Lower values indicate a better fitting model. Adding a Satorra-Bentler correction matrix to correct the standard errors was not possible due to the large number of items.
Hence, comparisons of factor models were done within rather than between groups. The possibility of using EDI-3 cut off scores to determine a clinical diagnosis of eating disorder was evaluated by conducting an analysis of sensitivity and specificity.
Sensitivity indicates the proportion of true positives correctly identified a sick patient diagnosed as sick , while specificity indicates the proportion of true negatives correctly identified a healthy person not receiving a diagnosis. These proportions change as the EDI cut off score is moved up or down, and may be expressed as a ROC curve describing the diagnostic discriminatory ability across the whole range of EDI cut off scores.
At one point of the ROC curve, the sensitivity and the specificity are at a maximum. A change window of. AUC is reported for each subscale within each diagnostic group. A no-discriminatory test has an AUC of. A non-parametric method of constructing standard errors was used. As the EDE interview Fairburn and Cooper was used for the patient sample only, the estimates of diagnostic accuracy are inflated if left uncorrected.
This was solved by assigning a particular diagnosis of eating disorder at random to individuals in the control sample according to the prevalence rate for that particular disorder.
In this study we used the generally accepted prevalence rates from two-stage community studies of 0. Due to the high ratio of patients to controls in the present study, yielding strongly upwardly biased base rates, positive and negative predictive values are not reported. Instead, likelihood ratios LR are reported indicating the chance ratio of a positive test result in diseased individuals true positives to that of a positive test result in non-diseased false positives.
Table 2 shows that the mean scores of the EDI-3 subscales were different between the three diagnostic groups. Differences between all three groups are found only on the B subscale.
However, each diagnostic group differentiates from the others as follows. Overall, Danish control norms were significantly lower than international norms see Fig.
Norms of Danish controls vs. In the manual Garner , means for the subscales DT, B, and BD are only reported for sub-groups of patients and not the total patient population, therefore not included in Fig. Norms of Danish patients vs.
Moreover, on the eating disorder specific subscales i. The internal consistency of the item scores was satisfactory for patients as well as controls see Table 3 , except for the AS subscale for controls.
The confirmatory factor models were examined separately for the patient and the non clinical control sample as there is reason to expect that healthy and mentally ill individuals may attach somewhat different meanings to the same set of questions. Items were specified to load on twelve primary latent factors, according to the manual Garner However, different ways of specifying the relationships between the factors were tested out. The base model M1 specified 12 independent or uncorrelated factors, which fitted the data least well as expected see Table 4 for model comparisons.
The second model specified a single general factor M2 explaining the covariance among the 12 primary latent factors, which improved model fit according to all fit indexes. A tentative alternative model M4 specifying one general risk factor, and two general psychological disturbance factors one latent factor for emotional dysregulation, perfectionism, ascetism and interoceptive deficits , and another latent factor for the remaining disturbance factors slightly improved model fit according to AIC.
The best fitting model was, however, a 12 factor model M5 allowing all factors to correlate freely. To test the trustworthiness of the preceding model specifications for the covariance data, a random model M6 specifying the 90 EDI items to load on the 12 respective factors in an unsystematic fashion produced a poorer absolute and relative fit, as expected.
All factors were allowed to correlate, as in model M5. Summarized, the correlated 12 factor model received best support. However, a more parsimonious second-order model, which has a much simpler factor structure than the correlation model, is to prefer if a worsening of fit is not substantial, which it was not.
Two observations speak for favouring model M3. Firstly, the improvement in fit was larger when moving from model M2 to M3, rather than from model M3 to M4, especially in the control sample. Secondly, an examination of the factor correlations among the three general factors indicated an extremely high correlation between the two psychological factors in model M4.
Taken together eating problems should be summarized in two main scores to differentiate eating problems: one representing a risk factor and another representing a psychological disturbance score, according to the author i. Garner The factor loadings from second order factor analysis of model M3 are displayed in Table 5.
The relatively large number of chi-squares compared to degrees of freedom, is not reassuring either. Following an inspection of the modification indices, the mediocre fit appears related to several items showing hugely correlated residuals as well as significant factor side-loadings. Hence some of the EDI items do not have adequate psychometric properties. The factor loadings for the second order two factor model M3 in Table 4 with risk and psychological disturbance as general factors accounting for the 12 primary factors.
The two general factors were allowed to correlate. The figures show that the interoceptive deficits subscale is the best predictor across all diagnostic groups, followed by low self-esteem and personal alienation. The bulimia subscale comes sixth overall, but is an excellent predictor of a diagnosis of BN with high sensitivity and specificity estimates.
Table 6 provides an overview of sensitivity, specificity, likelihood ratios and diagnostic accuracy of the three best and the worst predictors within each diagnostic group. Generally, increasing the cut off increases the specificity and reduces misclassification, but at the cost of increasing the number of false negatives patients not detected , which represents a more serious error.
Most ROC curves across the diagnostic groups are quite parallel over all levels of cut off scores, but with one notable exception. Yields six composites: one that is eating-disorder specific i. The item set from the original EDI, as well as items from the revision EDI-2 , has been carefully preserved so that clinicians and researchers can compare data collected previously with data from the EDI Includes clinical norms for adolescents in addition to U.
It also provides multisite nonclinical comparison samples. An independent and structured self-report form, the EDI-3 SC is easy to complete and provides data regarding frequency of symptoms i. Available in Spanish The EDI-3 materials, including the manual, have been translated into European Spanish and designed especially for Spanish-speaking clinicians and their clients. EDI-3 Bibliography. EDI-3 Fact Sheet. EDI-3 Introductory Kit. Manuals, books, and equipment.
EDI-3 e-Manual. The Early Development Instrument provides a holistic snapshot on how young children within a community are developing in five key domains:. Erikson provides coaching and technical assistance to use the accumulated data to better inform: strategic planning, proposal writing, and community visioning and planning.
Schools can use this data to identify and learn from strengths in the community. It can help initiate targeted conversations on how to set children up for success before they start kindergarten, and where there are opportunities to focus additional support.
The EDI data can also inform planning for future kindergarten students and address the needs of the current cohort of kindergarten children as they progress through school. Results help identify the strengths and challenges of the children in their schools, leading to targeted interventions for those children. EDI data are used to predict academic outcomes up to fourth grade.
The data instigates community conversations that inform advocacy action planning. EDI data also encourages equitable allocation of resources to address the needs of children and families. EDI data helps government plan equitable investments, inform policy, and evaluate program success over time. Maps built from the data can help focus investments and identify specific community needs.
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