Running head: PARENT AND ADOLESCENT DISCREPANCY
Discrepancies between Parent and Adolescent Responses to Survey Questions
Joanna R. Price
Mentored By: Dr. Ken Wallston and Dr. Shelagh Mulvaney
7 April 2010
Psychology 2990
Professor Smith
Vanderbilt University
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Abstract: This study examined the congruency of adolescent and parent perceptions of
adolescent diabetes self-management and problem solving. Survey responses were collected
from 115 adolescent- parent dyads. Parents’ and adolescent’ responses to problem solving and
self-management surveys were studied to determine the agreement between their responses
and the relationship to the adolescents’ hemoglobin A1c, age and gender. Simple discrepancy
scores were computed for each dyad for each questionnaire and were calculated by
subtracting the mean child score for each item from the mean parent score for the same item,
then summing over the items. A squared discrepancy score was also calculated for each dyad
per survey to magnify the discrepancy results. Bivariate correlations were run for the four
discrepancy scores along with A1c, child’s gender and age. The results indicate that while no
variables were significantly related with A1c, there was a high correlation between the parent
and adolescent scores.
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Living with a diagnosis of diabetes during the adolescent years can become a constant
struggle. Teenagers are trying to rely less on their parents for help and have to begin
balancing their diabetes care with the rest of their life. Diabetes care is a very complex, lifelong process, but it is essential for survival. Children often struggle with managing their
disease during the adolescent years because they are beginning to take full responsibility for
their diabetes management. They have to learn to balance their self-care, their school lives
and many other aspects of teenage life. Parents become less involved as the care
responsibilities shift to the adolescent. Self-management and problem solving become critical
skills needed in order to keep diabetes under control. There are many factors such as coping
behaviors and family involvement that have an impact on how adolescents self-manage their
diabetes. Diabetic self-management can be measured through HbA1c levels, which is the
amount of glycated hemoglobin in your blood. HbA1c measures control of blood sugar over
approximately three months. It is a good indicator of diabetes control over a period of several
months. Normal levels of HbA1c are 6% or less, however, in people with diabetes, they
should try to keep their HbA1c levels to 7% or less (Dugdale 2009).
This research study, part of a larger study being conducted by Dr. Shelagh Mulvaney,
targeted adolescents, aged 13-17, with Type I diabetes. As stated by the American Diabetes
Association (2008), Type I diabetes occurs when the body has an inability to produce insulin,
blocking cells from gaining needed glucose. Care for Type I diabetes usually includes
checking blood sugar multiple times a day, injecting insulin and calculating dietary intake.
One hundred and fifteen dyads consisting of adolescents with Type I diabetes and their
caregivers, mostly mothers, were given the same problem solving and self-management
questionnaires to complete. By subtracting the child’s responses to these questionnaire items
from the parent’s responses, we were able to determine the similarities and differences
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between each dyad on each questionnaire and use this information to infer the closeness of the
parent-child relationship. In order to best analyze the discrepancy between responses, scores
were calculated in two different ways. It is important to look at both the direction and
magnitude of the discrepancy scores. Simple differences were calculated to show the direction
of the discrepancy, whether parents or adolescents rated the adolescent higher in selfmanagement and problem solving. The squared differences heightened the amount of
discrepancy and showed which dyads had more differences. This study looked at whether
parent-child discrepancy scores showed any correlation with the adolescents’ diabetes control,
the age of the adolescent, and whether the adolescent was a male or female. One research
question looked at whether the degree of congruency between children’s and parent’s
questionnaire responses had an effect on the HbA1c levels of the adolescent. It was predicted
that the adolescents in the discrepant parent-adolescent dyads would have poor A1c levels. It
was also predicted that parent-child dyads would correlate across both questionnaires,
meaning the degree of discrepancy on one questionnaire will be correlated with the degree of
discrepancy on the other questionnaire. Other factors, including age, and gender were also
studied to see if they had a relationship with the discrepancy scores. These research questions
showed different ways to look at the responses between children and parents in relation to
diabetes management.
