sections of the National Survey of
Adolescents (NSA), a structured interview used in prior epide-
miological studies of child abuse and mental health functioning
among adolescents (Kilpatrick et al. 2000, 2003). Both abusive
and non-abusive traumas (e.g., motor vehicle accident) were
assessed using this measure in order to assure that those indi-
viduals in the control group had not been exposed to another
type of traumatic event. Child abusive events were assessed
with behaviorally-specific dichotomous questions, which in-
cluded: 1) sexual abuse (i.e., anal penetration, vaginal penetra-

tion, oral sex on the perpetrator, oral sex from the perpetrator,
digital penetration, fondling of the adolescent, forced fondling
of the perpetrator), 2) physical abuse (i.e., attacked with a
weapon; attacked with a stick, club, or bottle; attacked without
a weapon; threatened with a weapon; attacked with fists), 3)
severe abuse from a caregiver (i.e., beaten with fists or an
object
to the point where bruising or bleeding occurred), 4) witnessed
violence (i.e., witnessing a violent beating at home or in com-
munity). Table 2 includes a list of all questions included in this
study, grouped by type of trauma. Only the adolescents, and not
the caregivers, completed these trauma interviews.
In line with research indicating a dose-response relation-
ship between the severity of child abuse exposure and risk for
subsequent mental health disorders (Cisler et al. 2011a, 2011b,
2012; Kolassa et al. 2010a, 2010b), we calculated child abuse
severity as the sum of the unique categories of child abuse to
which the adolescent was exposed. That is, during the NSA,
participants were asked to retrospectively report on the pres-
ence of 29 unique types of child abuse, and their total severity
was the number of unique types of child abuse to which they
answered affirmatively. This child abuse severity variable was
then used in subsequent analyses testing mediation within the
child abuse group.
The semi-structured clinical interviews and trauma assess-
ments were conducted by a trained female clinical research
coordinator with several years of experience administering
these interviews who was working under the supervision of
a licensed clinical psychologist.
Emotion Dysregulation Assessment Adolescents completed
the Difficulties in Emotion Regulation Scale (DERS, Gratz
and Roemer 2004), a 36 item self-report or care-giver report
measure of six domains of emotion regulation: awareness of

negative emotions, emotional clarity, strategies to regulate
emotions, difficulty engaging in goal directed behavior during
negative emotions, nonacceptance of negative emotions, and
impulse control during negative emotions. Participants indi-
cate how often the items were true for them on a five-point
Likert scale (‘Almost never’ to ‘Almost always’). Subsequent
psychometric analyses of the DERS suggested the removal of
the awareness scale, which was only comprised of reverse
coded items and correlates poorly with the remaining latent
J Fam Viol (2017) 32:565–575 567
factors (Bardeen et al. 2012; Fairholme et al. 2013).
Accordingly, we did not use the awareness scale in the present
analyses. The psychometric properties of the DERS
within adolescent samples has been established (Weinberg
and Klonsky 2009). Chronbach’s alpha for the remaining
DERS items among this sample was .95. The total DERS
score was utilized as a comprehensive measure of emotion
regulation, as the individual subscales within this sample were
highly correlated.
Adolescent Clinical Symptom Assessment Adolescents also
completed the UCLA PTSD Reaction Index – Adolescent
version (Steinberg et al. 2004; 2013) and the adolescent ver-
sion of the Short Mood and Feelings Questionnaire (SMFQ).
The UCLA PTSD Index consists of 22 items assessing DSM-
IVre-experiencing, avoidance, and hyperarousal symptoms of
PTSD using a five-point Likert Scale (BNever^ to BAlmost
every day^). Cronbach’s alpha for the UCLA PTSD Index
among this sample was .96. For the present analyses, we used
a summed PTSD symptom severity score from all DSM-IV
symptom items. The SMFQ consists of 13 items assessing

