Should Schools Expect Poor Physical and Mental Health, Social Adjustment, and Participation Outcomes in Students with Disability?
Should Schools Expect Poor Physical and Mental Health, Social Adjustment, and Participation Outcomes in Students with Disability?
Sharmila Vaz 0 1 2 3
Reinie Cordier 0 1 2 3
Marita Falkmer 0 1 2 3
Marina Ciccarelli 0 1 2 3
Richard Parsons 0 1 2 3
Tomomi McAuliffe 0 1 2 3
Torbjorn Falkmer 0 1 2 3
0 1 School of Occupational Therapy and Social Work, Curtin University , Perth, Western Australia , Australia , 2 School of Education and Communication, CHILD programme, Institution of Disability Research Jonkoping University , Jonkoping , Sweden , 3 School of Pharmacy, Curtin University , Perth, Western Australia , Australia , 4 James Cook University, College of Healthcare Sciences, Occupational Therapy , Townsville, Queensland , Australia , 5 Rehabilitation Medicine, Department of Medicine and Health Sciences (IMH), Faculty of Health Sciences, Linkoping University & Pain and Rehabilitation Centre, UHL, County Council , Linkoping , Sweden
1 Funding: This project was funded by a Doctoral scholarship provided by the Centre for Research into Disability and Society and the School of Occupational Therapy and Social Work, Curtin University , Perth , Australia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
2 Academic Editor: Stefano Federici, University of Perugia , ITALY
3 Should Schools Expect Worse Outcomes in students with Disability?
The literature on whether students with disabilities have worse physical and mental health, social adjustment, and participation outcomes when compared to their peers without disabilities is largely inconclusive. While the majority of case control studies showed significantly worse outcomes for students with disabilities; the proportion of variance accounted for is rarely reported. The current study used a population cross-sectional approach to determine the classification ability of commonly used screening and outcome measures in determining the disability status. Furthermore, the study aimed to identify the variables, if any, that best predicted the presence of disability. Results of univariate discriminant function analyses suggest that across the board, the sensitivity of the outcome/screening tools to correctly identify students with a disability was 31.9% higher than the related Positive Predictive Value (PPV). The lower PPV and Positive Likelihood Ratio (LR+) scores suggest that the included measures had limited discriminant ability (17.6% to 40.3%) in accurately identifying students at-risk for further assessment. Results of multivariate analyses suggested that poor health and hyperactivity increased the odds of having a disability about two to three times, while poor close perceived friendship and academic competences predicted disability with roughly the same magnitude. Overall, the findings of the current study highlight the need for researchers and clinicians to familiarize themselves with the psychometric properties of measures, and be cautious in matching the function of the measures with their research and clinical needs.
Competing Interests: The authors have declared
that no competing interests exist.
Supporting the inclusion and participation of all students in the school setting is emphasised as
a universal need [1,2]. The concept of inclusion is based on a notion of social justice that
advocates equal access to all educational opportunities for all students, regardless of the presence of
a disability or any form of disadvantage . Educational policies in developed countries have
responded to this social justice agenda in different ways. In Australia, students with disabilities
continue to experience barriers to equitable participation , despite the governments
commitment to inclusive education reported in an array of documents and policies [1,2,5].
According to the 2009 Australia national records, 65.9% of 520 year old students with disabilities
attended mainstream schools; 24.3% attended special classes within mainstream schools, and
9.9% attended special education schools. This pattern was consistent regardless of the severity
of the disability .
Physical placement per se of students with disabilities in a mainstream setting does not
automatically result in the school being perceived as inclusive by the student [7,8]. Instead,
students with disabilities continue to experience barriers to equitable participation, some due to
the prejudices held by the general population; including beliefs about their needs, rights,
vulnerabilities and competencies . Research findings to date suggest that a persons diagnostic
category does not affect the intensity and diversity of his or her participation [10,11].
Most studies of statistical associations between child characteristics, type of disability, and
participation outcomes, report weak to moderate correlations . One possible
explanation for these moderate to weak associations is that disability is only one of several factors that
affect participation and that the effects of other factors are stronger. Important factors for
predicting participation in school activities of pupils with disabilities are child characteristics, such
as autonomy, locus of control and engagement; environmental factors such as adaptations of
the environment; and the attitudes of teachers and peers . Students with disabilities are
reported to participate less in structured and unstructured activities, and to experience limited
classmate interaction and recess participation compared with their peers without disabilities
. These reports of systemic exclusion prompted the need for the current study, in which
physical health, mental health (self-concept, coping), social adaptation (social skills, school
belongingness, loneliness and social dissatisfaction) and participation in school activities are
investigated in relation to having a disability. Each of these outcomes are defined and described
in relation to students with disabilities.
Physical and mental health functioning
Physical health. Population based studies report that having a disability has a significant
impact on childrens health and educational functioning . The extent of this impact appears
to be much greater among children with multiple disabilities. In fact, the impact on school
performance has been found to be even more pronounced for children reported to have learning
disabilities in addition to their physical impairments .
Mental health. Contemporary research indicates that conceptualising mental health as a
unidimensional construct is limiting . Mental health is a state of emotional and social
wellbeing that allows the individual to realise his or her own abilities, cope with normal stresses of
life, undertake productive activities, experience meaningful personal relationships, and make a
meaningful contribution to his or her community [21,22]. Mental health should arguably be
seen to reflect a multi-faceted and interactive construct encompassing the absence of
dysfunction in psychological, emotional, behavioural and social spheres and optimal function or
well-being (p. 128) ; not just the absence of disease.
