Data analysis and cleaning .1 Child anthropometric data

Child height, weight and BMIz were calculated according to the 2000 CDC growth charts.

[381] Use of CDC growth charts for children from 2 years of age is consistent with the recommendations of the National Health and Medical Research Council (NHMRC). [17]

As height and weight data in these works were by parental report it was considered that they may not be associated with the same degree of accuracy that would be found in a clinical research setting. It was therefore important to screen these data for what is often called biologically implausible values. There is no standard approach to assessing biologically implausible values and a recent review found 11 different approaches in the literature since the year 2000. [382] Lawman and colleagues also reported that of the large epidemiological studies found by their literature search, approximately 41% did not address biologically implausible value identification at all. No recommendations were made in relation to any one approach over any other method in this paper. [382]

135 The approach taken in this thesis to identify biologically implausible values involved creation of a ‘modified Z score.’ This method was suggested by the CDC since using the CDC BMI reference data to calculate BMIz that are then screened for biologically implausible values will lead to errors since, when constructing the CDC growth charts, Cole’s LMS method was not followed exactly, as described below. [383]

Most growth references are now calculated using Cole’s LMS method. [384] This method summarises the distribution of anthropometric data at any given age and for each gender in just three variables; L, M and S. The LMS parameters are the power in a Box-Cox transformation that normalises the distribution (L), the median (M) and the generalised coefficient of variation (S). Using these parameters any desired centile and z-scores can be calculated. In the LMS method, the LMS parameters are estimated from the data, then smoothed and then used to create centiles. When constructing the CDC growth charts this approach was not followed exactly and instead the required centiles were estimated from the data, then smoothed and used to calculate the LMS parameters; a subtle but important difference. The outcome of this difference in approach means that the LMS values produced in this way are not good at determining z-scores of extreme data points, and therefore for screening for biologically implausible values.

Despite attempting to alleviate this problem, it should be noted, however, that the calculation of a “modified z-score” is not universally supported (Cole TJ, personal communication 2017), as it uses arbitrary z-scores in its calculation and introduces asymmetry into the screening process with less variability below the median than above.

Bearing this in mind, it was chosen, therefore, to screen for biologically implausible values using a two-step approach. Firstly, all weight or height z-scores greater or less than 3 were discarded. Secondly, after regressing weight z-score on height z-score any residuals greater or less than 2.5 were also discarded.

3.2.5.2 Parent BMI

Parent’s self-reported weight and height were used to calculated BMI scores and BMI categories in accordance with the World Health Organizations classifications (Underweight

<18.50kg/m2; Normal weight 18.50 – 24.99kg/m2; Overweight ≥25.00kg/m2; Obese

≥30.00kg/m2). [385] Parent’s BMI were initially visually screened for very high or very low values, as considered to be biologically implausible.

136 3.2.5.3 Children’s eating behaviour questionnaire (CEBQ) sub-scales

Mean scores for each of the CEBQ sub-scales were created. Each sub-scale showed acceptable internal reliability, except slowness in eating, which was slightly below that desired Cronbach α 0.7; enjoyment of food (4 items; Cronbach α 0.866); food responsiveness (5 items; Cronbach α 0.786); satiety responsiveness (5 items; Cronbach α 0.705); slowness in eating (4 items; Cronbach α 0.676); and food fussiness (6 items;

Cronbach α 0.923). [386, 387] CEBQ sub-scales were normally distributed (skewness and kurtosis between 1 and -1).

3.2.5.4 Depression, anxiety and stress scale (DASS-21)

Mean scores for the parent’s depression, anxiety and stress scales were created. Mean scores for each scale showed high internal reliability; stress [7 items; Cronbach α 0.837]

anxiety [7 item; Cronbach α 0.742] depression [7 items; Cronbach α 0.886]. Each DASS scale was examined for normality (skewness and kurtosis between 1 and -1). Depression and anxiety scales were deemed skewed so transformed accordingly, however, the stress scale was normally distributed.

3.2.5.5 Feeding practice and structure questionnaire (FPSQ-28)

Mean scores for each of the 8 FPSQ-28 sub-scales were created. Of the scales reported in this study, only 5 scales showed acceptable internal reliability, based on Cronbach α 0.7 (reward for behaviour [4 items; Cronbach α 0.821], reward for eating [4 items; Cronbach α 0.762], persuasive feeding [6 item; Cronbach α 0.802], covert restriction [4 items;

Cronbach α 0.808], structured meal setting [3 items; Cronbach α 0.865]. Overt restriction showed questionable internal reliability [4 items; Cronbach α 0.604], and removal of items did not improve reliability. Similarly, structured meal timing showed questionable internal reliability [3 items; Cronbach α 0.670], and removal of items did not improve reliability.

Family meal setting was also included as a single item as recommended by Jansen, et, al (2016). [244] These findings were, however, consistent with the Cronbach α reported in FPSQ-28 validation studies. [244] All scales were deemed to be normally distributed (skewness and kurtosis between 1 and -1).

3.2.5.6 TV and electronic device use

The three items reflecting TV and electronic device use during meals showed below acceptable internal reliability (Cronbach’s α 0.457) and therefore were retained as three categorical items.

137 3.2.5.7 Frequency of family meals

The three items measuring the frequency of family meals showed less than acceptable internal reliability (Cronbach’s α 0.527). A total frequency of family meals score was created (out of 21) and was deemed to be normally distributed (skewness and kurtosis between 1 and -1).

3.2.5.8 General nutrition knowledge

General nutrition knowledge data were used to create a total nutrition knowledge score and knowledge sub-scores. Internal reliability for total nutrition knowledge was less than acceptable (Kuder-Richardson-20, as a dichotomous alternative to Cronbach’s alpha measure of reliability, reached 0.347, 13 items). Each of the sub-scores also had low internal reliability (heart disease, Cronbach’s α 0.407, 5 items; high/low salt, Cronbach’s α 0.194, 5 items; knowledge of dietary guidelines, Cronbach’s α 0.271, 3 items). Scores for total nutrition knowledge, as considered a continuous 13-item scale, were skewed so transformed accordingly.

3.2.5.9 Nutrition-related belief

Although each of the nutrition related beliefs were intended to be used as an individual scale, they appeared to have internal reliability only slightly below acceptable (Cronbach’s α 0.573). This level of reliability is, however, not unexpected given that the scale included only four items and Cronbach’s alpha is understood to increase as the number of items on a scale increase. The combined total nutrition-related belief scales, compiling all four belief items was deemed to be normally distributed (skewness and kurtosis between 1 and -1).

In addition to a total nutrition belief scales, all individual beliefs were retained as ordinal variables.

3.2.5.10 Home resources

All other items (e.g. cooking skills, shopping skills, availability fruit and vegetables, cooking facilities, food storage facilities, responsibility for food purchasing, responsibility for meal preparation) were retained as categorical variables.

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