depression symptoms using a three-point Likert Scale. A total
depression symptom severity score was created by summing
all the items. Cronbach’s alpha for the SMFQ among this
sample was .94.
Caregiver Clinical Symptom Assessment Caregivers addi-
tionally completed the Child Behavior Checklist (Achenbach
1991), a broad measure of childhood mental health difficulties
across several domains. For the purpose of the present analy-
ses, we focused on total internalizing symptoms, consisting of
the sum of the anxious/depressed, withdrawn/depressed, and
somatic complaints subscales. Cronbach’s alpha for the CBCL
Internalizing items among this sample was .93.
Additional Assessments Adolescent’s verbal IQ was mea-
sured using the Receptive One Word Picture Vocabulary Test
administered by a female research coordinator. This measure
Table 1 Demographic and clinical characteristics of the sample
Abuse-exposed (n = 61) Controls (n = 20)
Variable Mean (frequency) SD Mean (frequency) SD P value of
group difference*
Age 14.44 1.51 13.7 1.6 .06
Verbal IQ 96.05 14.81 105.7 16.4 .02
Ethnicity 46% Caucasian 80% Caucasian .053
20% African
41% African American
American
10% Biracial

3% Hispanic
Current PTSD 62% 0 <.001
Total number of abusive events 5.3 4.4 0 NA
Direct physical abuse 43% 0
Physical abuse from caregiver 44% 0
Sexual abuse 67% 0
Witnessed violence 88% 0
DERS nonacceptance 8.1 6.9 3.3 3.8 .006
DERS goals 11.1 5.2 6.2 5.4 <.001
DERS impulse 8.1 6.1 2.1 3.3 <.001
DERS strategies 11.0 8.3 3.0 3.1 <.001
DERS clarity 7.4 5.0 4.6 4.1 .03
DERS total 45.8 26.2 19.1 15.2 <.001
UCLA PTSD Index 26.98 19.9 .25 1.1 <.001
SMFQ 10.0 7.9 2.7 3.0 <.001
CBCL – anxious 7.4 5.9 2.1 2.4 <.001
CBCL – depressed 5.1 3.5 1.1 1.8 <.001
CBCL – somatic complaints 5.0 4.9 1.3 1.8 .002

SMFQ Short Mood and Feelings Questionnaire, CBCL Child
Behavior Checklist
*Given evidence of group differences in age, verbal IQ, and
ethnicity, the remainder of group comparisons includes these
variables as covariates
568 J Fam Viol (2017) 32:565–575
was included to characterize any group differences in general
cognitive function.
Statistical Analyses
Preliminary analyses assessed general linear model (GLM)
statistical assumptions and potential confounding factors that
differ between groups to be used as covariates in primary
analyses. Missing data was addressed directly during data col-
lection by having the research coordinator review all question-
naires and if an item was missing, the participant was notified
and asked to complete the item.
To test hypotheses that difficulties with emotion regulation
mediate the relationship between child abuse exposure and
PTSD, depression, and caregiver-reported internalizing symp-
tom severity, we conducted two sets of analyses to verify three
predictions of a mediation pathway (Baron and Kenny 1986;
MacKinnon et al. 2007; Preacher and Hayes 2008): 1) path a,
such that there is a significant relation between the indepen-
dent variable and the hypothesized mediator; 2) path b, such
that there is a significant relation between the proposed medi-
ator the dependent measure, controlling for the independent
variable; and 3) that the total effect, path c (direct relation

