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A caregiver questionnaire that assesses mealtime problems in children aged 2 to 6 years old was developed. Community caregivers (n = 712) completed the Mealtime Behavior Questionnaire (MBQ) and measures of child behavior and family mealtime behaviors and environment. Exploratory and confirmatory factor analyses revealed and validated the MBQ's 4 subscales (food refusal/avoidance; food manipulation; mealtime aggression/distress; and choking/, gagging/vomiting). Mealtime problems occurred from “sometimes” to “always” for 1% to 61% of the sample. The MBQ demonstrated excellent to fair internal consistencies, and preliminary evidence for validity was found.
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Children’s Health Care, 39:142–156, 2010
Copyright © Taylor & Francis Group, LLC
ISSN: 0273-9615 print/1532-6888 online
DOI: 10.1080/02739611003679956
Assessing Children’s Mealtime
Problems With the Mealtime Behavior
Kristoffer S. Berlin
Department of Psychiatry and Human Behavior, Brown Medical School;
and Bradley Hasbro Children’s Research Center, Rhode Island Hospital,
Providence, RI
W. Hobart Davies
Department of Psychology, University of Wisconsin–Milwaukee; and
Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI
Alan H. Silverman
Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI
Douglas W. Woods
Department of Psychology, University of Wisconsin–Milwaukee,
Milwaukee, WI
Elizabeth A. Fischer
Departments of Psychiatry & Behavioral Medicine, and Pediatrics,
Medical College of Wisconsin, Milwaukee, WI
Colin D. Rudolph
Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI
A caregiver questionnaire that assesses mealtime problems in children aged 2
to 6 years old was developed. Community caregivers .n D712/ completed the
Correspondence should be addressed to Kristoffer S. Berlin, PhD, Department of Psychology,
Ohio University, 200 Porter Hall, Athens, OH 45701. E-mail:
Mealtime Behavior Questionnaire (MBQ) and measures of child behavior and
family mealtime behaviors and environment. Exploratory and confirmatory factor
analyses revealed and validated the MBQ’s 4 subscales (food refusal/avoidance;
food manipulation; mealtime aggression/distress; and choking/, gagging/vomiting).
Mealtime problems occurred from “sometimes” to “always” for 1% to 61% of
the sample. The MBQ demonstrated excellent to fair internal consistencies, and
preliminary evidence for validity was found.
The survival, health, well-being, and development of all children depend on
eating a nutritionally balanced diet. Despite the seemingly instinctive ease with
which most children eat and feed, there are complex interactions among many
factors (genetic, biological, psychological, sociocultural, family, and environ-
mental) that may impede the development of healthy eating habits and behaviors.
There are alarmingly high prevalence rates of feeding problems in early child-
hood, with estimates ranging from 25% to 45% among typically developing
children (Bentovim, 1970; Linscheid, 1992; Manikam & Perman, 2000) and
reaching as high as 85% for children with developmental disabilities (Manikam
& Perman, 2000).
Behavioral feeding problems present across a range of behaviors and sever-
ities. They can include inappropriate mealtime behaviors, lack of self-feeding,
failure to advance texture from puree to table food texture, food refusal or
accepting only small quantities, picky eating, eating too slowly, frequent vom-
iting, eating too much, parental aversion to mealtimes, mealtime aggression
and distress, or poor dietary regimen adherence (Crist & Napier-Phillips, 2001;
Kerwin, 1999; Mackner, McGrath, & Stark, 2001).
Children with severe feeding difficulties are at risk for a variety of problems.
These include severe weight loss, malnutrition, lethargy, impaired intellectual–
emotional–academic development, growth retardation, aspiration, invasive medi-
cal procedures (e.g., placement of a nasogastric or gastrostomy tube), admission
to an inpatient unit for treatment of the feeding problem, or death (Budd et al.,
1992; Christopherson & Hall, 1978; Finney, 1986; Linscheid, 1978; Riordan,
Iwata, Wohl, & Finney, 1980; Sisson & Van Hasselt, 1989; Skuse, 1993; Whitten,
Pettit, & Fischhoff, 1969).
Eating and adequate nutrition also play key roles in the health and devel-
opment of children with medical conditions (Mackner et al., 2001). Among
the numerous medical conditions that require dietary modification, disregulated
eating and impaired nutritional status are linked with significant morbidity and
the exacerbation of the disease process (for a review, see Mackner et al., 2001).
Medical conditions in which nutrition plays a significant role include cancers,
celiac disease, coronary heart disease, chronic renal disease, congenital heart
disease, cystic fibrosis, encopresis, HIV infection, inflammatory bowel disease,
liver disease, osteoporosis, phenylketonuria, seizure disorders, short bowel syn-
drome, sickle cell disease, and type 1 and type 2 diabetes (Mackner et al., 2001).
Previous work to identify, measure, and assess behavioral feeding problems
has found that differences in parental report of feeding difficulties between
healthy and clinical groups are reflected in the frequency in which children
engage in the problematic behaviors, rather than fundamental differences in the
types or groupings of behaviors exhibited during mealtimes (Crist & Napier-
Phillips, 2001). Existing measures include About Your Child’s Eating (AYCE;
Davies, Ackerman, Davies, Vannatta, & Noll, 2007), the Behavioral Pediatric
Feeding Assessment Scale (Crist & Napier-Phillps, 2001), and the Children’s
Eating Behavior Inventory (Archer, Rosenbaum, & Streiner, 1991). These mea-
sures may list both desirable (“eats fruit”) and problematic behaviors (“My child
hates eating”), and may ask parents to endorse whether particular behaviors
are problematic and what they do to address these problems (Archer et al.,
1991; Crist & Napier-Phillps, 2001). Although useful, these measures of feeding
problems unfortunately confound the frequency of the child’s behavior problems
at mealtime with the caregivers’ strategies used to address the problematic
feeding behaviors and more general ratings of the mealtime environment. This
has left researchers and clinicians without a measure of feeding problems based
only on the frequency of child behaviors. Furthermore, this has made it difficult
to separate the child’s mealtime behavior problems from the strategies caregivers
use to address these problems. Therefore, this initial pilot study had four goals:
1. Develop a parent report questionnaire to assess the frequencies of mealtime
behavior problems in young children focused only on the frequency of
child behavior.
2. Ascertain the factor structure of this measure using exploratory factor
analysis (EFA).
3. Examine preliminary evidence for reliability and validity.
4. Determine the influence of parent gender and child age and gender on
caregiver report of mealtime behavior problems.
