Psychometric Functioning of a Spanish Translation of the Hoarding Rating Scale

Study Overview
Measure Reliability
Convergent and Divergent Validity
DIF Analyses

Study Overview

The core features of hoarding disorder (HD) include extreme difficulties with discarding everyday possessions and significant levels of clutter in living areas. HD is associated with high levels of distress and impairment, and it is often accompanied by excessive acquiring. Although hoarding is believed to be a worldwide phenomenon, cross-cultural research remains in nascent stages, hampered in part by a lack of validated measures in languages besides English. In the present study, we aimed to address this gap in the literature by validating a Spanish translation of the Hoarding Rating Scale (HRS), a widely used measure that maps onto core diagnostic criteria for HD.

Our sample (N=826) included participants responding in English (n = 555; 45.9% female; M age = 35.2; 7.3% Latinx) or Spanish (n = 271; 45.9% female; M age = 26.0; 69.6% Latinx) to a battery of questionnaires via Amazon’s Mechanical Turk. The Spanish HRS was translated using back-translation and editorial board methods. We first used an item-response theory (IRT) approach to test differential item functioning (DIF) of the English and Spanish versions of the HRS. Next, we examined convergent and divergent validity by comparing the strength of associations of each language version with other known, well-validated measures of hoarding and associated features.

R packages used: itemanalysis, lavaan, semTools, lordif

Measure Reliability

HRS: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), HRS1, HRS2, HRS3, 
    HRS4, HRS5))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
      0.92      0.92     0.9      0.69  11 0.0058  1.1 1.3     0.67

 lower alpha upper     95% confidence boundaries
0.9 0.92 0.93 

 Reliability if an item is dropped:
     raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
HRS1      0.89      0.90    0.88      0.69  8.7   0.0074 0.0048  0.67
HRS2      0.91      0.92    0.89      0.73 10.8   0.0060 0.0035  0.72
HRS3      0.90      0.90    0.88      0.70  9.5   0.0070 0.0073  0.70
HRS4      0.89      0.89    0.86      0.67  8.0   0.0080 0.0038  0.64
HRS5      0.89      0.89    0.86      0.67  8.0   0.0079 0.0023  0.65

 Item statistics 
       n raw.r std.r r.cor r.drop mean  sd
HRS1 548  0.87  0.87  0.84   0.79  1.0 1.5
HRS2 548  0.82  0.81  0.73   0.71  1.4 1.7
HRS3 548  0.85  0.85  0.79   0.76  1.2 1.6
HRS4 548  0.90  0.90  0.88   0.83  1.0 1.5
HRS5 548  0.89  0.90  0.88   0.84  0.8 1.4

Non missing response frequency for each item
        0    1    2    3    4    5    6    7    8 miss
HRS1 0.55 0.17 0.13 0.04 0.06 0.03 0.01 0.01 0.00 0.01
HRS2 0.43 0.19 0.17 0.06 0.09 0.03 0.01 0.01 0.01 0.01
HRS3 0.51 0.17 0.16 0.04 0.06 0.04 0.01 0.00 0.01 0.01
HRS4 0.56 0.18 0.12 0.05 0.05 0.02 0.01 0.00 0.01 0.01
HRS5 0.64 0.15 0.10 0.03 0.03 0.02 0.01 0.00 0.00 0.01

HRS: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), HRS1, HRS2, HRS3, 
    HRS4, HRS5))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean  sd median_r
      0.93      0.93    0.92      0.74  14 0.0064  1.9 1.9     0.74

 lower alpha upper     95% confidence boundaries
0.92 0.93 0.95 

 Reliability if an item is dropped:
     raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
HRS1      0.92      0.92    0.90      0.75  12   0.0078 0.00093  0.74
HRS2      0.92      0.92    0.90      0.73  11   0.0083 0.00175  0.74
HRS3      0.92      0.92    0.90      0.74  11   0.0079 0.00162  0.74
HRS4      0.92      0.92    0.90      0.74  12   0.0079 0.00191  0.74
HRS5      0.91      0.91    0.89      0.72  10   0.0090 0.00112  0.72

 Item statistics 
       n raw.r std.r r.cor r.drop mean  sd
HRS1 188  0.87  0.87  0.83   0.80  1.6 2.1
HRS2 188  0.89  0.89  0.86   0.83  2.0 2.2
HRS3 188  0.88  0.88  0.84   0.81  2.0 2.1
HRS4 188  0.88  0.88  0.84   0.81  2.1 2.1
HRS5 188  0.92  0.92  0.89   0.86  1.8 2.2

Non missing response frequency for each item
        0    1    2    3    4    5    6    7    8 miss
HRS1 0.53 0.08 0.10 0.06 0.12 0.05 0.04 0.02 0.01 0.31
HRS2 0.40 0.10 0.12 0.11 0.14 0.06 0.05 0.01 0.02 0.31
HRS3 0.37 0.12 0.13 0.08 0.16 0.06 0.04 0.02 0.01 0.31
HRS4 0.35 0.12 0.13 0.11 0.15 0.08 0.04 0.01 0.01 0.31
HRS5 0.47 0.10 0.07 0.08 0.13 0.05 0.06 0.01 0.01 0.31

DASS Anxiety: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), DASS2, DASS4, 
    DASS7, DASS9, DASS15, DASS19, DASS20))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.87      0.87    0.87       0.5 6.9 0.0082 0.44 0.56     0.49

 lower alpha upper     95% confidence boundaries
0.86 0.87 0.89 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
DASS2       0.87      0.87    0.86      0.53 6.7   0.0084 0.0064  0.50
DASS4       0.86      0.86    0.85      0.50 6.1   0.0093 0.0106  0.49
DASS7       0.86      0.86    0.85      0.50 6.0   0.0095 0.0104  0.48
DASS9       0.85      0.85    0.83      0.48 5.6   0.0101 0.0051  0.49
DASS15      0.84      0.85    0.83      0.48 5.5   0.0102 0.0047  0.48
DASS19      0.86      0.86    0.85      0.50 6.1   0.0093 0.0108  0.49
DASS20      0.85      0.85    0.84      0.49 5.8   0.0098 0.0049  0.48

 Item statistics 
         n raw.r std.r r.cor r.drop mean   sd
DASS2  546  0.67  0.67  0.58   0.54 0.56 0.78
DASS4  546  0.73  0.74  0.67   0.63 0.36 0.68
DASS7  546  0.74  0.75  0.68   0.64 0.35 0.72
DASS9  546  0.81  0.80  0.77   0.71 0.53 0.82
DASS15 546  0.81  0.81  0.79   0.73 0.42 0.75
DASS19 546  0.74  0.74  0.67   0.63 0.51 0.73
DASS20 546  0.78  0.78  0.74   0.69 0.37 0.70

Non missing response frequency for each item
          0    1    2    3 miss
DASS2  0.59 0.29 0.10 0.03 0.02
DASS4  0.74 0.17 0.08 0.01 0.02
DASS7  0.76 0.15 0.06 0.03 0.02
DASS9  0.66 0.19 0.13 0.03 0.02
DASS15 0.71 0.18 0.08 0.03 0.02
DASS19 0.60 0.30 0.07 0.02 0.02
DASS20 0.74 0.17 0.07 0.02 0.02

DASS Anxiety: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), DASS2, DASS4, 
    DASS7, DASS9, DASS15, DASS19, DASS20))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
       0.9       0.9    0.91      0.57 9.4 0.0093 0.54 0.63     0.59

 lower alpha upper     95% confidence boundaries
0.88 0.9 0.92 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
DASS2       0.91      0.91    0.91      0.64 10.7   0.0082 0.006  0.65
DASS4       0.88      0.89    0.90      0.57  8.0   0.0111 0.022  0.59
DASS7       0.88      0.89    0.89      0.57  8.0   0.0111 0.020  0.59
DASS9       0.88      0.89    0.89      0.57  8.0   0.0111 0.021  0.61
DASS15      0.88      0.88    0.87      0.54  7.2   0.0119 0.014  0.58
DASS19      0.88      0.88    0.89      0.56  7.6   0.0115 0.015  0.59
DASS20      0.88      0.88    0.88      0.56  7.5   0.0116 0.016  0.58

 Item statistics 
        n raw.r std.r r.cor r.drop mean   sd
DASS2  93  0.63  0.61  0.52   0.48 0.70 0.87
DASS4  93  0.80  0.80  0.75   0.72 0.49 0.80
DASS7  93  0.80  0.80  0.77   0.72 0.42 0.74
DASS9  93  0.80  0.80  0.77   0.72 0.60 0.78
DASS15 93  0.87  0.88  0.87   0.82 0.46 0.73
DASS19 93  0.83  0.83  0.81   0.76 0.60 0.85
DASS20 93  0.84  0.84  0.82   0.78 0.52 0.79

Non missing response frequency for each item
          0    1    2    3 miss
DASS2  0.53 0.29 0.14 0.04 0.66
DASS4  0.67 0.20 0.10 0.03 0.66
DASS7  0.71 0.18 0.09 0.02 0.66
DASS9  0.57 0.27 0.15 0.01 0.66
DASS15 0.68 0.18 0.14 0.00 0.66
DASS19 0.61 0.19 0.17 0.02 0.66
DASS20 0.66 0.18 0.15 0.01 0.66

DASS Depression: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), DASS3, DASS5, 
    DASS10, DASS13, DASS16, DASS17, DASS21))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.94      0.94    0.94      0.69  16 0.0039  0.7 0.78     0.69

 lower alpha upper     95% confidence boundaries
0.93 0.94 0.95 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
DASS3       0.93      0.93    0.92      0.69  13   0.0046 0.0060  0.69
DASS5       0.94      0.94    0.93      0.73  16   0.0039 0.0016  0.74
DASS10      0.93      0.93    0.92      0.67  12   0.0049 0.0042  0.68
DASS13      0.93      0.93    0.92      0.69  13   0.0047 0.0068  0.69
DASS16      0.93      0.93    0.92      0.68  13   0.0048 0.0062  0.68
DASS17      0.93      0.93    0.93      0.69  13   0.0046 0.0058  0.70
DASS21      0.93      0.93    0.92      0.69  14   0.0045 0.0048  0.69

 Item statistics 
         n raw.r std.r r.cor r.drop mean   sd
DASS3  546  0.86  0.87  0.84   0.81 0.61 0.86
DASS5  546  0.77  0.77  0.71   0.69 0.89 0.91
DASS10 546  0.90  0.90  0.89   0.86 0.70 0.94
DASS13 546  0.87  0.87  0.84   0.82 0.85 0.98
DASS16 546  0.89  0.89  0.87   0.85 0.70 0.90
DASS17 546  0.86  0.85  0.83   0.80 0.60 0.92
DASS21 546  0.85  0.85  0.83   0.79 0.58 0.89

Non missing response frequency for each item
          0    1    2    3 miss
DASS3  0.60 0.24 0.12 0.05 0.02
DASS5  0.41 0.36 0.17 0.06 0.02
DASS10 0.58 0.22 0.14 0.06 0.02
DASS13 0.47 0.29 0.14 0.09 0.02
DASS16 0.55 0.25 0.15 0.05 0.02
DASS17 0.64 0.17 0.12 0.06 0.02
DASS21 0.64 0.19 0.12 0.05 0.02