Having a disability, irrespective of type, increases the risk of developing mental health
problems and disorders because of associated adverse individual and environmental factors [24
26]. Estimates suggest that young people with an intellectual disability manifest behaviours and
experiences that may be indicative of mental health problems, three to four times more often
than their peers without disability; with between 4% and 18% having a mental health diagnosis
. Mental health problems in young people with a disability are often undiagnosed and
untreated; and impact on them acquiring skills necessary for their successful integration into the
Self-concept. Self-concept refers to an individuals belief about his or her behavioural
capabilities in a range of skills, knowledge and attitudes, that are drawn from various cognitive,
motor and social skills [28,29]. These beliefs are reflections of the persons actual abilities and
internalisation of the feedback obtained from significant others , and social comparison
with others in the same setting . A persons self-concept undergoes varying degrees of
adaptation during different life stages and experiences . Different social environments are
likely to influence an individual's self-concept in different ways.
Literature on the impact of disability on the self-concept of students in mainstream
educational settings is inconclusive, with inconsistencies reported between studies dependent on: a
schools mainstreaming philosophy; the dimension of self-concept explored; and nature and
severity of the participants disability. For example, students with learning disabilities
reportedly have lower academic self-concept when compared with their typically developing peers ;
but findings with regards to their global self-esteem are mixed, with some studies suggesting
lower global self-esteem in the learning disability subgroup , and others reporting no group
differences [35,36]. Students with hearing, learning, and physical disabilities are also reported
to have lower social and academic competence when compared to their non-disabled peers,
but show no differences in the level of reported physical self-concept [33,37]. The phenomenon
of elevated self-concept among students with externalising behaviors is also widely reported in
the disability literature , and is hypothesised to serve as a protective factor, buffering the
person from the negative effects of social and academic failures .
Coping skills. Coping involves the use of cognitive or behavioural strategies to manage
stress , and are related to self-regulation; a core component of healthy adaptation .
Perceived competence and coping skills may reduce psychological distress and buffer the
deleterious effects of stress, resulting in better adjustment [42,43]. However, few studies to date have
reviewed the coping skills of students with disabilities in an educational context. Existing work
in the area suggests that students with learning disabilities use passive cognitive avoidance and
more wishful thinking coping strategies when faced with academic stress-related events
[44,45]. They also tend to receive less peer support when coping with academic or
interpersonal problems, when compared with students without disability .
Social skills. Social skills include socially acceptable learned behaviours that enable
individuals to interact successfully with others and avoid socially undesirable responses . This
definition of social skills is a hybrid of the peer acceptance and behavioural definitions, and is
the most socially valid in the sense of predicting important social outcomes for children .
Development of social skills is regarded as a fundamental task for all children . Acquisition
or performance deficits in social skills may impede the quality of an individuals social
relationships and social adjustment . For example, deficits have been linked to social
adjustment problems, such as peer rejection, loneliness, reduced school belongingness, and early
withdrawal from school. A variety of pejorative outcomes beyond the school setting including
substance abuse, chaotic personal lives, and limited or absent post-secondary educational
experiences, have also been reported among students with disabilities who have social skills deficits
. Given the difficulties and the associated risk of poor social development, it is imperative
for educators and health professionals to identify and provide interventions for children who
experience problems in this developmental area .
Belongingness in school. The feeling of belongingness represents an active internal
experience of a strong psychological connection [53,54]. School belongingness is defined in terms
of the degree to which a student feels accepted and included within the school . When
students have a sense of belonging in school, they believe that the school community is incomplete
without them, and vice versa. Severity of disability has been shown to influence students
perceptions of belongingness in school. For example, research suggests that students with mild
learning disabilities have levels of school belongingness similar to their typically developing
peers, despite having lower academic performance and behavioural vulnerability . For
students with moderate and severe disabilities, school belongingness appeared to be dependent on
the students relationships within classroom-based social groups  and their involvement in
classroom activities .
Loneliness and social dissatisfaction in school. There are several definitions of loneliness
in research literature. Some scholars consider it to be a unidimensional construct that is a
discrepancy between desired and obtained social contacts . Other researchers consider
loneliness to be a multi-dimensional entity, comprised of several individual and relational aspects
. It is widely believed that school-aged children have a complex and multi-dimensional
conceptualisation of loneliness ; however, differences in conceptualisation are
inconsistently described in the literature. Indications about ones social network (i.e., being alone) and
reflection on subjective sadness have been specified by 911 year old students in an Australian
sample . Not all students conceptualised loneliness as a multi-faceted entity. Almost 40%
of the children in the Australian sample described loneliness without referencing distressing
emotions; 10% described loneliness without referencing social deficits and more than 80% did
not conceptualise being alone with loneliness. References to self-attributions (e.g., having no
courage to talk about their situation, being in ones own world, or being different) were used
when describing loneliness . These findings demonstrate the highly subjective nature of
loneliness, which has been identified as a key reason for the difficulties in understanding how
individuals experience loneliness .
The literature presents mixed findings about the impact of disability on students
perceptions of loneliness at school. Some findings suggest students with learning disabilities who are
enrolled in mainstream schools are less socially accepted, have fewer friends, and feel more
lonely when compared to their peers without disabilities . Other studies report no
group differences in loneliness between students with physical disability and their typically
developing peers; but those with Autism Spectrum Disorders have been found to have twice the
loneliness of other disability subgroups . Students with learning and other physical
disabilities have a higher degree of social dissatisfaction with their peer relationships . Difficulties
in reading and processing social cues, and difficulties in expressing emotions in social
situations have been identified as potential contributors to the increased vulnerability and
propensity for rejection by peers among students with disability [68,69].
