ItemAnalysis: Reliability and Validity Analysis
In Scale: Likert Type Questionnaire Item Analysis
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Performs an item analysis based on item-scale correlations, and then conducts factor analysis with one factor. Reports Cronbach alpha and single factor loadings, while it returns the original analyses from the psych package.
Usage
1
2
ItemAnalysis(prep, method="spearman", fm="gls",
nfactors=1, rcut= 0.3, score_type="z", exclude=c())
Arguments
prep
A ScaleData object pre-processed with PreProc.
nfactors
Number of factors to be extracted in validity analysis.
rcut
Lower bound for items' correlation to scale.
score_type
Type of standard scores to calculate ("z", "t", or "sten".)
method
Method to calculate the correlation matrix. Options are: "spearman" or "polychoric".
fm
Method for factor xtraction in the validity analysis.
exclude
Items to exclude from the analysis. Indices in the original order.
Details
This function is no more than a wrap-up for psych package alpha and fa functions. Use ?psych::alpha and psych::fa for details.
Available method for correlations are "spearman" and "polychoric". Available methods for factor extraction are "minres", "wls", "gls", "pa", "ml", "minchi".
Defining number of factors is included for sake of completeness. The intended use of the function is a quick and error-proof validity measure, and not factor model fitting. Adjusting the number of factors can only serve to see if there is a better model fit with more than one factor. Scores will be calculated for the first factor only. Of course if you need to use this function as a wrapper for psych::fa, you can always extract the object with YOUROBJECT$valid$model.
Default scoring is the sum of the standardized values times the first factor loadings. T-scores translate these to have a mean of 50 and an SD of 10, and STen scores, a mean of 5.5 and an SD of 2.
Value
A list of three objects. data is the dataset, passed on for other computations, rely is the output of the reliability analysis, and valid the output of the factor analysis:
data
The dataset used.
items
The item statements. If not provided value is NULL.
rely
A list of the following elements:
..alpha
Output of the psych::alpha function.
..k
Number of items
..title
Name of analysed object.
..suggest
List of 2: low_cor Items with low correlation to the rest of the scale, and a_drop Items whose deletion may improve reliability.
valid
A list of the following elements:
..model
Output of the psych::fa function
..method
