Exploratory and Confirmatory Factor Analysis of Measures of Controlling Feeding Practices Jansen E1*, Mallan K1, Nicholson J 2,3, Daniels L1   1 Institute of Health and Biomedical Innovation, School of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia 2 Queensland University of Technology, Brisbane, Australia 3 Murdoch Children’s Research Institute, Melbourne, Australia * t: 61 (0) 7 3138 6114 | e: e1.jansen@student.qut.edu.au Rationale A large number of studies have examined the role of parental feeding, notably controlling feeding practices (CFPs), as a determinant of children’s weight gain[1-3]. However, key constructs of controlling feeding practices such as pressure to eat, restriction, monitoring, overt and covert control have only been validated/evaluated in older children (mostly > 4 years)[4-6]. In a sample of Australian first-time mothers with 2 year old children, the factor structure of 24 items commonly used to assess controlling feeding practices was examined. Results continued EFA: The factors and their items identified by the Exploratory Factor Analysis are shown in Table 1. EFA on the 24 items revealed a 7-factor solution, fulfilling most of the criteria listed in Figure 1. Two of the 5 above factors were replicated exactly (i.e. ‘covert control’ and ‘monitoring’). New factors consisted of a) 2 items assessing ‘reward’, b) 2 items assessing restriction of sweets and high fat foods, and c) the remaining 4 ‘restriction’ items. ‘Overt control’ and ‘pressure’ were partially replicated with 1 item originally loading on the former, now loading on the latter factor. Table 1: Factor structure of controlling feeding practices (N=466) Methods Participants Participants from the intervention and control groups of the NOURISH RCT (in 2008)[7] – 698 mother-child dyads allocated at baseline, child age 4-7 months NOURISH promoted protective early feeding practices, healthy child eating behaviours & growth Data used here were collected during the 3rd assessment time point (follow-up) when children were approximately 2 years of age Valid data were available from 466 first-time mothers Measures Self-administered questionnaire 24 items, measured on 5 point Likert scales – 1 (lowest) to 5 (highest) – sourced from 2 questionnaires (including Cronbach’s α from original validation studies): Child Feeding Questionnaire[4] Restriction – 8 items (α = 0.73) Pressure – 4 items (α = 0.70) Monitoring – 3 items (α = 0.92) Overt vs. Covert Control Questionnaire[6] Overt control – 4 items (α = 0.71) Covert control – 5 items (α = 0.79) Data Analyses Multi-stage validation analysis of the factor structure of controlling feeding practices items (see Figure 1) Exploratory factor analysis (EFA): using principal axis factoring and oblique rotation; conducted on 224 randomly selected cases (i.e. sample 1) Confirmatory factor analysis (CFA): carried out on the remaining 242 cases; to confirm factor structure identified in EFA (i.e. sample 2) Internal consistency was examined via Cronbach’s alpha & coefficient H combining sample 1 and 2 Factors and items composing each factor Cronbach’s α Coefficient H Factor 1 – Covert Control: How often do you avoid... CC1: ... going with your child to cafes or restaurants which sell unhealthy foods CC2: ... buying lollies and snacks and bringing them into the house CC3: Not buy foods that you would like because you do not want your children to have them CC4: Try not to eat unhealthy foods when your child is around CC5: ... buying biscuits and cakes and bringing them into the house .820 873 Factor 2 – Rewards: I offer... Rest5: Sweet foods to my child as a reward for good behaviour Rest6: My child his favourite foods in exchange for good behaviour .833 .869 Factor 3 – Monitoring: How much do you keep track of the... Mo1: ... sweet foods that your child eats Mo2: ... snack food that your child eats Mo3: ... high fat foods that your child eats .931 .976 Factor 4 – Restriction: high fat & sweets: I have to be sure that my child does not eat too... Rest1: ... many sweet foods Rest2: ... much of his favourite foods .865 .939 Factor 5 – Restriction: general: If I did not guide or regulate my child’s eating, he would eat too... Rest3: I have to be sure that my child does not eat too much of his favourite foods Rest4: I intentionally keep some foods out of my child’s reach Rest7: ... many junk foods Rest8: ... much of his favourite foods .703 .782 Factor 6 – Overt Control: How often are you firm about... OC1: ... what your child should eat OC2: ... when your child should eat OC3: ... where your child should eat .691 .768 Factor 7 – Pressure Pr1: My child should always eat all of the food on his plate Pr2: I have to be especially careful to make sure my child eats enough Pr3: If my child says “I’m not hungry” I try to get him to eat anyway Pr4: If