Relevant Literature
There have been many research studies done on parent and child communication
related to diabetes care and self-management. Family involvement is an important part in selfmanagement because the family becomes a support system. Adolescents with diabetes reach a
point where they have to learn how to self-manage their diabetes in order to gain more
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independence. This process can be difficult for some, but family support and communication
can be a way to reduce the stress and anxiety of the situation.
Parental Support and Coping
An important factor in how children self-manage their diabetes is the degree of
support they have from family. Previous studies have looked specifically at parental and child
relationships and how these relate to self-care and management. Family support, especially
the parent-child relationship in the care of diabetes, plays a huge role in the success of
management. Berg et al. (2007) focused on the mother- child relationship in relationship to
diabetes management. The collaboration and support of 127 children, aged 10-15, with Type I
diabetes and their mothers were tested. Several important findings resulted from this study,
such as the child-parent involvement in diabetes management is important for children’s and
mothers’ emotional adjustment. According to Berg et al. (2007), better appraised support and
collaboration between mothers and their adolescents leads to better mood and less depressive
symptoms. Collaborative coping leads to mothers and children maintaining engagement and
successfully caring for diabetes.
McDougal (2002) focused on family support and normalization. This case study
determined that normalization leads to a nurturing family environment that helps children
adapt easier to their life with diabetes. Family support is an important factor in the
normalization process and can be achieved through successful adaption by parents and
promotes normalcy which helps with the child’s adaption to their condition. It is important to
recognize that this was a case study and looked at only one family. By looking at one family,
it was not representative of the population as a whole. Although this was a case study, this
article also included a literature review that focused on normalization and family environment
which was used to support the case study to strengthen the findings.
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Stallwood (2005) studied seventy-three caregivers and looked at how caregiver stress
and coping played a role in the care of children with diabetes. The results of this study
showed that families of younger children with diabetes had higher levels of stress, specifically
diabetes related stress. Perceived stress of caregivers may be a motivator for successful
glycemic control, meaning the more stress parents have about managing their child’s diabetes
leads to closer adherence to diabetes management. This study also stressed the importance of
assessing parental stress along with glycemic control and providing intervention for high
amounts of caregiver stress. For my research study, it is important to note the importance of
age and to analyze the data to determine if a child’s age plays a role in the correlations. With
the support of the studies mentioned above, it can be predicted that more parental support can
lead to better care of diabetes.
Parental Monitoring
The degree to which parents monitor and assist their children with diabetes care is an
important aspect to consider when looking at diabetes control, and becomes more challenging
particularly during adolescence when the child begins to spend more time away from home.
Lewandowski et al. (2006) discusses how conflict between parents and adolescents is
correlated with poor adherence to diabetes treatment. In that study, fifty-one mothers of
adolescents with diabetes completed questionnaires about conflict around diabetes, social
support and adolescent autonomy with their diabetes care. Results of the Lewandowski et al.
study show that higher levels of spousal support led to less diabetes-centered conflict and
closer adherence to treatment. The amount of conflict over diabetes care was correlated with
glycemic control. Discrepancy scores for that study were calculated by taking the absolute
value of the difference between mother and adolescent total scores on the decision-making
autonomy measure. This measure focused on the amount of discrepancy rather than a
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direction for the discrepancy. Lewandowski et al. discovered that mothers and adolescents
had a moderate level of discrepancy regarding who was responsible for diabetes care. The
Lewandowski at al. study shows how mothers and adolescents differed in their survey
answers on autonomy and how parental involvement with diabetes care leads to better
adherence to treatment.
Anderson et al. (1999) looked at how parent and adolescent teamwork plays a role in
diabetes management. The 85 patients in the study were assigned to one of three study
groups: teamwork, attention control or standard care. Adolescents were followed for 24
months and came in for regular medical appointments four times during that time period. The
standard care group just came in for routine visits. The teamwork intervention and the
attention control condition received 20-30 minute intervention sessions after each visit with
the doctor. The teamwork intervention focused on the importance of sharing responsibility
between parents and adolescents. The control group was given traditional diabetes
information without a focus on parental involvement. The results show that the families
assigned to the teamwork group had less conflict, and more adolescents in this group
improved their HbA1c levels compared to the other two groups. This study shows how
important parental involvement could be in diabetes care.
