1. Characteristics of health-related self-report measures for children aged three
to eight years: A review of the literature
Joanne Cremeens1
, Christine Eiser2
& Mark Blades2
1
St. Jude Children’s Research Hospital, Division of Behavioral Medicine, Memphis, TN, 38105-2794, USA
(E-mail: Joanne.Cremeens@stjude.org); 2
Department of Psychology, University of Sheffield, Sheffield, UK
Accepted in revised form 19 October 2005
Abstract
Aims: To review and make recommendations about the format and quality of health-related self-report
measures for children aged 3–8 years. Methods: Literature searches used to identify measures of QOL, self-
esteem, self-concept and mental health. The format (i.e., scale type, presentation style) and quality (i.e.,
item generation, reliability, validity, responsiveness) of measures were compared and evaluated. Results:
Fifty three measures were identified: QOL (n=25, 47%), self-esteem/concept (n=15, 28%), mental health
(n=13, 25%). Likert scales were used most frequently to represent response choices (n=34, 64%). The
authors of 11 (21%) measures provided justification for their scale choice. Items were most commonly
presented in written format (n=24, 45%). Item content was generated from the respondent population in
only 21 (40%) measures. Twenty-seven (51%) measures reported internal reliability between 0.70 and 0.90,
and 12 (23%) reported reproducibility in this range. Although validity was reported for 48 (91%) measures,
evidence for three or more aspects occurred for only 9 (17%). Eleven (21%) measures evidenced respon-
siveness to change. Conclusion: Authors should provide clearer evidence for reliability and responsiveness.
Newly developed instruments need to meet established standards, and further studies should assess the
impact of scale and presentation types on the psychometrics of measures.
Key words: Children, Outcome measurement, Quality of Life, Review
Abbreviations: QOL – quality of life; SAC – Scientific Advisory Committee
Introduction
Traditionally self-report measures of subjective
concepts such as quality of life (QOL) or mental
health have been aimed at children above 8 years,
and proxy reports have been used to gain infor-
mation for children below this age [1, 2]. However
the value of obtaining children’s self-reports about
their health, functioning, abilities, and emotions is
increasingly recognized within both medical care
and child health research.
Children’s legal status has changed over the last
four decades, with several high profile abuse cases
in the 1970s highlighting the value of children’s
own views in legal situations [3]. Gaining
information from children themselves for treat-
ment decisions is becoming necessary from a legal
standpoint [4, 5]. There is evidence that children
can: be competent reporters in legal contexts (for
example recalling events over long time periods);
resist misleading suggestions [e.g., 6, 7]; commu-
nicate their health needs and make decisions about
care if given age-appropriate information [e.g.,
8–10]. In turn, researchers and health professionals
are acknowledging that young children should be
more involved in decisions about their own lives,
families, and health care [11, 12].
Clinical trials designed to evaluate existing, new,
or alternative treatments have traditionally
focused on objective indices such as survival rates
Quality of Life Research (2006) 15: 739–754 Ó Springer 2006
DOI 10.1007/s11136-005-4184-x

2. or reduced morbidity [13]. The inclusion of
subjective outcome measures such as QOL can
help distinguish between treatment programs with
similar physical health outcomes, or between drugs
with apparently equal efficacy and safety [14, 15].
Self-reports on subjective states from patients
themselves can provide information on conse-
quences of treatment plans (such as behavioral or
psychological problems) that may not be captured
by traditional outcome indices [16]. Self-report
measures can help pediatricians and parents make
decisions about the care and treatment of sick
children by providing information about the
quality of children’s lives alongside survival time.
Researchers have criticized trials designers for
neglecting to incorporate the patient’s perspective
of outcomes in clinical trials [17–19]. There is
considerable interest and need for inclusion of
subjective child measures in the evaluation of
treatment outcomes for chronic childhood condi-
tions such as cancer [20].
There is evidence that the level of parent–child
agreement about children’s lives and functioning
can be quite poor [e.g., 21–24]. Discrepancies
between parent and child reports could reflect real
differences in perspectives, or parent’s lack of
knowledge or insight into their children’s lives and
opinions [25]. Lack of concordance between par-
ent and child reports emphasizes the need to
obtain both perspectives whenever possible [13].
Partly in response to this interest in, and need to
gain information about young children’s lives,
some researchers have developed self-report mea-
sures for children below 8 years [e.g., 26–30].
However there is no ‘gold standard’ regarding
format and content. A variety of response scales
and presentation styles varying in attractiveness
and appropriateness for children have been used
by authors. Guidelines regarding the most appro-
priate and helpful formats for child self-report
measures are needed.
The purpose of this paper is to begin this process
by reviewing the format and quality of health-
related self-report measures for children aged 3 and
8 years. This is a pragmatic review based on the
need to review existing child self-report instruments
(due to the proliferation of papers published in the
last 20 years on the development of new measures).