Data were from a sample of community caregivers of children aged 2 to 6
years old. Descriptive statistics and demographic information for the community
sample can be found in Table 1. The ethnic composition was 8% African
American, 6% Asian American, 81% Caucasian, 3% Latino, and 2% mixed
race or “other.” The average level of education (as a proxy for socioeconomic
status) was some college (MD13:79 years, SD D3:02 years). Data from
community participants were collected by 90 students enrolled in an advanced
Sample Demographic Information and Descriptive Statistics
Variable n
(% Male)
(% Female) M SD
EFA community Child age (years) 356 2.00 6.00 3.78 1.28
Parent age (years) 355 19.00 60.00 32.64 6.55
Child gender 355 nD179 (50.3%) nD176 (49.7%)
Parent gender 355 nD121 (34.1%) nD234 (65.9%)
CFA community Child age (years) 356 2.00 6.00 3.81 1.34
Parent age (years) 353 19.00 57.00 33.10 6.36
Child gender 356 nD189 (53.0%) nD167 (47.0%)
Parent gender 355 nD133 (37.6%) nD222 (62.4%)
Note. EFA Dexploratory factor analysis; CFA Dconfirmatory factor analysis.
undergraduate-graduate psychology laboratory course (offered during 3 consec-
utive summers). Each student obtained data from eight or more parents who
were known to them and who had a child between the ages of 2 and 6 years
old. To facilitate data entry and collection, participants completed surveys on Hard copies of the survey were made available to
participants without Internet access. The institutional review board approved the
study, and informed consent was indicated by the completion of the survey. The
only potential benefit of participating was extra credit for the recruiting student
in a psychology course if so allowed. Several steps were taken to ensure the
authenticity of the data: (a) both students and their respondents were blind to
the study’s hypotheses and aims, (b) response patterns of data uploaded were
reviewed to check for duplicate IP addresses and to screen for multiple entries
within a short time period, (c) students were required to attend research ethics
seminars, and (d) students were required to review and abide by the university’s
policy on academic dishonesty.
Mealtime Behavior Questionnaire (MBQ). Given the absence of estab-
lished measures based solely on the problematic behaviors of children during
mealtimes, 33 items were generated by psychologists and advanced psychol-
ogy trainees working as members of a multidisciplinary feeding team (see the
appendix for the MBQ). Each team member was asked to generate a range of
specific feeding problems likely to be encountered in the clinic or in the homes
of typically developing children. Items were placed on a 5-point frequency scale
ranging from 1 (never), 3 (sometimes), to 5 (always). Participants were asked,
“Please rate each behavior in terms of how often or frequently it happens.
Please rate each behavior as it occurred during mealtimes or feeding over the
past week.” The first round of data collection also asked parents to rate the level
of difficulty they experienced around each behavior. The difficulty ratings were
judged to be redundant and dropped (and excluded from further data collection)
when initial correlations between frequency and difficulty consistently exceeded
rD:85 for all factors.
AYCE (Davies et al., 2007). The AYCE is a valid and reliable 25-item
parent report measure that asks about parent beliefs and concerns regarding their
child’s eating, by asking about the frequency of child eating behaviors, parents’
mealtime interactions with the child, and their feeling about mealtimes. The
AYCE has three scales: Child Resistance to Eating (CRE), Positive Mealtime
Environment (PME), and Parent Aversion to Mealtime (PAM). The internal
consistency of the AYCE factors is satisfactory across various demographic
subgroups, with the alphas from the total normative sample being good for
CRE (.88) and PAM (.80), and acceptable for PME (.70). The AYCE has
demonstrated convergent validity, as all scales relate (in the expected direction)
with established measures of family functioning (Davies et al., 2007).
Analytic Plan
To determine the underlying factor structure of the MBQ items, EFA was
conducted following the suggestions of Russell (2002) and Fabrigar, Wegener,
MacCallum, and Strahan (1999) using a randomly selected half of community
data (SPSS select cases function). The other half of the community data was
utilized to conduct confirmatory factor analysis (CFA) to validate this factor
structure with an additional sub-sample and to determine the goodness of fit
based on a variety of indexes.
Because the traditional chi-square statistic as a test of absolute fit is sensitive
to sample size (Hu & Bentler, 1995), additional measures less influenced by
sample size were used, including the non-normed fit index (NNFI) the com-
parative fit index (CFI), and standardized root mean square residual (SRMR).
SRMR values of .08 demonstrate an adequate model fit (Hu & Bentler, 1999).
NNFI and CFI values range from 0 to 1, and values above .90 represent a
good model fit (Bollen, 1989; Hoyle & Panter, 1995). The root mean square
error of approximation (RMSEA; MacCallum, Browne, & Sugawara, 1996)
statistic was supplied as an indication of the population error variance (Browne
& Cudeck, 1993). Interpretation of RMSEA values in terms of fit are as follows:
good (<.05), acceptable (.05–.08), marginal (.08–.10), and poor (>.10; Browne
& Cudeck, 1993; Fabrigar et al., 1999; Hu & Bentler, 1999). Error terms
(unexplained variance) among observed variables from the same latent construct
were allowed to correlate if model fit was improved.
To gather preliminary evidence for construct validity, bivariate correlations
were examined (Messick, 1995). It was anticipated that the MBQ would have
significant positive relations with other measures of mealtime problems and
inverse relations with positive ratings of the mealtime environment and child age.
Internal consistency (alpha) coefficients were calculated for one index of relia-
bility. To estimate the prevalence of specific mealtime behavior problems over a
1-week period, the frequency and cumulative percentage of each MBQ item rated
3 (sometimes) or higher was calculated. Multivariate analyses of variance were
used to determine the influence of parent and child gender (and their interaction)
on caregiver report of mealtime behavior problems (the MBQ total score and
4 subscales) using an alpha level of p < :017 to control type 1 error (.05 of 3
effects: child gender, parent gender, and Child Gender Parent Gender).
EFA. To determine the number of factors to extract, a parallel analysis
(Reise, Waller, & Comrey, 2000) was conducted using MacParallel Analysis
(Watkins, 2000), a software program that contains tables of eigenvalues produced
by Monte Carlo simulations. Parallel analyses are a variant on the scree test,
where one plots the eigenvalues from actual data and data derived from factoring
a completely random set of data involving the same number of items and research
participants. The point at which the eigenvalues for the actual data dropped below
eigenvalues for the random data indicated the number of factors to be extracted.