character. The factor extraction method.
..loadings
numeric. The factor loadings
..kmo
list. KMO sampling adequacy statistics.
..bartlett
list. Bartlett's test of sphericity.
..scores
numeric. Factor scores (Standardized, see Details.)
Author(s)
Nikolaos Giallousis, psierevn@gmail.com.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
data(Depression98)
depressionScale <- Scale(data=Depression98,
orders=list(
c(16,19,11,9,1,17,5,18,4,8,2,12,
20,10,14,6,3,13,15,7),
c(1,18,4,15,7,8,3,14,20,6,19,16,
12,5,10,13,2,17,11,9)),
orders_id=c(
rep(1, 49),
rep(2, 49)),
reverse=c(3,4,13,14,18,20),
col_names= paste('q', 1:20, sep=''))
depressionScale
depressionPre <- PreProc(depressionScale)
depressionRel <- ItemAnalysis(depressionPre)
depressionRel
depressionRel <- ItemAnalysis(depressionPre, exclude=c(1, 3, 15, 13))
depressionRel
Example output
Loading required package: psych
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'ggplot2'
The following objects are masked from 'package:psych':
%+%, alpha
Attaching package: 'Hmisc'
The following object is masked from 'package:psych':
describe
The following objects are masked from 'package:base':
format.pval, units
Loading required package: MASS
$data
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
1 2 2 1 2 2 4 2 2 2 2 2 2 4 4 4 4 2 2 4 4
2 2 2 3 3 4 2 3 2 2 4 3 4 2 4 2 3 2 4 2 2
3 5 4 3 3 1 4 2 4 4 1 3 3 4 4 3 5 1 3 5 3
4 2 4 2 2 2 3 2 4 4 2 1 3 2 1 2 4 5 4 5 3
5 3 4 1 3 4 1 2 4 4 1 1 3 4 4 4 4 2 NA 4 4
6 3 3 1 3 2 4 1 3 4 1 1 1 4 1 5 4 5 1 2 3
7 3 2 2 5 4 4 3 1 3 2 3 2 4 3 4 4 2 4 5 4
8 1 1 1 1 1 5 5 5 5 1 1 1 5 5 5 4 1 5 1 1
9 2 4 1 4 5 5 3 4 4 2 1 3 4 4 2 4 4 4 4 4
10 2 2 1 2 3 4 2 4 4 2 2 2 4 2 4 4 4 3 4 3
11 1 1 1 1 4 1 1 4 4 1 1 1 4 1 4 4 5 5 2 4
12 1 1 1 3 1 1 2 3 5 1 1 2 2 4 4 2 2 5 2 2
13 2 4 1 1 1 5 1 5 4 1 1 1 5 1 3 5 5 5 1 4
14 4 4 2 1 1 2 1 5 5 1 1 1 4 1 5 5 5 5 1 5
15 4 2 1 1 1 2 1 4 5 1 1 1 5 1 5 1 4 5 1 1
16 5 4 2 3 5 4 4 3 1 1 1 5 2 4 4 4 5 4 4 4
17 1 5 1 3 1 4 1 2 3 1 1 4 4 2 2 1 2 4 1 4
18 4 4 1 2 2 1 1 4 1 1 2 2 3 5 2 3 1 3 2 5
19 3 2 1 4 1 2 1 4 2 1 1 1 4 1 5 3 2 5 4 3
20 2 3 1 2 1 3 1 3 1 1 1 3 3 4 2 1 1 3 3 4
21 3 2 1 5 1 1 1 1 2 2 1 2 4 1 4 1 5 4 2 2
22 4 4 2 2 4 4 1 3 2 1 1 3 4 3 4 4 3 2 4 4
23 2 2 1 2 4 4 1 4 4 1 1 1 4 4 4 4 4 2 4 4
24 5 5 1 3 4 2 2 2 2 1 2 2 3 3 4 3 2 3 3 3
25 3 5 1 3 4 2 1 3 3 1 1 5 3 5 3 3 2 4 2 4
26 5 5 1 2 3 4 2 4 5 1 1 2 4 4 4 4 2 4 2 4
27 2 2 1 2 2 5 2 2 2 1 1 3 4 4 4 5 5 2 4 5
28 4 2 1 4 1 4 1 4 1 1 1 2 4 3 3 4 5 4 4 3
29 5 5 1 4 3 4 2 1 2 1 1 4 4 4 4 5 4 2 4 4
30 1 2 1 3 1 2 1 3 3 1 1 1 4 1 4 3 5 3 3 3