We focused on QOL, self-esteem, self-concept, and
mental health measures. Literature reviews focus-
ing separately on QOL [e.g., 31–36], self-esteem and
self-concept [e.g., 37–39], and mental health [e.g.,
40, 41] measures are available. Despite existence of
these reviews, there were two reasons for this
review. First, no current review focuses specifically
on self-report measures for children aged between 3
and 8 years. Second, no current review had collec-
tively evaluated, or attempted to integrate, the
methodologies used in developing and validating
measures from all of these four conceptual areas.
Methods
Rationale for review
The principal rationale for inclusion of QOL, self-
esteem, self-concept, and mental health measures
was the overlap in definition and conceptualiza-
tion of these assessment areas. One of the most
widely cited definition of QOL is provided by the
World Health Organization (WHO) who defined
QOL as an individual’s physical health, psycho-
logical states, level of independence, social rela-
tionships, and their relationship to salient features
of the environment [42, 43]. Most definitions
emphasize also the subjective nature of QOL. For
example, Calman [44] argued that QOL is related
to the perceived gap between an individual’s hopes
and expectations and their present experience. It is
in this definition of QOL being related to satis-
faction with abilities and functioning, that we see
the link to self-esteem and self-concept measures.
Satisfaction with life has become an important
criterion of individual QOL [35].
Self-esteem has been defined as positive or neg-
ative attitudes about the self, degree of satisfaction
with self, and one’s feeling of perceived worth as
compared to others (i.e., an evaluative concept)
[45]. Self-concept has been conceptualized as the
way in which our view of self changes over time
due to experiences and social interactions (i.e., a
descriptive concept) [38]. In many studies, self-
concept and self-esteem have been used inter-
changeably, with no distinction made between
them [46]. It is also difficult to create a measure
that taps into only either the descriptive or the
evaluative aspects of self [38].
Assessment of mental health focuses on symp-
tomatology, but must also take into account
740

3. medical, familial, and social factors which may
have contributed to the referral [41]. Therefore,
measures of child mental health have included
items asking children about behavior, self-esteem,
family and peer relationships, and physical health
[41]. Due to these overlaps in definition, QOL, self-
esteem, self-concept, and mental health measures
have included similar domains and items. There-
fore, we included measures of these four assess-
ment areas in our review.
Search strategy and inclusion/exclusion criteria
The following databases were searched from 1980
to 2004: MEDLINE, ISI Web of Science, Psy-
cINFO, PubMed, Cochrane Central Register of
Controlled Trials, Embase, and ERIC-AT Test
Locator. Databases were searched from 1980 to
2004 to provide information on how the science of
self-report measurement has developed over time.
Text word and thesaurus searches were used to
minimize the chance of missing relevant articles.
The following keywords were searched: child,
childhood, children, pediatric, paediatric; quality
of life, QOL, HRQOL, health status, functional
status, well-being; self-esteem, self-concept, self-
competence, self-image; mental health, depression,
anxiety, fear; measure/s, instrument/s, tool/s.
Internet search engines were used to identify
additional references, and authors were contacted
to identify work in preparation or submitted.
Inclusion criteria were self-report measures of
QOL, self-esteem, self-concept, and mental health,
for children aged between 3 and 8 years. Papers
were excluded if they were targeted only at chil-
dren over 8 years or developed solely for proxy
report of the child’s functioning.
Data extraction and quality assessment
The format of measures was compared across the:
response scales (i.e., Likert, graphic, facial, or
visual analogue); justification/evidence reported
for choice of response scale; presentation styles
(i.e., written, pictorial, verbal, computerized or
props); target age ranges.
The quality of measures was assessed by com-
paring: the item generation methods (i.e., how the
content of items was developed); and the reliabil-
ity, validity, and responsiveness data. The psy-
chometric properties of measures have been the
most frequently cited criteria for evaluation of self-
report measures [e.g., 32, 33, 38], i.e., whether
scores on a given measure are reliable, valid, and
responsive.
Reliability is concerned with how well the items
measure an underlying construct, and whether
scores on a measure will remain consistent over
time. The internal consistency of items can be
tested at one time point by calculating the corre-
lations between items. Test–retest reliability can be
assessed by calculating the correlations between
scores at two time points. Criteria outlined by the
Scientific Advisory Committee (SAC) of the
Medical Outcomes Trust [47] state that internal
consistency and test–retest correlation values
above 0.70 are necessary for health assessment
measures. However, a coefficient value of $0.70
while acceptable, indicates an explained variance
of less than 50% in the variables measured. Some
authors have argued that high internal consistency
(i.e., up to 0.90) should be expected and desired
when validating self-report measures [e.g., 48, 49].
We evaluated measures using a coefficient of 0.70
or above as a minimum cut-off criteria for evi-
dence of reliability, with ideal coefficient values
being between 0.70 and 0.90 (Table 1).
