- Email: [email protected]
Confirmatory factor analysis of the Children's Eating Behaviour Questionnaire in a low-income sample
Eating Behaviors 13 (2012) 267–270
Contents lists available at SciVerse ScienceDirect
Eating Behaviors
Confirmatory factor analysis of the Children's Eating Behaviour Questionnaire in a low-income sample Martha A. Sparks ⁎, Cynthia L. Radnitz Fairleigh Dickinson University, 1000 River Road Teaneck, New Jersey 07666 United States
a r t i c l e
i n f o
Article history: Received 19 September 2011 Accepted 9 March 2012 Available online 16 March 2012 Keywords: Obesity Children Preschoolers Confirmatory factor analysis Hispanic African American
a b s t r a c t Although pediatric obesity is prevalent in low-income and African American and Hispanic communities, validated measures of child eating habits are lacking for these communities. In this study, confirmatory factor analysis was used to validate the hypothesized 7-factor structure of the Children's Eating Behaviour Questionnaire (CEBQ). The sample included 229 parent–child pairs, the majority low-income and Hispanic (57%) or African American (25%). The a priori structure of the CEBQ failed to replicate. Exploratory factor analysis revealed 3 factors: Disinhibition, Food interest, and Undereating, none predicting child BMI. Although limited by the observational, cross-sectional design, results indicate that the CEBQ needs additional study. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Over the past 30 years, obesity rates have nearly tripled among American preschoolers, to 12.5% (Ogden, Carroll, & Flegal, 2008). African American, Hispanic (Anderson & Whitaker, 2009), and poor children (Haas et al., 2003) are particularly at risk for obesity and subsequent health problems. Parent-report questionnaires are frequently used to measure children's eating behavior and provide an efficient method for gathering data. Validated measures of child eating behavior for low-income and African American and Hispanic groups are lacking, however, despite their greater risk of pediatric obesity. One promising measure is the Children's Eating Behaviour Questionnaire (CEBQ: Wardle, Guthrie, Sanderson, & Rapoport, 2001), a parent-report instrument assessing four “food approach” and three “food avoidance” dimensions of eating behavior that are theoretically related to child BMI. The food approach behaviors include: Food responsiveness (5 items); Enjoyment of food (4 items); Emotional overeating (4 items); and Desire to drink (3 items). The food avoidant scales consist of: Satiety responsiveness / slowness in eating (9 items); Emotional undereating (4 items); and Food fussiness (6 items). Evidence for the validity of the CEBQ is accumulating. Obese parents rated children significantly higher on the Food responsiveness, Emotional overeating, and Desire to drink scales than normal-
⁎ Corresponding author. Fax: + 1 201 692 2304. E-mail address: [email protected] (M.A. Sparks). 1471-0153/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eatbeh.2012.03.002
weight parents (Wardle, Guthrie, Sanderson, Birch, & Plomin, 2001). The Satiety responsiveness / slowness in eating, Food responsiveness, and Enjoyment of food subscales predicted the amount children ate following low- and high-energy preloads (Carnell & Wardle, 2007). Additionally, a 7-year longitudinal study demonstrated temporal stability for 7 of the 8 scales ranging from .29 to .55 (Ashcroft, Semmler, Carnell, van Jaarsveld, & Wardle, 2008). In an ethnically-diverse British sample of school-aged children, the CEBQ food approach scales were positively associated with BMI, and two food avoidance scales were negatively associated with BMI (Webber, Hill, Saxton, Van Jaarsveld, & Wardle, 2009). Dutch (Sleddens, Kremers, & Thijs, 2008) and Portuguese (Viana, Sinde, & Saxton, 2008) investigations of the CEBQ found significant relationships in the predicted directions between the various CEBQ subscales and child BMI. The aim of this study was to validate the CEBQ (Wardle, Guthrie, Sanderson, & Rapoport, 2001) on an ethnically-diverse, low-income, American preschool sample. We hypothesized that the a priori 7factor CEBQ model would replicate and that the food approach scales would be positively associated and the food avoidance scales would be negatively associated with child BMI. 2. Method 2.1. Participants A sample of 229 primary caregivers of 2–5 year old children enrolled in New York City-area Head Start preschools was recruited. Head Start is a federally-funded program for low-income children up to age 5. The majority of caregivers were female (90.8%), and the
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M.A. Sparks, C.L. Radnitz / Eating Behaviors 13 (2012) 267–270