Summary of the Literature
In summary, the literature on differences in physical and mental health, social adjustment, and
participation outcomes in students with and without disabilities is largely inconclusive. The
research studies included in this introduction used convenience samples and focussed on a few
disability subtypes (mainly mild intellectual disability, learning disability, Attention Deficit
Hyperactivity Disorder [ADHD] and Autism Spectrum Disorders). Case-control designs were
used to identify differences between students with and without disability, and commonly used
models to detect differences included a simple regression model using the t-test, or a
Meanvalue difference using the t-tests or the Mann-Witney U-test. The majority of these studies
showed significant between-group differences; however, the proportion of variance accounted
for was low. Therefore, to truly establish whether or not students differed on a variety of
outcome measures based on disability, a cross-sectional design with a representative sample of
students with and without disability, in mainstream schools is needed. Using a population
crosssectional approach, the classification accuracy of several outcome measures used with school
children can be estimated.
Consequently, the current study aimed to: a) assess the classification ability of commonly
used screening and outcome measures in determining the disability status of primary school
children; and b) identify the variables, if any, that best predicted the presence of disability.
Cross-sectional data from 395 students, parents and class-teachers from 75 primary schools
and 77 classrooms across metropolitan Perth and other major urban centres of Western
Australia were used. Data for this study were drawn from a large longitudinal study on the factors
associated with student adjustment across the primary-secondary transition [70,71]. Students
were categorised as having a disability if they were reported to have a disability by their primary
caregiver, which had an impact on the students daily functioning. To be eligible for the study,
their parent(s)/care-giver(s) needed to confirm that they were attending a mainstream class for
at least 80% of their school hours per week, with support provided as required. Thus, a broad
definition was used to categorise students into the disability group. Further details on the
studys design, recruitment, data collection, and sample characteristics have been published
elsewhere . Participation in the study was voluntary. Informed written consent was
obtained from school principals, parents, teachers, and written assent was obtained from students
to participate in this study. All participants were made aware that they could withdraw from
the study at any time without justification or prejudice. Ethics approval was obtained from
Curtin University Health Research Ethics Committee, in Western Australia (WA) (approval
number HR 194/2005).
Data collection instruments
Short Form Health Survey (SF-36). Items from the SF-36, a multipurpose short form
generic measure of health status were used to gain an understanding of parents perception of
their childs physical and overall health .
Strengths and Difficulties Questionnaire. The Strengths and Difficulties Questionnaire
(SDQ)  was developed as a brief screening tool that describes children and adolescents
behaviours, emotions and relationships. The SDQ aims to assess both negative and positive
attributes of behaviour across five domains (namely conduct problems, emotional symptoms,
hyperactivity, peer relationships and prosocial behaviour). The author suggests that the SDQ
can be used for screening, as part of a clinical assessment, as a treatment outcome measure, and
as a research tool [73,74]. The parent version of the Strengths and Difficulties Questionnaire
(SDQ) was used to measure overall mental health functioning [73,75,76]. The overall score was
derived by summing students emotional, conduct problems, hyperactivity/inattention, and
peer relationship scores. Higher scores indicate poorer overall mental health functioning.
The parent version of the SDQ is reported to have moderate to high weighted mean internal
consistency ( = 0.530.80) . Discriminate and predictive validity of the measure has
previously been reported . For the calculation of screening efficiency, the SDQ total and subscale
scores were classified into three categories (unlikely, possible/ query and probable/ of
concern). On the basis that approximately 10% of the child and adolescent populations exhibit
some kind of mental health problem, the probable/ of concern range included scores above
the 90th percentile. To calculate the values for screening efficiency, the SDQ groups were
dichotomised into diagnosis and no diagnosis . This dichotomisation was necessary
to calculate the screening efficiency in terms of sensitivity, specificity, positive predictive value
(PPV), and negative predictive value (NPV) (S1 Appendix). The SDQ has been found to
distinguish between those children/adolescents receiving treatment and those who are not, and
between particular diagnoses or problematic behaviour, at least as well as other, more established
instruments like the Rutter questionnaires, the Child Behaviour Checklist (CBCL), and the
Youth Self-Report (YSR) [73,75,79,81,82]. The SDQ has also been widely used in clinical
populations  and with adolescents with intellectual disability [84,85].
Adolescent Coping Scale. The Adolescent Coping Scale (ACS) is a self-report inventory
designed to support young people when examining their own coping behaviour. The ACS
helps to measure the usage and helpfulness of coping strategies in general and specific
situations . The ACS was designed for use in clinical, educational, and research contexts. This
self-report measure is based on the implicit assumption that groups of functional coping
actions are more likely to lead to adaptive outcomes, whereas dysfunctional strategies are more
likely to result in maladaptive outcomes. The ACS measures what people feel, think, or do to
cope . The scale uses a five-point Likert rating system, ranging from 1 (doesnt apply or
dont do it) to 5 (used a great deal) to rate each item. In line with evidence that suggests that an
individuals choice of coping strategy is mostly consistent , the General Form of the
instrument that addresses how people cope with concerns in general was used. The short form of the
ACS also allows for combining scales to produce measures of three empirically defensible
coping styles based on factor analysis. These three coping domains comprise two functional coping
styles (i.e., solving the problem, and reference to others), and one dysfunctional coping style
(i.e., non-productive coping). Internal consistencies are reported to range from = 0.50
(reference to others) to 0.66 (non-productive coping) . Test-retest reliabilities for the same
subscales on the general form range from r = .44 to .84 (Mean r = .69) .
Self-Perception Profile for Adolescents. Items from the Self-Perception Profile for
Adolescents (SPPA) measured student perceived competence in domains of academics, athletics,
social acceptance, physical appearance, close friendships, behavioural conduct and overall
selfworth . These competencies are understood to reflect the underpinnings of an individuals
self-worth and are intricately related to the latter, depending on the perceived value individuals
place on each domain . The SPPA scale uses a structured alternative format, with each
item requiring the individual to first decide on what kind of teenager he or she is most like, and
then respond to whether the description is sort of true or really true (p.4.) . For each
item, a score of 4 represents the most satisfactory, and a score of 1 the least satisfactory
self-assessment, after negatively worded items are reverse-coded. Domain scores are obtained by
calculating the mean of the five items within each subscale. Subscale scores with means closest to
4 are most positive and reflect a high perception of competency in the domain in question.
