- Open Access
Psychometric assessment of scales for a Model of Goal Directed Vegetable Parenting Practices (MGDVPP)
International Journal of Behavioral Nutrition and Physical Activityvolume 10, Article number: 110 (2013)
Vegetable intake has been related to lower risk of chronic illnesses in the adult years. The habit of vegetable intake should be established early in life, but many parents of preschoolers report not being able to get their child to eat vegetables. The Model of Goal Directed Behavior (MGDB) has been employed to understand vegetable parenting practices (VPP) to encourage a preschool child’s vegetable intake. The Model of Goal Directed Vegetable Parenting Practices (MGDVPP) provides possible determinants and may help explain why parents use effective or ineffective VPP. Scales to measure effective and ineffective vegetable parenting practices have previously been validated. This manuscript presents the psychometric characteristics and factor structures of new scales to measure the constructs in MGDVPP.
Participants were 307 parents of preschool (i.e. 3 to 5 year old) children, used for both exploratory (EFA) and confirmatory factor analyses (CFA). Data were collected via an internet survey. First, EFA were conducted using the scree plot criterion for factor extraction. Next, CFA assessed the fit of the exploratory derived factors. Then, classical test theory procedures were employed with all scales. Finally, Pearson correlations were calculated between each scale and composite effective and ineffective VPP as a test of scale predictive validity.
Twenty-nine subscales (164 items) within 11 scales were extracted. The number of items per subscale ranged from 2 to 13, with three subscales having 10 or more items and 12 subscales having 4 items or less. Cronbach’s alphas varied from 0.13 to 0.92, with 17 being 0.70 or higher. Most alphas <0.70 had only three or four items. Twenty-five of the 29 subscales significantly bivariately correlated with the composite effective or ineffective VPP scales.
This was the initial examination of the factor structure and psychometric assessment of MGDVPP scales. Most of the scales displayed acceptable to desirable psychometric characteristics. Research is warranted to add items to those subscales with small numbers of items, test their validity and reliability, and characterize the model’s influence on child vegetable consumption.
High vegetable intake has been inversely related to risk of heart disease and stroke, likely with several cancers , and obesity in the adult years . Vegetable intake tracks from the earliest years , supporting the likelihood that preference for  and habit of vegetable intake is established early in life, even as early as the preschool years .
Parents are believed to be important influences on child dietary intake, especially in the preschool years . However, many parents of preschoolers report difficulties in getting their child to eat vegetables . Separate vegetable parenting practices (VPP) dimensions have recently been identified that are likely effective (E) VPP for getting a child to eat and enjoy vegetables (e.g. Effective Responsiveness “I tell my child that vegetables taste good”) and ineffective (I) VPP in getting a child to eat vegetables (e.g. Ineffective Responsiveness “I give my child something to eat or drink if they are bored”) . Many parents of preschoolers use both EVPP and IVPP, suggesting that they are not aware of practices that are likely to be effective or not .
To design effective intervention programs we need to understand why parents might employ EVPP and IVPP. The existing research predicting specific feeding parenting practices has focused on psycho-pathological or sociological factors. For example, stress and depression predicted impaired feeding specific parenting, while perceived social support predicted improved parenting . Higher levels of maternal education were associated with mother’s higher use of controlling and lower use of emotional feeding practices . Mother’s parenting satisfaction was associated with less pressure on the child to eat and less food restriction . The next step in this line of investigation is to more narrowly focus the behavior (e.g. parenting practices to enhance child vegetable intake) and incorporate a model to identify the likely psychosocial predictors of the behavior.
A Model of Goal Directed Behavior (MGDB) obtained high levels of adult health behavior predictiveness [12–14] by incorporating “anticipated emotions” into the Theory of Planned Behavior (TPB), and inserting “desire” between the psychosocial predictors and intentions [12, 15]. Since “desire” was operationalized to embody “intrinsic motivation” [12, 15], constructs from Self Determination Theory that contribute to intrinsic motivation (autonomy, competence, relatedness)  were added to the model. Competence is similar to Social Cognitive Theory’s Self Efficacy construct [17–19]. Since habit (i.e. automated behavior)  and barriers  were strongly related to behavior, incorporating these variables should enhance predictiveness and understanding (See Figure 1). This previously unpublished enhanced MGDB provided the conceptual framework for this study.