Validity is concerned with whether measures
assess the construct intended. The guidelines out-
lined by the SAC [47] and used by existing reviews
of child measures [e.g., 32, 36] follow the tradi-
tional conceptualization of validity: content, cri-
terion-related and construct. Evidence for content
validity has been considered by the use of either an
expert panel to rate the appropriateness of items or
statistical procedures to identify how strongly the
item content defines each domain. Evidence of
criterion validity has been judged by the relation-
ship between scores on a measure with those of a
criterion measure. Evidence of construct validity
has been considered if scores on multiple measures
of the same or related constructs are correlated
(convergent validity), or alternatively scores on
measures assessing different concepts are not cor-
related (discriminant validity). Evidence for dis-
criminant validity has also been judged as present
when an instrument discriminates between groups
of individuals known to differ on an accepted
741

4. Table1.Thequalityofhealth-relatedself-reportmeasuresforchildren
Measure($yearinformation
onmeasurefirstpublished)
Country
of
development
Age
(yrs)
No.of
domains
(items)
Scale
(No.of
points);
presentation
Child
generated
items
Pilot
testing
undertaken
Internal
consistencya
Test–retest
reliabilitya
Validityb
ResponsivenessIndependent
testingc
Qualityoflifemeasures–generic
Pediatricqualityoflifequestion-
naire(PedsQLTM
,1998)[30]
USA5–184(23)Likert(3,5);
Written
ÖÖÖÖ–ÖÖÖÖÖ
C-QOL(2000)[53]UK5–81(62)Likert(5);
Written
ÖÖÖÖÖÖÖÖ–
Childhealthratinginventories
(CHRIs,1995)[54,55]d
USA5–127(30)Graphic(5);
Pictorial
ÖÖÖ–ÖÖÖÖ–
Childhealthandillnessprofile-
childedition(CHIP-CETM
,2001)
[29]
USA6–115(45)Graphic(5);
Pictorial
ÖÖÖÖÂÖÖ–Ö
Kiddy-KINDLR
(1998)[56]Germany4–76(19)Likert(3);
Verbal
–e
ÖÖÖ–ÖÖÖÖ
Child’shealthself-conceptscale
(CHSCS,1990)[57]
USA7–137(30)Likert(4);
Written
ÖÖÖÖÖ–Ö
Dutchchildren’squalityoflife
questionnaire(DUCATQOL,
2002)[58]
Netherlands6–164(25)Facial(5);
Written
––ÖÖÖÖÖÖ––
TedQL.4(2001)[59]d
UK3–81(22)Graphic(4);
Props
ÖÖÖÖÖÖ––
Personaladjustmentandroleskills
scale(PARSIII,1990)[60]
USA5–186(28)Likert(4);
Verbal
–e
–ÖÖ–ÖÖ–Ö
Genericchildren’squalityoflife
questionnaire(GCQ,1997)[28]
UK6–140(25)Likert(5);
Pictorial
ÖÖÖÖ–Ö––
Picturedchild’squalityoflifeself
questionnaire(AUQUEI,2002)
[61]d
France4–128(27)Facial(4);
Pictorial
––Ö–ÖÖÖ––
CAT-SCREEN(2000)[62]Germany6–126(24)Likert(3);
Computerized
–e
–Ö–Ö–Ö
DUX-25(2001)[63]d
Netherlands5–164(28)Facial(5);
Computerized
––ÖÖ–Ö––
Exeterhealth-relatedqualityoflife
measure(ExQoL,2000)[64]
UK6–121(12)Visualanalogue
(100);
Computerized
–e
––ÖÖ––
Qualityoflifescale-’Threewishes’
and‘Whatworriesyoumost’
(1990)[65]
USA7–121(30)Facial(5);
Written
–e
–Ö––––
Animatedcomputerprogram
(1999)[66]d
Netherlands5–121(35)Likert(4);
Computerized
–––––––
Nordicqualityoflifequestionnaire
forchildren(1993)[67]d
Sweden6–1820(75)Likert(-);
Written
–––––––
742

5. Qualityoflifemeasures–disease-specific
Pediatricasthmaqualityoflife
questionnaire(PAQLQ,1992)[68]
USA7–173(23)Likert(7);
Written
Ö–ÖÖÖÖÖÖÖÖ
Pediatricrhinoconjunctivitisqual-
ityoflifequestionnaire(PRQLQ,
1993)[69]
Canada6–125(23)Likert(7);
Verbal
ÖÖ–ÖÖÖÖÖÖ
Cysticfibrosisquestionnaire-child
version(CFQ-C,2000)[70]
France6–139(36)Likert(4);
Written
ÖÖÖÖ–ÖÖ–Ö
Howareyou?(HAY,2000)[71]Netherlands7–1310(80)Likert(4);Written–e
ÖÖÖÖÖÖÖ–
Aboutmyasthma(AMA,1998)
[72]
USA6–121(55)Likert(4);Written–e
ÖÖÖ–ÖÖÖ–
Childhoodasthmaquestionnaire
(CAQ,1993)[27]
UK4–72(14)Facial(4);Written–e