31 2 2 1 1 3 3 1 5 5 1 1 2 4 4 5 3 4 5 4 3
32 4 2 1 1 3 4 2 5 3 1 1 2 5 3 4 3 4 4 4 3
33 3 3 1 1 2 2 2 5 4 1 1 1 4 2 3 1 3 4 3 3
34 1 1 1 1 4 1 1 5 4 1 1 1 5 1 5 1 5 5 1 1
35 3 3 1 4 5 4 5 5 5 1 5 4 1 5 1 5 5 4 5 5
36 4 3 1 3 1 5 4 2 2 1 2 4 3 2 4 4 5 1 3 4
37 4 3 2 4 3 4 4 2 3 2 3 3 2 3 3 4 2 3 3 4
38 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
39 4 4 2 4 2 2 1 2 4 2 1 2 4 4 4 2 2 2 2 2
40 1 1 1 4 1 2 1 1 4 1 3 2 2 1 3 1 5 2 1 3
41 4 2 4 4 4 4 4 2 1 3 NA 4 2 4 1 5 2 2 4 4
42 1 5 1 3 3 1 2 3 5 1 1 1 4 4 4 4 5 4 2 2
43 2 2 1 2 4 4 1 4 5 1 1 1 4 1 3 2 4 4 2 2
44 2 3 1 3 3 2 2 4 4 1 1 2 4 2 4 2 2 3 2 4
45 5 5 1 4 3 4 3 2 1 1 1 1 3 1 3 2 5 5 5 3
46 4 2 1 1 2 2 3 4 3 1 1 2 4 1 4 1 5 4 1 2
47 4 5 1 4 1 2 2 2 2 1 1 2 4 4 4 4 5 4 2 4
48 3 2 2 3 2 2 2 3 3 1 1 4 3 3 3 2 4 4 3 2
49 2 2 4 2 1 5 4 2 2 2 1 1 5 2 4 4 2 3 2 3
50 1 5 2 1 4 5 2 5 5 5 2 4 3 1 4 5 5 3 5 2
51 4 1 1 4 5 5 2 4 5 1 1 4 5 4 4 1 5 5 2 2
52 1 1 1 1 3 1 1 3 4 4 1 5 5 4 4 5 2 5 1 5
53 4 3 2 3 3 4 3 2 3 3 2 2 5 4 3 2 1 3 2 2
54 4 4 1 4 4 4 1 2 4 1 1 2 4 1 4 4 5 2 2 4
55 3 3 1 3 4 4 2 4 2 1 1 2 4 4 4 4 4 4 4 2
56 1 2 1 1 2 2 1 5 5 1 5 1 4 2 4 2 5 4 2 2
57 1 3 1 4 1 1 1 3 4 1 1 1 5 3 4 3 5 5 4 4
58 1 2 1 2 5 4 5 5 4 1 1 2 4 3 4 3 5 4 4 4
59 2 2 1 1 4 2 1 5 4 1 1 1 4 2 5 2 4 4 2 2
60 3 4 1 3 4 4 1 4 2 1 1 2 4 2 4 4 4 2 4 4
61 2 1 1 2 4 3 2 3 5 1 1 2 2 2 3 3 5 3 1 3
62 5 5 2 2 3 3 5 4 5 1 1 5 5 1 4 2 1 1 4 1
63 1 2 3 4 5 4 4 2 3 1 4 5 2 1 3 1 4 3 1 4
64 4 4 3 3 1 3 3 3 3 1 2 2 2 1 2 2 2 5 4 2
65 4 4 2 3 2 2 2 3 3 2 2 3 2 4 3 4 4 4 3 3
66 2 3 1 2 2 4 1 4 4 1 1 2 4 2 3 4 5 5 2 3
67 4 2 1 1 3 2 2 4 5 1 1 1 4 3 5 4 4 3 4 4
68 2 5 1 4 4 1 1 2 4 1 1 4 5 1 3 1 4 4 2 1
69 2 4 1 2 5 1 2 3 3 1 1 2 4 1 4 3 3 4 4 2
70 2 4 1 2 3 2 1 4 2 1 1 1 5 1 4 2 5 2 4 2
71 4 4 1 2 2 1 1 3 3 1 1 2 4 1 3 2 2 4 1 1
72 2 2 1 2 3 1 1 5 5 1 1 1 4 2 5 1 4 4 1 2
73 5 5 2 3 5 4 1 4 4 5 2 4 4 4 4 4 5 4 5 4
74 4 5 1 3 2 4 1 3 3 1 1 2 4 1 3 2 5 2 1 4
75 3 1 1 2 5 4 1 5 4 1 1 4 4 1 5 3 4 4 3 3
76 2 2 1 3 3 2 1 4 4 1 1 2 4 1 4 2 5 4 4 2
77 2 2 1 2 4 4 1 2 3 1 1 2 4 2 4 4 5 4 3 4
78 2 2 1 2 3 3 2 4 4 1 1 2 4 2 3 3 5 4 3 3
79 1 1 1 1 3 2 1 5 4 1 1 1 4 1 4 2 4 4 4 2
80 3 4 1 4 4 2 1 3 3 1 1 2 4 4 3 4 4 3 4 2
81 5 4 1 2 2 2 1 3 2 1 1 1 4 2 3 2 5 4 4 2
82 2 2 1 2 3 2 2 3 4 1 1 2 4 2 3 2 5 4 4 2
83 2 1 1 3 4 2 1 3 4 1 1 1 3 1 2 4 5 4 2 2
84 3 3 2 2 3 2 2 3 3 1 1 2 4 3 4 2 3 3 4 2
85 3 2 2 3 3 3 1 2 3 1 1 2 4 3 3 4 3 3 3 4
86 2 3 1 2 4 3 3 4 4 2 3 2 3 1 4 3 4 4 4 2
87 4 2 2 2 2 2 2 2 4 2 2 2 4 2 4 2 4 4 2 2
88 2 2 1 4 3 2 2 1 5 1 1 2 4 5 2 4 2 4 2 4
89 1 1 1 2 3 1 1 5 3 1 1 1 5 1 5 1 5 4 3 1