Qualitative research conducted by the authors was used to generate items to populate scales within this model . The present manuscript reports preliminary psychometric analyses of newly generated items for a Model of Goal Directed Vegetable Parenting Practices (MGDVPP) scales and subscales. To our knowledge, this is the first report of the psychometrics of scales for MGDVPP.
Intensive qualitative interviews were conducted with parents of preschool children to generate items for MGDVPP scales . An internet survey including 192 items covering 11 scales was then employed using Survey Monkey . Exploratory factor analyses were conducted using the scree plot criterion for factor extraction. Next, confirmatory factor analyses were conducted to test the fit of the exploratory derived factors. Then, classical test theory procedures (i.e. item means, standard deviations, corrected item-total correlations, average inter-item correlations, Cronbach’s alpha) were employed with all empirically determined subscales. Last, bivariate Pearson correlations were calculated between each subscale and composite EVPP and IVPP as a test of predictive validity.
An internet survey was announced in a Children’s Nutrition Research Center (CNRC) newsletter distributed to 25,000 recipients; fliers were posted on participant volunteer billboards around the Texas Medical Center, public libraries and YMCA’s. We also sent personal emails to the CNRC list of volunteers, and listed the study on the Baylor College of Medicine (BCM) volunteer website. Inclusionary criteria were being a parent of a preschool child, able to read and write English, and having the child spend most of the time with that caregiver. Access to the internet survey implied access to both a computer and an internet connection. Given the low risk nature of the study, selecting the “participate” button in the survey was taken as evidence of consent. The Institutional Review Board of the Baylor College of Medicine reviewed and approved the research protocol. This sample was used for both the Exploratory and Confirmatory Factor Analyses.
Qualitative telephone interviews were conducted using a semi-structured script with a multicultural sample of parents of 3–5 year old children . The interview script consisted of twelve open-ended questions and several structured follow up questions, prompts, and probes. Interviews were taped; and verbatim transcripts created, coded and analyzed using thematic analysis. MGDB [12, 15] provided the theoretical framework and guided the questionnaire development and interpretation of results. Themes were identified from the transcripts and transformed into items for a questionnaire. Cognitive interviews were conducted to assess parent understanding of item wording; as a result, some items were simplified and others deleted. Based on theory, the 192 items were divided across 11 scales. Three category responses were employed for all scales given our repeated finding using item response modeling that respondents generally effectively used only two or three response categories [17–19].
Eighteen attitude items were generated, each starting with the stem: “If my child started eating more vegetables on most days…” A three category response was employed (1 = Disagree; 2 = Neither Agree nor Disagree; 3 = Agree). (See individual items in Table 1.)
Items were created for two different types of norms. Descriptive norms identified the respondents’ perceptions of what parents and children were currently doing in regard to the child’s eating of vegetables. We asked the respondents’ perception of the extent to which most parents get their child to eat more vegetables, to have their child eat enough vegetables, and the extent to which most children eat vegetables. Parents were asked to select from a three category response option which included: 1 = Disagree; 2 = Neither Agree nor Disagree; 3 = Agree, for each statement. Closer to the original formulation for TPB, normative expectations identified what the respondent believed other people expected them to do, and the extent to which the respondent wanted to please those people. Given the complexities of modern family structures and living arrangements, different respondents are likely responsive to the expectations of people in different social roles. To reduce this complexity we asked the respondent to identify “the three most important people who influence your decisions about your child in a good, or a bad way” from a menu (see Table 2). For each of these three role players, the respondent was asked to respond to two questions: “It is important to my [role person] that my child eats more vegetables”; and “It is important to me to please my [role person] when it comes to getting my child to eat more vegetables”. Parents were asked to select from a three category response (1 = Disagree; 2 = Neither Agree nor Disagree; 3 = Agree) for each statement. (See individual items in Table 3.)