The SPPA is reportedly a psychometrically robust measure; with acceptable internal
consistency scores for each subscale based on Cronbachs alpha . Comparable internal
consistency of the measure has also been established in populations of students with a learning disability
( = 0.89), and those with behavioural disorders ( = 0.85) . Robustness of the factor
pattern for students with learning disabilities, and students with behavioural disorders suggests
that domain distinctions are meaningful for these sub-groups, and that the instrument is valid
enough to be used effectively in special education research . Validity of the measure in an
equivalent Australian sample has been previously substantiated by other researchers [92,93].
Discriminant validity of the scholastic competence and the behavioural conduct subscales
among secondary school typically developing students, students with learning disability and
behavioural disorders has been substantiated previously .
Social Skills Rating System. The Social Skills Rating System is a multi-rater instrument
with a child, parent and teacher version, designed to assist professionals in screening and
classifying children suspected of having significant social behaviour problems . In this study,
Secondary Student Form of the SSRS (SSRS-SSF) was used to measure how frequently students
engaged in 39 social behaviours, categorised into assertion, self-control, cooperation, and
empathy domains. Subscales scores were added to compute total social skills scale scores, with
higher scores indicating higher frequency of use of social skills. The SSRS is deemed valid to
assess social skills in children with and without special needs [51,95]. Prior research suggests that
the total social skills scale version of the SSRS-SSF (frequency rating) has adequate internal
consistency ( = .83) to permit its independent use in samples of multi-racial American and
Australian adolescents with and without disabilities . The SSRS has been used in several
studies as a screening tool [97,98] and as a measure to assess treatment outcomes [99,100].
Studies have also found the SSRS to discriminate between the broad categories of students with
and without disabilities .
Psychological Sense of School Membership. Student perception of school belongingness
was measured using the 18-item Psychological Sense of School Membership (PSSM) scale .
Belongingness within this scale is operationalised in terms of the degree to which a student
feels accepted and included within the school . The PSSM is deemed to be useful for
research and planning interventions both at the level of the individual and the organisation.
Items include statements such as: I feel like a real part ofname of school; and People here
notice when Im good at something. Approximately one-third of the items are phrased in a
negative direction in an attempt to avoid the development of a response set bias. A five-point
Likert scale is used to collect responses, with choices ranging from 1 (not at all true) to 5
(completely true). A total mean score is calculated by summing the item scores and dividing
them by 18, to give a value ranging from 1 to 5; with a higher score indicative of greater
belongingness. The PSSM has been tested on middle school and secondary school students in both
urban and suburban communities in the United States of America . The PSSM has
satisfactory internal consistency ( = .80)  and a testretest reliability index of .78 (4-week
interval)  and .56 and .60 for boys and girls respectively (12-month interval) . Positive
correlations between PSSM scores and school success , Grade Point Average (GPA),
academic competence and self-efficacy  are documented. Higher PSSM scores indicate
greater perceived school belongingness. The PSSM has been shown to discriminate between groups
of students predicted to be different in terms of their sense of belonging in school .
Loneliness and Social Dissatisfaction Scale. To obtain an index of students feelings of
loneliness and dissatisfaction with peer relations, the Loneliness and Social Dissatisfaction
Scale (LSDS)  was administered. The rating scale is a self-administered questionnaire for
students aged 618 years. The 16 primary items are comprised of items on feelings of loneliness
(e.g., Im lonely), perceptions of peer relationships (e.g., I dont have any friends),
perceptions on how relationship provisions are being met (e.g., Theres nobody I can go to when I
need help), and perceptions of social competence (e.g., Im good at working with other
children). Students were asked to indicate the degree to which each statement was a true
description of themselves on a five-point scale ranging from 1 (not at all true) to 5 (always true), with
reverse ordering for particular items to minimise response set bias . One total score of
loneliness and social dissatisfaction was obtained for each student, as well as subscale scores for
loneliness and social dissatisfaction . The authors report satisfactory internal consistency
reliability, with Cronbachs = .79 .
The scale is widely used to assess self-perception of loneliness and social dissatisfaction in
children both with and without special needs [63,108,109]. Childrens self-report on this form
correlates significantly with peer status derived from sociometric measures, and also with the
teachers report of the childs social behaviour (Cassidy & Asher, 1992). The LSDS is designed
primarily as an outcome measure and has been used to examine changes in loneliness in young
people with physical disabilities .
School Participation Questionnaire. The nature and extent of participation in school
activities within the contexts of physical, social and psychological features of the school
environment was assessed by the School Participation Questionnaire (SPQ); a measure developed for
this study. Items from the National Survey of School Environments , the School
Microsystems subscale from the Involvement Microsystems Scale , and The Curriculum
Framework of Western Australia  were incorporated into this questionnaire. Students were
asked to report whether 14 school activities were available at their school. Availability was
operationalised as: offered by the school with appropriate adaptations that make it possible for
the student to take part. Students were also asked to rate how often they participated in each of
the 14 activities (if available), on a six-point frequency scale. The original version of the School
Microsystems subscale has demonstrated moderate internal consistency ( coefficient = .73)
Exploratory factor analysis was undertaken to ensure the validity of the School Participation
Questionnaire, prior to its use in the analysis. A minimum factor loading of .45 was set, and the
first three factors were obtained from a Principal Component Analysis. The
Kaiser-MeyerOlkin (KMO) measure of sampling adequacy was .79, above the recommended value of .60,
and Bartletts test of sphericity was significant (2 = 509.77, p < 0.05). The analysis showed
that the first factor (Participation in School Related Activities) explained 23.9% of the variance;
the second factor (Participation in Community Activities) explained 9.8% of the variance; and
the third factor (Participation in Out of School Activities) explained 8.1% of the variance in
participation. The three-factor solution was found to account for 41.7% of the variance
Data Management and Analysis
Data were analysed using the Statistical package for the Social Sciences (SPSS v.20). Only 0.9
2.5% of data were missing at scale levels. The estimation maximisation (EM) algorithm and
Littles Chi-square statistic identified data to be missing completely at random, with the
probability level set at .05 [114,115]. Standard guidelines recommended by tool developers were
followed to replace missing values. Where guidelines were not present, missing values were
replaced by mean scores. Independent samples ttests confirmed that the profiles of those
whose data were missing for various questions were similar to those who responded.
