Skip to main content

Table 5 Item-fit indexes applying the one-dimensional approach to the various short versions

From: Establishing the HLS-Q12 short version of the European Health Literacy Survey Questionnaire: latent trait analyses applying Rasch modelling and confirmatory factor analysis

  HLS-Q12b HL-SF12 [26] HLS-EU-Q16 [11]
Item Loc. Chi prob. Infit t Com. Loc Chi prob. DIF ordered Infit t Com. Loc Chi prob. DIF Ordered Infit t Com.
1                    
2 −0.097 0.997 1.01 0.2 0.269 − 0.041 0.699    0.99 −0.1 0.278 0.133 0.606    1.03 0.5 0.276
3                    
4              −0.236 0.703    1.00 0.0 0.338
5              −0.453 0.313 Edu, h > l   0.97 −0.7 0.387
6       0.137 0.348    1.05 1.0 0.183        
7 0.053 0.705 1.03 0.7 0.239               
8              −0.865 0.087   No 0.94 −1.1 0.376
9                    
10 0.892 0.761 0.97 −0.6 0.292 0.964 0.848    1.00 −0.1 0.266        
11              1.084 0.001* Age, y < o
Edu, h < l
  1.17 3.4a 0.139
12                    
13              −0.082 0.332 Gen m > f   0.97 −0.6 0.319
14 −0.625 0.275 1.00 0.0 0.260               
15       −0.842 0.025 Gen, m > f
Age, y > o
No 1.06 1.2 0.207        
16              −1.305 0.044    0.91 −1.6 0.443
17                    
18 0.420 0.357 0.98 −0.5 0.363 0.490 0.006    0.93 −1.6 0.416 0.687 0.633    1.00 0.1 0.299
19                    
20                    
21              −0.793 0.002* Edu, h > l No 0.95 −0.9 0.347
22                    
23 −1.158 0.065 0.98 −0.3 0.281 −0.997 0.431    0.96 −0.7 0.291 −0.908 0.385    0.96 −0.7 0.319
24                    
25                    
26       0.526 0.129 Age, y < o   1.07 1.5 0.187        
27                    
28 1.068 0.265 1.04 0.9 0.176         1.360 0.039    1.07 1.4 0.232
29                    
30 0.422 0.284 1.03 0.6 0.230 0.502 0.845 Edu, h < l   1.04 0.9 0.217        
31              1.167 0.166    0.98 −0.4 0.349
32 −1.135 0.618 0.98 −0.5 0.337               
33       −0.298 0.652    0.97 −0.5 0.321 −0.110 0.820    1.03 0.6 0.240
34                    
35                    
36                    
37              0.242 0.070 Age, y > o   1.06 1.2 0.220
38 0.702 0.484 1.06 1.3 0.196               
39       0.372 0.831    1.00 0.0 0.293 0.531 0.305    0.96 −0.7 0.366
40                    
41                    
42                    
43 −0.728 0.375 0.95 −0.9 0.426 −0.621 0.236    0.94 −1.1 0.385 −0.452 0.450    1.00 0.0 0.322
44 0.186 0.105 1.04 0.8 0.248               
45       −0.194 0.072    1.07 1.4 0.202        
46                    
47                    
  1. Note. This table shows the item-fit indexes when applying the one-dimensional approach to the various short versions. The HLS-EU-Q16 could not be deemed sufficiently unidimensional. The results are displayed for comparison purposes
  2. Chi square probability (Chi prob), differential item functioning (DIF), item location estimate (Loc) and unordered response categories (ordered) were obtained from Rasch modelling using RUMM2030 software. ConQuest 4 software was used for the Infit measures
  3. aA t–value > 1.96 and an infit-value > 1 indicate a poor fit with the Rasch model due to there being more variation in the data than expected given the model (item is under-discriminating)
  4. *Item with a significant misfit (p-value below Bonferroni-adjusted 5%)
  5. For items displaying uniform DIF, the relevant dichotomized person factor levels are indicated. For example, “Age, y > o” refers to the uniform DIF for the person factor age in favour of the y = younger respondents (47 years or younger) factor level as compared to o = older respondents (48 years or older) factor level. The “Highest completed education level” (Edu) factor has the l = low (primary and secondary school) and h = high (university or university college) levels, and the factor “Gender” (Gen) has the f = females and m = males levels
  6. Ordered: “No” refers to items with unordered response categories
  7. None of the items on the HLS-Q12 displayed DIF or unordered response categories
  8. Com: communalities obtained from CFA using the LISREL software
  9. bHLS-Q12 developed through the present study