A benefit of parallel analysis is that it eliminates the subjectivity associated
with visual inspection of the scree plots (Kaiser, 1970). The parallel analysis
conducted for this study indicated that a four-factor solution was most appro-
priate. Table 2 presents the eigenvalues and percentage of variance accounted
for by each factor initially and upon extraction and the randomly generated
eigenvalues used for the parallel analyses. Based on the suggestions of Russell
Eigenvalues and Percentage of Variance Accounted for by the Initial Four Factors
Initial Eigenvalues
Extraction Sums of
Squared Loadings Rotation Sums
of Squared
Factor Total
Generated Total
(%) Total
Food refusal/avoidance 9.11 27.60 1.70 8.52 25.82 6.39
Food manipulation 2.58 7.82 1.60 2.00 6.06 6.00
Mealtime aggression/distress 1.87 5.65 1.54 1.38 4.19 5.66
Choking/gagging/vomiting 1.82 5.53 1.47 1.26 3.80 3.20
Note. ND356.
(2002), EFA was then conducted using the correlation matrix, principal axis
factoring (to extract 4 factors), and promax rotation. Items with multiple factor
loadings (Items 4, 7, 21, and 25) were retained on the factor with the highest
loading. Items 15 and 29 were excluded from the factor scores given the item
content diverged from other factor items. Based on the content of the items, the
four factors were named food refusal/avoidance, food manipulation, mealtime
aggression/distress, and choking/gagging/vomiting. Table 3 shows the items and
pattern factor item loadings.
CFA model evaluation. An important assumption to satisfy the use of CFA
is that the measured variables have a multivariate normal distribution. Initial data
screening indicated departures from a normal distribution, with several items
(Items 5, 11, 14, 15, 29, 30, 31, and 32) having moderate skewness (>2)
and kurtosis (>5; West, Finch, & Curran, 1995). To address the violations
to distributional assumptions of CFAs, two suggested strategies were utilized
(Fabrigar et al., 1999; West et al., 1995). First, unweighted item sums of factor
subscales were used as item parcels-indicators of the latent variable total score
(rather than use individual items), as they (a) have more normal distributions,
(b) more closely approximate normally distributed continuous variables, (c) have
been found to produce path estimates identical to those obtained using individ-
ual items (Sass & Smith, 2006), and (d) decreased the number of parameters
estimated. The second strategy was to perform CFA using a robust maximum
likelihood estimation method in LISREL 8.54 (Jöreskog & Sörbom, 2003). This
method allowed for the computation of a Satorra–Bentler scaled (SB) and
robust standard errors, which adjust for multivariate kurtosis (Satorra & Bentler,
CFA was then employed to evaluate the factor structure of the MBQ for the
second half of the community sample. The initial results were mixed for the
community sample: Sattora-Bentler, .2/ D15:26; CFI D.97; NNFI D.91;
RMSEA D.140; and SRMR D.039. However, the model fit improved signifi-
cantly (p < :0001) and resulted in an excellent fit by allowing the error variance
to correlate between the food manipulation and choking/gagging/vomiting sub-
scales: Sattora-Bentler, .1/ D1:25; CFI D1.00; NNFI D1.00; RMSEA D
.027; and SRMR D.010. All subscales served as statistically significant (p <
:05) indicators of the MBQ total score, a latent variable reflecting various
typographies of mealtime behavior problems. Figure 1 presents the final com-
pletely standardized factor solution and associated errors. Unstandardized factor
loadings, the covariance matrices, and additional details of the CFA can be
obtained from Kristoffer S. Berlin.
Reliability and prevalence rates. The internal consistencies of the MBQ
total score (sum of 31 items, excluding 2 items) and subscales were good, on
Items and Item Factor Loadings From Factor Pattern Matrix
Variable 1 2 3 4
Food refusal/avoidance
24. Demanding alternative foods/forms of foods .665 .018 .058 .031
23. Eating too slowly .664 .201 .226 .031
22. Only eating a few foods .657 .123 .015 .076
17. Deal making (negotiation) .643 .208 .167 .118
16. Talking to keep from eating .618 .095 .044 .079
28. Verbally refusing to eat .601 .071 .146 .095
25. Playing with food .455 .380 .116 .054
4. Pushing spoon/food away .430 .306 .033 .104
33. Not sitting in chair .415 .275 .008 .046
21. Pushing away food from table .403 .309 .213 .101
3. Leaving the table .338 .007 .294 .018
27. Playing with toys rather than eating .268 .139 .212 .130
Food manipulation
12. Letting food drop out of mouth .044 .712 .085 .000
5. Throwing food .242 .675 .190 .089
13. Spitting out food .186 .541 .078 .039
14. Hiding food .135 .516 .061 .005
1. Hands in front of face .109 .460 .036 .010
11. Spitting at a person .077 .404 .185 .094
2. Packing food in the mouth .203 .392 .088 .009
Mealtime aggression/distress
8. Screaming .079 .084 .63 4 .103
9. Hitting others or objects .141 .246 .569 .006
10. Kicking others or objects .042 .116 .563 .003
7. Crying .318 .035 .549 .012
18. Reporting physical pain .181 .189 .509 .110
6. Refusing to come to the table .288 .018 .43 6 .125
19. Asking for comfort or assurance .253 .157 .424 .238
20. Flailing arms/legs .135 .226 .408 .054
30. Biting others .279 .295 .361 .076
31. Gagging .132 .033 .171 .890
32. Vomiting .176 .016 .155 .716
26. Choking or coughing on food or liquid .000 .143 .039 .608
Excluded items
29. Biting self .193 .446 .165 .044
15. Hitting self .114 .415 .170 .029
Note. Items retained on factors are indicated in bold.
FIGURE 1 Final measurement models with completely standardized estimates. Note.
MBQ DMealtime Behavior Questionnaire.
average (mean ˛D:83,SD D:06), and ranged from fair to excellent. Table 4
presents the total and subscale internal consistencies and descriptive statistics
for each sample (and combined community sample). Table 4 also presents the
percentage of respondents who endorsed an MBQ item at 3 or higher, reflecting
the prevalence of each mealtime behavior that occurred between “sometimes”
and “always” in the past week. The average prevalence rate across the 31 MBQ
items was 24.6% (SD D16:9%).
Validity. Construct validity was established through significant correlations
(see Table 5) in the expected directions using the MBQ total score with es-
tablished measures (Cohen & Swerdlik, 1999). For solely descriptive purposes,
MBQ subscales have also been included in this table. The MBQ total score
was significantly related to an established measure of family mealtime problems
(AYCE scales: CRE and PAM) and inversely to the PME rating and child age.
The goodness-of-fit statistics present in the CFA section provided evidence for
structural and content validity (Messick, 1995).