DASS Depression: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), DASS3, DASS5, 
    DASS10, DASS13, DASS16, DASS17, DASS21))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.93      0.93    0.93      0.65  13 0.0067 0.54 0.67     0.63

 lower alpha upper     95% confidence boundaries
0.92 0.93 0.94 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
DASS3       0.92      0.92    0.92      0.67  12   0.0075 0.0086  0.63
DASS5       0.93      0.93    0.92      0.67  12   0.0070 0.0106  0.68
DASS10      0.91      0.91    0.91      0.63  10   0.0084 0.0081  0.62
DASS13      0.92      0.93    0.92      0.68  13   0.0072 0.0095  0.68
DASS16      0.92      0.92    0.92      0.66  12   0.0077 0.0103  0.63
DASS17      0.91      0.91    0.91      0.63  10   0.0086 0.0064  0.62
DASS21      0.91      0.91    0.91      0.64  11   0.0082 0.0060  0.62

 Item statistics 
        n raw.r std.r r.cor r.drop mean   sd
DASS3  93  0.79  0.80  0.77   0.73 0.48 0.70
DASS5  93  0.80  0.79  0.74   0.71 0.81 0.90
DASS10 93  0.89  0.89  0.88   0.84 0.46 0.82
DASS13 93  0.78  0.78  0.72   0.70 0.59 0.81
DASS16 93  0.83  0.83  0.79   0.76 0.48 0.82
DASS17 93  0.90  0.90  0.90   0.86 0.48 0.82
DASS21 93  0.88  0.88  0.88   0.83 0.44 0.77

Non missing response frequency for each item
          0    1    2    3 miss
DASS3  0.62 0.28 0.09 0.01 0.66
DASS5  0.48 0.26 0.23 0.03 0.66
DASS10 0.70 0.18 0.08 0.04 0.66
DASS13 0.59 0.25 0.14 0.02 0.66
DASS16 0.69 0.17 0.11 0.03 0.66
DASS17 0.69 0.17 0.11 0.03 0.66
DASS21 0.71 0.16 0.11 0.02 0.66

DASS Stress: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), DASS1, DASS6, 
    DASS8, DASS11, DASS12, DASS14, DASS18))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.91      0.91     0.9      0.58 9.8 0.006 0.75 0.68     0.57

 lower alpha upper     95% confidence boundaries
0.9 0.91 0.92 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
DASS1       0.89      0.89    0.88      0.58 8.4   0.0069 0.0030  0.57
DASS6       0.89      0.89    0.88      0.58 8.2   0.0071 0.0052  0.56
DASS8       0.90      0.90    0.89      0.59 8.6   0.0068 0.0053  0.56
DASS11      0.89      0.89    0.88      0.57 7.9   0.0073 0.0044  0.56
DASS12      0.89      0.89    0.87      0.57 7.9   0.0074 0.0034  0.56
DASS14      0.90      0.90    0.89      0.61 9.3   0.0064 0.0035  0.60
DASS18      0.89      0.89    0.88      0.59 8.5   0.0069 0.0050  0.58

 Item statistics 
         n raw.r std.r r.cor r.drop mean   sd
DASS1  546  0.80  0.80  0.77   0.72 0.85 0.87
DASS6  546  0.82  0.82  0.78   0.74 0.71 0.85
DASS8  546  0.78  0.78  0.73   0.70 0.60 0.82
DASS11 546  0.84  0.84  0.81   0.77 0.83 0.85
DASS12 546  0.84  0.84  0.82   0.77 0.89 0.90
DASS14 546  0.73  0.74  0.67   0.64 0.68 0.82
DASS18 546  0.79  0.80  0.75   0.71 0.67 0.81

Non missing response frequency for each item
          0    1    2    3 miss
DASS1  0.41 0.38 0.16 0.05 0.02
DASS6  0.50 0.33 0.13 0.04 0.02
DASS8  0.58 0.27 0.12 0.03 0.02
DASS11 0.42 0.37 0.17 0.04 0.02
DASS12 0.40 0.37 0.17 0.06 0.02
DASS14 0.52 0.31 0.14 0.03 0.02
DASS18 0.51 0.34 0.12 0.03 0.02

DASS Stress: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), DASS1, DASS6, 
    DASS8, DASS11, DASS12, DASS14, DASS18))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
       0.9       0.9     0.9      0.56   9 0.0096 0.68 0.66     0.55

 lower alpha upper     95% confidence boundaries
0.88 0.9 0.92 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
DASS1       0.90      0.90    0.89      0.59 8.7   0.0098 0.0064  0.60
DASS6       0.89      0.89    0.88      0.58 8.1   0.0107 0.0065  0.55
DASS8       0.87      0.88    0.88      0.55 7.2   0.0119 0.0109  0.52
DASS11      0.88      0.88    0.88      0.55 7.3   0.0116 0.0092  0.55
DASS12      0.89      0.89    0.89      0.58 8.2   0.0106 0.0115  0.60
DASS14      0.88      0.88    0.88      0.55 7.3   0.0117 0.0105  0.54
DASS18      0.87      0.88    0.87      0.54 7.2   0.0118 0.0075  0.54

 Item statistics 
        n raw.r std.r r.cor r.drop mean   sd
DASS1  93  0.72  0.71  0.64   0.60 0.88 0.92
DASS6  93  0.75  0.75  0.71   0.66 0.66 0.79
DASS8  93  0.84  0.83  0.80   0.76 0.70 0.89
DASS11 93  0.81  0.82  0.79   0.75 0.55 0.73
DASS12 93  0.76  0.75  0.69   0.65 0.71 0.87
DASS14 93  0.82  0.83  0.80   0.76 0.62 0.76
DASS18 93  0.83  0.84  0.82   0.76 0.62 0.88

Non missing response frequency for each item
          0    1    2    3 miss
DASS1  0.43 0.31 0.20 0.05 0.66
DASS6  0.53 0.30 0.16 0.01 0.66
DASS8  0.54 0.28 0.13 0.05 0.66
DASS11 0.59 0.27 0.14 0.00 0.66
DASS12 0.55 0.20 0.24 0.01 0.66
DASS14 0.54 0.31 0.14 0.01 0.66
DASS18 0.59 0.25 0.11 0.05 0.66

SIR Clutter: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), SIR1, SIR3, SIR5, 
    SIR8, SIR10, SIR12, SIR15, SIR20, SIR22))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.93      0.93    0.94      0.61  14 0.0043 0.49 0.58     0.62

 lower alpha upper     95% confidence boundaries
0.92 0.93 0.94 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
SIR1       0.93      0.93    0.94      0.64  14   0.0045 0.0069  0.63
SIR3       0.92      0.93    0.93      0.61  13   0.0048 0.0107  0.61
SIR5       0.92      0.92    0.93      0.60  12   0.0049 0.0069  0.62
SIR8       0.92      0.92    0.93      0.60  12   0.0052 0.0093  0.60
SIR10      0.92      0.92    0.93      0.60  12   0.0050 0.0066  0.60
SIR12      0.92      0.93    0.94      0.61  13   0.0048 0.0112  0.62
SIR15      0.92      0.92    0.93      0.59  12   0.0052 0.0080  0.60
SIR20      0.93      0.93    0.94      0.62  13   0.0046 0.0106  0.62
SIR22      0.93      0.93    0.94      0.62  13   0.0047 0.0112  0.63

 Item statistics 
       n raw.r std.r r.cor r.drop mean   sd
SIR1  96  0.70  0.71  0.66   0.63 0.90 0.66
SIR3  96  0.80  0.81  0.79   0.75 0.40 0.61
SIR5  96  0.83  0.83  0.83   0.78 0.33 0.66
SIR8  96  0.86  0.86  0.85   0.82 0.56 0.75
SIR10 96  0.84  0.84  0.83   0.79 0.35 0.68
SIR12 96  0.80  0.80  0.76   0.74 0.46 0.69
SIR15 96  0.87  0.87  0.86   0.83 0.43 0.75
SIR20 96  0.79  0.77  0.74   0.71 0.53 0.91
SIR22 96  0.79  0.78  0.74   0.72 0.43 0.78

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR1  0.27 0.56 0.17 0.00 0.00 0.83
SIR3  0.67 0.27 0.06 0.00 0.00 0.83
SIR5  0.74 0.21 0.04 0.00 0.01 0.83
SIR8  0.57 0.31 0.09 0.02 0.00 0.83
SIR10 0.73 0.21 0.05 0.00 0.01 0.83
SIR12 0.65 0.26 0.08 0.01 0.00 0.83
SIR15 0.71 0.18 0.09 0.02 0.00 0.83
SIR20 0.68 0.18 0.09 0.04 0.01 0.83
SIR22 0.72 0.16 0.11 0.00 0.01 0.83

SIR Clutter: Spanish

Some items ( SIR10 ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), SIR1, SIR3, SIR5, 
    SIR8, SIR10, SIR12, SIR15, SIR20, SIR22))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.86      0.89    0.92      0.47 7.9 0.012 0.99 0.74     0.62

 lower alpha upper     95% confidence boundaries
0.84 0.86 0.89 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
SIR1       0.84      0.87    0.90      0.46  6.8   0.0147 0.1206  0.63
SIR3       0.83      0.86    0.89      0.44  6.3   0.0155 0.1139  0.60
SIR5       0.84      0.87    0.90      0.45  6.5   0.0151 0.1200  0.63
SIR8       0.83      0.86    0.89      0.44  6.3   0.0156 0.1131  0.60
SIR10      0.93      0.93    0.94      0.64 13.9   0.0062 0.0052  0.64
SIR12      0.83      0.87    0.90      0.45  6.5   0.0154 0.1224  0.62
SIR15      0.83      0.86    0.90      0.44  6.4   0.0158 0.1167  0.62
SIR20      0.83      0.87    0.90      0.45  6.4   0.0154 0.1167  0.62
SIR22      0.83      0.86    0.89      0.44  6.2   0.0157 0.1103  0.59

 Item statistics 
       n raw.r   std.r r.cor r.drop mean   sd
SIR1  91 0.743 0.75784  0.72   0.67 0.97 0.97
SIR3  91 0.824 0.83856  0.83   0.77 0.76 0.96
SIR5  91 0.786 0.79606  0.78   0.72 0.90 1.00
SIR8  91 0.824 0.83353  0.82   0.77 0.95 1.00
SIR10 91 0.063 0.00022 -0.15  -0.15 2.10 1.43
SIR12 91 0.807 0.80870  0.78   0.74 0.71 1.07
SIR15 91 0.823 0.82482  0.81   0.75 1.02 1.14
SIR20 91 0.804 0.81456  0.80   0.74 0.68 1.01
SIR22 91 0.837 0.85022  0.84   0.78 0.80 1.01

