Content uploaded by Carly Cheevers
Author content
All content in this area was uploaded by Carly Cheevers
Content may be subject to copyright.
Developing an Index of Well-Being for Nine-Year-Old
Irish Children
Carly Cheevers & Mick O’Connell
Accepted: 2 October 2012 / Published online: 17 October 2012
#
Springer Science+Business Media Dordrecht 2012
Abstract This paper outlines the development of an index of child well-being using
data from the first wave of the Child Cohort in the Growing up in Ireland study. This
national longitudinal study explores children’s lives by collecting data from 8,568
nine-year-old children, their caregivers and their teachers. Well-being indices are
useful to describe children’s circumstances, to monitor child outcomes, and to create
and assess the efficacy of social polices involving children. Traditionally, macro-level
data has been used in the construction of child well-being indices. However, micro-
level data is used in this paper to provide a child-centered perspective on their well-
being. This index is comprised of three domains; physical health, socia l & emotional
functioning and educational attainment. Fourteen measures were used in the creation
of these domains utilising data from children, caregivers and teachers on the child’s
current development. The domain content, protocol followed and confirmatory pro-
cess used in creating this index are discussed. Evidence is provided supporting the
inclusion of the domains and the factorial structure of the index. A child well-being
index of this sort is valuable as it manages to efficiently summarize the richness of
information provided by multiple informants on the multidimension al nature of child
well-being into a single index. Consequently, it can be easily used and understood by
the various stakeholders involved in services related to child welfare.
Keywords Indicators
.
Index
.
Well-being
.
Children
.
Development
1 Introduction
1.1 The Importance of Assessing the Well-Being of Children
Studying the well-being of children is a “significant emerging frontier” in developmen-
tal research (Pollard and Lee 2003 pp. 59). The recent proliferation in studies exam-
ining the well-being of children may in part be explained by the conversion of many
Child Ind Res (2013) 6:213–236
DOI 10.1007/s12187-012-9171-5
C. Cheevers (*)
:
M. O’Connell
School of Psychology, University College Dublin, Belfield Dublin 4, Ireland
e-mail: carly.cheevers@ucdconnect.ie
governments to accountability-based public policy (Ben-Arieh 2008). Furthermore,
stakeholders have become increasingly aware that assessing child well-being is
necessary to effectively plan, and examine the efficacy of, services for children
(Rutter and Stevenson 2008).
One of the most practical ways of exploring and measuring well-being is the use of
social indicators. Indicators are widely used statistical markers that denote a particular
phenomenon. They tend to be more common in the fields of economics and finance
but the advantages to thei r use in the social sciences are beginning to be realized.
Social indicators are unique as they provi de a bridge between empirical measurement
and theory (Frønes 2007), in that they give meaning to data, lend support to existing
theories and inform the development of new theories. In the study of well-being, social
indicators are essential as they can provide information on the condition of individuals,
changes and trends across time and furthermore can be utilized to set goals aiming to
improve well-being in areas of need (Ben-Arieh and Frønes 2011).
1.2 Key Developments in Child Indicator Research
The earliest discussion on social indicators pertaining to children is believed to have
been stimulated by President Herbert Hoover’s establishment of the Committee on
Recent Social Trends in America in 1929, and the group’s subsequent report, “Recent
Social Trends” in the United States (US) in 1933 (Zill et al. 1983). It is argued that
there has been a recent revival of the child indicators movement that can be traced
back to the social indicators movement of the 1960s (Ben-Arieh 2008). The dynamic
social climate of the 1960s acted as a catalyst for relevant stakeholders to seek out
ways to measure and monitor the conditions o f different groups in society, including
families and children (Aborn 1985; Land 2000 as cited in Ben-Arieh 2008).
Accordingly, since the late 1970s there has been an increasing interest in using social
indicators to monitor the well-being of children (Sanson et al. 2010).
In his discussion of the history of the child indicators movement, Ben- Arieh
(2008) posits that the launch of the United Nations Children’sFund’s (UNICEF)
annual report, “State of the World’s Children” in the early 1980s and the global
ratification of the United Nations’ Convention on the Rights of the Child (CRC) from
1990 to the present time have also been major driving forces in bringi ng the
measurement and use of child indicators into the spotlight.
The birth and subsequent development of the child indicators movement was
enabled by a number of theoretical and methodological shifts and advancements
(Ben-Arieh 2008). These theoretical shifts included: the genesis of the United
Nations’ CRC; the acknowledgement of childhood as a life stage; and the under-
standing of child development within an ecological framework.
Methodological developments which facilitated the evolution of the child indica-
tors movement included the acknowledgement of the subjective perspective–essen-
tially, the realization that children had an important role in studies of their childhood.
Furthermore, while historically research focused on the child as part of a family, the
child indicator movement now acknowledges the child as the unit of observation,
a separate distinct entity to be observed. Finally, this piqued interest in children’s
well-being shown by stakeholders and the research community resulted in the
creation of large numbers of data sets including administrative data, census,
214 C. Cheevers, M. O’Connell
surveys and academic research which provide rich information on children’s
lives.
The advancement of technology will certainly have facilitated the progression of
the child indicators movement. The proliferation of personal computers and their ever
increasing speed have provided researchers with access to vast numbers of datasets
and the ability to conduct complex statistical analyses with one single keystroke.
Without this technological and electronic growth the movement would not have
managed to progress as far, as rapidly.
1.3 Composite Indices
As the amount of indicators representing various aspects of well-being proliferates, so
too the effort to develop composite or summary indices of well-being emerges, with
increased attention being paid to combining a number of single “simple” indicators into
“constructed” composite indices. Given the multidimensional nature of well-being,
composite indices are particularly advantageous and can facilitate a holistic consider-
ation of well-being (Ben-Arieh and Goerge 2001). Using a single index can be useful
in that it is easier to report, track and monitor progress, and make comparisons across
groups (Moore et al. 2008; Sanson et al. 2010). Composite indices are easier for
stakeholders and the public to understand, they can be utilized to understand the
factors influencing child well-being and development and evaluate the impact of
policies and changing social conditions (Moore et al. 2008; Sanson et al. 2010).
There are a number of challenges faced in creating composite indices: which
measures to use as indicators; what domains of well-being to include; how to weight
the constituent parts; the application of cut-offs; how to deal with missing or lack of
data, and generally conceptualizing the entire process (Moore et al. 2007). An added
complexity is that it is sometimes argued that the validity of such measures is
questionable because the use of summa ry statistics can mask trends within their
constituent parts (Moore et al. 2007, 2008). Moore et al. (2008) consider this point,
but counter it with the reasoning that leading economic indices are composite
measures, and that this averaging effect smoothes volatility wi thin the data, as a
result providing a more accurate portrayal of the economic sit uation as a whole. The
same argument can apply to an index of well-being.
1.4 Macro-Level and Micro-Level Indices of Well-Being
Generally, composite indices of child well-being are created using population level
data (Sanson et al. 2010). The most prominent of these originate in the US, e.g., the
Kids Count Index created by the Annie E Casey Foundation (O’ Hare and Bramstedt
2003) and the Foundation for Child Deve lopment’s Child Well-Being Index (CWI;
Land et al. 2001). While population level indices use aggregate data to describe the
proportion of children in a population with a certain outcome, micro-level indices, in
which the child is the unit of observation, enable one to understand children at the
present time, and the developmental pathways related to functioning at an individual
level (Fernandes et al. 2012; Sanson et al. 2010). Micro-level indices effectively give
children a voice and offer a child-centered perspective to the concept of child well-
being (Moore et al. 2008). Fernandes et al’s(2012) recent review essay stated that
Developing an Index of Well-Being for Irish Children 215
utilizing micro-level data advances the field of measuring child well-being, yet
despite these desirable charact eristics there have been a limited number of attempts
to create micro-level indices of child well-being. The only efforts to do so have
employed data from large scale studies in Australia and the US and the main elements
of these studies are outlined in Table 1.