Parent and child gender differences. Using an alpha of p < :017,
no effect of parent gender, F .5; 675/ D1:86,pD:10 (partial 2D:014);
child gender, F .5; 675/ D2:309,pD:043 (partial 2D:017); or the Parent
Gender Child Gender interaction, F .5; 675/ D1:18,pD:32 (partial 2D
:009), was found on caregiver report of child mealtime behavior problems.
MBQ Total Score, Subscale, and Item Descriptive Statistics and Internal Consistencies
for Clinical and Combined Community Sample
Variable M SD ˛
to “Always”
in Past Week
MBQ total score (31 items) 55.29 14.65 0.91 MD23:2%
SD D17:1%
Food refusal/avoidance 26.53 8.06 0.89 MD40:6%
SD D12:4%
24. Demanding alternative foods/forms of foods 2.26 1.14 41.2%
23. Eating too slowly 2.37 1.14 44.4%
22. Only eating a few foods 2.73 1.13 60.9%
17. Deal making (negotiation) 2.34 1.14 46.0%
16. Talking to keep from eating 1.90 1.01 30.4%
28. Verbally refusing to eat 2.04 1.01 34.0%
25. Playing with food 2.26 1.00 42.7%
4. Pushing spoon/food away 2.12 1.03 39.3%
33. Not sitting in chair 2.56 1.07 52.9%
21. Pushing away food from table 1.81 0.91 24.4%
3. Leaving the table 2.52 1.05 52.3%
27. Playing with toys rather than eating 1.62 0.89 17.6%
Food manipulation 10.78 3.38 0.73 MD15:9%
SD D14:0%
12. Letting food drop out of mouth 1.53 0.78 14.0%
5. Throwing food 1.36 0.73 8.8%
13. Spitting out food 1.60 0.78 16.3%
14. Hiding food 1.27 0.65 8.8%
1. Hands in front of face 1.83 0.93 25.8%
11. Spitting at a person 1.14 0.44 3.2%
2. Packing food in the mouth 2.04 1.01 34.5%
Mealtime aggression/distress 14.09 4.71 0.81 MD16:2%
SD D9:3%
8. Screaming 1.53 0.84 14.7%
9. Hitting others or objects 1.48 0.79 13.5%
10. Kicking others or objects 1.35 0.67 8.6%
7. Crying 1.88 0.94 27.2%
18. Reporting physical pain 1.63 0.92 17.3%
6. Refusing to come to the table 1.87 0.97 26.4%
19. Asking for comfort or assurance 1.87 1.00 27.4%
20. Flailing arms/legs 1.37 0.69 9.1%
30. Biting others 1.09 0.37 1.5%
Choking/gagging/vomiting 3.87 1.59 0.76 MD6:3%
SD D3:5%
31. Gagging 1.30 0.66 6.5%
32. Vomiting 1.14 0.49 2.8%
26. Choking or coughing on food or liquid 1.43 0.75 9.7%
Excluded items
15. Hitting self 1.12 0.41 2.2%
29. Biting self 1.05 0.29 1.0%
MBQ Total Score and Subscale Correlations With the
About Your Child’s Eating Questionnaire
MBQ Total
Score MBQ 1 MBQ 2 MBQ 3 MBQ 4
MBQ 1: Food refusal/avoidance 0.912 1.000
MBQ 2: Food manipulation 0.757 0.544 1.000
MBQ 3: Mealtime aggression/distress 0.834 0.628 0.557 1.000
MBQ 4: Choking/gagging/vomiting 0.495 0.318 0.425 0.350 1.000
Child age 0.187 0.104 0.268 0.156 0.157
Child resistance to eating 0.708 0.758 0.394 0.539 0.302
Positive mealtime environment 0.450 0.438 0.296 0.401 0.144
Parent aversion to mealtime 0.684 0.673 0.441 0.576 0.277
Note. All correlations are significant at p < :001.
The MBQ was developed to provide researchers and clinicians with a validated
measure of the topographies of feeding difficulties in young children focused
solely on the frequency of child mealtime behaviors. Preliminary results of this
initial pilot study identified and validated a factor structure of the MBQ that
contained a total score and four subscales. These subscales reflect a variety of
problematic mealtime behaviors including food refusal/avoidance, food manipu-
lation, mealtime aggression/distress, and choking/gagging/vomiting. Overall, the
reliability of the MBQ and subscales was good and ranged from fair to excellent.
Preliminary analyses also suggest that the MBQ is valid in the assessment of
mealtime problems, as the MBQ relates (as anticipated) to the affective quality
of the mealtime environment, parental aversion to mealtimes, and parent report
of their child’s resistance to eating.
With regard to demographic influences, no effect of child or parent gender was
found on caregivers’ reports of their child’s mealtime behavior problems. Consis-
tent with previous research (Sanders, Patel, Grice, & Shepherd, 1993), younger
child age was associated with greater overall mealtime problems, food refusal
and avoidance, food manipulation, and mealtime aggression or distress. Child
age was not associated with parent report of choking, gagging, and vomiting.
With regard to the specific subscales, there is an interesting pattern of perfor-
mance for the choking/gagging/vomiting factor. This scale’s internal consistency
was good; however, it shows the weakest relations to other MBQ subscales and
to other measures of mealtime problems. One possibility for these finding is that
this scale may be tapping into feeding problems of a medical nature, more so
than behavioral feeding problems. For example, choking, gagging, and vomiting
can be signs of swallowing problems and aspiration (which are common in
children with feeding problems). This may account for the low correlations with
more behavioral measures. In light of this, it may be tempting to drop these
items for those wanting a more pure measure of behavioral feeding problems.
Substantial variability in the prevalence rates of feeding problems was ac-
counted for by the specific type of problem identified. The prevalence rates
of feeding problems across the MBQ subscales ranged from 6% to 41%, with
the most frequent types of mealtime behavior problems being food refusal and
avoidance. The single most common mealtime problem reported by parents
(60%) was that their child was “only eating a few foods.” These findings may
potentially suggest a developmental progression in the severity of pediatric
feeding problems that may begin with the relatively common food selectivity
and passive refusals, to more overt and aggressive types of behaviors.
This article contributes to the literature in a variety of novel ways. First, it
suggests that prevalence of mealtime problem rates vary according to the specific
behavioral topography and severity of the problem. Second, it provides a measure
of young children’s feeding difficulties that are not confounded by the caregiver’s
strategies for managing these problems. Third, this measure was developed and
validated using rigorous and advanced statistical techniques based on modern
quantitative recommendations (Fabrigar et al., 1999; Reise et al., 2000; Russell,
2002). No known measures of similar content have these advantages.