Normality checks for each independent variable were performed, and appropriate
transformations undertaken for variables that departed from normality . Linear regression
analyses were run to determine whether differences in subgroup mean scores existed [Disability Vs.
Typically developing student (TD)]; and if so, to examine the amount of variability in mean
score differences. Univariate Discriminant Function Analysis (DFA) was conducted to identify
the independent variables that could most accurately distinguish between students with and
without disability. Sensitivity, specificity, overall classification accuracy, PPV, NPV, positive
likelihood ratio (LR+), and negative likelihood ratio (LR-) of each model were tabulated. For
more information on how to interpret the indices refer to S1 Appendix.
An attempt was made to identify the independent variables that could best predict the
presence of disability in the student sample, using multivariate Discriminant Function and logistic
regression analyses. Models were developed using a forward stepwise strategy, with the
likelihood ratio used to determine the order of entry of variables. The standardised canonical
discriminant function coefficients and the unstandardised function coefficients for discriminant
analysis and the Wald statistic for logistic regression were used to evaluate the degree to which
each of the variables contributed to the discrimination between the two groups. The
contribution of the respective variables to the discrimination depended on the magnitude and the
direction of the coefficients.
Data from 395 students, their parents and class-teachers were collected. The mean age of the
student sample was 11.9 years (SD = 0.45 years, median = 12 years). Boys comprised 47.3%
(n = 187) of the sample. Based on the Australian Bureau of Statistics median income
categorisation , the majority of the sample (58%, n = 224) was from mid-range socio-economic
status (SES). A total of 17.5% (n = 65) of the sample were reported by a parent or primary
caregiver to have a disability. The predominant disabilities included cerebral palsy, ADHD, Autism
Spectrum Disorder, learning disabilities, and sensory disabilities (i.e., vision and/or hearing
Univariate DFA models were applied to determine the classification ability of each
independent variable in differentiating students with disability from their typically developing peers,
based on their physical (SF-36) and mental health functioning (overall mental health, perceived
competence, coping), social adaptation (social skills, belongingness, loneliness, social
satisfaction) and participation profiles.
Simultaneous linear regression models (in the case of continuous independent variables)
were fitted to determine differences in Mean-values of the sample due to health status
(disability only versus typically developing peers); and analyses were undertaken to estimate whether
between group differences in each of the subgroups differed beyond chance.
Childs physical and overall health (SF-36)
As shown in Table 1, based on parental report of their childs physical and overall health status,
60% of students with a disability could be accurately classified. The overall health status of a
child was a better marker for disability (PPV for disability = 40%) than physical health status
(PPV for disability = 25%). The samples parent ratings of physical and overall health were not
in the LR interval for being considered potentially useful in differentiating students (i.e., < 0.3
for LR- and > 7.0 for LR+) , based on the presence or absence of a disability.
Strengths and Difficulties Questionnaire: Total and subscales
The ability of different scores from the SDQ to correctly classify students with a disability from
their typically developing counterparts is displayed in Table 1. The sensitivity of the total SDQ
score to correctly screen students with disability was 79%; while the PPV or its ability to
correctly identify students with a disability from the mainstream population of students was 35%.
The PPV of the each SDQ subscale, in predicting mental health problems in children with a
disability ranged from 24%36%. The samples mental health scores were not in the LR interval
p < .001; R2 = .07
p < .001; R2 = .12
p < .001; R2 = .11
p < .001; R2 = .04
p < .001; R2 = .07
p < .001; R2 = .05
Note. IV = Independent variable; DG = disability group (discriminant variable); TD = typically developing; CC = Correct classification; SN = Sensitivity;
SP = Specificity; PPV = Positive predictive value; Negative predictive value; LR+ = Positive likelihood ratio; LR- = Negative likelihood ratio;
ns = not significant
Log transformed scorea: higher score = poorer physical health of child
Ordinal scoreb: higher score = poorer overall health of child
Log transformed scorec: higher score = poorer mental health functioning
Log transformed scored: higher score = greater peer problems
Log transformed scoree: higher score = greater hyperactivity
Log transformed total SDQ scoref: higher score = greater emotional problems
Log transformed total SDQ scoreg: higher score = greater conduct problems
Total adjusted scoreh: higher scores = greater use of problem solving coping strategies
Total adjusted scorei: greater use referencing to others
Total adjusted scorej: greater use of non-productive coping strategies
Mean raw scorek: Higher score = greater self-worth
Mean raw scorel: greater academic competence
Mean raw scorem: greater athletic competence
Mean raw scoren: greater physical competence
Mean raw scoreo: greater behavioural conduct competence
Mean raw scorep: greater close friendship competence
Mean raw scoreq: greater social acceptance competence.
for being considered potentially useful in differentiating students based on the presence or
absence of a disability . As shown in Table 1, group differences in mental health functioning
scores explained less than 1% of the variability in scores. We also undertook DFA using the
90% totals score dichotomisation scaling system recommended by the instrument developers.
However, performances in all the discriminant indices were worse than using the continuous
scores; thus the dichotomised results were not reported.