90 2 3 2 2 2 2 2 4 2 1 2 2 4 2 4 2 4 4 2 2
91 1 2 1 3 2 2 1 1 4 1 1 2 4 2 4 4 5 4 2 2
92 4 2 1 3 2 2 2 4 3 1 1 2 4 2 4 2 2 4 2 2
93 1 1 1 1 1 1 1 5 5 1 1 1 5 1 5 1 5 5 3 1
94 2 2 1 1 1 3 1 4 5 2 1 1 5 2 4 3 4 4 4 2
95 3 2 1 2 3 2 1 4 4 1 1 2 4 2 4 4 2 4 4 2
96 4 2 2 2 4 3 2 3 2 2 2 2 4 3 3 3 4 5 3 3
97 2 2 1 3 2 2 2 4 3 1 1 3 4 2 4 2 4 5 3 2
98 4 3 1 2 2 2 2 4 2 1 1 2 4 4 4 2 3 4 4 2
$orders
$orders[[1]]
[1] 16 19 11 9 1 17 5 18 4 8 2 12 20 10 14 6 3 13 15 7
$orders[[2]]
[1] 1 18 4 15 7 8 3 14 20 6 19 16 12 5 10 13 2 17 11 9
$orders_id
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[77] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
$reverse
[1] 3 4 13 14 18 20
$items
character(0)
$col_names
[1] "q1" "q2" "q3" "q4" "q5" "q6" "q7" "q8" "q9" "q10" "q11" "q12"
[13] "q13" "q14" "q15" "q16" "q17" "q18" "q19" "q20"
attr(,"class")
[1] "ScaleData"
Warning message:
In PreProc(depressionScale) :
Items' vector unspecified. out$items is NULL...
Reliability Analysis of depressionPre ScaleData object.
A spearman correlation matrix of 20 items was calculated and submitted to Reliability analysis.
The overall Cronbach's Alpha was 0.83 .
Item(s) that exhibited low correlation with the rest of the scale were:
1 and 3 .
Furthermore, deleting item(s) 1 and 3 may improve reliability.A gls factor analysis was conducted. Items were regressed to
a single factor. Their loadings are the following:
q1 q3 q15 q4 q13 q19 q8 q6
0.2265837 0.2900250 0.3400521 0.4109353 0.4173177 0.4259260 0.4320918 0.4347683
q16 q17 q14 q10 q7 q18 q2 q20
0.4515751 0.4582502 0.4727368 0.4729194 0.4816417 0.4997000 0.4998320 0.5111198
q5 q9 q11 q12
0.5168634 0.5349238 0.5471951 0.6541123
Reliability Analysis of depressionPre ScaleData object.
A spearman correlation matrix of 16 items was calculated and submitted to Reliability analysis.
The overall Cronbach's Alpha was 0.82 .
A gls factor analysis was conducted. Items were regressed to
a single factor. Their loadings are the following:
q4 q19 q6 q8 q17 q16 q10 q7
0.3996803 0.4115013 0.4171090 0.4350791 0.4368332 0.4404291 0.4488153 0.4824395
q14 q18 q2 q5 q20 q9 q11 q12
0.4997687 0.5103412 0.5116982 0.5121831 0.5279374 0.5492480 0.5515895 0.6456384
Scale documentation built on May 2, 2019, 1:27 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Performs an item analysis based on item-scale correlations, and then conducts factor analysis with one factor. Reports Cronbach alpha and single factor loadings, while it returns the original analyses from the psych package.