ÖÖÖÂÖÖ–Ö
Children’sdermatologylifequality
index–cartoonversion(CDLQI,
2003)[73]d
UK3–166(10)Likert(4);PictorialÖÖ–ÖÖÖ–Ö
Lifeactivitiesquestionnairefor
childhoodasthma(1993)[74]
USA5–171(71)Likert(5);WrittenÖ–ÖÖÖÖÖ––
Self-esteem/self-conceptmeasures–general/over-arching
Pictorialscaleofperceivedcompe-
tenceandsocialacceptance(PSPCSA,
1984)[75]
USA4–74(24)Graphic(4);PictorialÖÖÖÖ–ÖÖÖÖÖ
Physicalself-conceptquestionnaire
(PSDQ,1996)[76]
Australia7–189(70)Likert(6);WrittenÖ–ÖÖÖÖÖÖ–Ö
Perezself-conceptinventory(1982)
[77]
USA5.5up1(40)Likert(2);PictorialÖÖÖÖÖÖÖ––
Marylandpre-schoolself-concept
scale-revised(1982)[78]
USA4–61(20)Likert(2);PictorialÖ–ÖÖÖÖ–Ö
Pierspreschoolpictorialself-con-
ceptscale(1985)[79]
USA3–64(30)Likert(3);Pictorial–e
ÖÖÖÖÖÖ––
Preschoolandprimaryself-concept
scale(1982)[80]
USA4–91(7)Likert(4);Pictorial–ÖÖÖÖÖÖ––
Self-descriptionquestionnaire
(SDQ-I,1998)[81]
Australia5–86(38)Likert(4);Written–e
–ÖÖÂÖÖÖ––
Competenceperceptionssubjective
taskvaluebeliefs(1993)[82]
USA6.5up1(9)Graphic(7);Written–ÖÖÖ–ÖÖ––
Berkeleypuppetinterview(BPI,
1998)[83]
USA4.5–7.51(60)Likert(7);Props–e
–ÖÂÖÖ––
Puppetinterview(2001)[84]USA5–71(20)Likert(2);Props––––ÖÖ–Ö
IthinkIam(2002)[85]Russia7–95(32)Likert(2);Written––––ÖÖ––
Self-interview(1989)[86]USA3.5–7.51(50)Likert(2);Props––ÖÂÖ––
Self-conceptandmotivationinven-
tory(SCAMIN,1987)[87]
USA5up1(12)Facial(3);Written––––Ö
743

6. Table1.Continued
Measure($yearinformation
onmeasurefirstpublished)
Country
of
development
Age
(yrs)
Noof
domains
(items)
Scale
(No.of
points);
presentation
Child
generated
items
Pilot
testing
undertaken
Internal
consistencya
Test–retest
reliabilitya
Validityb
ResponsivenessIndependent
testingc
Self-esteem/self-conceptmeasures–specific
Children’sphysicalself-concept
scale(CPSS,1998)[88]
USA6–113(27)Graphic(4);
Written
ÖÖÖÖÖÖ––
Readingself-conceptscale(RSCS,
1995)[26]
NewZealand5–81(50)Likert(5);
Verbal
–e
ÖÖÖ–ÖÖ––
Mentalhealthmeasures–generic
Dominicquestionnaire-R(2000)
[89]d
USA6–117(90)Likert(2);
Computerized
–e
ÖÖÖÖÖÖ–Ö
Preschoolsymptomsself-report
(PRESS,1990)[90]
USA3up1(25)Likert(2);
Pictorial
Ö–ÖÖÖÖÖ––
Children’sself-reportquestion-
naire(SRQ,1985)[91]
Canada7–128(-)Likert(-);
Written
–––ÖÖÖÖ––
Pictorialinstrumentforchildand
adolescentpsychiatry(1994)[92]
USA6–167(137)Graphic(5);
Pictorial
–e
ÖÖ–ÖÖ–
Mentalhealthmeasures–depression
Children’sdepressioninventory
(CDI,1987)[93]
USA6up1(4)Likert(3);
Written
–ÖÖÖÖÖÖÖ–Ö
Shortchildren’sdepressioninven-
tory(SCDI,1980)[94]
USA7–171(13)Likert(4);
Written
––ÖÖÖÖ–Ö
Mentalhealthmeasures–anxiety/fear
Socialanxietyscaleforchildren
revised(SASC-R,1993)[95]
USA7up1(10)Likert(3);
Written
Ö–ÖÖÖÖÖ––
(Child)Medicalfearquestionnaire
(CMFQ,1986)[96]
USA4–71(12)Likert(3);
Written
––ÖÖ–ÖÖ–Ö
Hospitalfearsratingscale(1988)
[97]
USA6up1(25)Graphic(5);
Written
Ö––ÖÖÖ––
Levonnscale(1990)[98]USA3.5up1(39)Graphic(3);
Pictorial
––ÖÖÂÖ–Ö
Scarescale(1988)[99]USA3–131(1)Graphic(5);
Verbal
–Ö––ÖÖ––
Fearfacesscale(1982)[100]USA4up1(91)Facial(7);
Pictorial
––––ÖÖ––
Hospitalfearsquestionnaire(1981)
[101]
USA5up1(9)Graphic(5,8);
Verbal
Ö––––Ö–
ÖEvidencereported;–Evidencenotreported/notavailable;a
Internalconsistency/test–retestcoefficients:ÖÖ>0.70–0.90,Ö$0.70,Âbelow0.70;b
Validitytesting:ÖÖÖevidence
forthreeormoreaspects,ÖÖevidencefortwoaspects,Öevidenceforoneaspect;c
Furthertesting:Öbothauthorandindependentvalidationtesting;d
Measurecurrentlyunder
development/validation;e
Otheritemgenerationtechniqueused(i.e.,literaturereview,expertpanelrated,adaptedfromexistingchild/adolescentmeasures).