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR1  0.38 0.35 0.19 0.07 0.01 0.66
SIR3  0.53 0.25 0.16 0.04 0.01 0.66
SIR5  0.45 0.27 0.22 0.03 0.02 0.66
SIR8  0.44 0.26 0.21 0.09 0.00 0.66
SIR10 0.18 0.20 0.22 0.16 0.24 0.66
SIR12 0.63 0.13 0.16 0.05 0.02 0.66
SIR15 0.43 0.29 0.16 0.08 0.04 0.66
SIR20 0.63 0.15 0.13 0.09 0.00 0.66
SIR22 0.55 0.18 0.20 0.08 0.00 0.66

SIR Discarding: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), SIR4r, SIR6, SIR7, 
    SIR13, SIR17, SIR19, SIR23))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.89      0.91    0.91      0.58 9.6 0.0076 0.83 0.74     0.64

 lower alpha upper     95% confidence boundaries
0.87 0.89 0.9 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
SIR4r      0.92      0.92    0.92      0.67 12.2   0.0051 0.0081  0.67
SIR6       0.86      0.88    0.88      0.54  7.1   0.0098 0.0251  0.57
SIR7       0.86      0.88    0.89      0.56  7.5   0.0096 0.0297  0.57
SIR13      0.87      0.89    0.89      0.57  7.9   0.0092 0.0309  0.57
SIR17      0.86      0.88    0.89      0.56  7.5   0.0097 0.0282  0.57
SIR19      0.86      0.89    0.89      0.56  7.7   0.0095 0.0293  0.57
SIR23      0.88      0.90    0.90      0.59  8.7   0.0088 0.0368  0.67

 Item statistics 
       n raw.r std.r r.cor r.drop mean   sd
SIR4r 96  0.61  0.55  0.43   0.41 1.31 1.31
SIR6  96  0.88  0.89  0.90   0.84 0.83 0.82
SIR7  96  0.85  0.86  0.85   0.79 0.66 0.88
SIR13 96  0.81  0.83  0.80   0.74 0.64 0.85
SIR17 96  0.85  0.86  0.85   0.78 0.80 0.94
SIR19 96  0.83  0.84  0.81   0.76 0.95 0.94
SIR23 96  0.75  0.76  0.70   0.67 0.66 0.87

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR4r 0.35 0.27 0.18 0.10 0.09 0.83
SIR6  0.41 0.38 0.20 0.02 0.00 0.83
SIR7  0.56 0.27 0.11 0.05 0.00 0.83
SIR13 0.57 0.25 0.15 0.03 0.00 0.83
SIR17 0.47 0.32 0.17 0.02 0.02 0.83
SIR19 0.41 0.29 0.26 0.03 0.01 0.83
SIR23 0.56 0.25 0.17 0.01 0.01 0.83

SIR Discarding: Spanish

Some items ( SIR4r SIR17 ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), SIR4r, SIR6, SIR7, 
    SIR13, SIR17, SIR19, SIR23))

  raw_alpha std.alpha G6(smc) average_r  S/N   ase mean   sd median_r
       0.4      0.49    0.75      0.12 0.94 0.047  1.4 0.52    0.097

 lower alpha upper     95% confidence boundaries
0.31 0.4 0.49 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
SIR4r     0.728      0.79    0.84     0.383 3.73    0.027  0.16  0.58
SIR6      0.209      0.29    0.65     0.064 0.41    0.066  0.33 -0.13
SIR7      0.048      0.14    0.55     0.027 0.16    0.079  0.29 -0.14
SIR13     0.143      0.25    0.65     0.053 0.34    0.071  0.34 -0.13
SIR17     0.606      0.62    0.83     0.212 1.62    0.024  0.42  0.58
SIR19     0.085      0.20    0.61     0.041 0.26    0.076  0.31 -0.13
SIR23     0.133      0.24    0.64     0.051 0.32    0.072  0.32 -0.13

 Item statistics 
       n raw.r  std.r r.cor r.drop mean   sd
SIR4r 91 -0.62 -0.652 -0.87  -0.76 3.16 1.06
SIR6  91  0.69  0.732  0.72   0.51 0.86 0.93
SIR7  91  0.86  0.893  0.96   0.76 0.76 0.98
SIR13 91  0.75  0.779  0.76   0.56 0.89 1.08
SIR17 91  0.21  0.089 -0.21  -0.20 2.24 1.46
SIR19 91  0.80  0.831  0.85   0.63 0.77 1.11
SIR23 91  0.76  0.790  0.79   0.59 0.79 1.05

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR4r 0.02 0.07 0.15 0.24 0.52 0.66
SIR6  0.43 0.35 0.16 0.04 0.01 0.66
SIR7  0.56 0.19 0.19 0.07 0.00 0.66
SIR13 0.51 0.22 0.16 0.10 0.01 0.66
SIR17 0.16 0.20 0.13 0.24 0.26 0.66
SIR19 0.60 0.14 0.15 0.08 0.02 0.66
SIR23 0.55 0.21 0.16 0.05 0.02 0.66

SIR Acquiring: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), SIR2r, SIR9, SIR11, 
    SIR14, SIR16, SIR18, SIR21))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.82      0.85    0.86      0.44 5.5 0.012 0.68 0.62     0.45

 lower alpha upper     95% confidence boundaries
0.79 0.82 0.84 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
SIR2r      0.86      0.86    0.87      0.51 6.3   0.0091 0.018  0.59
SIR9       0.81      0.85    0.86      0.48 5.6   0.0126 0.026  0.59
SIR11      0.77      0.80    0.80      0.41 4.1   0.0155 0.027  0.41
SIR14      0.79      0.82    0.82      0.43 4.5   0.0142 0.031  0.45
SIR16      0.78      0.81    0.81      0.41 4.2   0.0148 0.029  0.44
SIR18      0.77      0.81    0.82      0.41 4.2   0.0155 0.027  0.41
SIR21      0.78      0.82    0.82      0.43 4.5   0.0146 0.029  0.44

 Item statistics 
       n raw.r std.r r.cor r.drop mean   sd
SIR2r 96  0.57  0.51  0.37   0.33 1.04 1.26
SIR9  96  0.58  0.59  0.48   0.43 0.75 0.83
SIR11 96  0.81  0.82  0.81   0.72 0.58 0.84
SIR14 96  0.73  0.76  0.73   0.66 0.34 0.61
SIR16 96  0.78  0.81  0.80   0.71 0.27 0.69
SIR18 96  0.80  0.81  0.79   0.70 0.92 0.97
SIR21 96  0.75  0.75  0.71   0.63 0.84 0.93

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR2r 0.43 0.34 0.08 0.05 0.09 0.83
SIR9  0.47 0.34 0.16 0.03 0.00 0.83
SIR11 0.57 0.32 0.07 0.01 0.02 0.83
SIR14 0.72 0.23 0.04 0.01 0.00 0.83
SIR16 0.82 0.11 0.04 0.01 0.01 0.83
SIR18 0.43 0.29 0.24 0.02 0.02 0.83
SIR21 0.45 0.31 0.21 0.01 0.02 0.83

SIR Acquiring: Spanish

Some items ( SIR2r ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), SIR2r, SIR9, SIR11, 
    SIR14, SIR16, SIR18, SIR21))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.75      0.74    0.85      0.29 2.8 0.017  1.2 0.66      0.6

 lower alpha upper     95% confidence boundaries
0.72 0.75 0.78 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r  S/N alpha se  var.r med.r
SIR2r      0.91      0.91    0.90      0.63 10.0   0.0089 0.0061  0.63
SIR9       0.68      0.67    0.83      0.25  2.0   0.0214 0.3625  0.63
SIR11      0.65      0.63    0.80      0.22  1.7   0.0237 0.3299  0.57
SIR14      0.66      0.64    0.81      0.23  1.8   0.0221 0.3624  0.60
SIR16      0.65      0.64    0.80      0.23  1.8   0.0239 0.3364  0.57
SIR18      0.67      0.64    0.80      0.23  1.8   0.0229 0.3275  0.57
SIR21      0.64      0.62    0.79      0.21  1.6   0.0242 0.3345  0.57

 Item statistics 
       n raw.r std.r r.cor r.drop mean   sd
SIR2r 91 -0.55 -0.55 -0.71  -0.68 3.26 0.98
SIR9  91  0.76  0.74  0.68   0.61 0.93 1.18
SIR11 91  0.84  0.84  0.82   0.75 0.92 1.00
SIR14 91  0.81  0.81  0.77   0.70 0.71 1.07
SIR16 91  0.83  0.83  0.80   0.73 0.91 1.11
SIR18 91  0.81  0.82  0.80   0.73 0.62 0.90
SIR21 91  0.87  0.87  0.86   0.79 0.74 1.05

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR2r 0.01 0.05 0.14 0.24 0.55 0.66
SIR9  0.52 0.21 0.13 0.11 0.03 0.66
SIR11 0.46 0.23 0.23 0.08 0.00 0.66
SIR14 0.63 0.13 0.16 0.05 0.02 0.66
SIR16 0.52 0.19 0.18 0.11 0.01 0.66
SIR18 0.63 0.18 0.15 0.04 0.00 0.66
SIR21 0.60 0.15 0.15 0.08 0.01 0.66

SIR Total: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), SIR1, SIR3, SIR5, 
    SIR8, SIR10, SIR12, SIR15, SIR20, SIR22, SIR4r, SIR6, SIR7, 
    SIR13, SIR17, SIR19, SIR23, SIR2r, SIR9, SIR11, SIR14, SIR16, 
    SIR18, SIR21))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.94      0.95    0.97      0.45  19 0.0036 0.65 0.57     0.45

 lower alpha upper     95% confidence boundaries
0.93 0.94 0.95 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
SIR1       0.94      0.95    0.97      0.45  18   0.0037 0.025  0.45
SIR3       0.94      0.95    0.97      0.44  18   0.0037 0.025  0.45
SIR5       0.94      0.95    0.97      0.44  18   0.0037 0.024  0.45
SIR8       0.94      0.95    0.97      0.44  17   0.0038 0.024  0.44
SIR10      0.94      0.95    0.97      0.44  17   0.0038 0.024  0.45
SIR12      0.94      0.95    0.97      0.45  18   0.0037 0.025  0.45
SIR15      0.94      0.94    0.97      0.44  17   0.0038 0.024  0.44
SIR20      0.94      0.95    0.97      0.45  18   0.0038 0.025  0.45
SIR22      0.94      0.95    0.97      0.44  18   0.0038 0.026  0.44
SIR4r      0.94      0.95    0.97      0.46  19   0.0034 0.024  0.47
SIR6       0.94      0.94    0.97      0.44  17   0.0039 0.024  0.44
SIR7       0.94      0.94    0.97      0.43  17   0.0039 0.024  0.42
SIR13      0.94      0.95    0.97      0.44  17   0.0038 0.025  0.44
SIR17      0.94      0.94    0.97      0.44  17   0.0039 0.025  0.42
SIR19      0.94      0.95    0.97      0.44  17   0.0039 0.025  0.42
SIR23      0.94      0.95    0.97      0.45  18   0.0037 0.026  0.46
SIR2r      0.94      0.95    0.97      0.46  19   0.0034 0.022  0.47
SIR9       0.94      0.95    0.97      0.46  19   0.0036 0.024  0.47
SIR11      0.94      0.95    0.97      0.45  18   0.0037 0.025  0.46
SIR14      0.94      0.95    0.97      0.44  17   0.0038 0.026  0.45
SIR16      0.94      0.95    0.97      0.45  18   0.0037 0.025  0.46
SIR18      0.94      0.95    0.97      0.45  18   0.0037 0.026  0.45
SIR21      0.94      0.95    0.97      0.45  18   0.0037 0.025  0.46