Finally, Ellis et al. (2007) emphasized the importance of parental monitoring of
diabetes care. This study gave self-report questionnaires to 99 adolescents and their
caregivers. These questionnaires rated parental supportive behaviors towards the adolescent’s
diabetes care. Parental monitoring was measured through the Parental Monitoring of Diabetes
Care scale (PMDC) and diabetes management was measured by the Diabetes Management
Scale (DMS). Based on the parent and adolescent reports, diabetes monitoring was correlated
with adherence to diabetes care. The Ellis et al. study showed that parental monitoring of their
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child’s diabetes care leads to better adherence and better adherence to care is correlated with
better HbA1c levels.
These studies all looked at parental monitoring of adolescents with diabetes and how
that influenced adherence to care regimens. All three studies mentioned showed that close
parental monitoring in the adolescents’ diabetes care was correlated to better adherence to
routines which, in turn, was correlated with better HbA1c levels. This showed that parental
monitoring is indirectly correlated with better HbA1c levels. With the support of these
studies, it was predicted that parental monitoring will lead to more congruency between
parent-adolescent dyads which will lead to better diabetes management. It is hypothesized
that if parents and children have high levels of agreement on one aspect of diabetes
management, such as the child’s ability to self-manage diabetes they should also have higher
agreement on the other aspects of diabetes management, such as the child’s ability to solve
diabetes self-management problems. If parents are not involved in their children’s diabetes
care, it is predicted that they will be less likely to be congruent with their adolescent on the
survey responses. These studies show the importance of parental involvement with their
adolescent’s diabetes care.
Parent and Child Congruency
An important concept for this research study is the idea of parent-child congruency in
responses to survey questions. Many past studies have looked at the relationship between
parent and child responses. Peterson et al. (2003) interviewed 137 preschoolers, 98
elementary children and their parents after a traumatic event. The dyads were asked to
describe the situation and their feelings. Narrative length, elaboration, cohesion, coherence
and contextual embeddedness were measured. The findings show that mothers’ and
daughters’ narratives were more cohesive and coherent than fathers’ and boys’ recollections
8
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of the event. It was also determined that mothers and daughters are similar for all accounts
measured, especially with older daughters. Fathers’ narrative styles were not highly correlated
with either daughters or sons. This study showed that mothers and daughters had more
agreement and cohesion than fathers and sons.
Coffman et al. (2006) was a follow-up to the Fullerton Longitudinal Study begun in
1979, following 130 infants and their parents. At the 15-16 year old wave, parents were given
the Parent-Child Relationship Inventory (PCRI) to test their relationship perceptions. The
PCRI measures satisfaction with parenting, involvement, communication, limit setting and
autonomy. At the same time adolescents were given several questionnaires about their
relationship with their parents. The results showed that mother-adolescent perceptions are
valid and reliable while father-adolescent relationships lack correspondence of perception.
This study showed that mothers and adolescents are fairly congruent in their perceptions of
the mother-child dyad relationship while fathers are less reliable.
Miller-Johnson et al. (1994) followed 88 children who were between the ages of 8 and
18, as well as their parents, and looked at how parental-adolescent relationships impacted
diabetes management. The Parent-Child Scales (PCS) were used to measure the parental
perspectives of their relationships with their adolescents. The different categories measured
were warmth, discipline, conflict and behavioral support. The results of this study showed that
parent-child dyad relationships are correlated with diabetes management, especially in terms
of conflict. Parent-child conflict is correlated with poor adherence to diabetes care. If a parent
and child have a lot of conflict, the child is at risk for poor diabetes adherence resulting in
poor health. This study shows how the parent child relationship is important in diabetes care
in adolescents.
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The studies reviewed in this section show how important parent-child congruency is
for the care and management of diabetes. As seen in Miller-Johnson et al. (1994), parent and
child conflict correlates with poor diabetes care. It is important for parents and adolescents to
be in agreement for the best diabetes management. Both Coffman et al. (2006) and Peterson et
al. (2003) show that mother-child dyads are more reliable and indicative of congruence. These
studies, along with others discover that the mother-daughter dyad is more likely to be
congruent than any other type of dyad. For this research study, it is important to note this
trend and to analyze the data to determine if gender plays a role in the correlations.