between the independent variable and the outcome measure),
weakens in the presence of the indirect effect (i.e., total effect c
equals the direct effect c’ minus the indirect effect ab).
Figure 1 illustrates the generic mediation framework and the
hypothesized mediation pathway in the current study.
First, we compared the child abuse exposed and control
participants on each subscale of the DERS, adjusting for co-
variates as needed, within a GLM framework using iteratively
reweighted least squares estimation (‘robustfit’ in Matlab
using a bisquare weighting function). These analyses
effectively test path a, such that there is a relation be-
tween child abuse severity and the hypothesized mediator,
emotion regulation. Given that, by DSM definition, our
healthy and non-trauma-exposed control group cannot have
any PTSD symptoms, analyses testing paths b and the indirect
effect ab were conducted solely within the child abuse group
(n = 61), which required re-establishing path a within this
restricted sample as well.
As recommended (Baron and Kenny 1986; MacKinnon
et al. 2007; Preacher and Hayes 2008), these analyses entailed
four separate multiple regression GLM analyses using itera-
tively reweighted least squares estimation: 1) path a, such that
child abuse severity is associated with emotion regulation, 2)
path b, such that emotion regulation is associated with clinical
symptom severity when controlling for child abuse severity, 3)
path c, such that child abuse severity is associated with clinical
symptom severity, and 4) that the indirect pathway, a x b,
Table 2 Trauma questions from the national survey of
adolescents, organized by abuse type
Physical abuse Physical abuse Witness violence Witness
violence Sexual abuse

Attacked you with a gun, knife, or
other weapon, regardless of
when it happened or whether
you told the police or other
authorities?
Has a caregiver ever
beat you up, hit
you with a fist, or
kicked you hard?
Heard or seen your
caregivers throw
objects at each other,
without hitting the
other person?
Seen someone actually
shoot someone else
with a gun?
Has a man or boy ever put a sexual part
of his body inside your private
sexual parts, inside your rear end, or
inside your mouth when you didn’t
want him to?
Physically attacked you without a
weapon, and you thought they
were trying to kill or seriously
injure you?
grabbed you
around the neck or

choked you?
Heard or seen them
(caregivers) throw ob-
jects that hit one an-
other?
Seen someone actually
cut or stab someone
else with a knife or
other sharp
weapon?
Has anyone, male or female, ever put
fingers or objects inside your private
sexual parts or inside your rear end
when you didn’t want them to?
Threatened you with a gun or
knife, but didn’t actually shoot
or cut you?
burned or scalded
you on purpose?
Heard or seen them
(caregivers) pushing or
shoving each other?
Seen someone being
molested, sexually
assaulted or raped?
Has anyone, male or female, ever put
their mouth on your private sexual

parts when you didn’t want them to?
Beat you up, attacked you, or hit
you with something like a stick,
club, or bottle so hard that you
were hurt pretty badly?
locked you in a
closet, tied you up,
or tied you to
something?
Heard or seen them
(caregivers) hitting
each other or beating
each other up with their
hands or fists?
Seen someone being
mugged or robbed?
Has anyone, male or female, ever
touched any of your private sexual
parts when you didn’t want them to?
Beat you up with their fists so hard
that you were hurt pretty badly?
threatened you
with a gun, knife,
or other weapon?
Heard or seen them
(caregivers) hitting or

beating each other with
objects, like stick, belt?
Seen someone
threaten someone
else with a knife, a
gun, or some other
weapon?
Has anyone, male or female, ever made
you touch their private parts when
you didn’t want to?
Has a caregiver ever spanked or hit
you so hard it caused bad
marks, bruises, cuts, or welts?
used a knife or
fired a gun at you
on purpose?
Heard or seen them
(caregivers) using a
weapon, like a gun or
knife on each other?
Seen someone beaten
up, hit, punched, or
kicked such that
they were hurt
pretty badly?
J Fam Viol (2017) 32:565–575 569

significantly differs from 0 (i.e., that the difference between
path c and c’ is significant). We tested the indirect ab pathway
using the percentile bootstrapping method (Preacher and
Hayes 2008), implemented in Matlab with 10,000 iterations
and resampling with replacement. These analyses were con-
ducted separately for each of the three outcome measures
(PTSD symptom severity, depression symptom severity, and
CBCL internalizing symptoms).
Results
Participant Characteristics and Preliminary Analyses
Preliminary analyses indicated non-normal distributions for
internalizing symptoms, child abuse severity, PTSD symptom
severity, depression severity, and DERS total and subscale
scores, which were corrected through square root transforma-
tions in all cases except child abuse severity, which was
corrected through log transformation. Participant characteris-
tics are provided in Table 1. As can be seen, the child abuse
and control groups differed either significantly or marginally
significantly in terms of age, IQ, and ethnicity; thus, these
variables were include as covariates in all subsequent analy-
ses. Adolescent girls who had experienced child abuse dem-
onstrated self-reported PTSD and depression symptoms and
greater caregiver-rated internalizing symptoms compared to
the control adolescent girls.
Comparison of DERS Scores between Child Abuse
and Control Groups
Between-group comparisons, adjusting for age, verbal IQ, and
ethnicity, demonstrated significantly higher scores among the
child abuse group for the DERS total score (t(76) = 4.5,
p < .001) as well as all subscale scores: nonacceptance