child's biological mother (78.6%). Most participants were Hispanic (57.2%) and African American (24.5%), with the remaining White (6.1%), Asian (2.2%), or interracial/other (7.8%). Two-thirds reported an annual household income of $20,000 or less. The mean age of the children (54% female) was 3.89 years (SD = .75). Obesity was prevalent in children (28.8%) and parents (45.9%). 2.2. Measures Measures were available in both Spanish and English. Each instrument was translated into Spanish and back-translated into English by different bilingual translators. 2.2.1. Demographic questionnaire Participants provided demographic information, including age, marital status, ethnicity, income, educational attainment, family structure, and relationship to the child enrolled in Head Start. 2.2.2. Children's Eating Behaviour Questionnaire The CEBQ is a 35-question parent-report measure assessing seven dimensions of children's eating behavior. Parents rate each item on a 5-point Likert-type scale with word anchors. Subscale scores are calculated by taking the mean of the item ratings; higher scores reflect more of the behavior in question. For the present sample, coefficient alpha estimates ranged from .68 to .78. 2.2.3. Anthropometric measures Trained research assistants measured children's and parents’ height and weight in duplicate using a calibrated medical balance beam scale with attached stadiometer (Detecto). BMI was calculated for parents and children, and children's BMIs were converted to BMI z-scores (Kuczmarski et al., 2002). 2.3. Procedure This study was approved by the Institutional Review Board of Fairleigh Dickinson University. It was part of a larger research project investigating the relationship between food habits, feeding behavior, and weight in Head Start preschool families. Participants completed pencil-and-paper measures, and parents and children were called individually to a private area for weighing and measuring. After participants completed the measures, researchers presented a brief, interactive program on encouraging healthy eating. Parents who completed the study were entered into lotteries to receive a food basket and a DVD player.
3. Results Although all items loaded significantly on the hypothesized factors (see Table 1), the a priori 7-factor model fit the data poorly according to all fit statistics (χ² (507, N = 179, p b .001; TLI = .642; CFI = .676; RMSEA = .086). Furthermore, only two factors correlated significantly with child BMI, Emotional overeating (r = .15, p = .03) and Satiety responsiveness / Slowness of eating (r = −.228, p = .001). Examination of residual values indicated a poor model fit, with many correlated errors and multiple items with significant loadings on several factors. For example, the item “My child is difficult to please with meals,” from the Food fussiness scale, cross-loaded on five other scales. The frequent cross-loadings indicate systemic problems with the scale, providing evidence that poor model fit was not due simply to the omission of one item in the confirmatory analysis. As additional changes (e.g. specifying correlated error terms) would not significantly improve model fit, an exploratory factor analysis approach was adopted with the goal of finding a more parsimonious scale with a clean factor structure. Using all 35 CEBQ items, a principal component analysis with direct oblimin rotation extracted nine factors with eigen values greater than 1, roughly corresponding to the CEBQ model originally proposed by Wardle, Guthrie, Sanderson, and Rapoport (2001). This model was not retained because items loaded on multiple factors and one factor contained only one item. Examination of the skree plot indicated that a 4-, 5-, or 6-factor model might fit the data. Thus, three principal axis factoring analyses were run limiting the number of factors to four, five, and six. For each model, items were retained if the loading on the primary factor was greater than .4 and loadings on other factors were less than .3. Factors with three or more items were retained. After low- and cross-loading items were eliminated from the 5- and 6-factor models, many of the remaining factors had fewer than three items. The 4-factor model retained three theoretically-unitary factors consisting of 17 items total and one factor with one item. This factor was eliminated, and the 17 remaining items were entered into a principal component analysis. On the resulting model, two items that cross-loaded > .3 were eliminated, and the remaining 15 items were reanalyzed. The 15 items loaded on three factors (see Table 2). The first factor named Disinhibition consisted of 6 questions from the Emotional overeating and Food responsiveness subscales. The second factor named Food interest contained three Enjoyment of food items and one Satiety responsiveness item with a negative factor loading. Finally, five items loaded on the third factor. These questions came from several of the food avoidance scales and together reflected Undereating. None of the revised scales correlated significantly with child BMI. 4. Discussion
2.4. Data analysis Structural equation modeling (AMOS version 17) was used to test whether the hypothesized 7-factor CEBQ (Wardle, Guthrie, Sanderson, & Rapoport, 2001) model fit the data. Due to a clerical error, item 34 was omitted on CEBQs administered to 30 participants. This item was therefore excluded from the analysis. Listwise deletion was used in cases of missing data; the final analysis included 179 participants. The tested model included 34 items set to load on seven factors. Model specifications included correlated factors, uncorrelated errors, and factor variance set to 1. We examined four fit indexes: the chisquare test, the Tucker-Lewis Index (TLI: Tucker & Lewis, 1973), the Comparative Fit Index (CFI: Bentler, 1990), and the root mean square error of approximation (RMSEA: Steiger, 1990). Hu and Bentler (1999) suggest that values ≥.95 on the TLI and CFI and ≤.06 on the RMSEA indicate good fit.