Usage
1 2 | ItemAnalysis(prep, method="spearman", fm="gls",
nfactors=1, rcut= 0.3, score_type="z", exclude=c())
|
Arguments
prep |
A ScaleData object pre-processed with PreProc. |
nfactors |
Number of factors to be extracted in validity analysis. |
rcut |
Lower bound for items' correlation to scale. |
score_type |
Type of standard scores to calculate ("z", "t", or "sten".) |
method |
Method to calculate the correlation matrix. Options are: "spearman" or "polychoric". |
fm |
Method for factor xtraction in the validity analysis. |
exclude |
Items to exclude from the analysis. Indices in the original order. |
Details
This function is no more than a wrap-up for psych package alpha and fa functions. Use ?psych::alpha and psych::fa for details.
Available method for correlations are "spearman" and "polychoric". Available methods for factor extraction are "minres", "wls", "gls", "pa", "ml", "minchi".
Defining number of factors is included for sake of completeness. The intended use of the function is a quick and error-proof validity measure, and not factor model fitting. Adjusting the number of factors can only serve to see if there is a better model fit with more than one factor. Scores will be calculated for the first factor only. Of course if you need to use this function as a wrapper for psych::fa, you can always extract the object with YOUROBJECT$valid$model.
Default scoring is the sum of the standardized values times the first factor loadings. T-scores translate these to have a mean of 50 and an SD of 10, and STen scores, a mean of 5.5 and an SD of 2.
Value
A list of three objects. data is the dataset, passed on for other computations, rely is the output of the reliability analysis, and valid the output of the factor analysis:
|
The dataset used. |
|
The item statements. If not provided value is NULL. |
|
A list of the following elements: |
..alpha |
Output of the |
..k |
Number of items |
..title |
Name of analysed object. |
..suggest |
List of 2: |
|
A list of the following elements: |
..model |
Output of the |
..method |
character. The factor extraction method. |
..loadings |
numeric. The factor loadings |
..kmo |
list. KMO sampling adequacy statistics. |
..bartlett |
list. Bartlett's test of sphericity. |
..scores |
numeric. Factor scores (Standardized, see Details.) |
Author(s)