Method
Design
In this descriptive, correlational design I compared and contrasted parental and
adolescent survey responses to determine where the congruencies were in their responses. I
looked at the answers from both questionnaires given to both parents and adolescents to
determine if discrepancy trends can be seen across both measures. I calculated bivariate
correlations to determine if the adolescents’ gender or age had an affect on the discrepancy
scores. Two different discrepancy scores were calculated for each questionnaire to show both
the direction of the discrepancy and the magnitude of the within-dyad differences. I also
looked at the HbA1c baseline levels of the teenagers to determine whether congruency
between dyad responses has any relation to blood glucose levels. The variable of child’s age
was also analyzed in the correlations to determine what effect, if any, age has on discrepancy
and/or HbA1c levels. Finally, gender was correlated with the discrepancy scores and HbA1c
to analyze whether males or females were more likely to be discrepant with their parents and
how that impacted diabetes self-management.
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Participants
The participants for the study were recruited through the Vanderbilt Eskind Diabetes
Clinic which is part of the Vanderbilt University Medical Center in Nashville, Tennessee1.
Adolescents between the ages of 13 and 17 who had been diagnosed with Type I diabetes for
at least the past six months were eligible to participate. A total of 115 adolescent-parent dyads
participated in this study by completing two questionnaires. The average age of the
adolescents was 15 years old with a standard deviation of 1.5. Of the adolescents that
participated, 63 were males, 51 were females and there was missing data on gender for one
person. Adolescents had a mean HbA1c level of 8.8% with a standard deviation of 1.76%. All
but a couple of the parents were moms.
Measures
Parents and adolescents were asked to fill out a number of questionnaires dealing with
diabetes barriers, problem solving and self-management. Both members of the family dyad
were given two of the same surveys: the Diabetes Behavior Management Survey (DBMS;
Iannotti, 2006), and the Problem Solving Behaviors Survey, a new measure created for this
research. Both questionnaires were filled out online. The DBMS, attached as Appendix G, had
41 questions where 39 of them were answered by clicking “never”, “seldom”, “half”,
“usually” or “always”. This questionnaire measured how frequently the adolescents carried
out necessary self-management tasks, such as checking blood glucose, dosing insulin, and
counting carbohydrates. The Problem Solving Behaviors Survey, attached as Appendix H,
had 27 questions with a response option of “never”, “seldom”, “half”, “usually” or “always”.
This questionnaire measured the ability of adolescents to deal with self-management problems
associated with their diabetes and diabetes care. The questionnaires given to both parents and
1
This research study was part of a larger study conducted by Dr. Shelagh Mulvaney.
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adolescents differed only in who was being addressed. Adolescents were asked questions
about themselves and parents were asked to answer questions about their child.
Procedures
Only data from adolescent-parent dyads that completed both questionnaires were
analyzed. The statistical software, SPSS, was used to calculate the degree of discrepancy
between the parent and adolescent responses to both questionnaires. First of all, mean scores
were calculated for the parents and the adolescents for both surveys, producing four separate
scores: parent self-management (Psm), child self-management (Csm), parent problem solving
(Pps), and child problem solving (Cps). These scores were calculated by taking the mean of
the responses for each questionnaire. Discrepancy scores were calculated two separate ways
to strengthen the research. Simple discrepancy scores were calculated by subtracting the child
mean from the parent mean for each item on each questionnaire. Two variables were created:
a simple discrepancy score for the DBMS (D_sm) and a simple discrepancy score for the
problem solving survey (D_ps). Squared discrepancy scores were also calculated, creating
two additional variables, a squared self-management discrepancy score (D2_sm) and a
squared problem solving discrepancy score (D2_ps). The squared discrepancy scores were
calculated by squaring the simple discrepancy score for each item for each survey and then
were summed across the items. In all, four discrepancy variables were calculated for each
dyad2.
The simple discrepancy scores showed the direction of the discrepancy through the
magnitude and direction of the values. The range of possible scores for the simple discrepancy
variables was from -4 to 4. Highly positive simple discrepancy scores mean the parent rates
2
After exploring the data through box plots, it was determined that subject number 117 had an extreme D_sm score on the self-management
study and subject number 120 had an extreme D2_ps score on the problem solving survey. In order to account for these extreme outliers,
both scores were recoded as missing data.