 Item statistics 
       n raw.r std.r r.cor r.drop mean   sd
SIR1  96  0.62  0.64  0.62   0.59 0.90 0.66
SIR3  96  0.70  0.72  0.71   0.67 0.40 0.61
SIR5  96  0.68  0.71  0.71   0.65 0.33 0.66
SIR8  96  0.77  0.79  0.79   0.75 0.56 0.75
SIR10 96  0.71  0.73  0.73   0.68 0.35 0.68
SIR12 96  0.67  0.69  0.68   0.63 0.46 0.69
SIR15 96  0.77  0.79  0.79   0.75 0.43 0.75
SIR20 96  0.68  0.69  0.68   0.64 0.53 0.91
SIR22 96  0.69  0.71  0.69   0.66 0.43 0.78
SIR4r 96  0.55  0.50  0.49   0.48 1.31 1.31
SIR6  96  0.81  0.81  0.81   0.78 0.83 0.82
SIR7  96  0.85  0.85  0.85   0.83 0.66 0.88
SIR13 96  0.75  0.75  0.74   0.72 0.64 0.85
SIR17 96  0.80  0.80  0.80   0.77 0.80 0.94
SIR19 96  0.78  0.77  0.77   0.74 0.95 0.94
SIR23 96  0.63  0.62  0.60   0.59 0.66 0.87
SIR2r 96  0.49  0.44  0.42   0.41 1.04 1.26
SIR9  96  0.48  0.48  0.45   0.43 0.75 0.83
SIR11 96  0.66  0.65  0.65   0.62 0.58 0.84
SIR14 96  0.73  0.73  0.72   0.70 0.34 0.61
SIR16 96  0.61  0.60  0.59   0.57 0.27 0.69
SIR18 96  0.68  0.68  0.66   0.64 0.92 0.97
SIR21 96  0.62  0.61  0.60   0.58 0.84 0.93

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR1  0.27 0.56 0.17 0.00 0.00 0.83
SIR3  0.67 0.27 0.06 0.00 0.00 0.83
SIR5  0.74 0.21 0.04 0.00 0.01 0.83
SIR8  0.57 0.31 0.09 0.02 0.00 0.83
SIR10 0.73 0.21 0.05 0.00 0.01 0.83
SIR12 0.65 0.26 0.08 0.01 0.00 0.83
SIR15 0.71 0.18 0.09 0.02 0.00 0.83
SIR20 0.68 0.18 0.09 0.04 0.01 0.83
SIR22 0.72 0.16 0.11 0.00 0.01 0.83
SIR4r 0.35 0.27 0.18 0.10 0.09 0.83
SIR6  0.41 0.38 0.20 0.02 0.00 0.83
SIR7  0.56 0.27 0.11 0.05 0.00 0.83
SIR13 0.57 0.25 0.15 0.03 0.00 0.83
SIR17 0.47 0.32 0.17 0.02 0.02 0.83
SIR19 0.41 0.29 0.26 0.03 0.01 0.83
SIR23 0.56 0.25 0.17 0.01 0.01 0.83
SIR2r 0.43 0.34 0.08 0.05 0.09 0.83
SIR9  0.47 0.34 0.16 0.03 0.00 0.83
SIR11 0.57 0.32 0.07 0.01 0.02 0.83
SIR14 0.72 0.23 0.04 0.01 0.00 0.83
SIR16 0.82 0.11 0.04 0.01 0.01 0.83
SIR18 0.43 0.29 0.24 0.02 0.02 0.83
SIR21 0.45 0.31 0.21 0.01 0.02 0.83

SIR Total: Spanish

Some items ( SIR10 SIR4r SIR17 SIR2r ) were negatively correlated with the total scale and 
probably should be reversed.  
To do this, run the function again with the 'check.keys=TRUE' option


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), SIR1, SIR3, SIR5, 
    SIR8, SIR10, SIR12, SIR15, SIR20, SIR22, SIR4r, SIR6, SIR7, 
    SIR13, SIR17, SIR19, SIR23, SIR2r, SIR9, SIR11, SIR14, SIR16, 
    SIR18, SIR21))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
       0.9      0.92    0.97      0.32  11 0.0065  1.2 0.61     0.58

 lower alpha upper     95% confidence boundaries
0.89 0.9 0.92 

 Reliability if an item is dropped:
      raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
SIR1       0.90      0.91    0.97      0.32 10.2   0.0070  0.25  0.58
SIR3       0.90      0.91    0.97      0.31  9.9   0.0072  0.24  0.57
SIR5       0.90      0.91    0.97      0.31 10.0   0.0071  0.25  0.58
SIR8       0.89      0.91    0.97      0.31  9.8   0.0073  0.24  0.57
SIR10      0.92      0.92    0.97      0.36 12.2   0.0052  0.24  0.59
SIR12      0.89      0.91    0.97      0.31  9.8   0.0073  0.24  0.57
SIR15      0.89      0.91    0.97      0.31  9.8   0.0073  0.24  0.57
SIR20      0.90      0.91    0.97      0.31  9.8   0.0072  0.24  0.57
SIR22      0.89      0.91    0.97      0.31  9.7   0.0073  0.24  0.57
SIR4r      0.93      0.94    0.98      0.40 14.7   0.0055  0.18  0.59
SIR6       0.90      0.91    0.97      0.31  9.9   0.0071  0.24  0.58
SIR7       0.89      0.91    0.97      0.31  9.7   0.0073  0.24  0.57
SIR13      0.90      0.91    0.97      0.31 10.0   0.0071  0.25  0.58
SIR17      0.92      0.93    0.97      0.36 12.5   0.0051  0.24  0.59
SIR19      0.89      0.91    0.97      0.31  9.7   0.0073  0.24  0.57
SIR23      0.89      0.91    0.97      0.31  9.7   0.0073  0.24  0.57
SIR2r      0.92      0.93    0.97      0.40 14.4   0.0056  0.19  0.59
SIR9       0.90      0.91    0.97      0.31 10.1   0.0071  0.25  0.58
SIR11      0.89      0.91    0.97      0.31  9.7   0.0073  0.24  0.57
SIR14      0.90      0.91    0.97      0.31 10.1   0.0071  0.25  0.58
SIR16      0.90      0.91    0.97      0.31  9.9   0.0072  0.24  0.57
SIR18      0.90      0.91    0.97      0.31  9.9   0.0072  0.24  0.57
SIR21      0.89      0.91    0.97      0.31  9.7   0.0073  0.24  0.57

 Item statistics 
       n  raw.r  std.r  r.cor r.drop mean   sd
SIR1  91  0.675  0.686  0.676  0.634 0.97 0.97
SIR3  91  0.790  0.797  0.798  0.762 0.76 0.96
SIR5  91  0.741  0.744  0.736  0.707 0.90 1.00
SIR8  91  0.818  0.825  0.821  0.793 0.95 1.00
SIR10 91  0.029 -0.017 -0.046 -0.072 2.10 1.43
SIR12 91  0.818  0.823  0.819  0.791 0.71 1.07
SIR15 91  0.823  0.825  0.819  0.794 1.02 1.14
SIR20 91  0.815  0.825  0.825  0.788 0.68 1.01
SIR22 91  0.838  0.851  0.851  0.815 0.80 1.01
SIR4r 91 -0.747 -0.754 -0.781 -0.777 3.16 1.06
SIR6  91  0.773  0.779  0.775  0.744 0.86 0.93
SIR7  91  0.856  0.862  0.863  0.835 0.76 0.98
SIR13 91  0.730  0.736  0.721  0.691 0.89 1.08
SIR17 91 -0.047 -0.094 -0.123 -0.149 2.24 1.46
SIR19 91  0.835  0.842  0.845  0.809 0.77 1.11
SIR23 91  0.834  0.844  0.844  0.809 0.79 1.05
SIR2r 91 -0.645 -0.655 -0.680 -0.683 3.26 0.98
SIR9  91  0.707  0.707  0.696  0.661 0.93 1.18
SIR11 91  0.854  0.859  0.857  0.833 0.92 1.00
SIR14 91  0.719  0.729  0.714  0.679 0.71 1.07
SIR16 91  0.767  0.773  0.760  0.731 0.91 1.11
SIR18 91  0.788  0.799  0.797  0.762 0.62 0.90
SIR21 91  0.836  0.848  0.850  0.811 0.74 1.05

Non missing response frequency for each item
         0    1    2    3    4 miss
SIR1  0.38 0.35 0.19 0.07 0.01 0.66
SIR3  0.53 0.25 0.16 0.04 0.01 0.66
SIR5  0.45 0.27 0.22 0.03 0.02 0.66
SIR8  0.44 0.26 0.21 0.09 0.00 0.66
SIR10 0.18 0.20 0.22 0.16 0.24 0.66
SIR12 0.63 0.13 0.16 0.05 0.02 0.66
SIR15 0.43 0.29 0.16 0.08 0.04 0.66
SIR20 0.63 0.15 0.13 0.09 0.00 0.66
SIR22 0.55 0.18 0.20 0.08 0.00 0.66
SIR4r 0.02 0.07 0.15 0.24 0.52 0.66
SIR6  0.43 0.35 0.16 0.04 0.01 0.66
SIR7  0.56 0.19 0.19 0.07 0.00 0.66
SIR13 0.51 0.22 0.16 0.10 0.01 0.66
SIR17 0.16 0.20 0.13 0.24 0.26 0.66
SIR19 0.60 0.14 0.15 0.08 0.02 0.66
SIR23 0.55 0.21 0.16 0.05 0.02 0.66
SIR2r 0.01 0.05 0.14 0.24 0.55 0.66
SIR9  0.52 0.21 0.13 0.11 0.03 0.66
SIR11 0.46 0.23 0.23 0.08 0.00 0.66
SIR14 0.63 0.13 0.16 0.05 0.02 0.66
SIR16 0.52 0.19 0.18 0.11 0.01 0.66
SIR18 0.63 0.18 0.15 0.04 0.00 0.66
SIR21 0.60 0.15 0.15 0.08 0.01 0.66