There are several important limitations of this study. These limitations include
the lack of racial diversity and the lack of data linking the MBQ to observed
meals. An additional limitation is that the sampling strategy used to collect
data did not provide estimations of participation rates. Therefore, it is unclear
if this sample was different from the large population in systematic ways. A
third limitation is that the eigenvalues from the EFA (e.g., amount of variance
explained from the factors) were rather low, suggesting higher than desired
error variance in the measure. Last, despite the many safeguards in place to
ensure data authenticity, a third limitation is that data may have been entered
from participants who were not parents of children aged 2 to 6 years old. The
coherence of the factor structure, the validity and reliability data, and the overall
pattern of results suggest that some preliminary confidence can be placed on
these data. It will be critical, however, for future studies to replicate the factor
structure using CFAs with independent clinical and community samples and to
expand the evidence for validity (cross-cultural, ecological, etc.) and clinical
utility (e.g., the sensitivity and specificity).
Implications for Practice
The MBQ appears to be a promising measure of mealtime behavior problems in
young children. Given that this study represents a pilot investigation, great care
must be taken to decide when or if to use this measure in practice. If used, the
MBQ could contribute to a multimethod, multidisciplinary assessment of young
children’s feeding problems, which could augment interview and observation
data by providing caregiver report information on potentially low base-rate
behaviors and a snapshot of a general 1-week pattern of behavior. This measure
could also be used prior to intake to help identify specific behaviors that would
require further assessment during a clinic visit. The descriptive data of the
MBQ can also be used to calculate zor tscores so that an individual case
presenting for treatment can be compared to a nonclinical sample to determine
“how discrepant” their factor scores are from the norms. A last potential use
would be to track clinical outcomes by re-administering the MBQ over the
course of treatment. Additional studies, however, are needed to further explicate
the clinical utility of the MBQ.
We thank the members of the Milwaukee Area Child Health Research Initiative
for their comments on an earlier version of this article.
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Mealtime Behavior Questionnaire
Please rate each behavior in terms of how often or frequently it happens. Please
rate each behavior as it occurred during mealtimes or feeding over the past week.
Never Sometimes Always
1. Hands in front of face 1 2 3 4 5
2. Packing food in the mouth 1 2 3 4 5
3. Leaving the table 1 2 3 4 5
4. Pushing spoon/food away 1 2 3 4 5
5. Throwing food 1 2 3 4 5
6. Refusing to come to the table 1 2 3 4 5
7. Crying 1 2 3 4 5
8. Screaming 1 2 3 4 5
9. Hitting others or objects 1 2 3 4 5
10. Kicking others or objects 1 2 3 4 5
11. Spitting at a person 1 2 3 4 5
12. Letting food drop out of mouth 1 2 3 4 5
13. Spitting out food 1 2 3 4 5
14. Hiding food 1 2 3 4 5
15. Hitting self 1 2 3 4 5
16. Talking to keep from eating 1 2 3 4 5
17. Deal making (negotiation) 1 2 3 4 5
18. Reporting physical pain 1 2 3 4 5
19. Asking for comfort or assurance 1 2 3 4 5
20. Flailing arms/legs 1 2 3 4 5
21. Pushing away food from table 1 2 3 4 5
22. Only eating a few foods 1 2 3 4 5
23. Eating too slowly 1 2 3 4 5
24. Demanding alternative foods/forms of foods 1 2 3 4 5
25. Playing with food 1 2 3 4 5
26. Choking or coughing on food or liquid 1 2 3 4 5
27. Playing with toys rather than eating 1 2 3 4 5
28. Verbally refusing to eat 1 2 3 4 5
29. Biting self 1 2 3 4 5
30. Biting others 1 2 3 4 5
31. Gagging 1 2 3 4 5
32. Vomiting 1 2 3 4 5
33. Not sitting in chair 1 2 3 4 5
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... All studies in a systematic review are analyzed using the signaling questions of the Quadas-2 which allows for comparison of articles over common terms. Fifty of the studies showed good "Methodological Performance" or the study methods had a low risk of introducing bias into the results (Archer et al., 1991;Barkmeier-Kraemer et al., 2017;Behar et al., 2018;Bell et al., 2014;Benjasuwantep et al., 2015;Berlin et al., 2010;Burrows et al., 2014;Cade et al., 2006;Cao et al., 2012;Castro et al., 2019;Christian et al., 2015;Collins et al., 2013;Davies et al., 2007;Gerasimidis et al., 2011;Hendy et al., 2009;Hulst et al., 2010;Huysentruyt et al., 2013;Jiang et al., 2014;Llewellyn et al., 2011;Lu et al., 2018;Lukens & Linscheid, 2008;Mallan et al., 2013;McCarthy et al., 2012;Meral & Fidan, 2014;Morino et al., 2015;Quah et al., 2017;Ramsay et al. n. d.;Rice et al., 2015;Rogers et al., 2018a;Rub et al., 2016;Seiverling et al., 2011Seiverling et al., , 2019Sheppard et al., 2014;Simpson et al. n. d.;Sirirassamee & Hunchangsith, 2016;Sleddens et al., 2008;Sparks & Radnitz, 2012;Steinsbekk et al., 2017;S. M. 1 Thoyre et al., 2014;van der Heul et al., 2015;Wardle et al., 2001;Watson et al., 2009;M. ...
... Thirty tools used various psychometric measures to quantify behaviors and determine potential for nutrition risk (Archer et al., 1991;Barkmeier-Kraemer et al., 2017;Behar et al., 2018;Benjasuwantep et al., 2015;Berlin et al., 2010;Cao et al., 2012;Davies et al., 2007;Domoff et al., 2015;Dovey et al., 2013;Hendy et al., 2013Hendy et al., , 2009Llewellyn et al., 2011;Mallan et al., 2014;Marshall et al., 2015;Jiang et al., 2014;Pados et al., 2018Pados et al., , 2017Quah et al., 2017;Ramsay et al. n. d.;Rogers et al., 2018a;Sirirassamee & Hunchangsith, 2016;Sleddens et al., 2008;Sparks & Radnitz, 2012;Steinsbekk et al., 2017;S. M. 1;Thoyre et al., 2014;Thoyre et al., 2018;van der Heul et al., 2015;Wardle et al., 2001;Williams et al., 2011). ...