(t = 3.6(76), p < .001), goal-directed behavior (t = 3.8(76),
p < .001), impulse control (t = 5.1(76), p < .001), strategies
(t = 4.0(76), p < .001), and clarity (t = 2.2(76), p = .03). Table 1
lists means and SD of the total score and subscale scores for
each group.
Mediation Analyses among the Child Abuse Group
A summary of mediation analyses across the dependent mea-
sures is provided in Table 3. All analyses reported below con-
trolled for age, verbal IQ, and ethnicity. Regarding path a
(which is identical across the dependent measures), there
was a significant positive relation between child abuse sever-
ity and PTSD symptom severity (t(56) = 3.08, p < .003).
Regarding path b across the dependent measures, there were
significant relations between DERS total score and PTSD
symptom severity (t = 3.94(55), p < .001), depression severity
(t = 7.8(55), p < .001), and caregiver-rated internalizing symp-
toms (t = 2.14(55), p = .04) upon controlling for child abuse
severity. Regarding path c across the dependent measures,
there were significant positive relations between child abuse
severity and PTSD symptom severity (t = 6.3(56), p < .001),
depression severity (t = 3.5(56), p < .001), and caregiver-rated
internalizing symptoms (t = 4.3(56), p < .001). The indirect
pathways ab (product of path a and b) were significantly
greater than zero across the dependent measures (see
Table 3), demonstrating that the indirect pathway from child
abuse severity to the dependent measure through DERS total
score significantly mediates the direct relation between child
abuse severity and each dependent measure. However, path c’
remained significant for each dependent measures, indicating
the robustness of the direct relationship between child abuse
exposure and clinical symptom severity.
Discussion

Overall Study
The goals of this study were to examine emotion regulation as
a possible mediator for the relationship between child abuse
severity and PTSD/depression severity in a sample of adoles-
cent females. This study aimed to address several important
limitations in the current literature by examining adolescents
with a high degree of abuse exposure, including a comparison
group of adolescents without trauma exposure, and utilizing
both self- and caregiver-report measures. Adolescents ex-
posed to child abuse demonstrated greater difficulties in all
aspects of emotion regulation relative to control females, as
has been previously demonstrated in adult and adolescent
samples. As hypothesized, emotion regulation significantly
mediated the relationship between child abuse severity and
symptoms of posttraumatic stress and depression. Moreover,
these results do not appear to be related to reporter bias, as
self-reported emotion regulation also mediated the relation-
ship between child abuse severity and caregiver-reported in-
ternalizing symptoms. These results strengthen previous find-
ings from other populations, emphasizing the mediating role
of emotion regulation in the occurrence of pathological symp-
toms in adolescents who are exposed to trauma. The current
results further supports prior data suggesting that emotion reg-
ulation potentially operates as a transdiagnostic risk factor for
mental health difficulties (McLaughlin et al. 2011) by exam-
ining a heavily-traumatized population and including post-
traumatic symptoms and caregiver-reported measures.
Research Implications
Although exposure to traumatic events is necessary in the de-
velopment of PTSD and an established risk factor in the
570 J Fam Viol (2017) 32:565–575