OCIR: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), OCIR1, OCIR2, 
    OCIR3, OCIR4, OCIR5, OCIR6, OCIR7, OCIR8, OCIR9, OCIR10, 
    OCIR11, OCIR12, OCIR13, OCIR14, OCIR15, OCIR16, OCIR17, OCIR18))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.91      0.91    0.95      0.37  11 0.0051 0.52 0.54     0.38

 lower alpha upper     95% confidence boundaries
0.9 0.91 0.92 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
OCIR1       0.91      0.91    0.95      0.38 10.3   0.0052 0.028  0.39
OCIR2       0.91      0.91    0.95      0.37  9.8   0.0055 0.029  0.38
OCIR3       0.91      0.91    0.95      0.37  9.8   0.0056 0.028  0.38
OCIR4       0.91      0.91    0.95      0.37  9.9   0.0054 0.030  0.38
OCIR5       0.92      0.92    0.95      0.39 11.0   0.0050 0.025  0.40
OCIR6       0.91      0.91    0.95      0.37  9.9   0.0055 0.027  0.38
OCIR7       0.91      0.91    0.95      0.38 10.2   0.0053 0.028  0.38
OCIR8       0.91      0.91    0.95      0.36  9.5   0.0056 0.028  0.38
OCIR9       0.91      0.91    0.95      0.36  9.7   0.0056 0.028  0.38
OCIR10      0.91      0.91    0.95      0.37  9.8   0.0054 0.029  0.38
OCIR11      0.91      0.91    0.95      0.37  9.9   0.0054 0.029  0.38
OCIR12      0.91      0.91    0.95      0.36  9.7   0.0056 0.027  0.38
OCIR13      0.92      0.92    0.95      0.39 10.9   0.0049 0.026  0.40
OCIR14      0.91      0.91    0.95      0.38 10.3   0.0052 0.029  0.39
OCIR15      0.91      0.91    0.95      0.36  9.6   0.0057 0.027  0.38
OCIR16      0.91      0.91    0.95      0.38 10.4   0.0052 0.028  0.39
OCIR17      0.91      0.91    0.95      0.39 10.7   0.0051 0.027  0.39
OCIR18      0.91      0.91    0.95      0.37  9.8   0.0055 0.027  0.38

 Item statistics 
        n raw.r std.r r.cor r.drop mean   sd
OCIR1  96  0.58  0.56  0.54   0.51 0.56 0.83
OCIR2  96  0.70  0.70  0.69   0.66 0.62 0.80
OCIR3  96  0.73  0.72  0.71   0.68 0.85 0.95
OCIR4  96  0.68  0.68  0.66   0.63 0.42 0.71
OCIR5  96  0.37  0.39  0.36   0.30 0.28 0.72
OCIR6  96  0.70  0.68  0.67   0.64 0.62 0.99
OCIR7  96  0.61  0.60  0.59   0.56 0.66 0.82
OCIR8  96  0.77  0.79  0.79   0.74 0.49 0.79
OCIR9  96  0.76  0.74  0.73   0.71 0.93 1.08
OCIR10 96  0.68  0.70  0.69   0.64 0.25 0.62
OCIR11 96  0.68  0.69  0.68   0.63 0.38 0.78
OCIR12 96  0.75  0.73  0.73   0.70 0.53 0.93
OCIR13 96  0.43  0.42  0.39   0.35 0.78 0.93
OCIR14 96  0.55  0.57  0.54   0.49 0.38 0.76
OCIR15 96  0.77  0.76  0.76   0.73 0.72 1.01
OCIR16 96  0.54  0.55  0.52   0.48 0.31 0.77
OCIR17 96  0.43  0.47  0.45   0.38 0.21 0.52
OCIR18 96  0.73  0.72  0.72   0.69 0.45 0.88

Non missing response frequency for each item
          0    1    2    3    4 miss
OCIR1  0.61 0.24 0.12 0.01 0.01 0.83
OCIR2  0.54 0.32 0.10 0.03 0.00 0.83
OCIR3  0.44 0.35 0.14 0.06 0.01 0.83
OCIR4  0.69 0.23 0.06 0.02 0.00 0.83
OCIR5  0.83 0.08 0.06 0.01 0.01 0.83
OCIR6  0.62 0.22 0.08 0.05 0.02 0.83
OCIR7  0.52 0.33 0.12 0.01 0.01 0.83
OCIR8  0.67 0.21 0.09 0.03 0.00 0.83
OCIR9  0.48 0.23 0.20 0.07 0.02 0.83
OCIR10 0.83 0.09 0.06 0.01 0.00 0.83
OCIR11 0.77 0.11 0.09 0.01 0.01 0.83
OCIR12 0.69 0.17 0.08 0.05 0.01 0.83
OCIR13 0.51 0.25 0.19 0.05 0.00 0.83
OCIR14 0.74 0.19 0.04 0.02 0.01 0.83
OCIR15 0.57 0.23 0.12 0.05 0.02 0.83
OCIR16 0.82 0.09 0.03 0.05 0.00 0.83
OCIR17 0.83 0.14 0.02 0.01 0.00 0.83
OCIR18 0.74 0.14 0.07 0.04 0.01 0.83

OCIR: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), OCIR1, OCIR2, 
    OCIR3, OCIR4, OCIR5, OCIR6, OCIR7, OCIR8, OCIR9, OCIR10, 
    OCIR11, OCIR12, OCIR13, OCIR14, OCIR15, OCIR16, OCIR17, OCIR18))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.95      0.95    0.97      0.52  19 0.0044 0.86 0.82     0.52

 lower alpha upper     95% confidence boundaries
0.94 0.95 0.96 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
OCIR1       0.95      0.95    0.96      0.52  19   0.0046 0.0140  0.53
OCIR2       0.95      0.95    0.96      0.52  18   0.0047 0.0144  0.52
OCIR3       0.95      0.95    0.96      0.54  20   0.0043 0.0083  0.54
OCIR4       0.95      0.95    0.96      0.52  18   0.0047 0.0140  0.52
OCIR5       0.95      0.95    0.96      0.52  18   0.0047 0.0140  0.53
OCIR6       0.95      0.95    0.96      0.51  18   0.0048 0.0139  0.52
OCIR7       0.95      0.95    0.96      0.51  18   0.0049 0.0132  0.51
OCIR8       0.95      0.95    0.96      0.51  18   0.0048 0.0128  0.51
OCIR9       0.95      0.95    0.96      0.52  19   0.0046 0.0139  0.53
OCIR10      0.95      0.95    0.96      0.51  18   0.0048 0.0128  0.51
OCIR11      0.95      0.95    0.96      0.51  18   0.0048 0.0139  0.51
OCIR12      0.95      0.95    0.96      0.52  18   0.0047 0.0135  0.51
OCIR13      0.95      0.95    0.96      0.51  18   0.0048 0.0134  0.51
OCIR14      0.95      0.95    0.96      0.51  18   0.0049 0.0125  0.51
OCIR15      0.95      0.95    0.96      0.52  19   0.0046 0.0143  0.53
OCIR16      0.95      0.95    0.96      0.52  19   0.0046 0.0129  0.53
OCIR17      0.95      0.95    0.96      0.53  19   0.0045 0.0130  0.53
OCIR18      0.95      0.95    0.96      0.52  18   0.0047 0.0132  0.52

 Item statistics 
         n raw.r std.r r.cor r.drop mean   sd
OCIR1  122  0.71  0.71  0.69   0.67 0.87 1.10
OCIR2  122  0.75  0.75  0.73   0.71 1.02 1.11
OCIR3  122  0.51  0.50  0.48   0.44 1.26 1.17
OCIR4  122  0.72  0.72  0.71   0.68 0.83 1.13
OCIR5  122  0.73  0.72  0.71   0.69 0.87 1.16
OCIR6  122  0.80  0.80  0.79   0.77 0.96 1.13
OCIR7  122  0.83  0.83  0.82   0.81 0.91 1.11
OCIR8  122  0.79  0.79  0.79   0.76 0.73 1.14
OCIR9  122  0.69  0.68  0.67   0.64 0.98 1.19
OCIR10 122  0.79  0.79  0.78   0.76 0.63 1.01
OCIR11 122  0.79  0.79  0.79   0.76 0.77 0.97
OCIR12 122  0.76  0.77  0.75   0.73 0.83 1.07
OCIR13 122  0.80  0.80  0.79   0.77 0.87 1.09
OCIR14 122  0.84  0.84  0.84   0.82 0.72 1.06
OCIR15 122  0.70  0.69  0.67   0.65 1.10 1.21
OCIR16 122  0.70  0.70  0.69   0.66 0.66 1.06
OCIR17 122  0.65  0.65  0.64   0.61 0.73 1.12
OCIR18 122  0.75  0.75  0.74   0.71 0.75 1.09

Non missing response frequency for each item
          0    1    2    3    4 miss
OCIR1  0.52 0.24 0.12 0.11 0.02 0.55
OCIR2  0.43 0.25 0.20 0.07 0.03 0.55
OCIR3  0.33 0.28 0.25 0.08 0.06 0.55
OCIR4  0.56 0.20 0.12 0.08 0.03 0.55
OCIR5  0.54 0.21 0.11 0.10 0.03 0.55
OCIR6  0.48 0.24 0.17 0.08 0.03 0.55
OCIR7  0.50 0.22 0.17 0.08 0.02 0.55
OCIR8  0.63 0.15 0.12 0.06 0.04 0.55
OCIR9  0.48 0.24 0.15 0.08 0.05 0.55
OCIR10 0.64 0.18 0.11 0.04 0.02 0.55
OCIR11 0.52 0.25 0.16 0.06 0.01 0.55
OCIR12 0.53 0.22 0.16 0.07 0.02 0.55
OCIR13 0.50 0.26 0.14 0.07 0.03 0.55
OCIR14 0.60 0.19 0.14 0.04 0.03 0.55
OCIR15 0.44 0.22 0.16 0.14 0.03 0.55
OCIR16 0.64 0.17 0.10 0.07 0.02 0.55
OCIR17 0.61 0.17 0.13 0.03 0.05 0.55
OCIR18 0.60 0.16 0.16 0.05 0.03 0.55

OCIR Total Non-Hoarding: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), OCIR2, OCIR3, 
    OCIR4, OCIR5, OCIR6, OCIR8, OCIR9, OCIR10, OCIR11, OCIR12, 
    OCIR14, OCIR15, OCIR16, OCIR17, OCIR18))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.91      0.91    0.95      0.41  11 0.0051  0.5 0.56     0.43

 lower alpha upper     95% confidence boundaries
0.9 0.91 0.92 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r  S/N alpha se var.r med.r
OCIR2       0.91      0.91    0.94      0.41  9.8   0.0055 0.027  0.42
OCIR3       0.91      0.91    0.94      0.41  9.6   0.0056 0.025  0.43
OCIR4       0.91      0.91    0.95      0.41  9.8   0.0055 0.028  0.43
OCIR5       0.92      0.92    0.95      0.44 11.0   0.0050 0.022  0.44
OCIR6       0.91      0.91    0.94      0.41  9.9   0.0055 0.025  0.43
OCIR8       0.90      0.90    0.94      0.40  9.3   0.0057 0.027  0.41
OCIR9       0.91      0.91    0.94      0.41  9.6   0.0057 0.026  0.42
OCIR10      0.91      0.91    0.94      0.41  9.6   0.0055 0.028  0.42
OCIR11      0.91      0.91    0.94      0.41  9.6   0.0055 0.028  0.42
OCIR12      0.90      0.91    0.94      0.41  9.6   0.0057 0.024  0.42
OCIR14      0.91      0.91    0.95      0.42 10.3   0.0052 0.027  0.44
OCIR15      0.90      0.90    0.94      0.40  9.5   0.0058 0.025  0.42
OCIR16      0.91      0.91    0.95      0.42 10.3   0.0053 0.026  0.44
OCIR17      0.91      0.91    0.95      0.43 10.7   0.0052 0.024  0.44
OCIR18      0.91      0.91    0.94      0.41  9.6   0.0056 0.024  0.42