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the child more positively than the child rates him/herself. Highly negative simple discrepancy
scores mean the reverse. The second discrepancy scores were the simple discrepancy scores
squared for each item and then summed over the items. These scores highlighted the
magnitude of the discrepancy. The formula for these scores was: d^2= (mean parent scoremean child score)^2 for each dyad. These scores were labeled as D2_sm, the squared
discrepancy score for the self-management survey, and D2_ps, the squared discrepancy score
for the problem solving survey. The range of possible scores for the squared discrepancy
variables was from 0 to 16. The higher the squared discrepancy score, the greater the
discrepancy within the dyad, regardless of which family member was higher or lower than the
other. Also, by squaring each item’s simple discrepancy score, items with more disagreement
contributed more to the total squared discrepancy score. Correlations using the Pearson
Product-Moment Correlation were calculated between the different discrepancy scores and
with other factors, including child age, gender and A1c levels.
A1c Levels were measured in order to determine the adolescents’ control of their
diabetes. A1c levels show the average blood glucose levels an adolescent has over the course
of 8-12 weeks. Therefore, this test is one of the best indicators available of diabetes control.
Results
Even though the focus of this thesis was on the discrepancy scores, the first analysis
examined the correlations between HbA1c and each of the separate scores on the two
instruments. Scores were calculated for both the parents and the adolescents for each
questionnaire yielding four scores: parent self-management (Psm): child self-management
(Csm): parent problem solving (Pps): and child problem solving (Cps). The mean scores for
each of the questionnaires were correlated using the Pearson Product- Moment Correlation
with one another and with the child’s A1c levels. As seen in Table 1, only the child’s problem
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solving score was significantly related to A1c levels (r = -0.21, p < 0.05). The more the
adolescents felt they could solve diabetes-related problems, the better their glycemic control
(i.e., the lower their A1c value). None of the other three scores were significantly correlated
with A1c values.
As also seen in Table 1, the four scores were also highly correlated with each other.
Psm was significantly correlated with Csm (r = 0.53, p <0.01), meaning that the more
children felt they were managing their diabetes, the more their parents thought so as well. A
similar correlation occurred between Pps and Cps (r = 0.41, p < 0.01) showing that the more
children thought they were solving diabetes-related problems, the more their parents agreed.
Psm was also significantly correlated with Pps (r = 0.60, p < 0.01) meaning that the more
parents thought their adolescents were controlling their diabetes, the more they thought the
children were using problem solving skills to solve their diabetes-related problems. The same
is true for the correlation between Csm and Cps (r = 0.51, p < 0.01) which shows that the
more adolescents thought they were in control of their diabetes, the more they thought they
could use problem solving skills for diabetes-related problems. These results show how the
questionnaire responses were correlated with each other.
Table 2 shows the bivariate correlations between the four different discrepancy scores
and A1c values. A1c levels did not correlate with any of the discrepancy scores; however
certain discrepancy scores correlated with each other. The simple discrepancy in selfmanagement correlated with the simple discrepancy in problem solving (r = 0.46, p < 0.01),
and the squared discrepancy in self-management was significantly correlated with the squared
discrepancy in problem solving (r = 0.36, p < 0.01). These correlations show that, with both
of the discrepancy scores, if parent-child dyads were discrepant on one questionnaire, they
were more likely to be discrepant on the other questionnaire as well. It also shows that both
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discrepancy scores were measuring different aspects of discrepancy because they were not
significantly correlated with each other. In other words, the simple discrepancy score for selfmanagement did not correlate with the squared discrepancy for self-management, and the
same was the case for problem solving.
Correlations were also run between the four discrepancy variables and the children’s
ages and gender. Gender was coded as male (0) or female (1) and ages ranged from 13 to 18
years old. Child’s age was not significantly correlated to any other variable. Gender, however,
was significantly correlated to the simple discrepancy score for problem solving (r = 0.20, p <
0.05). When the child was female, she was more likely to be discrepant from her parent on the
problem solving questionnaire. Gender was also significantly correlated with the squared
discrepancy score for self-management (r = -0.26, p < 0.01). This result shows that, when
using squared discrepancy scores instead of simple discrepancy scores, males are significantly
more discrepant from their parents on self-management than are females.