In calculating the composite well-being scores, the American studies using data
from the NSAF and the NSCH followed the same procedure of choosing thresholds,
or cut-points, for all indicators and domains to dichotomize children into groups. The
studies using the NSAF chose cut-points to divide children into those experiencing
problematic well-being or not, and the studies using the NSCH divided children into
those who showed normative well-being or not. Conversely, Sanson et al. (2010)
retained all indicator variables and domain scores in a continuous form where
possible in order to exploit the valuable diversity of the data.
1.5 Measurement of Child Well-Being in Ireland
Ireland’s largest endeavor to measure child well-being has been the biennial publication
of State of the Nation’s Children reports since 2006 (Office of the Minister for Children
2006). State of the Nation’s Children reports are collaborations between government,
academic institutions and other relevant stakeholders to observe and monitor the
condition of children in a particular area or region (Ben-Arieh and Goerge 2001 ). The
State of the Nation’s Children reports describe how children in Ireland are faring
based on a National Set of Child Well-being Indicators. These indicators include
population measures of socio-demographics, children’s relationships, education,
health, social, emotional and behavioral outcomes, and form al and informal supports
(Office of the Minister for Children and Youth Affairs 2010). The publication of the
initial report in 2006 was the first undertaking in Ireland to describe and subsequently
monitor the holistic well-being of Irish children. It was Ireland’s preliminary step in
bringing a policy focus on child well-being to light (Fitzgerald 2004).
While the Irish State of the Nation’s Children report utilizes over 50 unique
indicators to provide a picture of how well Irish children are doing, a number of
studies have used population data to create composite indices of child well-being in
Ireland (Bradshaw et al. 2007; Bradshaw and Richard son 2009; UNICEF Innocenti
Research Centre 2006). The most recent study compared child well-being across 27
countries of the EU, Norway and Iceland using 43 indicators taken from administra-
tive and survey data (Bradshaw and Richardson 2009). The domains of well-being
examined were children’s material situation, housing and environment, health, sub-
jective well-being, education, children’s relationships, and risk and safety.
While these reports of population level indicators and indices are crucial for
describing the functioning of children in Ireland and informing their future, there
has been no Irish research using indices to describe child well-being and functioning
at the individual level.
1.6 The Current Paper
This paper is timely in that it creates a micro-level index of well-being of children in
Ireland, using the child as the unit of observation. This index was created using data
216 C. Cheevers, M. O’Connell
Table 1 Information on existing micro-level indices of child well-being
Name “Child well-being index”“Child well-being index”“Child well-being index”“Outcome index”“Child well-being index
(positive)”
Authors Moore et al. 2007 Vandivere and McPhee 2008 Moore et al. 2008 Sanson et al. 2010 Moore et al. 2012
Study National Survey of America’s
Families (NSAF)
(1997, 1999, 2002)
National Survey of America’s
Families (1997, 1999, 2002)
National Survey of
Children’s Health
(NSCH) (2003)
Longitudinal Study of
Australian Children
(LSAC) (2004)
National Survey of
Children’s Health (2007)
N Exact unknown. Survey
sample 0 >30,000 per wave
Exact unknown. Survey
sample 0 >30,000 per wave
a
31,096 & 37,820 5,107 & 4,983 Exact unknown. Survey
sample 0 91,642
Age 6–11 & 12–17 years 6–11 & 12–17 years 6–11 & 12–17 years 3–19 month & 4–5 years 6–11 & 12–17 years
Domains of
well-being
Health & Safety Health & Safety Physical Health Health & Physical
Development
Physical Health
Social & Emotional Development Social & Emotional Development Psychological Health Social & Emotional
Functioning
Psychological Health
Educational Achievement &
Cognitive Attainment
Education Social Health Learning Competency Social Health
Educational Achievement &
Cognitive Development
Educational Achievement &
Cognitive Development
No. indicators 17 17 69 6 & 16 30 & 32
a
Vandivere and McPhee 2008 also used data from the NSAF to create indices of well-being across 13 representative states, mirroring the indicators and domains used by Moore et
al. 2007
Developing an Index of Well-Being for Irish Children 217
from the Child Cohort of “Growing up in Ireland” (GUI), the National Longitudinal
Study of Children in Ireland. Two cohorts of children will be followed over the course
of the GUI study, the Infant Cohort who are followed from 9 months old and the
Child Cohort who begin participating at 9 years old.
1
This study is the first large scale
longitudinal study of children in Ireland and provides a rich data source on children
and their environment using reports from parents and other caregivers, teachers,
school principals and the children themselves. Additionally, information is obtained
through direc t measurements of the children, for example their scores on academic
tests and their physi cal measurements. The diverse information gleaned from the
study means that GUI is an ideal source for the creation of a micro-level index of
child well-being. This is the fir st opportunity in Ireland to accurately track the
progress of children across their childhood, and an index of well-being can enable
users to examine the factors that influence trajectories of positive and negative child
well-being and development.
1.6.1 Aims
The aim of this study is to describe the development and psychometric properties of
an Index of Child Well-Being in Ireland. The measures used, and methods of
calculating domain and index scores will be explained. Relationships between indi-
cators and domains will be examined, the overall factorial structure discussed and
examples of the index’s utility will be illustrated.
1.7 Considerations for Developing the Micro-Level Index of Well-Being
1.7.1 Conceptualization and Measurement
The practice of creating micro-level composite indices of child well-being is still in
the early stages, therefore there is no prescribed protocol to use or method to follow to
measure and calculate well-being scores (Moore et al. 2008). The creation of indices
is shaped by individua l differences in the conceptualization of child well-being,
theoretical underpinnings, the availability of data, the indicators and domains includ-
ed and the calculation methods employed (Moore et al. 2008). Therefore it is
necessary to clearly conceptualize and explain the measurement processes of any
attempts to create composite indices.
There are a number of issues to take into consideration in conceptualizing and
creating a composite index of well-being. A systematic review of the child well-being
literature conducted by Pollard and Lee (2003) found that both the definition of child
well-being and the tools used to measure the construct lacked consistency. This issue
is further compounded by a lack of consensus on what the key indicators and domains
to include in measuring child well-being are (Fernand es et al. 2012; Moore 1997;O’
Hare and Bramstedt 2003; O' Hare and Gutierrez 2012; Pollard and Lee 2003).
However, consistent themes are arising (Moore et al. 2008), as can be illustrated by
Pollard and Lee ’s(2003) finding that generally five distinct domains of well-being are
1
Although a very small proportion (1.7 %) of the sample included eight and 10-year-olds this Cohort is
referred to throughout the paper as the Cohort of 9-year-olds
218 C. Cheevers, M. O’Connell
studied; physical, social, psychological, cognitive and economic. These appear to be
the key areas contributing to a child’s overall well-being and development. Pollard
and Lee (2003) indicated that while definitions of well-being are not consistent in the
literature, there is a clear pattern to the conceptualizations of what contributes to child
well-being. Managing discrepancies in the measurement of well-being is the key to
achieving consensus in the literature. In Pollard and Lee ’s(2003)reviewthey
postulate that to accurately capture well-being it is vital that the measurement tool taps
the multidimensional nature of well-being. Unfortunately, while there is a multitude of
measures claiming to assess child well-being, the reality is that they are tapping into
more specific, unidimensional aspects of well-being rather than the construct as a whole
(Pollard and Lee 2003). Their study found that 80 % of the studies they reviewed
covered only one domain of well-being, 13 % covered two, almost 5 % covered three
domains and just over 2 % covered four domains of well-being, namely cognitive,
physical, psychological and social well-being (Pollard and Lee 2003).