744

7. criterion. Ideally authors should report evidence
for multiple aspects of validity [47].
We acknowledge that a more comprehensive
conception of validity has been defined, where all
three types of validity are taken to be different
facets of a single unified form of construct validity
[50]. However as many of the existing measures of
child self-report were developed and validated
using the traditional conception of validity, we
chose to evaluate included measures using this
conception. Implications of modern test theory for
future work are made in the discussion.
Responsiveness is related to whether scores on an
instrument are sensitive to changes over time. Based
on the SAC [47] guidelines, we considered evidence
of sensitivity where scores on a measure could de-
tect differences in outcome or real changes experi-
enced over time. For example, this might include
situations where scores on a QOL measure were
sensitive to changes occurring following a targeted
psychosocial intervention. We considered respon-
siveness to be one of the most important aspects of a
measure, following the establishment of reliability.
Constructs such as QOL, self-esteem/concept and
mental health are dynamic processes that do not
remain static over time. Developing (child) mea-
sures which are both sensitive to change over time
and show stability over practical short time periods
is a major challenge for researchers [48].
An additional impact on the validity of
measures is the source used to generate the content
of items. The item generation techniques used can
impact on the content validity of measures [51, 52].
We argue that use of the respondent population (in
this case, children) to generate the content of items
should be a classical principle for any self-report
instrument (especially where the respondent’s
perspective may be substantially different from the
developer’s perspective, such as with children, or
cognitively compromised and vulnerable groups).
Results
Search results
Fifty three measures met the inclusion criteria
(Table 1). More QOL (n=25) measures were
identified, compared to self-esteem/concept
(n=15) and mental health (n=13). The number of
domains in measures ranged from one (various
measures) to 20 (the Nordic Quality of Life
Questionnaire for Children). The total number of
items ranged from 1 [99] to 137 [92]. The majority
of measures were developed in the U.S. or Canada
(n=34). The remaining originated from Europe
(n=15), Australia or New Zealand (n=3), or
Russia (n=1).
Measures
53
Likert 34
(verbal/numerical)
QOL 16
Self-esteem/
self-concept 11
Mental health 7
Graphic 11
(pictorial/3D)
QOL 3
Self-esteem/
self-concept 3
Mental health 5
Facial 7
(cartoon/photo)
QOL 5
Self-esteem/
self-concept 1
Mental health 1
Visual analog 1
(visual/numerical)
QOL 1
Self-esteem/
self-concept 0
Mental health 0
Figure 1. Breakdown of the number of measures by response scale type.
745

8. Format of measures
Response scales
Four response scales were identified. These
included: Likert (i.e., written linear scale anchored
in sections with numbers and/or words); Graphic
(i.e., three-dimensional or pictorial scale with/
without word/number anchors); Facial expression
(i.e., pictorial linear scale anchored in sections with
cartoon or photographic faces); or Visual analogue
(i.e., visual linear scale anchored at ends with
numbers and/or words). As shown in Figure 1, the
most commonly used response scale was Likert
(n=34, 64%), in the context of either QOL (n=16)
or self-esteem/concept (n=11). The least com-
monly used was visual analogue scales (n=1, 2%).
(Figure 2 gives examples of scale types).
Justification/evidence for choice of response scale
Of the 53 measures included, the authors of 11
(21%) provided justification for their choice of
response scale. These were classified into three
areas: testing children’s understanding of a chosen
scale (n=7); comparing the psychometric proper-
ties of measures across different scales (n=2); or
basing their scale choice on children’s own pref-
erences for different types (n=2). The authors of
seven measures [27, 28, 59, 70, 82, 88, 92] assessed
children’s understanding of their chosen scale.
This was achieved either by asking children:
hypothetical questions (e.g., asking a question
where the appropriate point on the scale can be
predicted to a greater extent); or rating their an-
swer twice (e.g., rating answer on the chosen re-
sponse scale and then on an equivalent scale). The
authors of the CHIP-CE [29] and the TedQL.4 [59]
compared the psychometric properties of the same
measure across different response scales (i.e.,
which scale produced the most consistent and
reproducible responses from children).
Presentation styles
Presentation styles were classified into five cate-
gories: Written (i.e., child reads items from a
written questionnaire); Pictorial (i.e., items pre-
sented to child using visual aids such as cartoons,
drawings or photographs); Verbal (i.e., written
items read aloud to child); Computerized (i.e.,
items presented to child with the aid of a com-
puter); or Props (i.e., items presented by inter-
viewer verbally using three-dimensional aids, such
as puppets, teddy bears, dolls). As shown in
Figure 2. Example of the graphic scale from the Pictorial Instrument for Child and Adolescent Psychiatry [92] mental health
measure and the facial expression scale from the CAQ [27] QOL measure.