Discussion
This study was designed to look at the discrepancy between parent and child survey
responses to see if they related to adolescent self-management of diabetes. HbA1c levels were
used to measure diabetic control. As determined by the studies in the introduction, parentchild communication is a key factor in adolescent management of diabetes. Adolescents that
keep their parents informed and involved are more likely to have better diabetic control. This
study used discrepancy scores to measure parent-child communication. It was predicted that
the higher the discrepancy between the parent-child dyads, the higher the blood glucose
levels. Two different types of discrepancy scores were calculated and analyzed to determine
their relationships to each other and to A1c, child’s age, and gender. It was also predicted that
parent-child dyads with high discrepancy scores on one of the instruments (either self-
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management or problem solving) would also be discrepant on the other instrument.
Furthermore, it was predicted that discrepancy scores would be associated with higher HbA1c
values. No hypotheses were stated for the relationships between discrepancy scores and child
age or gender. Those analyses were purely exploratory.
This study aimed to determine if parent-child discrepancy affected self-management
of the adolescents’ diabetes. It was predicted that high amounts of discrepancy between
parents and their children would lead to poor diabetic control. First, the mean simple scores
for the parents and children for each survey (Psm, Csm, Pps, and Cps) were correlated with
A1c. Results show that the Cps score correlated with A1c levels. This shows that the
children’s responses to the problem solving questionnaire were correlated with blood glucose
levels. This result is important because it shows that the adolescent’s self-report of problem
solving behaviors is related to their diabetic control in terms of A1c. Correlations were then
run with the four discrepancy variables and it was determined that A1c levels were not
correlated significantly with any of the discrepancy scores.
Another main focus of this study was to look at different ways to measure discrepancy
between parent and adolescent responses to the same surveys. Two separate variables were
used to measure this discrepancy for each survey. The simple discrepancy scores, D_sm and
D_ps were important because they show the direction of the discrepancy through signs. A
negative discrepancy meant that the child scored him/herself higher on the survey questions
than did his/her parent. The second discrepancy score calculated was a squared discrepancy,
D2_sm and D2_ps, and these variables were important because they magnified the
discrepancy for each dyad. Dyads with small amounts of discrepancy on an item-by-item
basis had squared discrepancy scores that stayed relatively small and dyads with large
discrepancy became even larger. The different discrepancy scores for each survey were not
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significantly correlated, meaning the correlations between D_sm and D2_sm and between
D_ps and D2_ps were not significant, so it can be concluded that they measure different
aspects of discrepancy and so they are both important to use when trying to determine
discrepancy scores.
While the two different discrepancy variables for each survey were not correlated with
each other, the different types of discrepancy variables across surveys were correlated with
each other. Both simple discrepancy variables were correlated and both squared discrepancy
scores were correlated. This shows that dyads were discrepant across surveys. If a parentchild dyad had a high discrepancy on one survey, on average, they had a high discrepancy on
the other survey as well.
Gender also played a role in discrepancy scores. The results, depicted in Table 3, show
that females had, on average, higher discrepancy between their responses and their parents on
the problem solving survey. Results also showed that males were significantly more
discrepant on the squared discrepancy score for the self-management survey. Males and their
parents were more likely to disagree on self-management behaviors. It is important to note
that problem solving is an internally driven response that is harder to analyze by other people.
It is generally easier for parents to know their child’s self-management behaviors than their
problem solving behaviors.
During the difficult transition to self-management during adolescence, parents should
still monitor their child’s care and be involved in some way. It is very important, especially
during this time, to help the adolescent develop and stick to a care regimen that they can use
to help make the transition to self management easier.
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Limitations
This study has several limitations in regards to the participants. All participants were
recruited through the Vanderbilt Eskind Diabetes Clinic which is part of the Vanderbilt
University Medical Center. While there were 115 families that participated, it may not be
representative of the overall population of adolescents with type I diabetes. A parent of the
adolescent also participated in the study and in this case, almost all parents were female.