Adolescent Coping Scale
Univariate DFA suggested that the sensitivity of students coping scores in predicting disability
status ranged from below to just above chance (49%60%). Students problem solving coping
style had better precision (PPV) than other coping styles in determining disability membership
(PPV = 22%). The NPV of each coping subscale to correctly identify typically developing
students from a mainstream population of students ranged between 83%87%. The samples
coping scores were not in the LR interval for being considered potentially useful in differentiating
students based on the presence or absence of a disability . As shown in Table 1, although
linear regression analyses revealed significant group differences in coping styles between
typically developing students and students with a disability; less than 2% of the variability in coping
was explained by these scores.
Self-Perception Profile for Adolescents
Univariate DFA suggested that based on Harters self-reporting competence scales, 5370% of
the disability group could be correctly classified (Table 1). At best, the PPV was 30%. The
sensitivity of all competence subscales in correctly identifying students with disability was just
above chance (50%), apart from the academic competence and close friendship subscales
demonstrating sensitivity of 65% and 60% respectively. None of the samples scores were in the LR
interval for being considered potentially useful in differentiating students based on the
presence or absence of a disability . Although linear regression analyses revealed significant
group differences in perceived competence between typically developing students and the
students with a disability; less than 7% of the variability in competence was explained by
Social Skills Rating Scale
The total social skills scores, presented in Table 2, could correctly classify 60% of the disability
group. The PPV of the total social skills score to identify students with disability was 23%. The
PPV of the other subscales ranged from 19% to 23%. The samples social skills were not in the
LR interval for being considered potentially useful in differentiating students based on the
presence or absence of a disability . As with other independent variables, regression analyses
explained less than 3% of the between-group variability in Mean-values.
Psychological Sense of School Membership
Students school belongingness scores could correctly classify 54% of the mainstream student
sample, using disability as a discriminant factor. The sensitivity of the school belongingness
score in correctly screening students with disability based on their belongingness scores was
45% while its specificity (or ability to correctly identify typically developing students based on
their belongingness scores) was 56%. The PPV or the ability to correctly identify students with
a disability from the mainstream population of students based on their belongingness scores
Note. IV = Independent variable; DG = disability group (discriminant variable); SSRS = Social Skills Rating Scale; PSSM = Psychological Sense of School
Membership (scale); LSDS = Loneliness and Social Dissatisfaction Scale; TD = typically developing; CC = Correct classification; SN = Sensitivity;
SP = Specificity; PPV = Positive predictive value; Negative predictive value; LR+ = Positive likelihood ratio; LR- = Negative likelihood ratio;
ns = not significant
Total scorea: higher scores = more frequent use of total social skills
Total scoreb: greater frequency of assertion behaviours
Total scorec: greater frequency of empathy behaviours
Total scored: greater frequency of cooperation behaviours
Total scoree: greater frequency of self-control behaviours
Mean raw total scoref: higher scores = greater belongingness in school
Log transformed total scoreg: higher scores = greater LSDS
Log transformed total subscale scoreh: higher scores = greater loneliness in school
Log transformed total subscale scorei: higher scores = greater social dissatisfaction in school.
Loneliness and Social Dissatisfaction Scale
The loneliness and social dissatisfaction scaled-score (LSDS), and its subscales focussing on
loneliness only and social dissatisfaction could correctly classify between 6465% of students
with disability. The PPV or the ability of these subscales to identify students with disability
with lower loneliness and social dissatisfaction scores from a population of mainstream
students was 26%. The samples LSDS scores were not in the LR interval for being considered
potentially useful in differentiating students based on the presence or absence of a disability
. As shown in Table 2, group differences in mean belongingness and loneliness scores
explained less than 4% of the variability in scores.
School Participation Questionnaire
Based on the frequency of student reported participation in school activities, one could accurately
identify between 19%21% (PPV) of students with disability from a mainstream sample of
students with and without disabilities (Table 3). The samples participation scores were not in the LR
intervals for being considered potentially useful in discerning students based on the presence or
absence of a disability . Linear regression analyses revealed no significant differences in
participation components between typically developing students and a subgroup with a disability.
Summary of results
Using a series of univariate discriminant function analyses and other screening indices, we set
out to examine the ability of several measures in predicting the presence of disability in a
Community Activitiesa (0.85)
of School Activitiesa (1.02)
Note. IV = Independent variable; DG = disability group (discriminant variable); TD = typically developing; CC = Correct classification; SN = Sensitivity;
SP = Specificity; PPV = Positive predictive value; Negative predictive value; LR+ = Positive likelihood ratio; LR- = Negative likelihood ratio;
ns = not significant
aTotal score: higher scores = greater participation.
mainstream sample of students with if a student has a disability. The sensitivity of the included
scales ranged from 27.3% to 78.5%, with an overall mean sensitivity of 56.2%, while the
specificity of the scales ranged from 45.5% to 85.1%, with an overall mean specificity of 61.2%.
Our results indicated that the ability of the included scales for correctly classifying (CC)
disability ranged from 47.2 to 78.6%, with an overall mean CC of 60.4%. The CC indicates the
proportion of true results (both true positives and true negatives) in the sample. This means
that most scales have nearly an equal chance of either correctly or incorrectly classifying a
person as having a disability. However, the sensitivity and specificity of a test cannot be used to
estimate the probability of a child having a disability . Given that PPV describes the
probability of having a disability when the student has already been classified as having a
disability, PPV becomes an important index . The PPV ranged from 17.6% to 40.3%, with an
overall mean PPV of 24.2%.
Across the board, the sensitivity of independent variables to correctly identify students with
a disability was 31.9% higher than the related PPV. The lower PPV scores suggest that the
included independent variables had only a small chance (17.6% to 40.3%) to correctly identify
students with a disability from the mainstream population of students. These findings suggest
that students with a disability do not differ enough from their typically developing counterparts
on each independent variable measured in this study, for them to be identifiable. Thus, based
on a univariate DFA of the current sample, the presence of a disability did not seem to impact
on their physical and mental health, social adjustment, and participation outcomes.