development of depression, many individuals who experience
child abuse do not go on to develop these symptoms. Though
the rate of PTSD varies by type of trauma experienced, a meta-
analysis conducted by Alisic et al. (2014) found that children
and adolescents who appear most at risk are females who ex-
perience interpersonal trauma, such as the abusive events in-
cluded in the current study (Alisic et al. 2014). Specifically,
they found that 32.9% of females who had been exposed to
interpersonal trauma met criteria for PTSD. Given the variety of
outcomes in presentation that can occur in individuals who
have experienced abuse, continued work examining mediators
remains an important goal for risk-factor research. Specifically,
future work should examine how established mediators (e.g.,
emotion regulation, abuse characteristics) work in concert to
confer risk for impairing maladaptive symptoms associated
with experiencing abuse. For example, emotion regulation
should be tested as a mediator for other types of traumatic
events, such as natural disasters or motor vehicle accidents.
Moreover, established mediators should be confirmed through
longitudinal design, in order to establish the timing in which the
Table 3 Results of mediation
regression analyses across the
three dependent measures
Dependent measure Mediation path B t (Confidence interval) p
PTSD
a .55 3.08 .003
b 1.1 3.94 <.001

c 2.57 6.3 <.001
c’ 1.82 4.77 <.001
a x b indirect path .61 95% CI = .16–1.11 <.05
Depression
a .55 3.08 .003
b 1.2 7.8 <.001
c 1.05 3.5 <.001
c’ .41 2.02 .048
a x b indirect path .65 95% CI = .22–1.14 <.05
Internalizing symptoms*
a .55 3.08 .003
b .52 2.14 .04
c 1.38 4.3 <.001
c’ 1.08 3.2 .002
a x b indirect path .29 95% CI = .01–.65 <.05
Age, verbal IQ, and ethnicity were included as covariates in all
analyses. These analyses were conducted among
abuse-exposed adolescents only (n = 61)
*Symptoms are caregiver-reported

Child Abuse
Severity
Fig. 1 Illustration of the generic
mediation framework and the
hypothesized mediation pathway
in the current study
J Fam Viol (2017) 32:565–575 571
development of mental health symptoms, mediators (e.g., emo-
tion regulation abilities), and abusive incidents occur.
Clinical Implications
Early and middle adolescence represent time periods of devel-
opment in emotion regulation capacities as well as mental
health disorders, and thus, are sensitive times in which inter-
ventions may produce long-lasting change. Given the potential
problematic trajectory of individuals who have been exposed
to child abuse, intervention to alter this course is crucial. There
is a current call in the literature to examine mediators for treat-
ment responsiveness in high-risk populations. Particularly with
PTSD, research on adult individuals with severe symptoms
(mirroring our population in a different developmental period)
demonstrates that roughly 40% - 50% of individuals with
chronic PTSD symptoms fail to meet criteria for functional
improvement or symptom discontinuation after receiving a
course of treatment (Foa et al. 2002). Given that emotion reg-
ulation difficulties may mediate symptom presentation, per-
haps they also may mediate treatment responsiveness.
Addressing emotion dysregulation could be an important pre-
ventative area, as it associated with a variety of mental health

disorders. Moreover, given the high comorbidity rate between
PTSD and other mental health conditions, traumatized youth
could represent an ideal sample to target for such interventions.
However, there is disagreement in the field as to whether
the current evidence-based interventions for adolescent trau-
ma (e.g., Trauma-Focused Cognitive Behavioral Therapy, TF-
CBT) adequately build emotion regulation or whether a
phase-based approach including treatments specifically
targeting poor emotion regulation should be used. Indeed,
studies with adult PTSD patients have found greater improve-
ment in PTSD symptoms by adding a component specifically
addressing emotion regulation to the typical course of trauma
treatment (Bryant et al. 2013; Cloitre et al. 2010). For cases of
complex trauma, the creators of TF-CBT have suggested ex-
tending the emotion regulation/stabilization phase of treat-
ment, acknowledging the deficits in these skills for highly
traumatized youth (Cohen et al. 2012). However, limited re-
search has been done on the traditional and extended treatment
models of TF-CBT related to efficacy in building emotion
regulation. This is an important empirical question that should
be examined in future work.
Indeed, emotion regulation could be an important area to
target within family work for a variety of reasons. First, genetic
studies on emotion regulation (specifically alexithymia) sug-
gest that between 30% and 40% of the variability within this
trait can be accounted for by genetic influences (Jørgensen et al.
2007; Picardi et al. 2011), suggesting that parents/caregivers of
these children may also struggle with regulating emotions.
Second, developmental theorists posit that emotion regulation
during infancy and childhood is largely influenced by parental
behaviors (see Shipman et al. 2007 for a review), suggesting a
model in which parenting behaviors confer risk for poor emo-
tion regulation, subsequently conferring risk for developing