746

9. Figure 3, the most common presentation style was
written format (n=24, 45%), and 12 of these as-
sessed QOL. The least commonly used methods
were computers (n=5) and props (n=4).
Age ranges
As shown in Table 1, most measures targeted
children aged 6 years and above. The widest age
range (5–18 years) was found for the PedsQLTM
[30], however this measure contained age-specific
versions with parallel items with minor simplifi-
cation of language and adapted response scales.
Half of the QOL measures (13 out of 25) were
directed at children aged 6 years and older. Just
over one third (6/15) of the self-esteem/concept
measures were targeted at children as young as 3
and 4 years (Table 1).
Quality of measures
Item generation and development methods
The item generation methods were unclear for 18
(34%) measures. This omission did not necessarily
mean there was no rationale or justification for
content; however it was not reported. The authors
of 21 (40%) measures reported children themselves
informed the content of items (Table 1). Some
researchers interviewed children and asked them to
list, or talk about, relevant topics [e.g., 29, 59],
while others used children’s spontaneous com-
ments from pilot work to develop the vocabulary
and wording of their items [e.g., 86]. Authors of an
additional 14 (26%) measures used other tech-
niques to develop item content (namely adaptation
of wording or language of items from existing
child/adolescent measures, or generated from an
expert panel consisting of health professionals,
clinicians and/or psychologists).
The authors of 25 (47%) measures reported
further pilot testing of their items on children, and
resulting adaptation (e.g., alteration of wording, or
deletion/addition of items). Such pilot testing was
undertaken for over half (14/25) of the QOL mea-
sures, half of the self-esteem/concept measures (7/
15), and one third of mental health measures (4/13).
Reliability data
As shown in Table 1, the authors of 42 (79%)
measures reported internal reliability. The most
commonly used statistic for assessing internal
consistency was Cronbach’s alpha reliability coef-
ficient (n=40). Of these measures, 13 (26%) re-
ported values of $0.70, and 27 (51%) evidenced
values of between 0.70 and 0.90. Two measures
[QOL: 64; self-esteem/concept: 87] assessed inter-
nal reliability, but reported only values below the
minimum 0.70 criteria.
Reproducibility, using correlation coefficients
between two time points, was reported for 29
(55%) measures. Ten (19%) of these reported
values of $0.70, and 12 (23%) evidenced values
between 0.70 and 0.90. Seven (13%) measures
assessed reproducibility but reported only values
below the minimum 0.70 criteria (Table 1).
Measures
53
Written 24
QOL 12
Self-esteem/
self-concept 6
Mental health 6
Verbal 6
(interviewer-
administered)
QOL 3
Self-esteem/
self-concept 1
Mental health 2
Pictorial 14
QOL 5
Self-esteem/
self-concept 5
Mental health 4
Props 4
QOL 1
Self-esteem/
self-concept 3
Mental health 0
Computerized 5
QOL 4
Self-esteem/
self-concept 0
Mental health 1
Figure 3. Breakdown of the number of measures by presentation style type.
747

10. Validity data
As shown in Table 1, the authors of 48 (91%)
measures reported evidence for at least one form
of validity. Of these, 16 (30%) measures showed
evidence of one aspect of validity, 23 (43%) evi-
dence for two aspects, and 9 (17%) for three or
more aspects.
The authors of 23 (43%) measures assessed
content validity using one (or more) of three tech-
niques. Items were either rated for relevance,
depth, or breadth using an expert panel (n=4); or
content validity was supported by information
obtained from children themselves (n=6), or by
statistical methods such as factor analysis or prin-
cipal component analysis (n=15). It was noted that
the authors of two measures [29, 86] used more
than one technique to establish content validity.
The authors of 17 (32%) measures reported
assessment of criterion validity. Of the three as-
pects of criterion validity, concurrent validity was
most frequently assessed (n=15). Authors assessed
this using either observed behavior/pain/anxiety/
function scores, achievement tests or diary symp-
tom scores for comparisons. The authors of two
measures [84, 93] assessed predictive validity (i.e.,
investigated the relationship between scores on
their measures with criterion scores at a later time
point). No authors provided evidence for retro-
spective validity.
The authors of 23 (43%) measures assessed
convergent validity by assessing the relationship
between scores on their measure to scores on a
measure of a similar construct. Discriminant
validity was investigated in 22 (42%) measures by
assessing either: whether a given measure can dis-
tinguish between groups known to differ (n=20);
or whether scores on a given measures were
unrelated to other measures assessing different
concepts (n=2).
Responsiveness data
Responsiveness was reported for 11 (21%)
measures, shown by evidence that scores on these
measures were sensitive to real changes in QOL,
self-esteem/concept or mental health over time
(Table 1). For example, Le Coq et al. [71] exam-
ined whether scores on the HAY QOL measure
were sensitive to a change in children’s asthma
status between two time points (where a change in
symptoms was reported by parents).