The hypothesized 7-factor model of the CEBQ (Wardle, Guthrie, Sanderson, & Rapoport, 2001) failed to replicate. A series of exploratory factor analyses resulted in a 15-item, 3-factor version of the CEBQ in which each item loaded on only one factor. The 3-factor model is tentative since in the absence of replication data, it cannot be determined whether the exploratory analyses produced a generally-applicable model or one unique to our sample. Results point to some weaknesses in the original CEBQ scale. In the exploratory factor analyses, many items loaded on multiple factors, and examination of those items often revealed ambiguous content. For example, the item, “My child finishes his/her meal quickly,” could measure Slowness of eating as a reverse-coded item or enthusiasm about eating, a food approach concept. Beyond problems at the item level, the constructs measured by the CEBQ may be less distinct than Wardle, Guthrie, Sanderson, & Rapoport (2001) proposed. The three factors of the reduced CEBQ each contained items from more
M.A. Sparks, C.L. Radnitz / Eating Behaviors 13 (2012) 267–270
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Table 1 Standardized factor loadings for the a priori 7-factor CEBQ model. Enjoyment of Food Item 1 5 20 22 2 13 15 27 6 31 29 12 14 19 28 3* 4* 8 17 18 21 26 30 35 9 11 23 25 7 10* 16* 24 32* 33
Emotional Overeating
Desire to Drink Food Responsiveness
Satiety Responsiveness/Slowness of Eating
Emotional Undereating
Food Fussiness
0.528 0.796 0.567 0.711 0.566 0.586 0.781 0.596 0.444 0.786 0.838 0.657 0.684 0.674 0.564 0.516 0.471 0.495 0.544 0.415 0.650 0.564 0.495 0.469 0.722 0.486 0.520 0.722 0.579 0.718 0.596 0.384 0.591 0.367
*Reverse-coded items.
Table 2 Reduced CEBQ: 3-factor oblimin rotated solution of principal component analysis. Factor Scale 1 Disinhibition My child eats more when anxious If allowed to, my child would eat too much If given the chance, my child would always have food in his/her mouth My child eats more when worried Even if my child is full s/he finds room to eat his/her favorite food My child eats more when annoyed Food My child is interested in food Interest My child loves food My child enjoys eating My child leaves food on his/her plate at the end of a meal Undereating My child cannot eat a meal if s/he has had a snack just before My child eats more and more slowly during the course of a meal My child decides that s/he doesn't like a food even without tasting it My child eats less when angry My child eats less when s/he is tired
2
3
EOE FR
.829 − .066 .738 .199
.034 .103
FR
.672
.150
.259
EOE FR
.662 .610
.118 .240
.079 .194
EOE EF EF EF SR
.575 − .267 − .003 − .008 .816 − .107 .076 .808 − .122 .094 .784 − .186 − .136 − .604 .171
SR
.013 − .197
.702
SE
.203 − .212
.688
FF
− .034 − .054
.686
.153 − .198 .150 .016
.655 .562
EUE EUE
EF Enjoyment of food; EOE Emotional overeating; EUE Emotional undereating; FF Food fussiness; FR Food responsiveness; SE Slowness in eating; SR Satiety responsiveness.