emotional or behavioral symptoms. Taken together, these
findings suggest that targeting emotion regulation and its’
impact on parenting may help caregivers to support adolescent
gains within treatment and improve the caregiver-child relation-
ship. Although our study was not able to explicitly test
the development of emotion regulation skills or symptoms over
time, as it was cross-sectional in nature, it underscores the im-
portant role these skills have in impacting the association be-
tween prior adverse life events and current pathological symp-
tom presentations. Future research should examine whether
parent/caregiver emotion regulation mediates the relationship
between childhood abuse and child maladaptive symptoms and
whether these symptoms improve during evidence-based youth
trauma treatment. Fortunately, most evidence-based trauma
treatments for youth (e.g. Trauma-Focused Cognitive
Behavioral Therapy, Child Parent Psychotherapy) explicitly in-
volve considerable parent/caregiver work and thus already pro-
vide the framework to target parent/caregiver emotion regula-
tion, if needed.
Study Strengths
The greatest strength of this study is our unique sample, as we
examined adolescent females with a high degree of child
abuse severity. Prior to this study, emotion regulation as a
mediating factor in the relationship between child abuse and
PTSD/depressive symptoms had not been established in
highly-impaired adolescent samples, which are more repre-
sentative of those that present to treatment. Prevalence rates
reported by a variety of mental health clinics within the
National Traumatic Stress Network suggest an average of 3–
4 trauma types experienced by children ages 1.5 to 18 years
(Greeson et al. 2014). By comparison, adolescent girls in the
current sample were exposed to 5.3 child abusive traumas on
average; therefore, our sample is high-risk and largely repre-
sentative of highly-impaired individuals who present for treat-

ment. Moreover, this sample was diverse in nature, allowing
for generalization of these findings to a broader patient popu-
lation. We also controlled for several confounding variables,
including verbal IQ, age, and ethnicity, and can therefore be
more confident of the relationship between these variables.
Finally, utilizing caregiver- and self-report measures of mental
health symptoms helps to avoid reporter bias, as one could
easily argue that individuals with poor emotion regulation
may be biased in their self-report of emotional symptoms.
Study Limitations
Study strengths should be considered along with limitations.
Due to the cross-sectional nature of the study, true mediation
572 J Fam Viol (2017) 32:565–575
was not able to be measured. This would require a longitudi-
nal study that could fully establish the timing of abuse events
and symptom development. Despite this limitation, the results
are similar to previous findings on adult and undergraduate
samples without the cost and time of conducting a longitudi-
nal study with a difficult population to track and measure.
Examining highly-impaired individuals often leads to a small-
er sample size, as is this case with our study. However, statis-
tical measures were taken to correct for the smaller sample,
including bootstrapping techniques and iterative least squares
regression to minimize the impact of outliers. Moreover, al-
though the sample was small, recruitment took place within
the community in addition to trauma-specific clinics and the
participants were ethnically-diverse and largely representative
of the geographical location of the current study. Our study
also included several covariates (e.g., age, verbal IQ, ethnici-
ty), but did not include other variables that may impact this