... M. 1;Thoyre et al., 2014;Thoyre et al., 2018;van der Heul et al., 2015;Wardle et al., 2001;Williams et al., 2011). All of these tools incorporated survey items that gathered information about typical patterns of food intake and level of concern about a child's growth (Archer et al., 1991;Barkmeier-Kraemer et al., 2017;Behar et al., 2018;Benjasuwantep et al., 2015;Berlin et al., 2010;Cao et al., 2012;Davies et al., 2007;Hendy et al., 2009;Llewellyn et al., 2011;Mallan et al., 2013;Marshall et al., 2015;Mallan et al., 2014;Pados et al., 2018;Quah et al., 2017;Ramsay et al. n. d.;Rogers et al., 2018a;Sirirassamee & Hunchangsith, 2016;Sleddens et al., 2008;Sparks & Radnitz, 2012;Steinsbekk et al., 2017;S. M. 1;Thoyre et al., 2014;Thoyre et al., 2018;van der Heul et al., 2015;Wardle et al., 2001;Williams et al., 2011). ...
Introduction The aim of this systematic review is to identify existing pediatric feeding screening tools that have been shown to be valid and reliable in identifying feeding dysfunction in children. Method A database search produced 5862 relevant articles to be screened based on pre-determined inclusion/exclusion criteria. After full text review of 183 articles, 64 articles were included in the review. Results Forty-four studies detailed development and validation of unique feeding screening tools for the pediatric population. The remaining twenty studies were validations studies of already developed screening tools. Discussion Multiple screening tools identified were effective in determining feeding dysfunction in children. Several tools employed excellent techniques to measure reliability and validity for diverse pediatric populations. Careful consideration of the tools listed in this review will help practitioners determine the best method for feeding screening in their facility.
... Behavioral change was often measured using questionnaires and scales, including the behavioral pediatric feeding assessment scale (BPFAS) (Crist & Napier-Phillips, 2001), the children's eating behavior questionnaire (CEBQ) (Wardle et al., 2001), and the mealtime behavior questionnaire (MBQ) (Berlin et al., 2010). Unsurprisingly, such measures were used in behavioral interventions or across mixedmodality interventions which included behavioral components. ...
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Objective This scoping review identifies and describes psychological interventions for avoidant restrictive food intake disorder (ARFID) and summarizes how outcomes are measured across such interventions. Method Five databases (Cochrane, Embase, Medline, PsycInfo, Web of Science) were searched up to December 22, 2022. Studies were included if they reported on psychological interventions for ARFID. Studies were excluded if participants did not have an ARFID diagnosis and if psychological interventions were not delivered or detailed. Results Fifty studies met inclusion criteria; almost half were single‐case study designs (23 studies) and most studies reported on psychological interventions for children and adolescents with ARFID (42 studies). Behavioral interventions (16 studies), cognitive‐behavioral therapy (10 studies), and family therapy (5 studies), or combinations of these therapeutic approaches (19 studies) were delivered to support patients with ARFID. Many studies lacked validated measures, with outcomes most commonly assessed via physical health metrics such as weight. Discussion This review provides a comprehensive summary of psychological interventions for ARFID since its introduction to the DSM‐5. Across a range of psychological interventions and modalities for ARFID, there were common treatment components such as food exposure, psychoeducation, anxiety management, and family involvement. Currently, studies reporting on psychological interventions for ARFID are characterized by small samples and high levels of heterogeneity, including in how outcomes are measured. Based on reviewed studies, we outline suggestions for clinical practice and future research. Public Significance Avoidant restrictive food intake disorder (ARFID) is an eating disorder characterized by avoidance or restriction of food due to fear, sensory sensitivities, and/or a lack of interest in food. We reviewed the literature on psychological interventions for ARFID and the outcomes used to measure change. Several psychological interventions have been developed and applied to patients with ARFID. Outcome measurement varies widely and requires further development and greater consensus.
... Children's specific food avoidance behaviours (e.g. eating slowly, hiding food, gagging) will be inventoried with the Meal Behaviour Questionnaire (MBQ) [64]. ...
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Background Fussy eating is most often a developmentally typical behaviour, generally presenting during toddlerhood. However, up to half of parents of young children are concerned about fussy eating, and this concern may mediate the use of nonresponsive feeding practises, such as coercive or unstructured feeding and using food to reward eating. Despite the high prevalence of parental concern for fussy eating and the negative impacts nonresponsive feeding practises have on children’s health and diets, no previous digital intervention to improve the feeding practises of parents of toddlers concerned about fussy eating has been evaluated. Aim This article describes the protocol of a randomised controlled feasibility pilot aiming to evaluate Fussy Eating Rescue, a purely web app based intervention for parents of toddlers. The primary aim is to investigate feasibility and acceptability; secondary aims are to explore indications of intervention effect on parents’ feeding practises or children’s eating behaviours. Methods Fussy Eating Rescue features include: (1) a Tracker, that allows parents to track repeated offers of food, (2) Topics, providing information on fussy eating, effective feeding strategies, and general nutrition, (3) Rescues, containing quick references to material supporting Topics contents, (4) Recipes, and (5) SMS notifications. Parents of toddlers (12–36 months old, n = 50) who have concerns about fussy eating will be recruited via Facebook. Parents will be randomised to an intervention group, which receives access to the app for 6 weeks, or to wait-listed control. Outcomes will be assessed at baseline and 6 weeks after app use, using online questionnaires and app usage statistics. Primary outcomes include participant retention rate, intervention engagement, app usability, perceived ease in using the app, perceived usefulness of the app, and user satisfaction. Secondary outcome measures include parents’ feeding practises and children’s eating behaviours. Discussion Results will inform whether Fussy Eating Rescue is a feasible way to engage parents concerned for their toddler’s fussy eating behaviours. If feasible and acceptable to users, a larger trial will further examine the efficacy of the Fussy Eating app in improving parents’ feeding practises and children’s eating behaviours. Trial registration Prospectively registered with the Australian New Zealand Clinical Trials Registry on 15 July, 2021 (ACTRN12621000925842).
... Feeding questionnaires can be used to gather information prior to evaluation and to assess the child's medical, developmental, and environmental status. A number of pediatric feeding questionnaires have been developed to assess feeding strategies [32][33][34][35][36] and feeding skill deficits [37] and to assess special populations such as autism spectrum disorders [38]. Symptom Checklist-90 [39] and the Parenting Stress Index [40] questionnaires can provide an assessment of parents' psychosocial functioning and other caregiver factors that may affect feeding behaviors. ...
Full-text available
Feeding disorders are increasingly common in children, especially as medical advancements improve the life expectancy of children born with prematurity and complex medical conditions. The most common symptoms include malnutrition, refusal to eat and drink, food pocketing, disruptive feeding behavior, slow feeding, food selectivity or rigid food preferences, limited appetite, and delayed feeding milestones. A unifying diagnostic definition of pediatric feeding disorder has been proposed by a panel of experts to improve the quality of health care and advance research. Referral to specialized care should be considered when feeding problems are complex or difficult to resolve. In this review, we provide an overview of the evaluation and management of pediatric feeding disorders and information that may be useful when considering whether referral to specialized care may be beneficial.