than one original CEBQ subscale. This is not terribly surprising, given that the original scales are highly intercorrelated. Our results suggest that not every concept was equally well-defined. Desire to drink, was completely eliminated in the exploratory analyses. We also found a single undereating factor that incorporated items from each of the a priori food avoidance scales. Perhaps food-averse children engage in a number of interrelated avoidant eating behaviors. Additional investigation is needed first to more clearly define relevant child eating behaviors and second to derive questionnaire items that capture these behaviors. Alternately, our data might have fit the CEBQ model poorly because our sample differed considerably from those in previous studies (Ashcroft et al., 2008; Sleddens et al., 2008; Viana et al., 2008; Wardle, Guthrie, Sanderson, Birch, et al., 2001; Wardle, Guthrie, Sanderson, & Rapoport, 2001; Webber et al., 2009) which were conducted in Europe. With the exception of Webber, the samples were predominantly Caucasian and middle-income. The food approach and food avoidance behaviors presumably measured by the CEBQ may not apply to low-SES Hispanic and African American preschoolers. This explanation is unlikely, however, as external eating, satiety sensitiveness, and slow eating have been documented in laboratory studies of African American and Hispanic children (Drabman et al., 1979; Fisher, Arreola, Birch, & Rolls, 2007; Fisher, Cai, et al., 2007; Johnson & Taylor-Holloway, 2006). Perhaps results differed from previous studies because parents misunderstood the questions. Low-income mothers may understand eating questionnaire items quite differently than the researchers intend (Jain, Sherman, Chamberlin, & Whitaker,
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2004). Qualitative research methods such as focus groups could elucidate how parents interpreted CEBQ questions. A strength of this study is the diverse sample. Hispanics, African Americans, and low-income people are rarely studied despite their greater risk of obesity. It is a weakness that child eating behavior was assessed by parent report which may be sensitive to poor recall, demand characteristics, and social desirability. A laboratory test of child eating would have provided additional data against which to validate the self-report measures. We recruited a convenience sample and may have assessed parents who were especially interested in eating and health. These parents might differ in their feeding behaviors from other parents. However, the high prevalence of parent and child obesity in our sample suggests that even parents who participate in a healthy eating study have sub-optimal eating and feeding habits. Although the sample was larger than previous investigations of the CEBQ (e.g., Sleddens et al., 2008), it was not large enough to disaggregate the groups by ethnicity. Our results suggest that the CEBQ in its current form may not be applicable to all populations. The 15-item scale is preliminary and needs additional testing. The larger problem is a lack of clarity regarding child eating styles, which behavior constellations constitute discrete styles, and how eating styles relate to child weight outcomes. The high prevalence of obesity in our sample among both parents and children underscores the need for further research to construct psychometrically sound measures of children's eating behaviors.
Role of Funding Sources There were no external funding sources.
Contributors Dr. Sparks designed the study, conducted literature searches, collected data, conducted the statistical analysis, and prepared the manuscript. Dr. Radnitz designed and supervised the source study, consulted on study design, and advised the preparation of the manuscript and revisions. All authors have approved the final manuscript.
Conflict of Interest All authors declare that they have no conflicts of interest.
Acknowledgments The authors wish to thank Brooke Bailer, Carol Chu, Aliza Davis, Mihaela Dranoff, Rachel Goldman, Hsaio-wan Lin, and Carinna Scotti for their assistance collecting data; Dr. Steven A. Armeli who provided consultation for the statistical analyses; and the families and staff of Head Start.