relationship (such as income level, household composition)
that would be important to include in future studies.
Therefore, these results should be replicated in a larger sam-
ple, in order to ensure the generalizability of our findings.
Moreover, our study focused on early and middle adolescence
as important developmental periods for both emotion regula-
tion abilities and mental health disorders. It would be impor-
tant to assess whether these findings would remain true for
other periods of adolescence. Also, this study made use of
subjective measures of all variables, which are inherently sus-
ceptible to individual bias. Future work should examine this
relationship using objective measures of emotion regulation.
Similarly, no caregiver-report measure of emotion regulation
was obtained, which could have offered a more comprehen-
sive examination of these deficits. It is also important to note
that our measure of child abuse severity was defined as the
number of categories of child abuse to which the child was
exposed, as opposed to the total number of times that the child
has been abused independent of the number of categories. Our
methodology also focused on the number of categories so that
the child would only have to provide ‘yes/no’ responses to
whether a given category occurred, towards the goal of in-
creasing reliability, given that these were retrospective reports
of child abuse exposure. Assessing categories of trauma, as
opposed to number of traumatic episodes experienced is also
consistent with prior research (Kolassa et al. 2010a, b).
However, it is certainly possible that a child who was only
exposed to one category of child abuse could be exposed
multiple times to that category (e.g., several episodes of
witnessing domestic violence within this category). As such,
future research is needed that spans a range of methodology
for measuring trauma exposure to establish the robustness of
the current findings. Finally, our choice to frame abuse expo-
sure questions in a Byes/no^ format without asking partici-
pants to elaborate further also prevented the inclusion of sev-
eral other factors that would be important to examine in future

studies, including age of exposure, relationship to perpetrator,
or
perception of betrayal associated with the abuse.
Experiences of child abuse put youth at great risk for a
multitude of mental and physical health concerns. Work that
examines mediators in symptom presentations for highly-
traumatized populations can directly inform care for those
who arguably need it most. Our findings underscore the im-
portance of examining individual traits, such as emotion reg-
ulation, as potential mediators for the relationship between a
history of child abuse exposure and current symptoms of post-
traumatic stress and depression. These results also support
emotion regulation skills training as an important point of
intervention for youth with extensive child abuse exposure.
Acknowledgements This research was supported in part by
grants
MH097784 and DA036360 from the National Institutes of
Health.
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http://www.acf.hhs.gov/programs/cb/research-data-
technology/statistics-research/child-maltreatment
http://www.acf.hhs.gov/programs/cb/research-data-
technology/statistics-research/child-maltreatment
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Journal of Family Violence is a copyright of Springer, 2017. All
Rights Reserved.
Emotion...AbstractIntroductionChild AbuseChild Abuse and
Associated DifficultiesEmotion RegulationThe Relationship
between Emotion Regulation and Child AbuseThe Current
StudyMethodParticipantsAssessmentsStatistical
AnalysesResultsParticipant Characteristics and Preliminary
AnalysesComparison of DERS Scores between Child Abuse and
Control GroupsMediation Analyses among the Child Abuse
GroupDiscussionOverall StudyResearch ImplicationsClinical
ImplicationsStudy StrengthsStudy LimitationsReferences
Evaluating a Research Article/Research Article Summary
The articles reviewed for this assignment must report the results
of someone's research in an area of social research. The
research should have been carried out by the author(s). The
article must be directed at a scholarly audience.
Your review must be on an article reporting structured research,
that is, one with variables, statistical analyses, relationships
among variables, etc. The article may be about any Human
Growth and Development topic you choose. Check with me if
you have any doubts about your topic.
The following types of articles may NOT be used:
· Purely theoretical papers which discuss concepts and

propositions, but report no empirical research;
· Statistical or methodological papers where data may be
analyzed but the bulk of the work is on the refinement of some
new measurement, statistical or modeling technique;
· Review articles, which summarize the research of many
different past researchers, but report no original research by the
author;
· Popularizations or abridged reports, commonly found in
popular newsstand magazines such as Psychology Today or
books of readings designed for use by undergraduates;
· Extremely short reports with less than four pages devoted to
methods and findings.
Most research reports beginwith sections on theory and reviews
of others' research, so skim the whole article or read the
abstract, if there is one, to determine whether the author reports
actual research he or she has done. Psychology, as is true of all
scientific fields, is becoming increasingly complex in its
statistical analyses. A working rule is: if you can't understand
the statistical analyses presented in the results section, don't
choose the article.
All articles must receive my OK. No two students may review
the same article. It is OK to use articles you have to read for
another class, if they meet all of the above criteria.
Student Name
Date
PSY203-82
Research Article Summary
Outline for the Research Article Review
When writing the research article summary use the following