... Tools to assess feeding problems About your child's eating (AYCE))-revised Davies et al. (2007) AYCE is a parent reported tool to assess the feeding behaviour (Child resistance to eating, positive mealtime environment and parent aversion to mealtime) Brief autism mealtime behaviour inventory (BAMBI) Lukens and Linscheid (2008) BAMBI is an informant reported measure to acquire feeding and mealtime behaviour information in children with ASD. The items were categorized under three domains, i.e. limited variety, food refusal and features of autism Mealtime behavior questionnaire (MBQ) Berlin et al., (2010) MBQ is a parent reported tool assesses the feeding behaviour (food refusal/avoidance, food manipulation, mealtime aggression/ distress, and choking/gagging/ vomiting) Montreal children's hospital (MCH) feeding scale Ramsay et al., (2011) MCH is a parent reported tool to assess feeding skills and behaviours (oral motor, oral sensory, and appetite, maternal concerns about feeding, mealtime behaviour, maternal strategies used and family reactions to their child's feeding) Quality of life tools Cerebral palsy quality of life questionnaire for children (CP QOL-Child) Davis et al., (2007) CP QOL-Child is a parent reported tool. It assesses the quality of life of children with CP in term of social wellbeing and acceptance, functioning, participation and physical health, emotional well-being, pain, and impact of disability Child health questionnaire (CHQ) McCarthy et al., (2002) CHQ contains both self-reported and parent reported form which assesses physical functioning, general health perception and emotional/ behavioral aspect of children with CP Pediatric quality of life inventory (PedsQL) Varni et al., (2006) PedsQL contains both self-reported and parent reported form which assesses physical, emotional, social and school functioning Caregiver priorities and child health index of life with disabilities (CPCHILD) child's nose to make them swallow, shaking the child's head, pouring water in child's mouth followed by food and pushing back the food in mouth to facilitate swallowing were reported by the parent. ...
Full-text available
Children with developmental disabilities (DD) exhibit feeding and swallowing difficulties, which can have an impact on nutritional, developmental, and psychological aspects. The existing tools assess the nature of feeding problems and behaviors only. The present study aimed to assess the physical, functional, and emotional domains in children with DD with feeding issues using Feeding handicap index for children (FHI-C). For clinical validation, FHI-C was administered on the parents/caregivers of 60 children with cerebral palsy, 61 with autism spectrum disorder, 59 with intellectual disability and 60 typically developing children in the age range of 2 to 10 years. The results revealed that the mean scores (Total FHI-C and FHI-C domain scores) were significantly higher for all three clinical groups than for the control group, which revealed good clinical validity. Also, FHI-C was found to have significantly high test–retest reliability. The study presents a valid and reliable tool for assessing the psychosocial handicapping effects of feeding problems in children with DD. FHI-C provides a holistic picture about the psychosocial impact of feeding problems in children with DD and will assist the clinicians in prioritizing the goals for feeding therapy. The scores obtained can be used as reference for pre and post therapy comparison purposes.
... The prevalence of feeding difficulties in childhood ranges from 20 to 35% [34] and represents a highimpact clinical problem for children and families alike [35]. The consequences of these difficulties include growth faltering, malnutrition, lethargy, developmental delay, aspiration, invasive medical procedures (such as feeding tube placement), and hospitalization [36]. ...
Full-text available
Background The traditional spoon-feeding approach to introduction of solid foods during the complementary feeding period is supported by consensus in the scientific literature. However, a method called Baby-Led Introduction to SolidS (BLISS) has been proposed as an alternative, allowing infants to self-feed with no adult interference. To date, there have been no trials in the Brazilian population to evaluate the effectiveness of BLISS in comparison to the traditional approach. Methods/design To evaluate and compare three different complementary feeding methods. Data on 144 mother-child pairs will be randomized into intervention groups by methods: (A) strict Parent-Led Weaning; (B) strict Baby-Led Introduction to SolidS; and (C) a mixed method. Prospective participants from Porto Alegre, Brazil, and nearby cities will be recruited through the Internet. The interventions will be performed by nutritionists and speech therapists, at 5.5 months of age of the child, at a private nutrition office equipped with a test kitchen where meals will be prepared according to the randomized method. The pairs will be followed up at 7, 9, and 12 months of age. Data will be collected through questionnaires designed especially for this study, which will include a 24h child food recall, questionnaires on the child’s and parents’ eating behavior, oral habits, eating difficulties, and choking prevalence. At 12 months of age, children will undergo blood collection to measure hemoglobin, ferritin, and C-reactive protein, saliva collection for analysis of genetic polymorphisms, and oral examination. Anthropometric parameters (child and maternal) will be measured at the baseline intervention, at a 9 month home visit, and at the end-of-study visit at the hospital. The primary outcome will be child growth and nutritional status z-scores at 12 months; secondary outcomes will include iron status, feeding behavior, acceptability of the methods, dietary variety, choking, eating behaviors, food preferences, acceptance of bitter and sweet flavors, suction, oral habits, oral hygiene behavior, dental caries, gingival health status, and functional constipation. Discussion The trial intends to ascertain whether there are potential advantages to the BLISS complementary feeding method in this specific population, generating data to support families and healthcare providers. Trial registration Brazilian Clinical Trials Registry (ReBEC): RBR- 229scm number U1111-1226-9516. Registered on September 24, 2019.
... Subsequently, 108 items were identified from the conceptual model and previous questionnaires (i.e., the DEBQ-C [14,15], CEBQ [13], the Children Eating Behavior Inventory [16], the Oregon Research Institute Child Eating Behavior Inventory [17], Children's Binge Eating Disorder Scale [25], Mealtime Behavior Questionnaire [26]). In addition, a third interview was conducted among six caregivers and three nutrition experts to combine the characteristics of Chinese eating culture, and 60 additional items were identified capturing the ten-factor conceptual model. ...
Full-text available
Background The objective of this study was to develop a scale to assess eating behaviors of school-aged children (6–12 years old) in China. Methods To develop the scale, a literature review and qualitative interviews were conducted. The draft scale contained 115 items and went through three evaluations among three groups of caregivers ( n = 140, 400, 700) selected from suburban and urban kindergartens in Xi’an, Hanzhong, and Yanan, China, from March 2017 to October 2018. The psychometric properties of the scale were assessed using exploratory, confirmatory factor analysis, and variability analysis. Results The final scale consisted of 46 items across eight dimensions including food fussiness, satiety responsiveness, food responsiveness, bad eating habits, susceptible diet, restrained eating, enjoyment of food, and junk food addiction. The total cumulative variance contribution rate was 52.16%. The scale and dimensions' Cronbach’s α coefficients, Guttman split-half reliability, and test- retest reliability were all above 0.65. The fitting indices for the confirmatory factor analysis were all close to 1. The scores for education of caregiver, family structure, and the body mass index of children were different among dimensions and groups, thus suggesting good discriminative utility. Conclusions All of the results indicated that the scale has good reliability and construct validity for evaluating the eating behaviors of school-aged children in China.