Thirty perceived behavioral control items were generated starting with the stem “How easy would it be to get my child to eat more vegetables if I…”, using a three category difficulty response (1 = Difficult; 2 = Neither Easy nor Difficult; 3 = Easy). (See individual items in Table 4.)
Anticipated Emotion items systematically varied types of vegetables served (i.e. usual, new, liked, disliked) with eating behavior (ate it, refused it), since we believed consistent and inconsistent service and behavior would lead to diverse meaningful emotional responses. Thirty-two anticipated emotion items were generated starting with four different stems: “If I served my child a new vegetable and they ate it, I would feel…”; “If I served my child a new vegetable and they refused to eat it, I would feel…”; “If I served my child a vegetable that they liked, and they refused to eat it, I would feel…”; “If I served my child a vegetable that I knew they disliked, and they ate it, I would feel…”. Three agreement response categories were offered (1 = Disagree; 2 = Neither Agree nor Disagree; 3 = Agree). (See individual items in Table 5.)
Twenty habit items were generated starting with the stem “Without thinking about it…”, using a three category frequency response (1 = Always, 2 = Sometimes, 3 = Never). (See individual items in Table 6.)
Twenty-one competence/self efficacy items were generated with a three category response (1 = Not Sure, 2 = Somewhat Sure, 3 = Sure). (See individual items in Table 7.)
Twelve relatedness items were generated starting with the stem “If my child ate at least 3 portions of vegetables most days I would feel…”, using a three category agreement response (1 = Disagree, 2 = Neither Agree nor Disagree, 3 = Agree). (See individual items in Table 8.)
Using the same three category agreement response (1 = Disagree, 2 = Neither Agree nor Disagree, 3 = Agree), three autonomy items, twenty-six barrier items, and four desire (similar to the intrinsic motivation construct) items were generated. (See individual items in Tables 9, 10, and 11.)
Twenty-one intention items were generated starting with the stem “In the next month I plan to…”, using a three category intention response (1 = Will Not Do, 2 = May or may Not Do, 3 = Will Do). (See individual items in Table 12.)
In a separate manuscript  with data from this internet survey, we reported confirmatory factor analyses on only the EVPP and IVPP (separate items developed in the same way) with the same sample indicating the most interpretable structure had separate (completely independent) two-level factor structures . For the analyses reported herein, the values for the 14 effective items were summed (EVPP sum (possible range: 14–102), Cronbach’s alpha = 0.69) and the 14 ineffective items were summed (IVPP sum (possible range: 14–102), Cronbach’s alpha = 0.60) to obtain unweighted composite scales. Participants reported gender of participating parent, gender of selected child, ethnicity of parent, highest household educational attainment, and annual household income using standard questions.
The items for each of the 11 scales were submitted to exploratory factor analysis (principal components) with a varimax rotation, using the scree plot criterion for factor extraction using SPSS . Exploratory factor analysis was used for data reduction and to examine whether the 11 scales were uni-dimensional or consisted of several underlying factors (i.e. subscales). Items not loading on a factor (factor loading <0.4) or loading on more than one factor were deleted from the scale and the analysis reconducted with the reduced set of items. Percentage of variance in the items accounted for by a factor was estimated using the eigenvalues. The exploratory factor structure was submitted to a confirmatory factor analysis (structural equation modeling) using the same sample to obtain model fit indices using Mplus . Hu and Bentler’s two-index presentation strategy  were employed to access the data-model fit. The combinational rules include 1) TLI of 0.96 or higher and an SRMR of 0.09 or lower; 2) RMSEA of 0.06 or lower and an SRMR of 0.09 or lower 3) CFI of 0.96 or higher and an SRMR of 0.09 or lower. Subscale means and standard deviations were calculated and range of scores noted. Cronbach’s alpha and the average inter-item correlation  were calculated for each subscale. When the number of items is small (e.g. 5 or less), an average inter-item correlation between 0.15 and 0.50 is considered an indication of acceptable internal consistency depending on the generality-specificity of the construct . Pearson correlations were calculated among MGDVPP subscales and between each MGDVPP subscale and composite scales of EVPP and IVPP.