References Anderson, S. E., & Whitaker, R. C. (2009). Prevalence of obesity among US preschool children in different racial and ethnic groups. Archive of Pediatric and Adolescent Medicine, 163, 344–348. Ashcroft, J., Semmler, C., Carnell, S., van Jaarsveld, C. H. M., & Wardle, J. (2008). Continuity and stability of eating behaviour traits in children. European Journal of Clinical Nutrition, 62, 985–990. Bentler, P. M. (1990). Comparative fit indexes in structural equation models. Psychological Bulletin, 107, 238–246. Carnell, S., & Wardle, J. (2007). Measuring behavioural susceptibility to obesity: Validation of the child eating behaviour questionnaire. Appetite, 48, 104–113. Drabman, R. S., Cordua, G. D., Hammer, D., Jarvie, G. J., & Horton, W. (1979). Developmental trends in eating rates of normal and overweight preschool children. Child Development, 50, 211–216. Fisher, J. O., Arreola, A., Birch, L. L., & Rolls, B. J. (2007). Portion size effects daily energy intake in low-income Hispanic and African American children and their mothers. The American Journal of Clinical Nutrition, 86, 1709–1716. Fisher, J. O., Cai, G., Jaramillo, S. J., Cole, S. A., Comuzzie, A. G., & Butte, N. F. (2007). Heritability of hyperphagic eating behavior and appetite-related hormones among Hispanic children. Obesity, 15, 1484–1495. Haas, J. S., Lee, L. B., Kaplan, C. P., Sonneborn, D., Phillips, K. A., & Liang, S. (2003). The association of race, socioeconomic status, and health insurance status with the prevalence of overweight among children and adolescents. American Journal of Public Health, 93, 2105–2110. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. Jain, A., Sherman, S. N., Chamberlin, L. A., & Whitaker, R. C. (2004). Mothers misunderstand questions on a feeding questionnaire. Appetite, 42, 249–254. Johnson, S. L., & Taylor-Holloway, L. A. (2006). Non-Hispanic white and Hispanic elementary school children's self-regulation of energy intake. American Journal of Clinical Nutrition, 83, 1276–1282. Kuczmarski, R. J., Ogden, C. L., Guo, S. S., Grummer-Strawn, L. M., Flegal, K. M., Mei, Z., et al. (2002). 2000 CDC growth charts for the United States: methods and development. Vital and Health Statistics, 11(246), 1–190. Ogden, C. L., Carroll, M. D., & Flegal, K. M. (2008). High body mass index for age among US children and adolescents, 2003–2006. JAMA : The Journal of the American Medical Association, 299, 2401–2405. Sleddens, E. F. C., Kremers, S. P. J., & Thijs, C. (2008). The Children's Eating Behaviour Questionnaire: Factorial validity and association with body mass index in Dutch children aged 6–7. International Journal of Behavioral Nutrition and Physical Activity, 5 online (http://www.ijbnpa.org/content/5/1/49) Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. Viana, V., Sinde, S., & Saxton, J. C. (2008). Children's Eating Behaviour Questionnaire: Associations with BMI in Portuguese children. British Journal of Nutrition, 100, 445–450. Wardle, J., Guthrie, C., Sanderson, S., Birch, L., & Plomin, R. (2001). Food and activity preferences in children of lean and obese parents. International Journal of Obesity, 25, 971–977. Wardle, J., Guthrie, C. A., Sanderson, S., & Rapoport, L. (2001). Development of the Children's Eating Behaviour Questionnaire. Journal of Child Psychology and Psychiatry, 42, 963–971. Webber, L., Hill, C., Saxton, J., Van Jaarsveld, C. H. M., & Wardle, J. (2009). Eating behavior and weight in children. International Journal of Obesity, 33, 21–28.
Contents lists available at SciVerse ScienceDirect
Eating Behaviors
Confirmatory factor analysis of the Children's Eating Behaviour Questionnaire in a low-income sample Martha A. Sparks ⁎, Cynthia L. Radnitz Fairleigh Dickinson University, 1000 River Road Teaneck, New Jersey 07666 United States
a r t i c l e
i n f o
Article history: Received 19 September 2011 Accepted 9 March 2012 Available online 16 March 2012 Keywords: Obesity Children Preschoolers Confirmatory factor analysis Hispanic African American
a b s t r a c t Although pediatric obesity is prevalent in low-income and African American and Hispanic communities, validated measures of child eating habits are lacking for these communities. In this study, confirmatory factor analysis was used to validate the hypothesized 7-factor structure of the Children's Eating Behaviour Questionnaire (CEBQ). The sample included 229 parent–child pairs, the majority low-income and Hispanic (57%) or African American (25%). The a priori structure of the CEBQ failed to replicate. Exploratory factor analysis revealed 3 factors: Disinhibition, Food interest, and Undereating, none predicting child BMI. Although limited by the observational, cross-sectional design, results indicate that the CEBQ needs additional study. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Over the past 30 years, obesity rates have nearly tripled among American preschoolers, to 12.5% (Ogden, Carroll, & Flegal, 2008). African American, Hispanic (Anderson & Whitaker, 2009), and poor children (Haas et al., 2003) are particularly at risk for obesity and subsequent health problems. Parent-report questionnaires are frequently used to measure children's eating behavior and provide an efficient method for gathering data. Validated measures of child eating behavior for low-income and African American and Hispanic groups are lacking, however, despite their greater risk of pediatric obesity. One promising measure is the Children's Eating Behaviour Questionnaire (CEBQ: Wardle, Guthrie, Sanderson, & Rapoport, 2001), a parent-report instrument assessing four “food approach” and three “food avoidance” dimensions of eating behavior that are theoretically related to child BMI. The food approach behaviors include: Food responsiveness (5 items); Enjoyment of food (4 items); Emotional overeating (4 items); and Desire to drink (3 items). The food avoidant scales consist of: Satiety responsiveness / slowness in eating (9 items); Emotional undereating (4 items); and Food fussiness (6 items). Evidence for the validity of the CEBQ is accumulating. Obese parents rated children significantly higher on the Food responsiveness, Emotional overeating, and Desire to drink scales than normal-
⁎ Corresponding author. Fax: + 1 201 692 2304. E-mail address: [email protected] (M.A. Sparks). 1471-0153/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eatbeh.2012.03.002
weight parents (Wardle, Guthrie, Sanderson, Birch, & Plomin, 2001). The Satiety responsiveness / slowness in eating, Food responsiveness, and Enjoyment of food subscales predicted the amount children ate following low- and high-energy preloads (Carnell & Wardle, 2007). Additionally, a 7-year longitudinal study demonstrated temporal stability for 7 of the 8 scales ranging from .29 to .55 (Ashcroft, Semmler, Carnell, van Jaarsveld, & Wardle, 2008). In an ethnically-diverse British sample of school-aged children, the CEBQ food approach scales were positively associated with BMI, and two food avoidance scales were negatively associated with BMI (Webber, Hill, Saxton, Van Jaarsveld, & Wardle, 2009). Dutch (Sleddens, Kremers, & Thijs, 2008) and Portuguese (Viana, Sinde, & Saxton, 2008) investigations of the CEBQ found significant relationships in the predicted directions between the various CEBQ subscales and child BMI. The aim of this study was to validate the CEBQ (Wardle, Guthrie, Sanderson, & Rapoport, 2001) on an ethnically-diverse, low-income, American preschool sample. We hypothesized that the a priori 7factor CEBQ model would replicate and that the food approach scales would be positively associated and the food avoidance scales would be negatively associated with child BMI. 2. Method 2.1. Participants A sample of 229 primary caregivers of 2–5 year old children enrolled in New York City-area Head Start preschools was recruited. Head Start is a federally-funded program for low-income children up to age 5. The majority of caregivers were female (90.8%), and the
268
M.A. Sparks, C.L. Radnitz / Eating Behaviors 13 (2012) 267–270
child's biological mother (78.6%). Most participants were Hispanic (57.2%) and African American (24.5%), with the remaining White (6.1%), Asian (2.2%), or interracial/other (7.8%). Two-thirds reported an annual household income of $20,000 or less. The mean age of the children (54% female) was 3.89 years (SD = .75). Obesity was prevalent in children (28.8%) and parents (45.9%). 2.2. Measures Measures were available in both Spanish and English. Each instrument was translated into Spanish and back-translated into English by different bilingual translators. 2.2.1. Demographic questionnaire Participants provided demographic information, including age, marital status, ethnicity, income, educational attainment, family structure, and relationship to the child enrolled in Head Start. 2.2.2. Children's Eating Behaviour Questionnaire The CEBQ is a 35-question parent-report measure assessing seven dimensions of children's eating behavior. Parents rate each item on a 5-point Likert-type scale with word anchors. Subscale scores are calculated by taking the mean of the item ratings; higher scores reflect more of the behavior in question. For the present sample, coefficient alpha estimates ranged from .68 to .78. 2.2.3. Anthropometric measures Trained research assistants measured children's and parents’ height and weight in duplicate using a calibrated medical balance beam scale with attached stadiometer (Detecto). BMI was calculated for parents and children, and children's BMIs were converted to BMI z-scores (Kuczmarski et al., 2002). 2.3. Procedure This study was approved by the Institutional Review Board of Fairleigh Dickinson University. It was part of a larger research project investigating the relationship between food habits, feeding behavior, and weight in Head Start preschool families. Participants completed pencil-and-paper measures, and parents and children were called individually to a private area for weighing and measuring. After participants completed the measures, researchers presented a brief, interactive program on encouraging healthy eating. Parents who completed the study were entered into lotteries to receive a food basket and a DVD player.