The conceptualization and measurement of this Index of Child We ll-Being in
Ireland was informed largely by the efforts of previous studies to create micro-level
indices of child well-being. Methodological elements of each study were considered
and for a number of reasons it was decided to model this index on the Outcome Index
created by Sanson et al. (2010). Firstly, the Outcome Index was constructing using
data from Growing up in Australia which is a similar study to GUI and which also uses
comparable measures. Secondly, it was preferable to exploit the information as contin-
uous data as their study did, to avoid both the loss of fine detail that occurs when
continuous data is dichotomized, and the subjective and sometimes arbitrary process of
choosing cut-points to define what is and is not considered to be a state of well-being.
Furthermore, a more accurate portrayal of a continuum of well-being emerges when the
distribution of continuous data is used and according to Sanson et al. 2005 (pp. 22),
using the data in this manner is “more statistically and mathematically principled”.
1.7.2 The Choice of Domains and Measures for Inclusion
The current study aims to create a composite index of child well-being consisting of
three domains of well-being: physical health, social and emotional functioning and
educational attainment. These domains reflect those used by the other large scale micro-
level indices, but also mirror the areas of well-being most prevalent in the research
literature (Pollard and Lee 2003) and align with key domains of child development:
physical, psychological, social and intellectual (Eccles and Gootman 2002).
O' Hare and Gutierrez (2012) recently reviewed the domains included in 1 8
comprehensive composite indices of child well-being and found that on average the
indices included approximately five domains, ranging between two and seven
domains. The lack of consistency in the field (Fernandes et al. 2012) is highlighted
by the use of over 100 domain names in these studies, over 60 of which were unique
names (O' Hare and Gutierrez 2012). The most popular domain types included were
education, health and material well-being. The authors concluded that a comprehensive
index of child well-being not including these three domains ‘is questionable’ (O' Hare
and Gutierrez 2012 pp. 13) and that because over half of the studies incorporated six
or seven domains in their indices this was perhaps an indication of the number of
domains that should be used in such an index. However, there is one point regarding
Developing an Index of Well-Being for Irish Children 219
the studies included in this review that we feel has been overlooked in reaching these
conclusions; that is, the different theoretical standpoints in regards to the inclusion or
exclusion of contextual/social environmental factors in the overall indices of well-
being. Three of the cited studies excluded contextual measures when constructing
their composite well-being indices (Moore et al. 2008; Sanson et al. 2010; Vandivere
and McPhee 2008). Accordingly, these studies utilized on average just over three
domains of well-b eing in their indices of child well-being which is important to
consider, particularly in regards to the current study. However, two of the studies
(Moore et al. 2008; Vandivere and McPhee 2008) did include separate indices of
contextual well-being, which when merged with their indices of child well-being,
were considered to represent the overall condition of the child.
According to Moore et al. (2012) the pract ice of combining measures describing
how children are faring with measures pertaining to their contexts or environments
confounds ‘the determinants of well-being with child outcomes’ (pp. 199). Moore et
al. (2008) warned against including contextual measures in composite indices of well-
being as it is conceptually important to differentiate between actual child well-being
and the resources that may facilitate or impede well-being. Micro-level indices do not
need to assume causal relations between for example household income and a child’s
well-being status as these relationships can be empirically tested. In the cases where
social environmental factors are included in indices with child outcomes, one is
implicitly modeling how a child inherits their parents’ circumst ance, thereby dis-
counting the intricate mechanisms through which risk, vulnerability, resilience and
protective factors function in these relationships. Following recommendations by
Vandivere and McPhee (2008), Moore et al. 2008 and Brown (2006), this study will
not be combining outcome measures representing child well-being with measures of
the child’s social environment.
1.7.3 Well-Being and Well-Becoming
The variables, or indicators, comprising each domain of well-being were chosen to
place the individual child within a contextual space representing not only their “well-
being”, but their “well-becoming”. This is in keeping with Sanson et al ’s(2010)
guideline that there should be empirical evidence of predictive relationships between
the measures of current functioning used and a child’s fut ure functi onin g. The
variables are not only indicative of children’s current functioning but they represent
areas crucial for their development into healthy, able, active citizens of society.
Additionally, the variables were chosen to represent positive and negative elements
of well-being, as advised by Moore and Lippman (2005). Positive indicators repre-
sent a positive construct (measured on a c ontinuum or one-dimensionally), for
example emotional adjustment, whereas negative indicators portray a negative state,
such as externalizing behaviours (measured one-dimensionally) (Pollard and Lee
2003). Unfortunately “well-being is often framed within a model of child deficits
rather than a model of child strengths” (Pollard and Lee 2003 pp. 69), in that well-
being has tende d to be represented by the absence of problems, ins tead o f th e
presence of flourishing and competency factors. The downside to this is that policy
may place more emphasis on interventions for problems, to the detriment of research
into promoting children’s assets. As a result there has been a call for research into
220 C. Cheevers, M. O’Connell
children’s strengths in order to fully understand what makes children thrive. Young
people respond well to the recognition and nurturance of their strengths, and identi-
fying these strengths can help establish what facilitates positive developmental
trajectories (Moore and Lippman 2005).
1.7.4 Weighting Indicators
One of the measurement challenges involved in creating composite indices is the
issue of weighting the constituent indicators and domains in terms of their contribu-
tion toward the overall construct measured. Again, due to the newness of the process
of creating micro-level indices of child well-being, so far all of the applicable studies
have weighted the constituent domains equally. This also appears to be the case
for t he composite indices of population level child well-being (Fernandes et al.
2012). At the present time equal weighting is optimal because there is no appropriate
statistical and theoretical alternative mechanism of weighting (Hagerty and Land
2007). Moore and Theokas (2008) find equal weighting in this case to be acceptable
because no domain dominates the entire construct, as is the nature of general well-
being.
1.7.5 Type of Indicators and Informers
Moore et al. (2007) suggest that the best pract ice for choosing the variables to create
indices of well-being is to include objective, observa ble and subjective indicators.
This study uses all three types of indicators from multiple sources, including the main
caregiver, the child’s teacher and the child themselves. The child self-report indicator
pertains to their health and although the children are young (9-years-old), studies
have shown that children as young as five can reliably report their health and health
related quality of life (see review by Riley 2004; Varni et al. 2007).
2 Method
2.1 The Growing Up in Ireland Study
The objective for Wave 1 of the Child Cohort was to interview a random sample of
8,000 nine-year-old chi ldren, t heir par ents/guardians, carers, teach ers and s chool
principals (Growing up in Ireland Team 2010). Figures from the Irish Census in
2006 indicated there were 56,497 nine-year-olds living in Ireland at the time, thus the
project aimed to survey approximately 14 % of the study population (Growing up in
Ireland Team 2010). To be eligible for incl usion, children had to be born between
November 1st, 1997 and October 31st, 1998 (Murray et al. 2011).
2.1.1 Sampling Frame and Sample Design
The sample was selected in two stages using the national primary education system as
the sampling frame. The school acted as the primary sampling unit (psu) and the pupil
was the secondary sampling unit (ssu) (Murray et al. 2011).
Developing an Index of Well-Being for Irish Children 221
& Stage 1: A random sample of 1,105 schools
2
was selected on a stratified
systematic basis in whic h schools were stratified according to coun ty, co-
educational status, disadvantaged status,
3
size (as measure d b y numb er of
9-year-olds) and religious denomination.
4
A total of 910 schools consented to
participate, resulting in a school response rate of 82.3 % (Growing up in Ireland
Team 2010).