Fathers were not really represented in this study. It is hard to tell how these limitations
affected the overall study, but it is necessary to account for them.
Another limitation of the study was that there are many other variables that may
impact A1c levels. Blood glucose levels measure more than diabetic self-management,
including hormonal changes, and it is difficult to isolate one variable. Calculating significant
correlations using A1c levels can be hard with so many factors playing a role.
Future Research
Several important questions remain after completing this study that will require future
research. One big question is what other factors determine self management? It would be
important to look at what else plays a role in diabetes care, more specifically in selfmanagement. It is also important to research other ways to measure self-management of
diabetes, other than HbA1c levels. If self-management could be measured in more ways than
A1c, the results of this study would be enhanced.
In order to determine the reliability and validity of the different discrepancy variables,
it is important to utilize these measurements in other studies. Future studies looking at
discrepancy should calculate the different scores to see if they are correlated. It would be
important to make sure that these discrepancy scores were still separate measurements, both
add unique aspects and different ways to look at discrepancy. This study is the first to use
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both formulas to measure discrepancy so it is necessary to make sure the results duplicate in
other studies.
Conclusion
This study measured different aspects of parent-child discrepancy in order to
determine how discrepancy impacted self-management of diabetes. Diabetes management
was measured using baseline A1c levels of the adolescents. Correlations showed that A1c
levels were not correlated with any the discrepancy scores, however it was correlated with the
child problem solving score. This means that adolescents who ranked themselves highly on
the Problem Solving Behaviors Survey had better glycemic control. This shows how
important it is for adolescents transitioning into self-management of their diabetes to learn and
be able to use problem solving techniques. Children that can successfully handle diabetes
related problems have better control.
The results of this study showed that the discrepancy scores correlated amongst each
other. It was determined that if a dyad was highly discrepant on one survey, they were likely
to be discrepant across both. This finding shows that parents and adolescents are consistent
with discrepancy across both surveys. It is important that parents and children be on the same
page in terms of diabetes care and self-management. While this study did not find predicted
correlations with A1c levels, it did show that it is important to measure the different aspects of
discrepancy. Both discrepancy scores looked at the congruency between parents and children
from different points of view. This study determined that different types of discrepancy scores
can be useful when analyzing different parts of discrepancy by focusing on direction of
discrepancy and magnitude.
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Miller-Johnson, S., Emery, R.E., Marvin, R.S., Clarke, W., Lovinger, R., Martin, M.
(1994). Parent-child relationships and the management of insulin-depemdemt diabetes
Discrepancies 21
mellitus. Journal of Consulting and Clinical Psychology, 62(3), 603-610.
Peterson, C., Roberts, C. (2003). Like mother, like daughter: similarities in narrative style.
Developmental Psychology, 39(3), 551-562.
Stallwood, L. (2005). Influence of caregiver stress and coping on glycemic control of young
children with diabetes. Journal of Pediatric Heath Care, 19(5), 293-300.
Stark, P.B. (2010). Scatterplots. SticiGui Statistics. Berkeley, CA. Retrieved 18 March 2010.
< http://www.stat.berkeley.edu/~stark/Java/Html/ScatterPlot.htm>.
Discrepancies 22
Table 1. Pearson Product-Moment Correlations Between the Individual Scores and A1c
A1c
A1c
3
Pps4
Correlation
N
113
-.141
1
.168
-.211*
.026
.405**
.000
1
112
99
114
-.128
.604**
.314**
.214
.000
.002
96
97
97
98
Correlation
-.083
.341**
.555**
.534**
Sig. (2-tailed)
.382
.001
.000
.000
N
112
98
113
97
Correlation
Sig. (2-tailed)
Correlation
N
1
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
3
The A1c variable is the baseline HbA1c blood glucose levels of the adolescents.
The Pps variable is the mean parent scores across items on the Problem Solving Behaviors Survey.
5
The Cps variable is the mean child scores across items on the Problem Solving Behaviors Survey.
6
The Psm variable is the mean parent scores across items on the DBMS Survey.
7
The Csm variable is the mean child scores across items on the DBMS Survey.