Independent testing undertaken
As shown in Table 1, 21 (39%) measures were
validated using independent testing beyond that
performed by the developers of the measure. Such
further testing can help provide increased evidence
for the psychometric properties of measures. It
was noted when this review was performed that
various newly developed measures, namely QOL
instruments [e.g., 55, 63, 73], were still under
evaluation by developers or had not been available
long enough to be tested (and used) by other
researchers.
Discussion
Evaluating the format of existing child self-report
measures
Fifty three child self-report measures have been
described in Table 1. They represent the range of
techniques and formats currently used in child
outcome measures. Likert response scales have
been most commonly employed to represent re-
sponse choices to children, mostly for measures to
assess QOL or self-esteem/concept. It has been
assumed that ratings produced by children on
Likert scales are valid. However Chambers and
Johnston [102] have shown evidence that 5–6 year
old children tend to select responses at the ex-
tremes of Likert-type scales when rating subjective
states (such as emotions).
We identified a small selection of measures that
used facial and graphic response scales to repre-
sent choices to children, specifically more recently
developed QOL instruments [e.g., 29, 61, 58]. It
may be that graphic scales (e.g., differing in size or
color) offer children more information on how to
grade their answers to items. However there is
some evidence that young children’s ratings may
not actually be equivalent across different types of
response scales, i.e., their responses may vary
depending on the type of scale used [103–105], and
the validity of different scale types has not been
determined [106]. Therefore we argue that
researchers need empirical evidence to support
scale choice. We identified 11 measures where au-
thors provided such justification, most commonly
by testing children’s understanding of chosen scale
types. We argue that direct comparisons of the
748

11. psychometric properties across scale types should
be incorporated in the development stages of new
child measures. Some work has been conducted
into the predictors of children’s ability to use
response scales [e.g., 102, 104, 106]. Shields et al.
[106] reported that cognitive ability and chrono-
logical age were the best predictors of kindergarten
children’s accurate use of a visual analogue scale.
Further research is needed to establish the cogni-
tive ability and age needed for successful use of
other scale types.
Items were most commonly presented to chil-
dren in a written format. Pictorial presentation
was identified in a smaller selection of measures.
Using pictures or props to present items to young
children could potentially benefit researchers in
gaining reliable responses. Visual aids can also be
useful in facilitating children’s understanding of
items, reducing demands on their memory,
engaging and maintaining their attention, and
avoiding reliance on verbal or reading skills that
may be lacking in younger children [107–108]. In
addition, computers have been utilized by devel-
opers of five measures [62–64, 66, 89]. The value of
this format becomes apparent for busy clinic set-
tings, and in theory many existing measures using
written formats could be converted to computer-
ized presentation. Studies are needed to determine
how data collected electronically correlates to the
original mode of administration, and also the
value of this new medium for enhancing the
quality of child self-reports.
The measures we identified were targeted at a
range of ages, with some being aimed at wide age
ranges (e.g., 6–14 years), and others focusing on
narrower age ranges (e.g., 6–8 years). Our review
included any measures for children aged between 3
and 8 years, which meant that measures met the
inclusion criteria if they were targeted at for
example children aged between 7 and 13 years.
Measures targeted at a wide age range may be less
appropriate for the youngest children, as the
content and format may not have been developed
specifically with these children in mind. There are
differences in the cognitive capabilities and lin-
guistic skills of children under and over 8 years
[109–111], and this raises the issue as to whether
any one measure will be appropriate for a large
range of ages.
One solution to the age range problem is to use
a similar instrument across a wide age range while
screening for children’s ability to understand and
rate items (e.g., using a calibration task). McGrath
et al. [112] developed a calibration task to assess
whether children could make proportional judg-
ments using scales (using ratings of different sized
circles). Cummins [113] used a similar task to test
cognitively compromised adults’ rating abilities
before completion of a QOL instrument (using
wooden cubes of varying sizes). Another solution
is to keep items parallel but make minor simplifi-
cations of language and in length/type of response
scale used. Authors of measures such as the CAQ
[27], CHIPTM
[29], KINDLR
[56], and the Peds-
QLTM
[30] have developed age-specific versions of
their instruments.
It is also relevant here to consider the impact of
developmental level on child measures. Develop-
mental issues have not received sufficient attention
in relation to child outcome measurement [35].
There is a need to include a developmental
framework in the development and validation of
child measures, which takes into account that
constructs such as QOL and self-esteem can have
different meanings to children of different ages, or
to the same children over time [36]. Factors that
are important to children lives may vary with
different stages of development, as well as the
personal relevance of different domains [36]. Re-
search is needed to document changes in percep-
tions of health, QOL, self and mental health that
occur as a function of specific developmental
stages.
Evaluating the quality of existing child self-report
measures
The item generation techniques for over one third
(n=18) of measures were not reported, and this
omission is a serious one. The methods used to
generate information for items can impact directly
on the validity of a measure [51, 52]. Of those
measures where authors did report how items were
generated, only 40% (n=21) reported using chil-
dren themselves to inform content. We urge
developers of new measures to consider using
information from children (respondents) as a
complement to researcher/expert panel generated
749

12. item content, to avoid inclusion of items that are
essentially meaningless to young children [114].