406 participants provided informed consent, entered the questionnaire website and initiated the questionnaire; 16 participants were deleted because they did not have a 3 to 5 year old child, or the child did not spend most days with that parent or guardian. Complete data were obtained from 307 participants. Since the demographic questions were at the end of the survey, we do not have the necessary data to compare the 83 participants who provided incomplete data with the 307 who provided complete data. Almost 90% of respondents were female, but slightly more of the children were male (53.1%) (Table 13). A plurality of respondents were white (37.1%), with representation from all major ethnic groups in Houston (19.5% Black/African American, 10.1% Hispanic, 14.0% Asian, and 19.2% Other). The sample was well educated with over half (64.5%) having a college degree or more. Over half (54.1%) had an annual household income of $60,000 or higher. The mean (±sd) Effective Vegetable Parenting Scale score was 23(±3.6); and the mean (±sd) Ineffective Vegetable Parenting Scale score was 34.4 (±3.1) . Eleven scales with 192 items were submitted to exploratory and confirmatory factor analyses with 164 items retained in 29 subscales. The psychometric results for the eleven scales are found in Tables 1, 14, and 3 through 12.
Acceptable fit was obtained for most of the scales, and predictive validity with EVPP and/or IVPP was obtained for 25 of 29 subscales (Table 14). Exceptions to acceptable fit include the below. Confirmatory factor analysis revealed marginally acceptable fit for the four factor structure among Anticipated Emotions items (bottom of Table 5). Cronbach’s alphas varied from 0.66 to 0.92 and average inter-item correlations ranged from 0.32 to 0.62 (Table 14) suggesting the internal consistency for the subscales with 4 items were acceptable. Confirmatory factor analysis revealed marginally acceptable model fit for the two factor structure among Competence/Self Efficacy items (bottom of Table 7). Cronbach’s alphas for the two subscales, however, were 0.85 and 0.76. The confirmatory factor analysis for the three Autonomy items could not attain positive definite status (Table 9). Cronbach’s alpha for the scale was 0.31 while the average interitem correlation was 0.17 which was at the lower end of the range of acceptable (Table 14). Despite this low internal consistency reliability, it was significantly inversely correlated with EVPP (r = −0.23, p < 0.001) (Table 14). Confirmatory factor analysis revealed marginally acceptable fit for the four factor solution among Intentions items (bottom of Table 12).
Exploratory factor analyses of each of the 11 original scales separately indicated there were 29 subscales with 2 to 13 items per subscale; three subscales had 10 or more items; 12 subscales had 4 items or less. Model fit was acceptable in most cases. Cronbach’s alphas for the subscales ranged from 0.13 to 0.92 with 17 being 0.70 or higher. Most alphas <0.70 included only three or four items, but acceptable average inter-item correlations . Twenty-five of 29 subscales significantly bivariately correlated with composite effective or ineffective VPP.
To our knowledge, this is the first report of the psychometric characteristics of theory based scales and subscales to predict a parent’s use of VPP. Most studies using TPB  or MGDB [12–15] used single dimensional scales for each predictive construct. Our approach, alternatively, found single dimensions did not adequately fit the items for most scales/constructs. Using the scree plot criterion and interpretability, exploratory analyses obtained one to four dimensions per scale/construct.
A number of subscales (12/29) had internal reliabilities less than 0.7 which is generally considered low . Low scale reliability attenuates relationships with other variables . Most of these subscales included only 3 or 4 items. Since Cronbach’s alpha is sensitive to the number of items, for subscales with few items an average inter-item correlation in the range of 0.15 to 0.50 is considered an indicator of an acceptable level of internal consistency . Of the 12 subscales with 4 items or less, the average inter-item correlation was in the acceptable range for 9 of them, and for 2 it exceeded the range. This suggests that a true dimension was detected, but additional work is needed to generate new items to expand the subscale, test dimensionality, and re-assess the psychometrics of the new subscales and scales. Since norms have a long history as a part of the Theory of Planned Behavior , the Descriptive Norms subscale should be retained, but further developed to enhance its reliability.