 Item statistics 
        n raw.r std.r r.cor r.drop mean   sd
OCIR2  96  0.69  0.69  0.67   0.63 0.62 0.80
OCIR3  96  0.74  0.72  0.72   0.68 0.85 0.95
OCIR4  96  0.68  0.68  0.66   0.63 0.42 0.71
OCIR5  96  0.40  0.43  0.38   0.32 0.28 0.72
OCIR6  96  0.70  0.67  0.66   0.63 0.62 0.99
OCIR8  96  0.78  0.79  0.79   0.74 0.49 0.79
OCIR9  96  0.76  0.73  0.72   0.70 0.93 1.08
OCIR10 96  0.72  0.74  0.72   0.68 0.25 0.62
OCIR11 96  0.72  0.74  0.72   0.67 0.38 0.78
OCIR12 96  0.76  0.74  0.74   0.71 0.53 0.93
OCIR14 96  0.57  0.58  0.55   0.50 0.38 0.76
OCIR15 96  0.79  0.77  0.76   0.73 0.72 1.01
OCIR16 96  0.57  0.58  0.55   0.50 0.31 0.77
OCIR17 96  0.45  0.50  0.47   0.40 0.21 0.52
OCIR18 96  0.75  0.73  0.72   0.69 0.45 0.88

Non missing response frequency for each item
          0    1    2    3    4 miss
OCIR2  0.54 0.32 0.10 0.03 0.00 0.83
OCIR3  0.44 0.35 0.14 0.06 0.01 0.83
OCIR4  0.69 0.23 0.06 0.02 0.00 0.83
OCIR5  0.83 0.08 0.06 0.01 0.01 0.83
OCIR6  0.62 0.22 0.08 0.05 0.02 0.83
OCIR8  0.67 0.21 0.09 0.03 0.00 0.83
OCIR9  0.48 0.23 0.20 0.07 0.02 0.83
OCIR10 0.83 0.09 0.06 0.01 0.00 0.83
OCIR11 0.77 0.11 0.09 0.01 0.01 0.83
OCIR12 0.69 0.17 0.08 0.05 0.01 0.83
OCIR14 0.74 0.19 0.04 0.02 0.01 0.83
OCIR15 0.57 0.23 0.12 0.05 0.02 0.83
OCIR16 0.82 0.09 0.03 0.05 0.00 0.83
OCIR17 0.83 0.14 0.02 0.01 0.00 0.83
OCIR18 0.74 0.14 0.07 0.04 0.01 0.83

OCIR Total Non-Hoarding: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), OCIR2, OCIR3, 
    OCIR4, OCIR5, OCIR6, OCIR8, OCIR9, OCIR10, OCIR11, OCIR12, 
    OCIR14, OCIR15, OCIR16, OCIR17, OCIR18))

  raw_alpha std.alpha G6(smc) average_r S/N    ase mean   sd median_r
      0.94      0.94    0.96      0.51  15 0.0056 0.86 0.81     0.51

 lower alpha upper     95% confidence boundaries
0.93 0.94 0.95 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se  var.r med.r
OCIR2       0.93      0.93    0.95      0.51  14   0.0060 0.0162  0.51
OCIR3       0.94      0.94    0.95      0.54  16   0.0053 0.0081  0.53
OCIR4       0.93      0.94    0.95      0.51  14   0.0060 0.0156  0.51
OCIR5       0.93      0.94    0.95      0.51  14   0.0059 0.0156  0.51
OCIR6       0.93      0.93    0.95      0.50  14   0.0061 0.0157  0.51
OCIR8       0.93      0.93    0.95      0.50  14   0.0062 0.0138  0.50
OCIR9       0.93      0.94    0.95      0.51  15   0.0058 0.0155  0.51
OCIR10      0.93      0.93    0.95      0.50  14   0.0061 0.0138  0.51
OCIR11      0.93      0.93    0.95      0.50  14   0.0061 0.0153  0.50
OCIR12      0.93      0.93    0.95      0.50  14   0.0061 0.0149  0.51
OCIR14      0.93      0.93    0.95      0.49  14   0.0062 0.0137  0.50
OCIR15      0.93      0.94    0.95      0.51  15   0.0058 0.0162  0.53
OCIR16      0.93      0.94    0.95      0.51  14   0.0059 0.0141  0.51
OCIR17      0.94      0.94    0.95      0.51  15   0.0058 0.0145  0.52
OCIR18      0.93      0.93    0.95      0.50  14   0.0060 0.0142  0.51

 Item statistics 
         n raw.r std.r r.cor r.drop mean   sd
OCIR2  122  0.74  0.74  0.72   0.70 1.02 1.11
OCIR3  122  0.50  0.49  0.46   0.42 1.26 1.17
OCIR4  122  0.73  0.73  0.71   0.68 0.83 1.13
OCIR5  122  0.72  0.72  0.70   0.67 0.87 1.16
OCIR6  122  0.79  0.79  0.78   0.75 0.96 1.13
OCIR8  122  0.80  0.80  0.80   0.77 0.73 1.14
OCIR9  122  0.70  0.69  0.67   0.64 0.98 1.19
OCIR10 122  0.79  0.79  0.79   0.76 0.63 1.01
OCIR11 122  0.79  0.79  0.78   0.76 0.77 0.97
OCIR12 122  0.77  0.77  0.76   0.73 0.83 1.07
OCIR14 122  0.83  0.83  0.83   0.80 0.72 1.06
OCIR15 122  0.70  0.70  0.67   0.65 1.10 1.21
OCIR16 122  0.72  0.73  0.71   0.68 0.66 1.06
OCIR17 122  0.67  0.67  0.65   0.62 0.73 1.12
OCIR18 122  0.75  0.76  0.74   0.71 0.75 1.09

Non missing response frequency for each item
          0    1    2    3    4 miss
OCIR2  0.43 0.25 0.20 0.07 0.03 0.55
OCIR3  0.33 0.28 0.25 0.08 0.06 0.55
OCIR4  0.56 0.20 0.12 0.08 0.03 0.55
OCIR5  0.54 0.21 0.11 0.10 0.03 0.55
OCIR6  0.48 0.24 0.17 0.08 0.03 0.55
OCIR8  0.63 0.15 0.12 0.06 0.04 0.55
OCIR9  0.48 0.24 0.15 0.08 0.05 0.55
OCIR10 0.64 0.18 0.11 0.04 0.02 0.55
OCIR11 0.52 0.25 0.16 0.06 0.01 0.55
OCIR12 0.53 0.22 0.16 0.07 0.02 0.55
OCIR14 0.60 0.19 0.14 0.04 0.03 0.55
OCIR15 0.44 0.22 0.16 0.14 0.03 0.55
OCIR16 0.64 0.17 0.10 0.07 0.02 0.55
OCIR17 0.61 0.17 0.13 0.03 0.05 0.55
OCIR18 0.60 0.16 0.16 0.05 0.03 0.55

OCIR Hoarding: English


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 0), OCIR1, OCIR7, 
    OCIR13))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.79       0.8    0.76      0.57   4 0.016 0.67 0.73     0.51

 lower alpha upper     95% confidence boundaries
0.76 0.79 0.83 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
OCIR1       0.67      0.67    0.51      0.51 2.1    0.028    NA  0.51
OCIR7       0.63      0.63    0.46      0.46 1.7    0.031    NA  0.46
OCIR13      0.86      0.86    0.75      0.75 6.0    0.012    NA  0.75

 Item statistics 
        n raw.r std.r r.cor r.drop mean   sd
OCIR1  96  0.86  0.87  0.81   0.69 0.56 0.83
OCIR7  96  0.88  0.89  0.84   0.73 0.66 0.82
OCIR13 96  0.79  0.78  0.56   0.52 0.78 0.93

Non missing response frequency for each item
          0    1    2    3    4 miss
OCIR1  0.61 0.24 0.12 0.01 0.01 0.83
OCIR7  0.52 0.33 0.12 0.01 0.01 0.83
OCIR13 0.51 0.25 0.19 0.05 0.00 0.83

OCIR Hoarding: Spanish


Reliability analysis   
Call: alpha(x = select(filter(HRSdat, HRSlang == 1), OCIR1, OCIR7, 
    OCIR13))

  raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
      0.84      0.84     0.8      0.64 5.3 0.017 0.88 0.96     0.64

 lower alpha upper     95% confidence boundaries
0.81 0.84 0.87 

 Reliability if an item is dropped:
       raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
OCIR1       0.85      0.85    0.74      0.74 5.6    0.018    NA  0.74
OCIR7       0.69      0.69    0.53      0.53 2.3    0.037    NA  0.53
OCIR13      0.78      0.78    0.64      0.64 3.6    0.026    NA  0.64

 Item statistics 
         n raw.r std.r r.cor r.drop mean  sd
OCIR1  122  0.83  0.83  0.68   0.63 0.87 1.1
OCIR7  122  0.91  0.91  0.86   0.79 0.91 1.1
OCIR13 122  0.87  0.87  0.78   0.70 0.87 1.1

Non missing response frequency for each item
          0    1    2    3    4 miss
OCIR1  0.52 0.24 0.12 0.11 0.02 0.55
OCIR7  0.50 0.22 0.17 0.08 0.02 0.55
OCIR13 0.50 0.26 0.14 0.07 0.03 0.55

Convergent and Divergent Validity

Correlation Table: English

corstarsl(select(filter(HRSdat, HRSlang==0), HRStot, SIRtot, CIRtot, OCIRhoard, OCIRtotNH, DASSdep, DASSanx, DASSstress))

Correlation Table: Spanish

corstarsl(select(filter(HRSdat, HRSlang==1), HRStot, SIRtot, CIRtot, OCIRhoard, OCIRtotNH, DASSdep, DASSanx, DASSstress))

