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Table 2 Psychometric analysis conducted

From: Translation and psychometric evaluation of the German version of the IcanSDM measure – a cross-sectional study among healthcare professionals

Psychometric measure

Criteria

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity

These tests ensure that correlations between variables can be accounted for by a smaller set of factors [60]. KMO value should be higher than .05 and Bartlett’s test value should be less than .05 to fulfil the criteria for calculating a factor analysis [57, 60].

Normed chi-squared statistic (Chi2/df)

Chi2/df is an indicator for model fit, dependent on sample size and should be as small as possible. A ratio between 2 and 3 indicate a good data fit [67].

Comparative fit indexes (CFI)

CFIs is an indicator for model fit. It ranges from 0 to 1 and higher values indicate better fit. Values above .95 indicate a good model fit [65, 68].

Tucker-Lewis Index (TLI)

TLI is an indicator for model fit. It corrects for complexity of the model and is sensitive to small sample sizes. Values above .95 indicate good fit [66].

Root mean square error of approximation (RMSEA)

RMSEA is an absolute index which describes closeness to fit. Values below .05 indicate a good fit, values between .05 and .08 indicate an adequate fit, values between .08 and 1 indicate a moderate fit and values above 1 are unacceptable [69].

Akaike Information Criterion (AIC)

AIC is a parsimony model fit index. It can be used to compare fit of competing models with smaller values indicating better fit [65, 67].

Parsimonious Normed Fit Index (PNFI)

PNFI is a parsimony model fit index. It ranges between 0 and 1 and higher values indicate a more parsimonious fit [67]. No threshold levels are recommended and it has to be analysed in combination with other goodness of fit indices [65].

Analysis of frequencies for item response distributions

Floor and ceiling effects were assumed if more than 15% of participants choose the lowest or highest possible score [56]. For analogue scales, no cut-off values exist. According to Bortz & Döring, items with difficulties below 20% show a floor effect, items with difficulties above 80% show a ceiling effect. Additionally, a skewness below − 2 indicates a floor effect, a skewness above + 2 indicates a ceiling effect [59].

Corrected item-total correlations

If items correlate with the total score of above .30, they measure the same underlying concept. Items with lower correlations should be removed because they do not add exploratory power to the measure [60, 61].

Item difficulties

Item difficulties are calculated by dividing item means by the maximal value of the answer range (0–10) and multiplying it with 100. Item difficulty should be near to 50%, and items should not differ much in their difficulty level [57].

Inter-item correlations

Inter-item correlations ensure association between items. High inter-item correlations of above .80 indicate that items ask the same questions and might be redundant [60, 61].

Cronbach’s α

Cronbach’s α is a measure for reliability and internal consistency. A value of at least .70 is acceptable and higher coefficients indicate a more stable measure [57, 60, 70].

  1. Note: This table has been adapted from Lindig et al. [53]