4
Csm
99
Sig. (2-tailed)
Csm7
Psm
97
N
Psm6
Cps
1
Sig. (2-tailed)
Cps5
Pps
1
114
Discrepancies 23
Table 2. Pearson Product-Moment Correlations Between the Discrepancy Scores and A1c
A1c
A1c
Correlation
D_sm
D_ps
D2_sm
D2_ps
1
Sig. (2-tailed)
N
D_sm8
Correlation
-.042
Sig. (2-tailed)
.686
N
D_ps9
99
Correlation
.057
.455**
Sig. (2-tailed)
.581
.000
97
98
99
Correlation
.080
.057
-.073
Sig. (2-tailed)
.433
.576
.474
98
99
99
100
Correlation
-.060
-.039
-.124
.360**
Sig. (2-tailed)
.564
.702
.225
.000
96
97
98
98
N
D2_ps11
1
97
N
D2_sm10
113
N
1
1
1
98
**. Correlation is significant at the 0.01 level (2-tailed).
8
The D_sm variable is the simple discrepancy scores between parent-adolescent dyads across items on the
DBMS Survey.
9
The D_ps variable is the simple discrepancy scores between parent-adolescent dyads across items on the
Problem Solving Behaviors Survey.
10
The D2_sm variable is the squared discrepancy scores between parent-adolescent dyads across items on the
DBMS Survey.
11
The D2_ps variable is the squared discrepancy scores between parent-adolescent dyads across items on the
Problem Solving Behaviors Survey.
Discrepancies 24
Table 3. Pearson Product-Moment Correlations Between the Discrepancy Scores, A1c,
Child’s Gender and Age
A1c
D_sm
D_ps
D2_sm
D2_ps
Gender
Childs
Age
A1c
Pearson
1
Correlation
Sig. (2-tailed)
N
D_sm
Pearson
113
-.042
1
Correlation
Sig. (2-tailed)
N
D_ps
.686
97
99
.057
.455**
.581
.000
97
98
99
.080
.057
-.073
.433
.576
.474
98
99
99
100
-.060
-.039
-.124
.360**
.564
.702
.225
.000
96
97
98
98
98
.145
.126
.199*
-.258**
.015
Sig. (2-tailed)
.126
.217
.050
.010
.882
N
112
98
98
99
97
114
Pearson
.018
-.041
.046
-.032
-.056
.046
Sig. (2-tailed)
.847
.685
.656
.752
.585
.630
N
112
98
98
99
97
114
Pearson
1
Correlation
Sig. (2-tailed)
N
D2_sm
Pearson
1
Correlation
Sig. (2-tailed)
N
D2_ps
Pearson
1
Correlation
Sig. (2-tailed)
N
Gender
Pearson
1
Correlation
Childs Age
1
Correlation
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
114
Discrepancies 25
Appendix A
Individual Score Means and Standard Deviations
N
Psm
Csm
Pps
Cps
98
114
99
114
Valid N (listwise)
96
Minimum Maximum
2.76
2.88
2.35
2.04
4.53
4.62
4.23
4.62
Mean
Std.
Deviation
3.7418
3.7115
3.2955
3.4710
.34933
.38066
.44612
.52297
Discrepancies 26
Appendix B
Discrepancy Score Means and Standard Deviations
N
D_sm
D_ps
D2_sm
D2_ps
99
99
100
98
Valid N (listwise)
97
Minimum Maximum
-1.03
-1.62
.27
.50
1.06
1.04
3.19
4.85
Mean
Std.
Deviation
.0318
-.1863
1.3401
2.4036
.34927
.52716
.62791
.91009
Discrepancies 27
Appendix C
Histogram for D_sm Variable
Discrepancies 28
Appendix D
Histogram for D_ps Variable
Discrepancies 29
Appendix E
Histogram for D2_sm Variable
Discrepancies 30
Appendix F
Histogram for D2_ps Variable
Discrepancies 31
Appendix G
Diabetes Behavior Management Survey( DBMS)
Discrepancies 32
Discrepancies 33
Discrepancies 34
Discrepancies 35
Discrepancies 36
Discrepancies 37
Discrepancies 38
Discrepancies 39
Discrepancies 40
Appendix H
Problem Solving Behaviors Survey
Discrepancies 41
Discrepancies 42
Discrepancies 43