Given that the instruments included in the study are also used to measure differences
between groups, we calculated mean score differences for each of the independent variable scales
between the disability and typically developing group. For most independent variable scales
(19 out of 29) there were significant differences when comparing the mean scores of the
disability group with the typically developing group. The overall trends suggest that, when mean
performance on a measure is used to determine if groups differ, the independent variable scales
used in the study do well in detecting differences. However, when they are used to differentiate
and classify groups they performed poorly.
We calculated the R2 to determine how much of the variance was explained by the
independent variables. The results indicated that the R2s ranged between 1 and 12%.
The extremely low R2 values indicate that there are a number of other determinants for each
outcome that were not included in the equation, making the models poor predictors for each
outcome. However, the models do identify a clear difference in the mean scores between the
disability and the typically developing groups (p < .05) for some outcomes. Hence the low R2
Upper 95% C.I. Standardised Canonical
for Exp(B) Discriminant Function
1The standardised discriminant function coefficients serve the same purpose as beta weights in multiple regressions (partial coefficient): they indicate the
relative importance of the independent variable in predicting disability status within the study population. They allow you to compare variables measured
on different scales. Coefficients with large absolute values correspond to variables with greater discriminating ability
2The structure matrix table shows the correlations of each variable with each discriminant function; the correlations then act similarly to factor loadings in
agreater score = worse health
bSDQpeer problems was not a significant predictor in the logistic regression model
SPPA = SelfPerception Profile for Adolescents.
explains why the sensitivity scores are low, as their ability to predict the disability status of a
particular respondent (having a disability or typically developing) is very poor because of the
large variance. There are many other factors that influence the value of the dependent variable
other than the disability status.
Logistic regression analysis
By applying a multivariate DFA with a combination of four independent variables: presence of
hyperactivity-inattention (parental report); identification of the child to have problems in peer
relationships (parental report); lower perceived academic competence (child self-report); and
physical health status (parental report), the groups with and without disability appeared to
separate more clearly ( = 0.816, 2(4) = 79.03, p < .001), with an R-canonical = 0.429, and 83.8%
correct classification (sensitivity = 28.8%, specificity = 94.8%, PPV = 52.78, NPV = 86.9).
Table 4 shows the standardised canonical coefficients and the structure weights of the
independent variables that contributed in this multivariate model.
The logistic regression analyses suggested that the full model was statistically significant
against a constant only model, indicating that the same set of independent variables were
significantly associated with the disability status (2 = 61.534(4) < 0.001). However, Nagelkerkes
pseudo R2 of 0.243 indicated that its predictive value may not be strong. Prediction success
overall was 86% (97.9% for the typically developing group and 27.3% for the disability group).
The Wald criterion demonstrated that close friendship competence, presence of peer problems,
hyperactivity, and poor physical health significantly predicted disability. The Odds Ratios
presence of hyperactivity increased the odds of having a disability by factors of 2.89;
poor overall health increased the odds of having a disability by 2.13;
a unit reduction in perceived close friendship competence decreased the odds of having a
disability by a factor of 0.59; and
unit reduction in academic competence decreased the odds of having a disability by 0.62.
We conducted logistic regression to determine which factors were significantly associated
with having a disability on a multivariate level. While significant, the multivariate analyses
optimising the inclusion of all independent variables suggested that the sensitivity was at best
28.8% to correctly identify a person with a disability. Moreover, the low overall pseudo R2 of
the model raises the issue of its generalisability. Indeed, the only findings that were congruent
with previous research, given these multivariate analyses were that poor health and
hyperactivity increased the odds of having a disability about two times, while poor close perceived
friendship and academic competences predicted disability with roughly the same magnitude.
Nevertheless, a summation of all the findings of the current study suggests that schools should
not necessarily expect poor academic, social, mental health and participatory outcomes in
students with a disability. The analysis shows that children with these conditions obtained scores
on these instruments which are not very different from those which typically developing
The premise for this paper was to determine if we can identify adolescents with disabilities
from a community sample of young adolescents in their final year of primary school with
commonly used screening and outcome measures. That is, can we employ commonly used
screening and outcome measures to accurately predict if children, known to have a disability (i.e., a
predefined group), are accurately classified by the measures as having a disability? The answer
to the questionin shortis that the measures used in this study performed poorly in
correctly classifying children with a disability.
Over the past several decades, educational policies in many countries have been geared
towards the inclusion of students with disabilities in mainstream educational programs
[120,121]. Teachers are likely to receive information about their next inbound students and
may form assumptions about student functioning based on the knowledge that the student has
a disability. However, existing research presents mixed findings on whether students with
disabilities studying in mainstream schools differ from their typically developing peers. Notably,
these studies have used convenience sampling techniques, focussed on few disability subtypes
(mainly mild intellectual disability, learning disability, ADHD, Autism Spectrum Disorders),
and used univariate tests to substantiate group differences (e.g., [10,11]). The present study is
the first to actually examine whether or not it is possible to accurately determine the disability
status of a student based on their physical and mental health, social adjustment, and
participation outcomes. The measures we used did not accurately capture the factors that are required
to separate them into distinct groups: typically developing students and students with a
disability. None of the measures had LR+ values that can be considered useful in differentiating
students with and without disabilities. This finding further substantiates our claim that in a
community sample of students with and without disabilities, the measures used in the current
study had limited discriminant ability in accurately awarding membership or identifying
students at-risk who would be eligible for further assessment.