Regressions

HRS.SIR.mod.Eng <- lm(scale(HRStot) ~ scale(SIRtot) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.SIR.mod.Eng, digits = 3, show.std=T)

	scale(HRStot)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.065	0.000	-0.159 – 0.028	-0.121 – 0.121	0.169
SIRtot	0.573	0.756	0.454 – 0.692	0.599 – 0.914	<0.001
DASSdep	0.020	0.026	-0.124 – 0.163	-0.163 – 0.214	0.786
DASSanx	0.112	0.145	-0.024 – 0.248	-0.031 – 0.321	0.105
DASSstress	-0.028	-0.036	-0.224 – 0.168	-0.285 – 0.214	0.778
OCIRtotNH	0.000	0.000	-0.124 – 0.125	-0.164 – 0.165	0.998
Observations	96
R² / R² adjusted	0.660 / 0.641

modelEffectSizes(HRS.SIR.mod.Eng)

lm(formula = scale(HRStot) ~ scale(SIRtot) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                      SSR df pEta-sqr dR-sqr
(Intercept)        0.3964  1   0.0209     NA
scale(SIRtot)     18.8002  1   0.5035 0.3449
scale(DASSdep)     0.0153  1   0.0008 0.0003
scale(DASSanx)     0.5523  1   0.0289 0.0101
scale(DASSstress)  0.0165  1   0.0009 0.0003
scale(OCIRtotNH)   0.0000  1   0.0000 0.0000

Sum of squared errors (SSE): 18.5
Sum of squared total  (SST): 54.5

HRS.SIR.mod.clutter.Eng <- lm(scale(HRS1) ~ scale(SIRclutter) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.SIR.mod.clutter.Eng, digits = 3, show.std=T)

	scale(HRS1)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.079	-0.000	-0.196 – 0.038	-0.151 – 0.151	0.184
SIRclutter	0.500	0.655	0.364 – 0.635	0.477 – 0.834	<0.001
DASSdep	-0.083	-0.108	-0.261 – 0.096	-0.342 – 0.125	0.359
DASSanx	0.182	0.234	0.012 – 0.352	0.015 – 0.452	0.036
DASSstress	0.053	0.067	-0.188 – 0.294	-0.238 – 0.372	0.665
OCIRtotNH	-0.111	-0.146	-0.262 – 0.040	-0.344 – 0.053	0.149
Observations	96
R² / R² adjusted	0.477 / 0.448

modelEffectSizes(HRS.SIR.mod.clutter.Eng)

lm(formula = scale(HRS1) ~ scale(SIRclutter) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                      SSR df pEta-sqr dR-sqr
(Intercept)        0.5753  1   0.0196     NA
scale(SIRclutter) 17.1074  1   0.3723 0.3099
scale(DASSdep)     0.2723  1   0.0094 0.0049
scale(DASSanx)     1.4488  1   0.0478 0.0262
scale(DASSstress)  0.0603  1   0.0021 0.0011
scale(OCIRtotNH)   0.6799  1   0.0230 0.0123

Sum of squared errors (SSE): 28.8
Sum of squared total  (SST): 55.2

HRS.SIR.mod.discarding.Eng <- lm(scale(HRS2) ~ scale(SIRdiscarding) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.SIR.mod.discarding.Eng, digits = 3, show.std=T)

	scale(HRS2)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.010	-0.000	-0.131 – 0.110	-0.140 – 0.140	0.865
SIRdiscarding	0.668	0.793	0.525 – 0.812	0.623 – 0.964	<0.001
DASSdep	-0.013	-0.016	-0.196 – 0.170	-0.232 – 0.201	0.887
DASSanx	-0.038	-0.045	-0.213 – 0.136	-0.248 – 0.158	0.663
DASSstress	0.017	0.019	-0.228 – 0.261	-0.261 – 0.300	0.892
OCIRtotNH	-0.086	-0.102	-0.245 – 0.073	-0.291 – 0.086	0.284
Observations	96
R² / R² adjusted	0.548 / 0.522

modelEffectSizes(HRS.SIR.mod.discarding.Eng)

lm(formula = scale(HRS2) ~ scale(SIRdiscarding) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                         SSR df pEta-sqr dR-sqr
(Intercept)           0.0098  1   0.0003     NA
scale(SIRdiscarding) 28.9653  1   0.4869 0.4293
scale(DASSdep)        0.0069  1   0.0002 0.0001
scale(DASSanx)        0.0649  1   0.0021 0.0010
scale(DASSstress)     0.0063  1   0.0002 0.0001
scale(OCIRtotNH)      0.3942  1   0.0127 0.0058

Sum of squared errors (SSE): 30.5
Sum of squared total  (SST): 67.5

HRS.SIR.mod.acquiring.Eng <- lm(scale(HRS3) ~ scale(SIRacquiring) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.SIR.mod.acquiring.Eng, digits = 3, show.std=T)

	scale(HRS3)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.077	-0.000	-0.195 – 0.040	-0.132 – 0.132	0.195
SIRacquiring	0.621	0.711	0.478 – 0.765	0.547 – 0.875	<0.001
DASSdep	0.023	0.026	-0.157 – 0.203	-0.179 – 0.231	0.802
DASSanx	-0.034	-0.039	-0.204 – 0.135	-0.229 – 0.152	0.687
DASSstress	0.128	0.141	-0.113 – 0.370	-0.125 – 0.408	0.295
OCIRtotNH	-0.027	-0.031	-0.181 – 0.127	-0.207 – 0.145	0.728
Observations	96
R² / R² adjusted	0.599 / 0.576

modelEffectSizes(HRS.SIR.mod.acquiring.Eng)

lm(formula = scale(HRS3) ~ scale(SIRacquiring) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                        SSR df pEta-sqr dR-sqr
(Intercept)          0.5521  1   0.0186     NA
scale(SIRacquiring) 24.0155  1   0.4520 0.3312
scale(DASSdep)       0.0204  1   0.0007 0.0003
scale(DASSanx)       0.0528  1   0.0018 0.0007
scale(DASSstress)    0.3590  1   0.0122 0.0050
scale(OCIRtotNH)     0.0393  1   0.0013 0.0005

Sum of squared errors (SSE): 29.1
Sum of squared total  (SST): 72.5

HRS.SIR.mod.Span <- lm(scale(HRStot) ~ scale(SIRtot) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.SIR.mod.Span, digits = 3, show.std=T)

	scale(HRStot)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.146	-0.000	0.024 – 0.268	-0.120 – 0.120	0.020
SIRtot	0.320	0.316	0.110 – 0.530	0.109 – 0.522	0.003
DASSdep	-0.006	-0.006	-0.258 – 0.247	-0.256 – 0.245	0.965
DASSanx	0.120	0.119	-0.159 – 0.398	-0.158 – 0.396	0.395
DASSstress	0.200	0.198	-0.048 – 0.447	-0.047 – 0.444	0.112
OCIRtotNH	0.280	0.289	0.063 – 0.497	0.065 – 0.514	0.012
Observations	91
R² / R² adjusted	0.687 / 0.668

modelEffectSizes(HRS.SIR.mod.Span)

lm(formula = scale(HRStot) ~ scale(SIRtot) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                     SSR df pEta-sqr dR-sqr
(Intercept)       1.9277  1   0.0624     NA
scale(SIRtot)     3.1420  1   0.0978 0.0340
scale(DASSdep)    0.0007  1   0.0000 0.0000
scale(DASSanx)    0.2495  1   0.0085 0.0027
scale(DASSstress) 0.8780  1   0.0294 0.0095
scale(OCIRtotNH)  2.2391  1   0.0717 0.0242

Sum of squared errors (SSE): 29.0
Sum of squared total  (SST): 92.5

HRS.SIR.mod.clutter.Span <- lm(scale(HRS1) ~ scale(SIRclutter) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.SIR.mod.clutter.Span, digits = 3, show.std=T)

	scale(HRS1)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.128	-0.000	-0.026 – 0.282	-0.147 – 0.147	0.101
SIRclutter	0.312	0.299	0.056 – 0.568	0.053 – 0.544	0.018
DASSdep	0.126	0.121	-0.191 – 0.443	-0.184 – 0.427	0.432
DASSanx	0.196	0.190	-0.159 – 0.552	-0.154 – 0.534	0.276
DASSstress	-0.095	-0.092	-0.408 – 0.217	-0.394 – 0.210	0.545
OCIRtotNH	0.272	0.273	0.004 – 0.539	0.004 – 0.541	0.047
Observations	91
R² / R² adjusted	0.528 / 0.501

modelEffectSizes(HRS.SIR.mod.clutter.Span)

lm(formula = scale(HRS1) ~ scale(SIRclutter) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                     SSR df pEta-sqr dR-sqr
(Intercept)       1.4956  1   0.0314     NA
scale(SIRclutter) 3.1873  1   0.0645 0.0325
scale(DASSdep)    0.3384  1   0.0073 0.0035
scale(DASSanx)    0.6547  1   0.0140 0.0067
scale(DASSstress) 0.2007  1   0.0043 0.0020
scale(OCIRtotNH)  2.2130  1   0.0457 0.0226

Sum of squared errors (SSE): 46.2
Sum of squared total  (SST): 98.0

HRS.SIR.mod.discarding.Span <- lm(scale(HRS2) ~ scale(SIRdiscarding) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.SIR.mod.discarding.Span, digits = 3, show.std=T)

	scale(HRS2)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.136	-0.000	-0.013 – 0.285	-0.141 – 0.141	0.073
SIRdiscarding	0.259	0.246	0.050 – 0.469	0.048 – 0.445	0.016
DASSdep	-0.171	-0.164	-0.480 – 0.137	-0.458 – 0.131	0.273
DASSanx	0.260	0.249	-0.077 – 0.596	-0.073 – 0.572	0.128
DASSstress	0.425	0.406	0.123 – 0.728	0.117 – 0.695	0.006
OCIRtotNH	0.103	0.102	-0.153 – 0.359	-0.152 – 0.357	0.425
Observations	91
R² / R² adjusted	0.567 / 0.541

modelEffectSizes(HRS.SIR.mod.discarding.Span)

lm(formula = scale(HRS2) ~ scale(SIRdiscarding) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                        SSR df pEta-sqr dR-sqr
(Intercept)          1.6807  1   0.0373     NA
scale(SIRdiscarding) 3.1000  1   0.0667 0.0310
scale(DASSdep)       0.6209  1   0.0141 0.0062
scale(DASSanx)       1.2018  1   0.0270 0.0120
scale(DASSstress)    3.9813  1   0.0841 0.0398
scale(OCIRtotNH)     0.3271  1   0.0075 0.0033

Sum of squared errors (SSE): 43.3
Sum of squared total  (SST): 100.0

HRS.SIR.mod.acquiring.Span <- lm(scale(HRS3) ~ scale(SIRacquiring) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.SIR.mod.acquiring.Span, digits = 3, show.std=T)

	scale(HRS3)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.133	0.000	-0.020 – 0.286	-0.145 – 0.145	0.087
SIRacquiring	0.298	0.282	0.053 – 0.542	0.050 – 0.514	0.018
DASSdep	-0.144	-0.137	-0.464 – 0.175	-0.441 – 0.167	0.372
DASSanx	0.155	0.148	-0.192 – 0.503	-0.184 – 0.481	0.377
DASSstress	0.322	0.307	0.011 – 0.632	0.011 – 0.603	0.042
OCIRtotNH	0.221	0.219	-0.047 – 0.489	-0.047 – 0.485	0.105
Observations	91
R² / R² adjusted	0.545 / 0.518

modelEffectSizes(HRS.SIR.mod.acquiring.Span)

lm(formula = scale(HRS3) ~ scale(SIRacquiring) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                       SSR df pEta-sqr dR-sqr
(Intercept)         1.6114  1   0.0340     NA
scale(SIRacquiring) 3.1398  1   0.0643 0.0313
scale(DASSdep)      0.4328  1   0.0094 0.0043
scale(DASSanx)      0.4237  1   0.0092 0.0042
scale(DASSstress)   2.2853  1   0.0476 0.0228
scale(OCIRtotNH)    1.4464  1   0.0307 0.0144