& Stage 2: To select pup ils, the sch ools were divided into two g roups–those
with less than 40 eligible children, and those with more than 40 eligible
children. In the former group, all children were recruited into the sample
and i n the larger schools an upper threshold of 40 children were randomly
selected by the school principal using a random number set (Murray et al.
2011). Out of 17,054 eligible students, 57 % (n0 9,645) consented to participate
and of those who consented, 8,655 completed the survey successfully. Data on
8,568 of these students are available in the final dataset
5
(Growing up in Ireland
Team 2010).
Sample Weighting Sample weights were constructed to reflect the sample desig n
using school and family variables to ensure the selected sample was representative
of the population from which it was drawn.
2.1.2 Data Collection
Survey Administration in Schools Principals completed a school questionnaire,
and t eachers completed two questionnaires: one about themselves and a second
on the study child. Trained interviewers administered two self-complete assess-
ments t o the children in the schools: academic tests and the Piers-Harris I I self-
concept scale (Piers et al. 2002). All school surveys were completed on paper
(Murray et al. 2011).
Survey Administration in Homes In the home, trained interviewers administered
questionnaires to primary caregivers (PCGs), secondary caregivers (SCGs) and
the study child through Computer Assisted Personal Interviewing (CAPI).
Additionally, all participants answered self-complete paper questionnaires of sen-
sitive items, the child completed a “One day time use diary” and the interviewers
measured the height and weight of the child and caregivers (Murray et al. 2011).
A short, self-complete paper quest ionnaire was sent to non-resident parents and other
caregivers who cared for the child for at least 8 h per week on a regular basis (Murray
et al. 2011).
2
From the national total of 3,177 primary schools (excluding 80 schools designated as only for infants and
schools with no 9-year-olds enrolled)
3
In the Republi c of Ireland, schools within a catchment area of a low income community may be
designated as disadvantaged
4
State primary schools in the Republic of Ireland are ‘denominated’ as having a particular religious ethos
by the Department of Education and Skills using one of nine religious categories
5
This disparity in figures is a result of dropping cases when requested by the family or when the level of
missingness made the data unusable
222 C. Cheevers, M. O’Connell
2.2 The Current Study
2.2.1 Sample
Participants included in this study were 8,568 children and their primary caregivers
and teachers who participated in Wave 1 of the Child Cohort of the Growing Up in
Ireland study.
Primary Caregivers The majority of primary caregivers were biological mothers
(94 %, n0 8,172), 2.1 % (n0 185) were fathers, and the remaining were the child’s
grandparent, foster parent, adoptive parent or other relation. The mean age of primary
caregivers was 39.28 years old (SD0 5.7), 93 % (n0 7,971) were Irish citizens, and
82 % (n0 7,014)werelivingwithapartner.Threein10hadcompletedlower
secondary school or below (30 %, n0 2,585), 37 % (n0 3,145) achieved upper
secondary school qualifications and one third (33 %, n0 2,838) had obtained a non-
degree qualification or higher on leaving school. Just over half (54 %, n0 4,586) were
currently employed or self-employed and 28 % (n0 2,408) lived in a household in
receipt of social welfare payments.
Children Children were aged between eight and 10 years old, with 98 % (n0 8,423)
aged 9 years. Just over half (51 %, n0 4,381) were male, 95 % were Irish citizens
(n0 8,119) and 82 % were living with two parents in the household (n0 7,015).
2.2.2 Procedure
Data Collection Access to the dataset was obtained through an application to the
Irish Social Science Data Archive (ISSDA). An Anonymised Microdata File (AMF),
the publicly available anonymised dataset, was received on a Compact Disc (CD) and
version 18 of the Statistical Package for the Social Sciences (SPSS) was utilized by
the researcher to access the data.
Developing the Index of Well-Being The initial stages of developing the Index
involved a number of steps to choose and prepare suitable variables for inclusion
(Fig. 1 below illustrates the final items included in the index). First, a review of the
research literature was conducted to ascertain ind ividual child factors whic h de-
scribed their current well-being status and which were also predictive of future
functioning. Following consultation of the GUI quest ionnaires, these factors were
then matched to relevant items and scales used in the study. The variables chosen
were examined in terms of their missingness, the psychometric properties of the
multi-item measures were assessed and all were deemed acceptable for inclusion (see
Table 1 for descriptives of the variables used
6
). The relationships between the
variables in each domain were then assessed for redundancy and overly high corre-
lations. This was to ensure that weighting of domains was not imb alanced by
including variables which tapped into the same underlying construct, and again all
6
Any results reported are based on the weighted dataset
Developing an Index of Well-Being for Irish Children 223
the variables were deemed acceptable. In preparing the variables for inclusion in the
index, where applicable, variables were recoded so that higher scores indica ted
positive outcomes.
Calculating the Index of Well-Being The four steps involved in calculating the
index a re outlined below. These steps followthemethodusedbySansonetal.
(2005) and Sanson et al. (2010) in their creation of the two LSAC Outcome Indices.
Their method to deal with missing data was also employed and is described in more
detail below.
Step 1– Standardizing all variables
In the first step, all 14 component variables were standardized to z
scores. There were a number of special cases:
& While a very small proportion of 1.7 % (n0 145) of the children were
not aged nine, age trends in some variables were still apparent. The
following variables showed age trends and were therefore standardized
by the age of the child: child health status, child’s long term illness or
disability, SDQ Prosoci al Beh avior score, and literacy skills.
& Children completed Level 2, 3 or 4 of the Drumcondra Primary Reading
Vocabulary Test-Revised and Drumcondra Primary Mathematics Test-
Revised depended on which class they belonged to. As a result, test
scores were standardized by the test level taken.
INDEX OF WELL-BEING
SOCIAL & EMOTIONAL
FUNCTIONING DOMAIN
Internalizing
1.SDQ
Emotional
Symptoms T-
Externalizing
1.SDQ Conduct
Problems T-
2.SDQ
Hyperactivity T-
Social
Competence
1.SDQ
Prosocial
Behaviour T +
2.SDQ Peer
Problems T-
EDUCATIONAL ATTAINMEN
T
DOMAIN
Literac
y
1.Drumcondra
Vocabulary Score C+
2.Reading Ability P+
3.Literacy Skills T+
Numerac
y
1.Drumcondra
Maths Score C+
2.Maths
Performance P+
3.Maths
Performance T+
PHYSICAL
HEALTH
DOMAIN
Physical Health
1.Health Status P+
2.Long Term Illness
or Disability C-
3.BMI C+
Ke
y
P Primary Caregiver - Negative measure
C Child
T Teacher + Positive measure
Fig. 1 The constituent measures, subdomains and domains of the Index of Well-Being
224 C. Cheevers, M. O’Connell
& The standardization of BMI involved a number of steps:
1. Firstly the continuous BMI score was standardized as z scores.
2. To account for age and gender, cut-point data from the International
Obesity Task Force (Cole et al. 20 00) was used to create a cate-
goric al variable of BMI indicat ing whe ther th e child was non-
overweight, overweight or obese.
3. Using this categorical variable, the original BMI standardized
variable was restructured to be meaningful, so that higher scores
equate to more positive outcomes compared to lower scores:
& Overweight and obese children’s scores were multiplied by −1.
& Children who were non-overweight and who scored below 0 on
the original BMI standardized score were multiplied by −1. In this
way, underweight, overweight and obese children all score below
0 and the non-overweight children score above 0, keeping in line
with the process for higher scores to mean positive outcomes.
& Finally, the variable was standardized to z scores again.
Step 2– Creating subdomain scores
The second step in the calculation process was to create the five subdomain
scores (the Internalizing, Externalizing, Social Competence, Literacy and
Numeracy subdomains). This involved firstly calculating the mean score of
the component indicators within each subdomain, and then standardizing the
resultant mean scores to z scores.