outline to present the information. The sections should be
labeled with the Roman numeral; however, the information
should be written in complete sentences in a logical paragraph
structure. The paper should be identified in the manner seen
above.
I. Give the Citation for the article.
a. Provide the citation for the article in APA or MLA format.
II. Evaluating the Introduction
a. What is the chosen topic for the research paper?
b. What is the purpose of the research study/paper?
c. What is the hypothesis of the research study provided in the
paper?
III. Experimental design/Data collection
a. What is the source of the data/How was the data acquired?
(That is, questionnaire, intensive interview, documents, existing
statistical information, observations, laboratory manipulations,
field manipulations, etc.)
b. Is the study a quantitative or qualitative study?
c. What was the sample population?
d. How was the data analyzed? (Discuss the statistical test(s)
performed and what data was presented.)
IV. Discussion of results
a. Briefly summarize the findings and conclusions concerning
the data mentioned by the author(s).
V. Overall Evaluation
a. Give your overall evaluation of the methods used in this
article: What things were done well? What were done poorly?
How much trust do you put in the findings?
b. Look at this article's "packaging," that is, the theoretical
introduction and the discussion or interpretation at the end. Do
you feel that the actual methods and results support the
theoretical and interpretive claims of the author? Why?

c. What possible ethical issues might have arisen in the process
of doing this research? Do you think the researcher's ethical
decisions were all justified, or are some questionable? Why?
d. To sum up, what do you feel you have learned worth knowing
from this article? (If your answer is "nothing", explain why.)
(Please note: this question is about the article and refers to the
quality of information it contains.)
Research Article Summary Rubric
Criteria
Grading Points
Score
I. Research article selection (20pts.)
· Article was approved by instructor.
· Student included the citation for the article provided adhering
to APA format.
II. Content of the summary (100pts.)
Student evaluated the article and presented the answers to the
following questions:
· What is the chosen topic for the research paper?
· What is the purpose of the research study/paper?
· What is the hypothesis of the research study provided in the
paper?
· What is the source of the data/How was the data acquired?
(That is, questionnaire, intensive interview, documents, existing
statistical information, observations, laboratory manipulations,
field manipulations, etc.)
· Is the study a quantitative or qualitative study?
· What was the sample population?
· How was the data analyzed? (Discuss the statistical test(s)
performed and what data was presented.)
· Briefly summarize the findings and conclusions concerning the
data mentioned by the author(s).

III. Overall Evaluation (40 pts.)
Student provided the overall evaluation of the article to include
the following:
· Give your overall evaluation of the methods used in this
article: What things were done well? What were done poorly?
How much trust do you put in the findings?
· Look at this article's "packaging," that is, the theoretical
introduction and the discussion or interpretation at the end. Do
you feel that the actual methods and results support the
theoretical and interpretive claims of the author? Why?
· What possible ethical issues might have arisen in the process
of doing this research? Do you think the researcher's ethical
decisions were all justified, or are some questionable? Why?
· To sum up, what do you feel you've learned worth knowing
from this article? (If your answer is "nothing", explain why.)
(Please note: this question is about the article and refers to the
quality of information it contains.)
IV. Content, clarity, and organization of ideas. (40pts.)
Student correctly adheres to the following when submitting the
final draft for grading:
· Submission appropriately labeled.
· No inclusion of first or second person reference (ie, no I, We,
You, etc).
· No more than 2 grammar mistakes. (-2pts each)
· No spelling mistakes. (-2pts each)
· Typed, Double spaced, Times New Roman or Arial, 10-12-pt
font.
Total Grade:
Instructor Comments:

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