Sum of squared errors (SSE): 45.7
Sum of squared total  (SST): 100.4

HRS.CIR.mod.Eng <- lm(scale(HRStot) ~ scale(CIRtot) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.CIR.mod.Eng, digits = 3, show.std=T)

	scale(HRStot)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.051	0.000	-0.157 – 0.056	-0.138 – 0.138	0.350
CIRtot	0.428	0.546	0.307 – 0.548	0.392 – 0.700	<0.001
DASSdep	-0.050	-0.065	-0.212 – 0.113	-0.280 – 0.149	0.546
DASSanx	0.051	0.066	-0.103 – 0.205	-0.133 – 0.266	0.511
DASSstress	0.156	0.199	-0.058 – 0.369	-0.073 – 0.470	0.150
OCIRtotNH	0.127	0.167	-0.009 – 0.263	-0.012 – 0.347	0.067
Observations	96
R² / R² adjusted	0.559 / 0.534

modelEffectSizes(HRS.CIR.mod.Eng)

lm(formula = scale(HRStot) ~ scale(CIRtot) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                      SSR df pEta-sqr dR-sqr
(Intercept)        0.2365  1   0.0097     NA
scale(CIRtot)     13.2765  1   0.3555 0.2436
scale(DASSdep)     0.0982  1   0.0041 0.0018
scale(DASSanx)     0.1167  1   0.0048 0.0021
scale(DASSstress)  0.5628  1   0.0229 0.0103
scale(OCIRtotNH)   0.9177  1   0.0367 0.0168

Sum of squared errors (SSE): 24.1
Sum of squared total  (SST): 54.5

HRS.CIR.mod.Span <- lm(scale(HRStot) ~ scale(CIRtot) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.CIR.mod.Span, digits = 3, show.std=T)

	scale(HRStot)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.153	0.000	0.024 – 0.283	-0.128 – 0.128	0.021
CIRtot	-0.005	-0.005	-0.163 – 0.153	-0.163 – 0.153	0.950
DASSdep	0.017	0.017	-0.253 – 0.287	-0.251 – 0.284	0.902
DASSanx	0.183	0.182	-0.112 – 0.478	-0.112 – 0.475	0.222
DASSstress	0.246	0.243	-0.018 – 0.509	-0.018 – 0.504	0.067
OCIRtotNH	0.417	0.430	0.200 – 0.634	0.206 – 0.655	<0.001
Observations	92
R² / R² adjusted	0.639 / 0.618

modelEffectSizes(HRS.CIR.mod.Span)

lm(formula = scale(HRStot) ~ scale(CIRtot) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                     SSR df pEta-sqr dR-sqr
(Intercept)       2.1580  1   0.0603     NA
scale(CIRtot)     0.0016  1   0.0000 0.0000
scale(DASSdep)    0.0060  1   0.0002 0.0001
scale(DASSanx)    0.5927  1   0.0173 0.0064
scale(DASSstress) 1.3447  1   0.0384 0.0144
scale(OCIRtotNH)  5.7037  1   0.1450 0.0612

Sum of squared errors (SSE): 33.6
Sum of squared total  (SST): 93.2

HRS.CIR.item.Eng <- lm(scale(HRS1) ~ scale(CIRtot) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.CIR.item.Eng, digits = 3, show.std=T)

	scale(HRS1)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.071	-0.000	-0.181 – 0.040	-0.143 – 0.143	0.208
CIRtot	0.526	0.667	0.401 – 0.651	0.508 – 0.826	<0.001
DASSdep	-0.143	-0.188	-0.312 – 0.025	-0.408 – 0.033	0.095
DASSanx	0.112	0.144	-0.048 – 0.272	-0.062 – 0.349	0.168
DASSstress	0.154	0.194	-0.068 – 0.375	-0.086 – 0.475	0.171
OCIRtotNH	-0.061	-0.080	-0.202 – 0.080	-0.265 – 0.105	0.392
Observations	96
R² / R² adjusted	0.531 / 0.505

modelEffectSizes(HRS.CIR.item.Eng)

lm(formula = scale(HRS1) ~ scale(CIRtot) + scale(DASSdep) + scale(DASSanx) + 
    scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                      SSR df pEta-sqr dR-sqr
(Intercept)        0.4616  1   0.0175     NA
scale(CIRtot)     20.0778  1   0.4369 0.3637
scale(DASSdep)     0.8211  1   0.0308 0.0149
scale(DASSanx)     0.5550  1   0.0210 0.0101
scale(DASSstress)  0.5468  1   0.0207 0.0099
scale(OCIRtotNH)   0.2124  1   0.0081 0.0038

Sum of squared errors (SSE): 25.9
Sum of squared total  (SST): 55.2

HRS.CIR.item.Span <- lm(scale(HRS1) ~ scale(CIRtot) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.CIR.item.Span, digits = 3, show.std=T)

	scale(HRS1)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.130	0.000	-0.028 – 0.287	-0.151 – 0.151	0.105
CIRtot	0.080	0.077	-0.112 – 0.272	-0.109 – 0.264	0.410
DASSdep	0.113	0.109	-0.214 – 0.440	-0.207 – 0.425	0.495
DASSanx	0.297	0.288	-0.060 – 0.654	-0.058 – 0.634	0.102
DASSstress	-0.067	-0.064	-0.386 – 0.252	-0.372 – 0.243	0.678
OCIRtotNH	0.364	0.366	0.101 – 0.627	0.102 – 0.631	0.007
Observations	92
R² / R² adjusted	0.497 / 0.467

modelEffectSizes(HRS.CIR.item.Span)

lm(formula = scale(HRS1) ~ scale(CIRtot) + scale(DASSdep) + scale(DASSanx) + 
    scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                     SSR df pEta-sqr dR-sqr
(Intercept)       1.5413  1   0.0303     NA
scale(CIRtot)     0.3927  1   0.0079 0.0040
scale(DASSdep)    0.2690  1   0.0054 0.0027
scale(DASSanx)    1.5654  1   0.0308 0.0160
scale(DASSstress) 0.0994  1   0.0020 0.0010
scale(OCIRtotNH)  4.3395  1   0.0809 0.0443

Sum of squared errors (SSE): 49.3
Sum of squared total  (SST): 98.0

HRS.OCIR.mod.Eng <- lm(scale(HRStot) ~ scale(OCIRhoard) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==0))
tab_model(HRS.OCIR.mod.Eng, digits = 3, show.std=T)

	scale(HRStot)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	-0.059	0.000	-0.159 – 0.042	-0.130 – 0.130	0.250
OCIRhoard	0.502	0.662	0.380 – 0.623	0.502 – 0.822	<0.001
DASSdep	-0.008	-0.011	-0.161 – 0.145	-0.212 – 0.191	0.918
DASSanx	0.142	0.184	-0.005 – 0.289	-0.006 – 0.374	0.058
DASSstress	0.012	0.015	-0.197 – 0.221	-0.250 – 0.281	0.908
OCIRtotNH	0.047	0.063	-0.084 – 0.179	-0.111 – 0.236	0.475
Observations	96
R² / R² adjusted	0.609 / 0.587

modelEffectSizes(HRS.OCIR.mod.Eng)

lm(formula = scale(HRStot) ~ scale(OCIRhoard) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 0))

Coefficients
                      SSR df pEta-sqr dR-sqr
(Intercept)        0.3170  1   0.0147     NA
scale(OCIRhoard)  16.0240  1   0.4291 0.2940
scale(DASSdep)     0.0026  1   0.0001 0.0000
scale(DASSanx)     0.8734  1   0.0394 0.0160
scale(DASSstress)  0.0032  1   0.0001 0.0001
scale(OCIRtotNH)   0.1217  1   0.0057 0.0022

Sum of squared errors (SSE): 21.3
Sum of squared total  (SST): 54.5

HRS.OCIR.mod.Span <- lm(scale(HRStot) ~ scale(OCIRhoard) + scale(DASSdep) + scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data=filter(HRSdat, HRSlang==1))
tab_model(HRS.OCIR.mod.Span, digits = 3, show.std=T)

	scale(HRStot)
Predictors	Estimates	std. Beta	CI	standardized CI	p
(Intercept)	0.164	-0.000	0.039 – 0.289	-0.123 – 0.123	0.011
OCIRhoard	0.323	0.329	0.088 – 0.558	0.089 – 0.569	0.008
DASSdep	0.009	0.009	-0.248 – 0.266	-0.246 – 0.263	0.947
DASSanx	0.226	0.225	-0.058 – 0.511	-0.058 – 0.507	0.118
DASSstress	0.200	0.198	-0.054 – 0.455	-0.053 – 0.450	0.121
OCIRtotNH	0.145	0.150	-0.134 – 0.425	-0.139 – 0.439	0.305
Observations	92
R² / R² adjusted	0.668 / 0.649

modelEffectSizes(HRS.OCIR.mod.Span)

lm(formula = scale(HRStot) ~ scale(OCIRhoard) + scale(DASSdep) + 
    scale(DASSanx) + scale(DASSstress) + scale(OCIRtotNH), data = filter(HRSdat, 
    HRSlang == 1))

Coefficients
                     SSR df pEta-sqr dR-sqr
(Intercept)       2.4631  1   0.0737     NA
scale(OCIRhoard)  2.6814  1   0.0797 0.0288
scale(DASSdep)    0.0016  1   0.0001 0.0000
scale(DASSanx)    0.8999  1   0.0282 0.0097
scale(DASSstress) 0.8827  1   0.0277 0.0095
scale(OCIRtotNH)  0.3837  1   0.0122 0.0041

Sum of squared errors (SSE): 31.0
Sum of squared total  (SST): 93.2

DIF Analyses

Run DIF Model

HRSdif.beta <- lordif(HRSdat[c(108:112)], HRSdat[,25], criterion = "Beta",beta.change = .05, minCell = 5)

View Table of DIF Output

datatable(HRSdif.beta$stats, extensions = c('FixedColumns','FixedHeader'), options = list(pageLength=10, dom = 'tip', scrollX = TRUE, scrollY=TRUE, fixedHeader=TRUE, fixedColumns = list(leftColumns = 2),
  initComplete = JS("
    function(settings, json) {
      $(this.api().table().header()).css({
        'background-color': '#000',
        'color': '#fff'
      });
    }")
))

The McFadden’s beta of .0686 indicates the presence of DIF for item 1. Examining the chi square and pseudo-R2 values, it appears that there is a trend toward uniform DIF for this item.

Notably, all items had the number of reponse options reduced from 9 to 7 due to sparseness.

Visualize Results of DIF Analyses

plot.lordif(HRSdif.beta, labels = c("English", "Spanish"))