Nikolaos Giallousis, psierevn@gmail.com.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | data(Depression98)
depressionScale <- Scale(data=Depression98,
orders=list(
c(16,19,11,9,1,17,5,18,4,8,2,12,
20,10,14,6,3,13,15,7),
c(1,18,4,15,7,8,3,14,20,6,19,16,
12,5,10,13,2,17,11,9)),
orders_id=c(
rep(1, 49),
rep(2, 49)),
reverse=c(3,4,13,14,18,20),
col_names= paste('q', 1:20, sep=''))
depressionScale
depressionPre <- PreProc(depressionScale)
depressionRel <- ItemAnalysis(depressionPre)
depressionRel
depressionRel <- ItemAnalysis(depressionPre, exclude=c(1, 3, 15, 13))
depressionRel
|
Example output
Loading required package: psych
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'ggplot2'
The following objects are masked from 'package:psych':
%+%, alpha
Attaching package: 'Hmisc'
The following object is masked from 'package:psych':
describe
The following objects are masked from 'package:base':
format.pval, units
Loading required package: MASS
$data
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20
1 2 2 1 2 2 4 2 2 2 2 2 2 4 4 4 4 2 2 4 4
2 2 2 3 3 4 2 3 2 2 4 3 4 2 4 2 3 2 4 2 2
3 5 4 3 3 1 4 2 4 4 1 3 3 4 4 3 5 1 3 5 3
4 2 4 2 2 2 3 2 4 4 2 1 3 2 1 2 4 5 4 5 3
5 3 4 1 3 4 1 2 4 4 1 1 3 4 4 4 4 2 NA 4 4
6 3 3 1 3 2 4 1 3 4 1 1 1 4 1 5 4 5 1 2 3
7 3 2 2 5 4 4 3 1 3 2 3 2 4 3 4 4 2 4 5 4
8 1 1 1 1 1 5 5 5 5 1 1 1 5 5 5 4 1 5 1 1
9 2 4 1 4 5 5 3 4 4 2 1 3 4 4 2 4 4 4 4 4
10 2 2 1 2 3 4 2 4 4 2 2 2 4 2 4 4 4 3 4 3
11 1 1 1 1 4 1 1 4 4 1 1 1 4 1 4 4 5 5 2 4
12 1 1 1 3 1 1 2 3 5 1 1 2 2 4 4 2 2 5 2 2
13 2 4 1 1 1 5 1 5 4 1 1 1 5 1 3 5 5 5 1 4
14 4 4 2 1 1 2 1 5 5 1 1 1 4 1 5 5 5 5 1 5
15 4 2 1 1 1 2 1 4 5 1 1 1 5 1 5 1 4 5 1 1
16 5 4 2 3 5 4 4 3 1 1 1 5 2 4 4 4 5 4 4 4
17 1 5 1 3 1 4 1 2 3 1 1 4 4 2 2 1 2 4 1 4
18 4 4 1 2 2 1 1 4 1 1 2 2 3 5 2 3 1 3 2 5
19 3 2 1 4 1 2 1 4 2 1 1 1 4 1 5 3 2 5 4 3
20 2 3 1 2 1 3 1 3 1 1 1 3 3 4 2 1 1 3 3 4
21 3 2 1 5 1 1 1 1 2 2 1 2 4 1 4 1 5 4 2 2
22 4 4 2 2 4 4 1 3 2 1 1 3 4 3 4 4 3 2 4 4
23 2 2 1 2 4 4 1 4 4 1 1 1 4 4 4 4 4 2 4 4
24 5 5 1 3 4 2 2 2 2 1 2 2 3 3 4 3 2 3 3 3
25 3 5 1 3 4 2 1 3 3 1 1 5 3 5 3 3 2 4 2 4
26 5 5 1 2 3 4 2 4 5 1 1 2 4 4 4 4 2 4 2 4
27 2 2 1 2 2 5 2 2 2 1 1 3 4 4 4 5 5 2 4 5
28 4 2 1 4 1 4 1 4 1 1 1 2 4 3 3 4 5 4 4 3
29 5 5 1 4 3 4 2 1 2 1 1 4 4 4 4 5 4 2 4 4
30 1 2 1 3 1 2 1 3 3 1 1 1 4 1 4 3 5 3 3 3
31 2 2 1 1 3 3 1 5 5 1 1 2 4 4 5 3 4 5 4 3
32 4 2 1 1 3 4 2 5 3 1 1 2 5 3 4 3 4 4 4 3