Step 3– Creating domain scores
Calculating domain scores involved the same process as above, of
calculating the standardized mean score of the component of each domain
subdomains (or in the case of Physical Health which has no subdomains,
the single indicators). These scores were standardized with a mean (M) of
100 and standard deviation (SD) of 10.
Step 4– Creating the index scores
This involved calculating t he standardized mean score of t he three
domains of Physical Health, Social & Emotional Functioning, and
Educational Attainment (only for cases in which all three domain scores
were present
7
). The variable was standardized with a mean of 100 and
standard deviation of 10. Additionally, the top and bottom 15 percentiles
for the index and each domain were used to create variables indicating the
groups of students performing best and worst in terms of their overall well-
being, and more specifically their physical health, social, emotional and
educational well-being. These cut-points are not intended to be clinically
meaningful; rather they are based on the general statistical view that
scoring less than one standard deviation below the mean of a population
(which approximates to 15 % of the sample) indicates a difficulty (Sanson
et al. 2005). Thus, the same logic was applied to the positive end of the
scale to observe which children are thriving.
7
Scores were available for the full sample on Physical Health and Educational Attainment, but data were
missing for 3.85 % of the cases on the Social and Emotional Functioning domain
Developing an Index of Well-Being for Irish Children 225
Dealing with Missing Data In the event of data missing from one or more variables
within a subdomain, or from one or more subdomains within a domain, a mean score
was still calculated using the available data. However, the resultant mean scores
would be skewed, as the few er scores used to create the mean score, the larger the
standard deviation. Accordingly, children missing data will have scores further from
their “real mean”–the mean that is technically reflective of their true scores. To
correct for this, cases were grouped together according to their level of missingness,
and their subdomain/domain scores were then divided by the group standard
deviation.
Validation of the Index In order to assess the validity of the constructed index,
the relationships between the domains were examined using Pearson correla-
tions. Following this a forced one-factor Principal Components Analysis (PCA)
was conducted as a means to examine if the three domains load adequately
onto a single factor, and to what extent do these domains explain the variance
in this underlying factor. Finally, continuous and categorical forms of the index
and domains were used to examine the relationship of demographic factors to
well-bei ng s cor es.
2.2.3 Measures
Physical Health Domain
Child health status
Primary caregivers described their child’s general health in the past year on a Likert
type scale from 1 “Very healthy, no problems”,to4“Almost always unwell”.
Child long term illness and impact on school
Children answered two items; 1) If they had a long term illness, disability or
medical condition diagnosed by a doctor and 2) If they did, whether it impacted
on their participation in school. A new variable was created on a scale of 1 “Yes,
has a condition and affects school”,2“Yes, has a condition but does not affect
school” and 3 “No, does not have a condition”.
Body Mass Index (BMI)
The GUI dataset provides children’s weight in kilograms (KG) and height in
centimeters. Child BMI was calculated by dividing the child’s measured weight
in KG by their height in meters squared.
Social & Emotional Functioning Domain The complete social and emotional func-
tioning domain consists of five subscales from the widely used Strengths a nd
Difficulties Questionnaire (SDQ; Goodman 1997). The SDQ was rigorously devel-
oped on a foundation of theory using information on facets of child psychological
maladjustment (using the DSM-IV; American Psychiatric Association 1994 and ICD-
10; World Health Organization 1993) and factor analysis (Goodman and Scott 1999).
Many studies have examined the scale’s psychometric properties, deeming it to be
reliable and valid (Goodman 2001; Muris et al. 2003; Smedje et al. 1999 ). Caregivers
and teachers rated children on 25 items pertaining to their psychol ogical adjustment
on a scale of 0 “Not True”,1“Somewhat True” and 2 “Certainly True”. A study by
the measure’s author found that child behavior was best predicted using primary
226 C. Cheevers, M. O’Connell
caregiver and teacher reports, and that both reports possessed roughly equivalent
predictive ability (Goodman et al. 2000). Accordingly, the mean scores of caregiver
and teacher reports combined are used in this study.
Internalizing Subdomain
SDQ Emotional Symptoms Scale (α 0 .73)
Scores on this scale were obtained by calculating the children’s mean scores on
five items related to negative emotional states.
Externalizing Subdomain
SDQ Conduct Problems Scale (α 0 .67)
Scores on this scale were obtained by calculating the children’s mean scores on
five items related to disruptive, disobedient and aggressive behavior.
SDQ Hyperactivity Scale (α 0 .85)
Scores on this scale were obtained by calculating the children’s mean scores on
five items related to inattentive, overactive behavior.
Social Competence Subdomain
SDQ Prosocial Behavior Scale (α 0 .77)
Scores on this scale were obtained by calculating the children’s mean scores on
five items related to positive, considerate, sociable behavior.
SDQ Peer Problems Scale (α 0 .67)
Scores on this scale were obtained by calculating the children’s mean scores on
five items related to problems in relationships with peers.
Educational Attainment Domain
Literacy Subdomain
Drumcondra Primary Reading Test–Revised (DPRT-R)–Vocabulary
Children’s scores on the Vocabulary section of the Irish national, standardized,
curriculum based DPRT-R were used as a measure of their reading achievement.
Reading Ability
Primary caregivers rated how well their child’s reading was relative to other
children of his/her age on a scale from 1 “Poor” to 5 “Exc ellent”.
Overall Literacy Skills (α 0 .91)
Teachers were to rate children on three items regarding their reading, writing, and
comprehension ability relative to children in their age group on a scale from 1 “Below
Average” to 3 “Above Average ” . An exploratory factor analysis indicated that these
three items loaded onto one factor and explained 84.5 % of the variance in the factor.
Accordingly, an overall teacher-rated ‘literacy’ scale was created using the child’s
mean score on these three items.
Numeracy
Drumcondra Primary Mathematics Test–Revi sed (DPMT-R)
Children’s scores on Part A, Form A of the Irish national, standardized,
curriculum based DPMT-R were used as a measure of their mathematics
achievement.
Mathematics Performance (caregiver report)
Primary caregivers rated how well their children were performing in math-
ematics relative to other children of their age on a s cale from 1
“Poor” to 5
“Excellent”.
Developing an Index of Well-Being for Irish Children 227
Mathematics Performance (teacher report)
Teachers were asked to rate children on their mathematics academic performance relative
to others in their age group on a scale from 1 “Below Average” to 3 “Above Average”.
3 Results
Table 2 displays the descriptive statistics of all of the variables included in the index
of well-being.
3.1 Relationships Between Indicators
Table 3 shows Pearson Correlations between the varia bles included in the Index.
Associations between scores on each variable ranged f rom negligible to large
(according t o Cohen 1992), with two relationships not reaching significance.
These were the correlation between child BMI and scores on prosocial behavior
and reading ability.