3. Results Although all items loaded significantly on the hypothesized factors (see Table 1), the a priori 7-factor model fit the data poorly according to all fit statistics (χ² (507, N = 179, p b .001; TLI = .642; CFI = .676; RMSEA = .086). Furthermore, only two factors correlated significantly with child BMI, Emotional overeating (r = .15, p = .03) and Satiety responsiveness / Slowness of eating (r = −.228, p = .001). Examination of residual values indicated a poor model fit, with many correlated errors and multiple items with significant loadings on several factors. For example, the item “My child is difficult to please with meals,” from the Food fussiness scale, cross-loaded on five other scales. The frequent cross-loadings indicate systemic problems with the scale, providing evidence that poor model fit was not due simply to the omission of one item in the confirmatory analysis. As additional changes (e.g. specifying correlated error terms) would not significantly improve model fit, an exploratory factor analysis approach was adopted with the goal of finding a more parsimonious scale with a clean factor structure. Using all 35 CEBQ items, a principal component analysis with direct oblimin rotation extracted nine factors with eigen values greater than 1, roughly corresponding to the CEBQ model originally proposed by Wardle, Guthrie, Sanderson, and Rapoport (2001). This model was not retained because items loaded on multiple factors and one factor contained only one item. Examination of the skree plot indicated that a 4-, 5-, or 6-factor model might fit the data. Thus, three principal axis factoring analyses were run limiting the number of factors to four, five, and six. For each model, items were retained if the loading on the primary factor was greater than .4 and loadings on other factors were less than .3. Factors with three or more items were retained. After low- and cross-loading items were eliminated from the 5- and 6-factor models, many of the remaining factors had fewer than three items. The 4-factor model retained three theoretically-unitary factors consisting of 17 items total and one factor with one item. This factor was eliminated, and the 17 remaining items were entered into a principal component analysis. On the resulting model, two items that cross-loaded > .3 were eliminated, and the remaining 15 items were reanalyzed. The 15 items loaded on three factors (see Table 2). The first factor named Disinhibition consisted of 6 questions from the Emotional overeating and Food responsiveness subscales. The second factor named Food interest contained three Enjoyment of food items and one Satiety responsiveness item with a negative factor loading. Finally, five items loaded on the third factor. These questions came from several of the food avoidance scales and together reflected Undereating. None of the revised scales correlated significantly with child BMI. 4. Discussion
2.4. Data analysis Structural equation modeling (AMOS version 17) was used to test whether the hypothesized 7-factor CEBQ (Wardle, Guthrie, Sanderson, & Rapoport, 2001) model fit the data. Due to a clerical error, item 34 was omitted on CEBQs administered to 30 participants. This item was therefore excluded from the analysis. Listwise deletion was used in cases of missing data; the final analysis included 179 participants. The tested model included 34 items set to load on seven factors. Model specifications included correlated factors, uncorrelated errors, and factor variance set to 1. We examined four fit indexes: the chisquare test, the Tucker-Lewis Index (TLI: Tucker & Lewis, 1973), the Comparative Fit Index (CFI: Bentler, 1990), and the root mean square error of approximation (RMSEA: Steiger, 1990). Hu and Bentler (1999) suggest that values ≥.95 on the TLI and CFI and ≤.06 on the RMSEA indicate good fit.
The hypothesized 7-factor model of the CEBQ (Wardle, Guthrie, Sanderson, & Rapoport, 2001) failed to replicate. A series of exploratory factor analyses resulted in a 15-item, 3-factor version of the CEBQ in which each item loaded on only one factor. The 3-factor model is tentative since in the absence of replication data, it cannot be determined whether the exploratory analyses produced a generally-applicable model or one unique to our sample. Results point to some weaknesses in the original CEBQ scale. In the exploratory factor analyses, many items loaded on multiple factors, and examination of those items often revealed ambiguous content. For example, the item, “My child finishes his/her meal quickly,” could measure Slowness of eating as a reverse-coded item or enthusiasm about eating, a food approach concept. Beyond problems at the item level, the constructs measured by the CEBQ may be less distinct than Wardle, Guthrie, Sanderson, & Rapoport (2001) proposed. The three factors of the reduced CEBQ each contained items from more
M.A. Sparks, C.L. Radnitz / Eating Behaviors 13 (2012) 267–270
269
Table 1 Standardized factor loadings for the a priori 7-factor CEBQ model. Enjoyment of Food Item 1 5 20 22 2 13 15 27 6 31 29 12 14 19 28 3* 4* 8 17 18 21 26 30 35 9 11 23 25 7 10* 16* 24 32* 33
Emotional Overeating
Desire to Drink Food Responsiveness
Satiety Responsiveness/Slowness of Eating
Emotional Undereating
Food Fussiness
0.528 0.796 0.567 0.711 0.566 0.586 0.781 0.596 0.444 0.786 0.838 0.657 0.684 0.674 0.564 0.516 0.471 0.495 0.544 0.415 0.650 0.564 0.495 0.469 0.722 0.486 0.520 0.722 0.579 0.718 0.596 0.384 0.591 0.367
*Reverse-coded items.