33 3 3 1 1 2 2 2 5 4 1 1 1 4 2 3 1 3 4 3 3
34 1 1 1 1 4 1 1 5 4 1 1 1 5 1 5 1 5 5 1 1
35 3 3 1 4 5 4 5 5 5 1 5 4 1 5 1 5 5 4 5 5
36 4 3 1 3 1 5 4 2 2 1 2 4 3 2 4 4 5 1 3 4
37 4 3 2 4 3 4 4 2 3 2 3 3 2 3 3 4 2 3 3 4
38 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
39 4 4 2 4 2 2 1 2 4 2 1 2 4 4 4 2 2 2 2 2
40 1 1 1 4 1 2 1 1 4 1 3 2 2 1 3 1 5 2 1 3
41 4 2 4 4 4 4 4 2 1 3 NA 4 2 4 1 5 2 2 4 4
42 1 5 1 3 3 1 2 3 5 1 1 1 4 4 4 4 5 4 2 2
43 2 2 1 2 4 4 1 4 5 1 1 1 4 1 3 2 4 4 2 2
44 2 3 1 3 3 2 2 4 4 1 1 2 4 2 4 2 2 3 2 4
45 5 5 1 4 3 4 3 2 1 1 1 1 3 1 3 2 5 5 5 3
46 4 2 1 1 2 2 3 4 3 1 1 2 4 1 4 1 5 4 1 2
47 4 5 1 4 1 2 2 2 2 1 1 2 4 4 4 4 5 4 2 4
48 3 2 2 3 2 2 2 3 3 1 1 4 3 3 3 2 4 4 3 2
49 2 2 4 2 1 5 4 2 2 2 1 1 5 2 4 4 2 3 2 3
50 1 5 2 1 4 5 2 5 5 5 2 4 3 1 4 5 5 3 5 2
51 4 1 1 4 5 5 2 4 5 1 1 4 5 4 4 1 5 5 2 2
52 1 1 1 1 3 1 1 3 4 4 1 5 5 4 4 5 2 5 1 5
53 4 3 2 3 3 4 3 2 3 3 2 2 5 4 3 2 1 3 2 2
54 4 4 1 4 4 4 1 2 4 1 1 2 4 1 4 4 5 2 2 4
55 3 3 1 3 4 4 2 4 2 1 1 2 4 4 4 4 4 4 4 2
56 1 2 1 1 2 2 1 5 5 1 5 1 4 2 4 2 5 4 2 2
57 1 3 1 4 1 1 1 3 4 1 1 1 5 3 4 3 5 5 4 4
58 1 2 1 2 5 4 5 5 4 1 1 2 4 3 4 3 5 4 4 4
59 2 2 1 1 4 2 1 5 4 1 1 1 4 2 5 2 4 4 2 2
60 3 4 1 3 4 4 1 4 2 1 1 2 4 2 4 4 4 2 4 4
61 2 1 1 2 4 3 2 3 5 1 1 2 2 2 3 3 5 3 1 3
62 5 5 2 2 3 3 5 4 5 1 1 5 5 1 4 2 1 1 4 1
63 1 2 3 4 5 4 4 2 3 1 4 5 2 1 3 1 4 3 1 4
64 4 4 3 3 1 3 3 3 3 1 2 2 2 1 2 2 2 5 4 2
65 4 4 2 3 2 2 2 3 3 2 2 3 2 4 3 4 4 4 3 3
66 2 3 1 2 2 4 1 4 4 1 1 2 4 2 3 4 5 5 2 3
67 4 2 1 1 3 2 2 4 5 1 1 1 4 3 5 4 4 3 4 4
68 2 5 1 4 4 1 1 2 4 1 1 4 5 1 3 1 4 4 2 1
69 2 4 1 2 5 1 2 3 3 1 1 2 4 1 4 3 3 4 4 2
70 2 4 1 2 3 2 1 4 2 1 1 1 5 1 4 2 5 2 4 2
71 4 4 1 2 2 1 1 3 3 1 1 2 4 1 3 2 2 4 1 1
72 2 2 1 2 3 1 1 5 5 1 1 1 4 2 5 1 4 4 1 2
73 5 5 2 3 5 4 1 4 4 5 2 4 4 4 4 4 5 4 5 4
74 4 5 1 3 2 4 1 3 3 1 1 2 4 1 3 2 5 2 1 4
75 3 1 1 2 5 4 1 5 4 1 1 4 4 1 5 3 4 4 3 3
76 2 2 1 3 3 2 1 4 4 1 1 2 4 1 4 2 5 4 4 2
77 2 2 1 2 4 4 1 2 3 1 1 2 4 2 4 4 5 4 3 4
78 2 2 1 2 3 3 2 4 4 1 1 2 4 2 3 3 5 4 3 3
79 1 1 1 1 3 2 1 5 4 1 1 1 4 1 4 2 4 4 4 2
80 3 4 1 4 4 2 1 3 3 1 1 2 4 4 3 4 4 3 4 2
81 5 4 1 2 2 2 1 3 2 1 1 1 4 2 3 2 5 4 4 2
82 2 2 1 2 3 2 2 3 4 1 1 2 4 2 3 2 5 4 4 2
83 2 1 1 3 4 2 1 3 4 1 1 1 3 1 2 4 5 4 2 2
84 3 3 2 2 3 2 2 3 3 1 1 2 4 3 4 2 3 3 4 2
85 3 2 2 3 3 3 1 2 3 1 1 2 4 3 3 4 3 3 3 4
86 2 3 1 2 4 3 3 4 4 2 3 2 3 1 4 3 4 4 4 2
87 4 2 2 2 2 2 2 2 4 2 2 2 4 2 4 2 4 4 2 2