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Introduction. Les problèmes du comportement alimentaire constituent l’une des manifestations les plus couramment associées au trouble du spectre autistique. Bien que transversale à toute condition médicale, cette problématique est plus prévalente et sévère auprès de la population autiste pédiatrique. Inévitablement, ces problèmes du comportement affectent l’état nutritionnel des enfants et peuvent produire des répercussions physiologiques importantes. Le présent travail de recherche propose donc d’adresser prioritairement cette problématique par son dépistage, avec pour objectif de vérifier l’adéquation des outils de dépistage pédiatrique du comportement alimentaire à l’égard de la population autiste. Méthodologie. Pour ce faire, une revue de l’ensemble des outils de dépistage du comportement alimentaire publiés avant avril 2015 a été réalisée. Il a été déterminé que les outils retenus devaient cibler les enfants âgés de 6 mois à 12 ans atteints ou non d’autisme. Un processus systématique de recherche à quatre étapes a été utilisé afin d’interroger les cinq bases de données identifiées. Résultats. Cette recherche a recensé 11 articles décrivant 10 outils de dépistage. Une analyse systématique de leurs caractéristiques a permis de déterminer que ces derniers ciblent principalement les enfants âgés de 2 à 6-7 ans et sont tous administrés aux parents. Treize dimensions alimentaires ont été identifiées parmi les items employés par chacun de ces outils. Toutefois, aucun d’entre eux ne permet une évaluation compréhensive de l’ensemble des dimensions alimentaires relevées. De plus, la majorité des outils recensés minimisent l’importance de la validité de contenu et de construit. Conclusion. L’emploi de ces outils auprès d’une population autiste est inadéquat à plusieurs égards. L’inconsistance des dimensions alimentaires évaluées, l’analyse superficielle de l’environnement alimentaire et le cloisonnement du comportement alimentaire autiste expliquent cette inadéquation. Recommandations. Nous hésitons à recommander l’utilisation de ces outils auprès d’une population autiste. Le cas échéant, ces derniers devraient être adjoints d’une investigation scientifique approfondie de l’environnement alimentaire pédiatrique, ainsi que des dimensions lui étant associées.
The chapter highlights the feeding and swallowing issues seen in children with neuro-developmental disorders, types, and extent of the problem across different disorders; its relation with the neuro-development of the child; effect on the quality of life of the parents/caregivers along with the child, specifically in the Indian context. It also focuses on the importance of assessment, team approach, and review of available tests for the assessment of feeding and swallowing problems in these children. The chapter is also going to give a few insights into the challenges faced by speech-language pathologists during the assessment of the feeding and swallowing issues in these children in the Indian scenario. The chapter will also include a section on applications of ICF model to feeding and swallowing issues in children with neurodevelopmental disorders.
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Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Full-text available
An examination of the use of exploratory and confirmatory factor analysis by researchers publishing in Personality and Social Psychology Bulletin over the previous 5 years is presented, along with a review of recommended methods based on the recent statistical literature. In the case of exploratory factor analysis, an examination and recommendations concerning factor extraction procedures, sample size, number of measured variables, determining the number of factors to extract, factor rotation, and the creation of factor scores are presented. These issues are illustrated via an exploratory factor analysis of data from the University of California, Los Angeles, Loneliness Scale. In the case of confirmatory factor analysis, an examination and recommendations concerning model estimation, evaluating model fit, sample size, the effects of non-normality of the data, and missing data are presented. These issues are illustrated via a confirmatory factor analysis of data from the Revised Causal Dimension Scale.
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Structural equation modeling allows several methods of estimating the disattenuated association between 2 or more latent variables (i.e., the measurement model). In one common approach, measurement models are specified using item parcels as indicators of latent constructs. Item parcels versus original items are often used as indicators in these contexts to avoid estimation problems or solve issues associated with multivariate normality of the data. One concern associated with the use of item parceling is that no single "correct" approach exists to construct the parcels. Despite the controversy associated with selecting the most appropriate parceling method, less is understood with regard to how these methods influence the structural or path coefficients. By means of simulated and empirical data, this article addresses some commonly used strategies to model disattenuated structural coefficients between latent variables. Results revealed that when a single unidimensional scale is used to represent a latent construct, the use of individual items, item parcels, or an appropriate representation of measurement error through a single observed variable all will result in identical disattenuated structural coefficient estimates. Implications for the future of item parceling are discussed.
This article reviews methodological issues that arise in the application of exploratory factor analysis (EFA) to scale revision and refinement. The authors begin by discussing how the appropriate use of EFA in scale revision is influenced by both the hierarchical nature of psychological constructs and the motivations underlying the revision. Then they specifically address (a) important issues that arise prior to data collection (e.g., selecting an appropriate sample), (b) technical aspects of factor analysis (e.g., determining the number of factors to retain), and (c) procedures used to evaluate the outcome of the scale revision (e.g., determining whether the new measure functions equivalently for different populations).
Problems relating to feeding occur in approximately one out of four children. The prevalence of eating disorders is even higher among developmentally disabled individuals. For example, it has been estimated that 80% or more of these persons exhibit maladaptive feeding behaviors that can lead to undesirable consequences for physical, social, and educational/vocational development (Perske, Clifton, McClean, & Stein, 1977). More conservative figures are reported by Jones (1982), who summarized findings from a number of researchers indicating that 19% to 61% of mentally retarded clients at inpatient or outpatient centers experience eating problems.
A framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented. We emphasize the value of confidence intervals for fit indices, and we stress the relationship of confidence intervals to a framework for hypothesis testing. The approach allows for testing null hypotheses of not-good fit, reversing the role of the null hypothesis in conventional tests of model fit, so that a significant result provides strong support for good fit. The approach also allows for direct estimation of power, where effect size is defined in terms of a null and alternative value of the root-mean-square error of approximation fit index proposed by J. H. Steiger and J. M. Lind (1980). It is also feasible to determine minimum sample size required to achieve a given level of power for any test of fit in this framework. Computer programs and examples are provided for power analyses and calculation of minimum sample sizes. (PsycINFO Database Record (c) 2012 APA, all rights reserved)