I did not guide or regulate my child’s eating, he would eat much less than he should OC4: How often are you firm about how much your child should eat .783 .842 Abbreviations: CC = Covert Control, Rest = Restriction, Mo = Monitoring, OC = Overt Control, Pr = Pressure CFA: Confirmatory Factor Analysis of this 7-factor model indicated ‘reasonable’ model fit to the data (see Figure 2 and Table 2) once paths between error terms of items restriction2 + monitoring3 and overt control4 + pressure1 respectively were included. Item OC4 (How often are you firm about how much your child should eat) loaded more strongly on the construct ‘pressure’ than its original ‘overt control’ factor (standardized regression weight, no modification indices: .49 and .33 respectively). Figure 2: Final CFA of the CFP items showing standardized estimates Figure 1: Procedure of multi-stage validation analysis followed in this study Table 2: CFA ‘reasonable’ fit indices Data preparation Cases with valid data Systematic missing data (Little's MCAR test) Outliers Case allocation (EFA & CFA) EFA & checking of assumptions Sample size (case/item; missing data; homogeneous constructs across sample) Items (items/construct; metric or dichotomous) Factorability (R-matrix r>0.3; Kaiser-Meyer-Olkin overall measure of sampling adequacy (MSA) >0.6; individual MSA on item level >0.5; Bartlett’s test of sphericity p<0.05) Outliers among items (poor MSA <0.6; low communality <0.1; low factor loading; split/cross-loading) Non-linearity Multicollinearity (r>0.8- 0.9 between items; determinant of R-matrix: >.00001) Extraction (Principal Components Analysis, Principal Axis Factoring etc.) Rotation (orthogonal or oblique) Factor correlation matrix r>0.3 Theory Number of factors to retain Kaiser criterion: factors with eigenvalues >1.0 % of variance explained Scree plot Item/factor loading >0.4 on a single factor Final factor structure based on theoretical conceptualisation + simple structure High loading on only 1 factor Several highly loading items for each factor No split loading (cross-loading) CFA Missing data imputation Regression imputation with EM (expectation-maximization) estimation adjustment Bollen-Stine bootstrap p-value if non-normal (i.e. kurtosis critical ratio >±1.96) Check full model for: Item/face validity (standardized regression weight >0.7; estimates are positive & significant) Item reliability (squared multiple correlations=R2>0.5  more explained than unexplained variance = low measurement error) Fit indices (χ2 + df; (Bootstrap) p-value; Normed χ2; SRMR; GFI & AGFI; TLI; CFI; RMSEA ) Decision for change Standardized residual covariances >1.96  problem with these items Modification indices (except for negative covariances of error terms) Reliability Cronbach’s alpha + Coefficient H Fit indices Cut-offs 7 factor model Item validity Standardized regression weight >0.7 15 items  9 items x Item reliability Squared multiple correlations (R2) >0.5 χ2 (df) + Normed χ2 1) p- value 2) bootstrap 1.00-2.00 >0.05 430.986 (229) +  x GFI >.90 x (.87) TLI CFI RMSEA .05-.08 SRMR <.050 x (.063) Abbreviations: GFI = Goodness-of-Fit Index, TLI = Tucker Lewis Index, CFI = Comparative Fit Index, RMSEA = Root Mean Square Error of Approximation, and SRMR = Standardized Root Mean Square Residual Conclusions The new 7-factor-structure for measuring controlling feeding practices in children ≤ 2 years of age was stable across 2 samples. Internal consistency of factors was better than in the original studies[4,6] and particularly coefficient H indicated good reliability of all factors. Construct validity can be further assessed by associating individual factors and child outcomes (i.e. weight status). Further development and validation of new items may be required, given some low fit-indices. Results Children (48% boys) in the sample were 24 (SD±1) months old with weight-for-age z-score = 0.6 (SD±0.9). Their mothers were 33 (SD±5) years old, 65% had a university degree, and 97% were married or in a defacto relationship. Strengths and Limitations + Simultaneous usage of various commonly applied controlling feeding practice measures + The relatively large sample size allowed several additional steps in the validation analysis and supported the robustness of the result Maternal controlling feeding practices were self-reported References [1] Clark et al. J Public Health 2007;29(2):132-141; [2] Faith et al. Obesity Research 2004;12 (11):1711-22; [3] Ventura & Birch. Int J Behav Nutr Phys Act 2008;5:15; [4] Birch et al. Appetite 2001;36(3):201-10; [5] Musher-Eizenman & Holub. J Pediatr Psychol 2007;32(8):960-72; [6] Ogden et al. Appetite 2006;47(1):100-6; [7] Daniels et al. Bmc Public Health 2009;9:387 Acknowledgements NOURISH is funded by NHMRC ID 426704. K Mallan’s post doctoral fellowship is funded by HJ Heinz Co.