More attention needs to be given to the content of
items, and the language used. Estimates of reading
age could be used to assess the appropriateness of
items within new child measures.
Although nearly 80% (n=42) of authors
assessed the internal reliability of children’s scores
on their measures, only 51% (n=27) of measures
evidenced values between 0.70 and 0.90. We argue
that high internal consistency (>0.70–0.90) should
be desired when developing new measures. Issues
concerning ‘bandwidth’ versus ‘fidelity’ warrant
discussion here [48]. Small sets of items (i.e., 5–10
items) focusing on similar aspects of a given con-
struct will almost always give high internal con-
sistency values, however assessment of constructs
such as QOL often require items spanning a wide
range of different aspects [35]. This can mean that
high correlations between items may be harder to
evidence, or if achieved may reflect a tendency to
respond similarly to different items (termed
‘response set’ syndrome [48]).
Test–retest reliability was reported for half the
measures (n=29), but only 23% (n=12) of mea-
sures evidenced values between 0.70 and 0.90.
Although the SAC guidelines [47] advocated high
reproducibility values for new measures, for dy-
namic constructs such as QOL low values may
actually represent a real change in levels rather
than a measurement artifact [48]. Therefore sen-
sitivity to change may be a more important crite-
rion than reproducibility in the context of
measuring constructs such as QOL, self-esteem, or
anxiety/depression.
Modern test theory [48] is favored at least within
the adult QOL literature [115–118], as an alterna-
tive to classic psychometric principles. Researchers
such as Schwartz and Rapkin [48] and Bjorner
et al. [119] have discussed alternatives to tradi-
tional psychometrics that will better apply to
measurement of subjective states such as QOL.
Application of modern test theory to child self-
report measurement needs consideration, as the
psychometric evidence for currently available
measures has been couched within classical test
theory and the true score tradition [48].
Based on the traditional concept of validity,
authors of 91% (n=48) of measures reported
evidence for at least one form of validity. Content
and construct validity were most frequently
assessed. The guidelines set out by the SAC [47]
state that it is necessary to provide evidence for all
three types of validity for outcome measures.
Evidence for three or more aspects of validity was
reported for only 17% (n=9) measures. The lack
of attention to establishing validity can seriously
negate the quality of available measures for future
research.
It is relevant to discuss the date of development
of measures, to consider whether our quality
judgments were confounded by recency. Newer
instruments may be of higher quality than older
instruments (due in part to the development of the
science of measurement over the last two decades).
However for many of these newly developed
measures the authors may not have had the
opportunity to establish and report psychometric
concepts (especially those aspects which require
some passage of time such as predictive validity),
or had time for measures to be tested (and used) by
others. Indeed the last decade has shown a large
increase in the development of health-related QOL
measures [120]. Two newly developed computer-
ized QOL instruments evidence this recency effect,
the CAT-SCREEN [62] and the DUX-25 [63],
showing low levels of psychometric validation.
However these computerized measures have not
been as widely available for use in child research as
say the PedsQLTM
[30] or the PAQLQ [68].
The authors of only 11 measures considered the
responsiveness of scores on their measures over
time. Many authors reported evidence for the
reproducibility of children’s scores over a short
time period, but did not consider whether their
measures were sensitive to change over longer
periods of time. While it is important that a mea-
sure can produce reproducible scores over a short
time period, a measure also needs to be sensitive to
important changes over time [121]. Again this lack
of attention within the literature needs to be
redressed; either by assessing the sensitivity of
existing measures or ensuring assessment in the
validation stages of new child measures. Estab-
lishing the responsiveness of subjective outcome
measures is a pertinent issue considering calls for
the inclusion of these measures within longitudinal
clinical trials.
750

13. Final comment
In conclusion, there are a variety of measures
currently available for assessing subjective out-
comes through child self-report. However there are
shortcomings with some of the currently available
instruments, specifically in relation to internal
reliability, validity and responsiveness. In addi-
tion, a variety of different response scales and
presentation styles have been employed to repre-
sent items in child measures. There is a need for a
set of minimum standards for child self-report
measures, and further research is needed to reach a
consensus as to the most appropriate formats for
child-centered instruments. Despite the limitations
with current measures and the difficulties in
developing new instruments, it is important that
children have the opportunity to describe their
own subjective experience. Children can provide
important information about their health, self-
image and mental state, information that is often
substantially different from parents’ or health care
professionals’ perspectives.
Acknowledgement
This review was funded in part by a University
of Sheffield grant awarded to the second author.
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Address for correspondence: Joanne Cremeens, St. Jude Chil-
dren’s Research Hospital, Division of Behavioral Medicine,
Memphis, TN 38105-2794, USA
Phone.: 001-901-495-2358; Fax: 001-901-495-4701
E-mail: Joanne.Cremeens@stjude.org
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