Factorial validity (CFA) could not be established for four scales even though internal consistency reliability was acceptable for all but the Autonomy scale. The CFA for the Autonomy items could not achieve positive definite status. Several direct estimation methods (weighted least squares, mean-adjusted weighted least squares, and variance-adjusted weighted least squares) were tried, but to no avail. The low Cronbach’s alpha (0.31), the consistently low corrected item total correlations (0.15, 0.19, 0.25), and the low average inter-item correlation (0.17) suggested that autonomy is a complex construct and the items we included tapped multiple dimensions, which were not highly interrelated. Since Autonomy included only three items, more development of this scale and possible subscales is warranted.
We had no theoretical foundation for theoretically deducing which MGDVPP subscales would correlate with EVPP or IVPP. Despite some low reliabilities, 25 of 29 subscales correlated with one or the other of the composite EVPP or IVPP. Parent Values (a Relatedness subscale) significantly inversely correlated with EVPP and IVPP. Similarly, most Intentions subscales inversely correlated with EVPP and IVPP. It is likely that respondents did not know which VPP were effective or ineffective, which may have influenced these relationships. It is possible that respondents thought the Intention items should only be answered positively if they were not already doing it, but intending to do it in the next month. Future research with these scales will need to address these issues.
Thirty intercorrelations among subscales were tested; 9 were not significant; 5 were significant at p < 0.05, 1 at p < 0.01, and 15 at p < 0.001. The subscales tended to be intercorrelated in expected directions within scales. The highest correlation was 0.51 between the Perceived Barriers of Respondent Doesn’t Like Vegetables and Cost of Vegetables. Intersubscale correlations will need to be validated in future studies. While not high enough in this sample to constitute multicollinearity, it is possible that future studies will identify different dimensions combining subscales in the current sample.
The strengths of this research include use of a broad innovative theoretical model to predict behaviors (here vegetable parenting practices); qualitative methods to generate items from the target group; and narrowly focused on parents of a developmentally similar age group. A number of limitations exist. The sample was limited in size and diversity. Further research is needed with larger samples to permit more sophisticated analyses and with more diverse samples to test generalizability across gender, ethnicity, and socioeconomic status. The internet survey method did not allow collecting and matching data from a second time point, thereby precluding an assessment of test-retest reliability; and the same sample was employed for exploratory and confirmatory factor analyses. Predictive validity was tested with cross-sectional data; these need to be verified with longitudinal data. Additional research with larger samples should use Item Response Modeling (IRM) to better understand the sequencing of items, difficulties across the latent constructs, the matching of item distributions with participant distributions, and to assess differences in item responses (i.e. differential item functioning) by demographic characteristics [30, 31]. IRM would also permit efficient reduction of items in the subscales with larger numbers by identifying items redundant at location along the latent variable . Twenty-nine subscales were identified. While model testing research should include all 29 to verify (or disconfirm) the current findings, investigators with a more practical or applied intent may wish to select subscales most clearly related to their efforts. The four subscales that did not correlate with EVPP or IVPP, and the ones that correlated in unexpected directions, need further testing in other samples.
Although further development is warranted, these scales and subscales can be used in studies attempting to understand why parents might use effective and ineffective vegetable parenting practices.
Baylor College of Medicine
Comparative fit index
Children’s Nutrition Research Center
Effective vegetable parenting practices
Item response modeling
Ineffective vegetable parenting practices
Model of goal directed behavior
Model of goal directed vegetable parenting practices
Root mean square error of approximation
Standardized root mean-square residual
Tucker lewis index
Vegetable parenting practices.
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This research was funded by a grant from the National Institute of Child Health and Human Development (HD058175) and institutional support from the US Department of Agriculture, Agricultural Research Service (Cooperative Agreement no. 58-6250-6001). This manuscript does not represent the views of the USDA. The authors have no conflict of interest.
The authors declare that they have no competing interests.
TB was principal investigator for the overall project and wrote the first draft of the manuscript. AB was the project manager. T-AC was the data manager and statistician. DT, TO, and SH contributed conceptually to the measures. CD participated in review of analyses. JB was the project coordinator. All authors reviewed, critiqued and approved this manuscript.
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