88 2 2 1 4 3 2 2 1 5 1 1 2 4 5 2 4 2 4 2 4
89 1 1 1 2 3 1 1 5 3 1 1 1 5 1 5 1 5 4 3 1
90 2 3 2 2 2 2 2 4 2 1 2 2 4 2 4 2 4 4 2 2
91 1 2 1 3 2 2 1 1 4 1 1 2 4 2 4 4 5 4 2 2
92 4 2 1 3 2 2 2 4 3 1 1 2 4 2 4 2 2 4 2 2
93 1 1 1 1 1 1 1 5 5 1 1 1 5 1 5 1 5 5 3 1
94 2 2 1 1 1 3 1 4 5 2 1 1 5 2 4 3 4 4 4 2
95 3 2 1 2 3 2 1 4 4 1 1 2 4 2 4 4 2 4 4 2
96 4 2 2 2 4 3 2 3 2 2 2 2 4 3 3 3 4 5 3 3
97 2 2 1 3 2 2 2 4 3 1 1 3 4 2 4 2 4 5 3 2
98 4 3 1 2 2 2 2 4 2 1 1 2 4 4 4 2 3 4 4 2
$orders
$orders[[1]]
[1] 16 19 11 9 1 17 5 18 4 8 2 12 20 10 14 6 3 13 15 7
$orders[[2]]
[1] 1 18 4 15 7 8 3 14 20 6 19 16 12 5 10 13 2 17 11 9
$orders_id
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[77] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
$reverse
[1] 3 4 13 14 18 20
$items
character(0)
$col_names
[1] "q1" "q2" "q3" "q4" "q5" "q6" "q7" "q8" "q9" "q10" "q11" "q12"
[13] "q13" "q14" "q15" "q16" "q17" "q18" "q19" "q20"
attr(,"class")
[1] "ScaleData"
Warning message:
In PreProc(depressionScale) :
Items' vector unspecified. out$items is NULL...
Reliability Analysis of depressionPre ScaleData object.
A spearman correlation matrix of 20 items was calculated and submitted to Reliability analysis.
The overall Cronbach's Alpha was 0.83 .
Item(s) that exhibited low correlation with the rest of the scale were:
1 and 3 .
Furthermore, deleting item(s) 1 and 3 may improve reliability.A gls factor analysis was conducted. Items were regressed to
a single factor. Their loadings are the following:
q1 q3 q15 q4 q13 q19 q8 q6
0.2265837 0.2900250 0.3400521 0.4109353 0.4173177 0.4259260 0.4320918 0.4347683
q16 q17 q14 q10 q7 q18 q2 q20
0.4515751 0.4582502 0.4727368 0.4729194 0.4816417 0.4997000 0.4998320 0.5111198
q5 q9 q11 q12
0.5168634 0.5349238 0.5471951 0.6541123
Reliability Analysis of depressionPre ScaleData object.
A spearman correlation matrix of 16 items was calculated and submitted to Reliability analysis.
The overall Cronbach's Alpha was 0.82 .
A gls factor analysis was conducted. Items were regressed to
a single factor. Their loadings are the following:
q4 q19 q6 q8 q17 q16 q10 q7
0.3996803 0.4115013 0.4171090 0.4350791 0.4368332 0.4404291 0.4488153 0.4824395
q14 q18 q2 q5 q20 q9 q11 q12
0.4997687 0.5103412 0.5116982 0.5121831 0.5279374 0.5492480 0.5515895 0.6456384
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