3.2 Relationships Between Domains
The associations between each domain of the index w ere all statistically signifi cant
(p <.001), and ranged from small to medium in magnitude (Cohen 1992). The
relationship of the strongest magnitude (r0 .38) was that between Social and
Emotional Functioning (N0 8,238) and Educational Attainment (N 0 8,568). The
magnitude of the relationship between Social and Emotional Functioning and
Table 2 Descriptive statistics of variables included in the index of child well-being
Domain Subdomain Variables N Mean SD % missing
Physical health – Child health status 8568 2.71 0.5 0.0
Child long term illness 8484 2.83 0.5 1.0
Child BMI 8089 17.95 3.1 5.6
Social & emotional
functioning
Internalizing Emotional symptoms (SDQ) 8236 8.22 1.6 3.9
Externalizing Conduct problems (SDQ) 8229 8.90 1.2 4.0
Hyperactivity (SDQ) 8220 7.08 2.3 4.1
Social Competence Peer Problems (SDQ) 8221 8.85 1.3 4.1
Prosocial behavior (SDQ) 8225 8.58 1.4 4.0
Educational
attainment
Literacy Drumcondra vocabulary
test score
8340 0.02 1.0 2.7
Reading ability 8562 3.77 1.0 0.1
Literacy Skills 8234 2.17 0.7 3.9
Numeracy Drumcondra maths
test score
8417 −0.76 0.9 1.8
Maths performance (caregiver) 8560 3.63 1.0 0.1
Maths performance (teacher) 8218 2.16 0.7 4.1
228 C. Cheevers, M. O’Connell
Table 3 Pearson correlations between standardized variables used in the index of well-being
12345678910111213
Physical health domain
1. Child health status –
2. Long term illness .33*** –
3. BMI .06*** .06*** –
Social/emotional domain
Internalizing subdomain
4. Emotional symptoms (SDQ) .20*** .10*** .05*** –
Externalizing subdomain
5. Conduct problems (SDQ) .08*** .08*** .03** .27*** –
6. Hyperactivity (SDQ) .09*** .09*** 03* .25*** .56*** –
Social competence subdomain
7. Peer approval (SDQ) .13*** .12*** 12*** .45*** .43*** .37*** –
8. Prosocial behavior (SDQ) .03** .05*** .00 .14*** .51*** .40*** .36*** –
Cognitive domain
Literacy subdomain
9. Drumcondra vocabulary score .05*** .03** .05*** .19*** .22*** .35*** .14*** .06*** –
10. Reading ability .07*** .04** .00 .12*** .14*** .33*** .07*** .10*** .50*** –
11. Literacy skills .06*** .05*** .07*** .21*** .24*** .47*** .19*** .14*** .62*** .53*** –
Numeracy subdomain
12. Drumcondra mathematics score .06*** .03** .07*** .23*** .21*** .36*** .19*** .05*** .62*** .32*** .51*** –
13. Mathematics performance (caregiver) .07*** .04*** .05*** .21*** .16*** .32*** .15*** .08*** .36*** .48*** .42*** .44*** –
14. Mathematics performance (teacher) .05*** .05*** .10*** .24*** .21*** .40*** .20*** .10*** .48*** .36*** .70*** .53*** .50***
N ranged from 7788 to 8568
*p<.05 **p<.01 ***p<.001
Developing an Index of Well-Being for Irish Children 229
Physical Health (N0 8,568) was small, with a co-efficient r0 .18 and the weakest
correlation was between Physical Health and Educational Attainment (r0 .11).
3.3 Factorial Structure of the Index
Table 4 displays the component matrix of the forced one-factor PCA. All factor
loadings were acceptable and additionally, the three variables explained 48.96 % of
the factor’s variance.
3.4 Illustrative Examples of Using Scores on the Index of Well-Being and its Domains
Figure 2 illustrates the mean scores of males and females on all three domains
of well-being. Independent samples T-tests indicated that males score statistically
significantly higher than females on the Physical Health and Educational Attainment
domains (t(8566) 0 2.03, p<.05 and t(8566) 0 3.13, p<.01, respectively). Conv ersely,
females score higher on the Social and Emotional Functioning domain
(t(8236) 0 −10.41, p<.001).
Figure 3 shows a steep linear gradient in the relationship between children’sscores
on the Index and the income quintile they belong to, ranging from the mean score of 96.2
(SD0 10.0) of children in the lowest income quintile to a mean score of 103.4 (SD0 8.7)
of children in the highest income quintile. A One Way Analysis of Variance indicated
there was a significant relationship between children’s well-being scores and their family
income, F(4,7703) 0 126.11, p<.0001. Tukey post-hoc comparison test s indicated
that scores at each income level were statistically significantly different.
3.5 Profiles of Children Performing in the Bottom and Top 15 % of the Index
of Well-Being
Table 5 illustrates the demographic information of the 15 % of children scoring
lowest on the Index of Well-Being compared to the total sample and the 15 % of
children who scored highest on the Index. The results indicated that the group scoring
in the bottom 15 % were overrepresented by children from single parent, low income,
urban households, with unemployed, lower educat ed primary caregivers. Conversely,
the group scoring in the highest 15 % was overrepresented by children with
employed, highly educated primary caregivers in two parent, high income families.
Table 6 indicates the number of index domains in which children were categorized
into the top or bottom 15 %. Firstly, 36.1 % of children (n0 2,978) did not score in the
top or bottom in any domai n. Almost 64 % (n0 5,235) of children did not score in the
top 15 % on either the Physical Health, Social & Emotional Functioning or
Educational Attainment domains. Just over 28 % (n0 2,359) of the children scored
Table 4 Component matrix of
forced 1 factor principal compo-
nents analysis
Domain Component matrix
Social & emotional functioning .806
Educational attainment .761
Physical health .490
230 C. Cheevers, M. O’Connell
in the top 15 % in one domain, 7 % scored in the top 15 % in two domains and 0.8 %
(n0 68) scored in the top 15 % in all three domains. In relation to the bottom 15 %,
65.6 % (n0 5,408) of children did not score in the bottom 15 % of any domains. One
quarter of the children (n0 2,071) scored in the bottom 15 % in one domain, 7 .6 %
(n0 643) scored in the bottom 15 % in two domains, and just over 1 % (n0 116)
scored in the bottom 15 % in all three domains.
There were also cases of students scoring in the bottom 15 % and top 15 % simulta-
neously. Just over 5 % (n0 445) of children scored in the top 15 % and the bottom
15 % in one domain, while 0.9 % (n0 78) scored in the top 15 % on one domain and
in the bottom 15 % of two domains. Finally, 0.6 % (n0 50) of children scored in the
top 15 % in two domains, while scoring in the bottom 15 % on one domain.
4 Discussion
4.1 Structure of the Index
Analyses indicated that the components within each domain were statistically signif-
icantly associated with one another. While the associations between components
92.0
94.0
96.0
98.0
100.0
102.0
104.0
Lowest 2nd 3rd 4th Hi
g
hest
Mean Score on Index of Child Well
Being
Fig. 3 Mean scores on the Index of Well-Being per income quintile
97.0
98.0
99.0
100.0
101.0
102.0
Physical Health
Domain
Social & Emotional
Functionin
g
Domain
Educational
Attainment Domain
Mean Scores on the Domains of the
Index of Well
Being
Male
Female
Fig. 2 Mean scores on the three domains of Index of Well-Being for males and females
Developing an Index of Well-Being for Irish Children 231
Table 6 Number of children scoring in the top and bottom 15 % in each domain
Number of domains scoring in top 15 % Total
0123
Number of domains scoring
in bottom 15 %
0 2978 1836 526 68 5408
(36.1 %) (22.3 %) (6.4 %) (0.8 %) (65.6 %)
1 1576 445 50 – 2071
(19.1 %) (5.4 %) (0.6 %) (25.1 %)
2 565 78 ––643
(6.9 %) (0.9 %) (7.8 %)
3116 –––116
(1.4 %) (1.4 %)
Total 5235 2359 576 68 8238
(63.5 %) (28.6 %) (7.0 %) (0.8 %) (100.0 %)
Table 5 Demographic profile of the lowest performing 15 % of children, the total sample and the highest
performing 15 % of children
Bottom
15 %
Total
sample
Top 15 %
N% N %N %
Child gender Male 653 52.9 4381 51.1 615 49.8
Female 582 47.1 4187 48.9 620 50.2
Household location Urban area 598 48.4 3832 44.8 549 44.6
Rural area 637 51.6 4721 55.2 681 55.4
Household type Single Parent 1 or 2 children 229 18.5 980 11.4 91 7.4
Single Parent 3 or more children 137 11.1 573 6.7 38 3.0
Couple 1 or 2 children 418 33.8 3004 35.1 436 35.3
Couple 3 or more children 451 36.5 4011 46.8 671 54.3
Primary caregiver education None or primary 178 14.4 549 6.4 19 1.6
Lower Secondary 445 36.0 2035 23.8 134 10.9
Hi Sec/TechVoc/UppSec+Tech/Voc 399 32.3 3145 36.7 455 36.8
Non Degree 128 10.4 1363 15.9 236 19.1
Primary Degree 64 5.2 962 11.2 254 20.6
Postgraduate 21 1.7 514 6.0 137 11.1
Household employment No caregiver employed 413 33.4 1498 17.5 101 8.2
One caregiver employed 365 29.5 2915 34.0 458 37.1
Two caregivers employed 458 37.1 4152 48.5 675 54.7
In receipt of social welfare Yes 613 49.6 2408 28.2 165 13.4
No 622 50.4 6147 71.8 1067 86.6
232 C. Cheevers, M. O’Connell
within the Social & Emotional Functioning and Educational Attainment domains
were of medium to large magnitude, the relationships between child BMI and the
other two variables (health status and long term illness) in the Physical Health domain
were very small with co-efficients of .06. Despite this, these indicators were the most
theoretically meaningful direct measures of actual physical health available in the
dataset so they were all included. Associations between scores on each domain were
statistically significant and ranged from small to medium. This highlights not only the
multidimensional nature of well-being but that wel l-being does not occur uniformly.