Measures can have different prognostic and/or analytical functions. Therefore measures can
be prognostically used to: a) predict a later outcome; b) determine suitability for a particular
intervention; c) report on the responsiveness to a particular intervention; or d) determine the
amount of intervention required (dosage) . Measures may also be used analytically to: a)
explain or understand the contexts; b) classify or identify subgroups of interest; c) allow
exploration of relationship between factors; d) detect within-subject change or between-subgroup
differences; e) enable comparison of groups of interest to other population subgroups or norms
. If an outcome measure is used to evaluate changes in a person over time, the measure
must be able to detect this change . In this study we were specifically interested in the
ability of the measures to accurately classify and identify a subgroup (i.e., disability), that is, the
identification accuracy of the measures.
Identification accuracy, which refers to an assessments ability to accurately diagnose the
presence or absence of a condition, is arguably of greater importance than other psychometric
criteria, as it indicates the overall precision of making a diagnosis. A discriminant analysis is
conducted, which evaluates the measures convergent validity to judge its ability to distinguish
typical from atypical functioning. Discriminant analysis is carried out using mathematical
calculations that contrast different variables, and that take into account variance in scores to
reach an overall identification. The sensitivity and specificity data assist researchers and
clinicians to gauge the overall identification accuracy of an assessment. Furthermore, if the
diagnosis of the condition is known, the PPV should be included as an important index.
Identification accuracy may vary due to the prevalence of a disorder; and the population or
setting (clinical vs. community). This means that even if a screening instrument is
psychometrically valid and reliable, it may be unlikely to be helpful in identifying individuals at-risk if it is
not usable within the given setting . Screening is a procedure designed to identify people
who have, or who are at risk of having, an illness, disease or disorder [125,126]. Screening is an
initial procedure to determine who is eligible for further assessment, and can be used to identify
those who are likely to benefit from immediate interventions or preventive counselling because
they are considered to be at-risk. For example, the utility of the SDQ is different in clinical
versus community populations [127,128]. In a clinical population, we assume the presence of
psychosocial problems. Therefore, the SDQ should inform us about types of psychosocial
problems, the duration, and perception of these problems. In a community setting, we assume
the presence of some but not all psychosocial problems; hence, the SDQ should be sensitive in
detecting those children in the community who are at risk of having psychosocial problems.
When using group difference indices that report on the magnitude of differences between
and within groups (e.g., using Mean-values), researchers are able to identify patterns; however,
researchers do not tend to report on the classification of the groups. The R2 is an indicator of
how useful the measure is to predict a persons membership of a group based on their score. In
our study the R2s were very low (112%), indicating that the assessments used were poor
predictors of students being classified as having a disability. As such, no combination of measures
could adequately separate respondents into their correct disability category. This is of concern
as both the SDQ and SSRS are screening tools that are routinely used to both classify children
as being at-risk of having a disability, as well as outcome measures to report on effect size
following an intervention. Our findings suggests that these measures may be appropriate for use
as outcome measures in calculating changes over time in groups (responsiveness), but the
groups need to be predefined. However, in an earlier study we found that the SSRS has a large
measurement error (ME) , a construct closely related to responsiveness. ME (i.e., variability
within stable subjects) sets the boundaries of the minimal detectible true change of an outcome
measure. Thus, to evaluate true change over time, both the responsiveness and the ME needs to
be taken into account where by the responsiveness values need to be wider than the ME.
Therefore, the combined findings suggest that the SSRS may not be useful as either screening or an
outcome measure. Moreover, the findings highlight the importance for researchers to not only
report the differences in Mean-values when comparing two or more groups, but also to
calculate and report the R2. R2 estimates the component of variance in the outcome which is
attributable to the set of independent variables in the model.
There is a real need for clinicians and researchers to understand issues related to validity
and reliability that accompany the use of measures as part of their diagnostic battery. First and
foremost, the stated purpose of the measure intended for use should match the clinical purpose.
The purpose of a measure is an important component of any assessment tool, as assessments
are conducted for very different diagnostic reasons. For instance, some assessments are
administered to diagnose the presence or absence of a condition, as well as to determine the severity
level of the condition or to establish the required dosage. As such, researchers and clinicians
need to be cognisant of the purpose of a given test in order to collect data reflecting their
diagnostic needs. Importantly, some measures might purport to serve a specific purpose, but offer
no data to substantiate the validity of using a test for that purpose.
Second, researchers and clinicians must carefully decide which psychometric properties of a
given measure should be considered as most essential and, thus, more important to focus upon
in selecting assessments for diagnostic use. One of the most important considerations for
clinicians in selecting a measure must be the identification accuracy. However, it is likely that
information from multiple sources, collected in various environments, is required for making
appropriate clinical decisions.
Conclusion and Future Direction for Research
By using a cross-sectional design and a sample representative of students with and without
disabilities, studying within the mainstream school system, the current study concludes that the
measures used to determine physical and mental health, social adjustment and participatory
outcomes were not helpful in distinguishing between students with and without a disability.
Our findings also highlight the importance of considering the design and purpose of
measures. Some measures are designed to serve the function of screening tools and as such to
correctly classify and accurately predict grouping; while other measures are designed to accurately
measure change over time in a predefined group (responsiveness). Screening measures
therefore need to have sound sensitivity, specificity, PPV and NPV and we are less concerned with
responsiveness. Conversely, outcome measures that are designed to accurately detect change
over time need to be responsive; but the groups under investigation need to have been classified
a priori. As such, researchers and clinicians need to familiarise themselves with the relative
importance of various psychometric properties in relation to measurement functions and be
cautious in matching the function of the measures with their research and clinical needs.
Conceived and designed the experiments: SV TF RC. Performed the experiments: SV.
Analyzed the data: SV RP TF RC. Contributed reagents/materials/analysis tools: SV TF RP. Wrote
the paper: SV RC TF MF RP MC TM. Critically reviewed the manuscript: RC.
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