Table 2 Reduced CEBQ: 3-factor oblimin rotated solution of principal component analysis. Factor Scale 1 Disinhibition My child eats more when anxious If allowed to, my child would eat too much If given the chance, my child would always have food in his/her mouth My child eats more when worried Even if my child is full s/he finds room to eat his/her favorite food My child eats more when annoyed Food My child is interested in food Interest My child loves food My child enjoys eating My child leaves food on his/her plate at the end of a meal Undereating My child cannot eat a meal if s/he has had a snack just before My child eats more and more slowly during the course of a meal My child decides that s/he doesn't like a food even without tasting it My child eats less when angry My child eats less when s/he is tired
2
3
EOE FR
.829 − .066 .738 .199
.034 .103
FR
.672
.150
.259
EOE FR
.662 .610
.118 .240
.079 .194
EOE EF EF EF SR
.575 − .267 − .003 − .008 .816 − .107 .076 .808 − .122 .094 .784 − .186 − .136 − .604 .171
SR
.013 − .197
.702
SE
.203 − .212
.688
FF
− .034 − .054
.686
.153 − .198 .150 .016
.655 .562
EUE EUE
EF Enjoyment of food; EOE Emotional overeating; EUE Emotional undereating; FF Food fussiness; FR Food responsiveness; SE Slowness in eating; SR Satiety responsiveness.
than one original CEBQ subscale. This is not terribly surprising, given that the original scales are highly intercorrelated. Our results suggest that not every concept was equally well-defined. Desire to drink, was completely eliminated in the exploratory analyses. We also found a single undereating factor that incorporated items from each of the a priori food avoidance scales. Perhaps food-averse children engage in a number of interrelated avoidant eating behaviors. Additional investigation is needed first to more clearly define relevant child eating behaviors and second to derive questionnaire items that capture these behaviors. Alternately, our data might have fit the CEBQ model poorly because our sample differed considerably from those in previous studies (Ashcroft et al., 2008; Sleddens et al., 2008; Viana et al., 2008; Wardle, Guthrie, Sanderson, Birch, et al., 2001; Wardle, Guthrie, Sanderson, & Rapoport, 2001; Webber et al., 2009) which were conducted in Europe. With the exception of Webber, the samples were predominantly Caucasian and middle-income. The food approach and food avoidance behaviors presumably measured by the CEBQ may not apply to low-SES Hispanic and African American preschoolers. This explanation is unlikely, however, as external eating, satiety sensitiveness, and slow eating have been documented in laboratory studies of African American and Hispanic children (Drabman et al., 1979; Fisher, Arreola, Birch, & Rolls, 2007; Fisher, Cai, et al., 2007; Johnson & Taylor-Holloway, 2006). Perhaps results differed from previous studies because parents misunderstood the questions. Low-income mothers may understand eating questionnaire items quite differently than the researchers intend (Jain, Sherman, Chamberlin, & Whitaker,
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2004). Qualitative research methods such as focus groups could elucidate how parents interpreted CEBQ questions. A strength of this study is the diverse sample. Hispanics, African Americans, and low-income people are rarely studied despite their greater risk of obesity. It is a weakness that child eating behavior was assessed by parent report which may be sensitive to poor recall, demand characteristics, and social desirability. A laboratory test of child eating would have provided additional data against which to validate the self-report measures. We recruited a convenience sample and may have assessed parents who were especially interested in eating and health. These parents might differ in their feeding behaviors from other parents. However, the high prevalence of parent and child obesity in our sample suggests that even parents who participate in a healthy eating study have sub-optimal eating and feeding habits. Although the sample was larger than previous investigations of the CEBQ (e.g., Sleddens et al., 2008), it was not large enough to disaggregate the groups by ethnicity. Our results suggest that the CEBQ in its current form may not be applicable to all populations. The 15-item scale is preliminary and needs additional testing. The larger problem is a lack of clarity regarding child eating styles, which behavior constellations constitute discrete styles, and how eating styles relate to child weight outcomes. The high prevalence of obesity in our sample among both parents and children underscores the need for further research to construct psychometrically sound measures of children's eating behaviors.
Role of Funding Sources There were no external funding sources.
Contributors Dr. Sparks designed the study, conducted literature searches, collected data, conducted the statistical analysis, and prepared the manuscript. Dr. Radnitz designed and supervised the source study, consulted on study design, and advised the preparation of the manuscript and revisions. All authors have approved the final manuscript.
Conflict of Interest All authors declare that they have no conflicts of interest.
Acknowledgments The authors wish to thank Brooke Bailer, Carol Chu, Aliza Davis, Mihaela Dranoff, Rachel Goldman, Hsaio-wan Lin, and Carinna Scotti for their assistance collecting data; Dr. Steven A. Armeli who provided consultation for the statistical analyses; and the families and staff of Head Start.
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