In this respect, domain scores might be better to use when the focus is on a particular
facet of well-being. Finally, a Principal Components Analysis illustrated that the three
domains loaded onto a single construct, lending further support to the structure and
relevance of this Index of Well-Being.
4.2 Illustrative Uses of Continuous Scores
The gender and income comparisons of children’s scores on the index illustrated
ways in which continuous forms of the index and its domain can be utilized . Females
scored higher than males on the Social & Emotional Functioning domain, as is a
typical finding in the lit erature (Buchmann et al. 2008). Conversely, males out-
performed females in the Physical Health and Educational Attainment domains.
Females were more likely to be overweight or obese in this study, which may explain
the better position of physical health well-being in males than females (Williams et al.
2009). On closer examination of the Educational Attainment domain, males per-
formed significantly better than females on the Numeracy Subdomain, while the
opposite was true for the Literacy Subdomain. These patterns are typical of the trends
in Irish children’s gender differences in mathematical and reading skills (Eivers et al.
2010). A clear income gradient were observed in relation to children’s well-being
which is parti cularly interesting considering the data collection for this study was
conducted during a period of economic boom when government assistance and
spending for children and families was at its peak.
4.3 Illustrative Uses of Categorical Scores
The potential of using the index categorically by applying cut-points to the top and
bottom of the score distribution was illustrated with the comparison of the demo-
graphic profiles of the children included in the bottom and top 15 % of the Index of
Well-Being with the overall sample. The results indicated that this form of the index
also functioned as expected, with the bottom 15 % of children being overrep-
resented by children from single parent, low income, urban households, with
unemplo yed , low er ed uc at ed pr im ary ca re gi ver s . Conver s ely, in the top 15 %,
children with employed, highly educated primary caregivers in two parent, high
income familie s were overrepresented. These parental an d household demo-
graphics are all associated with children’s development and well-being in the
research literature.
The number of domains in which children scored in the top 15 % and in the bottom
15 % on each domain of the index provides another perspective from which to examine
well-being. The results indicated that less than 1 % of children were consistently doing
Developing an Index of Well-Being for Irish Children 233
well across the different domains of well-being. Additionally, a significant proportion of
just over one third of children scored in the bottom 15 % on at least one
domain of well-being. The interrelated nature of these three domains mean that
the difficulty a child is experiencing in one area could start to impinge on other
aspects of their well-being. Thus it is important to look at the different
combinati o ns of childre n doin g well and perfor mi ng poorly to see a tr ue pict ur e
of how all of the children are doing.
4.4 Conclusions and Future Directions
This study describes the conceptualization and process involved in the development
of a composite index of child well-being in Ireland. The Index of Child Well-Being
was created using data from the first Irish longitudinal study of children, Growing up
in Ireland (GUI), and consists of three domains constructed using fourteen indicators
of children’s physical, social and e motional, and e duca tion al development. Th e
indicators used were subjective and objective measure s from multiple informer s
including the child themselves. In this way, it is a step closer to looking at the child’s
life through a holistic lens. Furthermore, positive and negative indicators were
included and as a result this index enables a shift away from the typical “deficit”
approach to the measurement of well-being and one can look at either ends of the
spectrum, of remediation and promotion of well-being. Analyses of the structure of
the index and relationships between the index and particular demographic variables
indicate it is functioning as expected. This composite index addresses the need to
portray a complex and multidimensional concept like well-being in a parsimonious,
understandable way.
The ecological framework within which the GUI study was developed means the
full potential of this index can be realized with the current wave of data and future
waves of the Child Cohort. Research using this index is currently underway to assess
those individual and contextual factors, and proximal processes (Bronfenbrenner and
Morris 2006) that either promote or hinder w ell-being. As illustrated in the
descriptives presented in this paper, caregiver employment and education are
clearly associated with differences in child well-being, but i t is more important
to understand the mechanisms through which these factors are having an
impact, therefore future work is vital.
Using the next wave of data for the Child Cohort, the index can be used to assess
which factors at aged nine appear to predict well-being at aged 13. Furthermore, the
trajectories of groups of children scoring in the top and bottom 15 % can be examined
for changes across time. The profiles of these children would be valuable to assess
patterns in the contextual factors placing a child at risk for poor well-being as well as
the factors that promote well-being.
This index provides an opportunity for stakeholders to gather important informa-
tion on what influences children’s well-being and more specifically, children’s phys-
ical health, their social and emotional functioning, and their educational attainment.
This type of statistic will be more easily understood by people outside of the
academic sphere, such as the media, the public and the government. Furthermore, it
can help inform the development of programs and policies for children and families
and examine changes in well-being across time.
234 C. Cheevers, M. O’Connell
References
American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.).
Washington: Author.
Ben-Arieh, A. (2008). The child indicators movement: past, present, and future. Child Indicators Research,
1(1), 3–16.
Ben-Arieh, A., & Frønes, I. (2011). Taxonomy for child well-being indicators: a framework for the analysis
of the well-being of children. Childhood, 18(4), 460–476.
Ben-Arieh, A., & Goerge, R. (2001). Beyond the numbers: how do we monitor the state of our children?
Children and Youth Services Review, 23(8), 603–631.
Bradshaw, J., & Richardson, D. (2009). An index of child well-being in Europe. Child Indicators Research,
2, 319–351.
Bradshaw, J., Hoelscher, P., & Richardson, D. (2007). An index of child well-being in the European Union.
Social Indicators Research, 80(1), 133–177.
Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In R. M.
Lerner (Ed.). Theoretical models of human development. Volume 1 of handbook of child psychology
(6th ed.). Editors-in-chief: W. Damon & R. M. Lerner. Hoboken, NJ: Wiley.
Brown, B. (2006). Does the CWI measure representative domains of child well-being? (Paper presented at
the forum “Review of the Child Well-Being Index,” co-sponsored by Foundation for Child Develop-
ment and the Brookings Institution). Retrieved from http://www.fcd-us.org/resources/resources_
show.htm?doc_id0464428.
Buchmann, C., DiPrete, T. A., & McDaniel, A. (2008). Gender inequalities in education. Annual Review of
Sociology, 34, 319–337.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
Cole, T. J., Bellizzi, M. C., Flegal, K. M., & Dietz, W. H. (2000). Establishing a standard definition for child
overweight and obesity worldwide: international survey . British Medical Journal, 320(7244), 1240–1243.
Eccles, J. S., & Gootman, J. A. (Eds.). (2002). Community programs to promote youth development.
Washington: National Academy Press.
Eivers, E., Close, S., Shiel, G., Millar, D., Clerkin, A., Gilleece, L., & Kiniry, J. (2010). The 2009 national
assessments of mathematics and English reading. Dublin: Department of Education and Skills.
Fernandes, L., Mendes, A., & Teixeira, A. A. C. (2012). A review essay on the measurement of child well-
being. Social Indicators Research, 106(2), 239–257.
Fitzgerald, E. (2004). Counting our children: An analysis of official data sources on children and childhood
in Ireland. Dublin: Children’s Research Centre, University of Dublin, Trinity College, Dublin.
Frønes, I. (2007). Theorizing indicators. Social Indicators Research, 83(1), 5–23.
Goodman, R. (1997). The strengths and difficulties questionnaire: a research note. Journal of Child
Psychology and Psychiatry, 38, 581–586.
Goodman, R. (2001). Psychometric properties of the strengths and difficulties questionnaire. Journal of the
American Academy of Child and Adolescent Psychiatry, 40(11), 1337–1345.
Goodman, R., & Scott, S. (1999). Comparing the strengths and difficulties questionnaire and the child
behaviour checklist: is small beautiful? Journal of Abnormal Child Psychology, 27,17–24.
Goodman, R., Ford, T., Simmons, H., Gatwa rd, R., & Meltzer, H. (2000). Usin g the Strengths and
Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample.
The British Journal of Psychiatry, 177, 534–539.
Growing up in Ireland team. (2010). Sample design and response in wave 1 of the 9-year cohort of
growingupinIreland. Retrieved from htt p://www.ucd.ie/issda/static/ documen tation/es ri/GUI-
SampleDesign9YearCohort.pdf.
Hagerty, M., & Land, K. (2007). Constructing summary indices of quality of life: a model for the effect of
heterogeneous importance weights. Sociological Methods & Research, 35, 455–496.
Land, K., Lamb, V. L., & Mustillo, S. K. (2001). Child and youth well-being in the United States, 1975–
1998: some findings from a new index. Social Indicators Research, 56, 241–320.
Moore, K. A. (1997). Criteria for indicators of child well-being. In R. M. Hauser, B. B. Brown, & W. R.
Prosser (Eds.), Indicators of child well-being (pp. 36–44). New York: Russell Sage.
Moore, K. A., & Lippman, L. H. (2005). Introduction and conceptual framework. In K. A. Moore & L. H.
Lippman (Eds.), What do children need to flourish? conceptualising and measuring indicators of
positive development. New York: Springer Science+Business Media.
Moore, K. A., & Theokas, C. (2008). Conceptualizing a monitoring system for indicators in middle
childhood. Child Indicators Research, 1(2), 109–128.
Developing an Index of Well-Being for Irish Children 235
Moore, K. A., Vandivere, S., Lippman, L., McPhee, C., & Bloch, M. (2007). An index of the condition of
children: the ideal and a less-than-ideal U.S. example. Social Indicators Research, 84, 291–331.
Moore, K. A., Theokas, C., Lippman, L., Bloch, M., Vandivere, S., & O'Hare, W. (2008). A microdata child
well-being index: conceptualization, creation, and findings. Child Indicators Research, 1(1), 17–50.
Moore, K. A., Murphey, D., Bandy, T. (2012). Positive child well-being: an index based on data for
individual children. Maternal and Child Health Journal, 16 Suppl 1.
Muris, P., Meesters, C., & van den Berg, F. (2003). The strengths and difficulties questionnaire (SDQ).
European Child & Adolescent Psychiatry, 12(1), 1–8.
Murray, A., McCrory, C., Thornton, M., Williams, J., Quail, A., Swords, L., Doyle, E., & Harris, E. (2011).
Technical report number 1. Growing up in Ireland. National longitudinal study of children. Child
cohort. Design, instrumentation and procedures for the child cohort. Dublin: Office of the Minister for
Children and Youth Affairs.
O' Hare, W. P., & Gutierrez, F. (2012). The use of domains in constructing a comprehensive composite
index of child well-being. Child Indicators Research. doi:10.1007/s12187-012-9138-6.
O’ Hare, W. P., & Bramstedt, N. L. (2003). Assessing the KIDS COUNT composite index. Kids Count
working paper. Baltimore: Annie E. Casey Foundation.
Office of the Minister for Children. (2006). State of the nation’s children. Ireland 2006. Dublin: The
Stationery Office.
Office of the Minister for Children and Youth Affairs. (2010). State of the nation’s children. Ireland 2010.
Dublin: Government Publications.
Piers, E. V., Harris, D. B., & Herzberg, D. S. (2002). Piers-Harris children’s self-concept scale, second
edition (Piers-Harris 2). Los Angeles: Western Psychological Services.
Pollard, E. L., & Lee, P. D. (2003). Child well-being: a systematic review of the literature. Social Indicators
Research, 61(1), 59–78.
Ril ey, A. W. (2004). Evidence that school-age children can self-report on their health. Ambulatory
Pediatrics, 4, 371–376.
Rutter, M., & Stevenson, J. (2008). Using epidemiology to plan services: A conceptual approach. In M.
Rutter, D. Bishop, D. Pine, S. Scott, J. Stevenson, E. Taylor, & A. Thapar (Eds.), Rutter’s child and
adolescent psychiatry (5th ed.). Oxford: Wiley-Blackwell.
Sanson, A., Misson, S., Wake, M., Zubrick, S. R., Silburn, S., Rothman, S. M., Dickenson, J. (2005). LSAC
technical paper #2. Summarising children’s wellbeing: The LSAC outcome index. Australia: Australian
government. Australian institute of family studies.
Sanson, A. V., Misson, S., Hawkins, M. T., & Berthelsen, D. (2010). The development and validation of
Australian indices of child development-part I: conceptualization and development. Child Indicators
Research, 3(3), 275–292.
Smedje, H., Broman, J. E., Hetta, J., & Von Knorring, A. L. (1999). Psychometric properties of a
Swedish version of the “strengths and difficulties questionnaire”. European Child & Adolescent
Psychiatry, 8,63–70.
UNICEF Innocenti Research Centre. (2006). Child poverty in perspective: An overview of child well-being
in rich countries. Innocenti report card 7. Florence: UNICEF Innocenti Research Centre.
Vandivere, S., & McPhee, C. (2008). Methods for tabulating indices of child well-being and context: an
illustration and comparison of performance in 13 American states. Child Indicators Research, 1(3),
251–290.
Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). How young can children reliably and validly self-
report their health-related quality of life?: an analysis of 8,591 children across age subgroups with the
PedsQL™ 4.0 generic core scales. Health and Quality of Life Outcomes, 5(1), 1–10.
Williams, J., Greene, S., Doyle, E., Harris, E., Layte, R., McCoy, S., et al. (2009). Growing up in Ireland.
The lives of 9-year olds. Report 1. Dublin: Office of the Minister for Children and Youth Affairs.
World Health Organization. (1993). The ICD-10 cl assification of mental and behavioural dis orders:
Diagnostic criteria for research. Geneva: Author.
Zill, N., Sigal, H., & Brim, O. G. (1983). Development of childhood social indicators. In E. F. Zigler, S. L.
Kagan, & E. Klugman (Eds.), Children, families, and government: Perspectives on American social
policy (pp. 188–222). New York: Cambridge University Press.
236 C. Cheevers, M. O’Connell
