composite: Composite
In multicon: Multivariate Constructs
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Returns a mean composite for each observation (row) in the set
Usage
1
Arguments
set
A matrix or data.frame of the scores in the columns to be averaged
R
A numeric vector specifying the columns in set that should be reverse scored prior to averaging.
Zitems
A logical indicating whether the items should be standardized (Z-scored) before creating a composite. This is probably most useful when items have been measured on different scales.
maxScore
A numeric element indicating the maximum possible score on each scale. If R = NULL then this is not needed. If not provided, composite will try to find the maximum possible score on its own.
rel
A logical indicating whether the reliability information (alpha, avg r, etc.) for the composite should be printed (not returned however).
nomiss
A numeric vector specifying the proporiton of valid cases in set (i.e. data that must not be NA) for the mean to be returned
tr
Amount of trimming to be done before calculating the mean
Details
This function is used to create a unit-weighted composite of the variables listed in the columns of the matrix or data.frame "set" for each row. The nomiss option lets one specify the proportion of valid cases required for the composite mean to be computed. By default, the mean is computed if at least 80 precent of the data in the the row are valid, the mean results otherwise NA results.
Value
Returns a list of composite scores corresponding to each row of 'set'
Author(s)
Ryne A. Sherman
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
data(RSPdata)
names(RSPdata)
# Forming a composite:
# We will form a composite extraversion variable using BFI scores.
# First put the variables into one data.frame
ext.vars <- data.frame(RSPdata$sBFI1, RSPdata$sBFI6, RSPdata$sBFI11,
RSPdata$sBFI16, RSPdata$sBFI21, RSPdata$sBFI26, RSPdata$sBFI31, RSPdata$sBFI36)
head(ext.vars) # Looks good
# Three items need to be reverse scored
ext.comp <- composite(ext.vars, R = c(2,5,7), rel = TRUE)
ext.comp
# Let's say we want to include the CAQ item "04 - Is a talkative Individual" in our
# extraversion composite. But is is measured on a 1 to 9 scale while the BFI variables
# are measured on a 1 to 5 scale. We should set Zitems=TRUE to Z-score all of the
# items before compositing.
ext.comp2 <- composite(data.frame(ext.vars, RSPdata$sCAQ004),
R = c(2,5,7), rel= TRUE, Zitems = TRUE)
describe(ext.comp2) # mean is zero
Example output
Loading required package: psych
Loading required package: abind
Loading required package: foreach
[1] "SID" "ssex" "sBFI1" "sBFI2" "sBFI3"
[6] "sBFI4" "sBFI5" "sBFI6" "sBFI7" "sBFI8"
[11] "sBFI9" "sBFI10" "sBFI11" "sBFI12" "sBFI13"
[16] "sBFI14" "sBFI15" "sBFI16" "sBFI17" "sBFI18"
[21] "sBFI19" "sBFI20" "sBFI21" "sBFI22" "sBFI23"
[26] "sBFI24" "sBFI25" "sBFI26" "sBFI27" "sBFI28"
[31] "sBFI29" "sBFI30" "sBFI31" "sBFI32" "sBFI33"
[36] "sBFI34" "sBFI35" "sBFI36" "sBFI37" "sBFI38"
[41] "sBFI39" "sBFI40" "sBFI41" "sBFI42" "sBFI43"
[46] "sBFI44" "sEXT" "sAGR" "sCON" "sNEUR"
[51] "sOPEN" "sCAQ001" "sCAQ002" "sCAQ003" "sCAQ004"
[56] "sCAQ005" "sCAQ006" "sCAQ007" "sCAQ008" "sCAQ009"
[61] "sCAQ010" "sCAQ011" "sCAQ012" "sCAQ013" "sCAQ014"
[66] "sCAQ015" "sCAQ016" "sCAQ017" "sCAQ018" "sCAQ019"
[71] "sCAQ020" "sCAQ021" "sCAQ022" "sCAQ023" "sCAQ024"
[76] "sCAQ025" "sCAQ026" "sCAQ027" "sCAQ028" "sCAQ029"
[81] "sCAQ030" "sCAQ031" "sCAQ032" "sCAQ033" "sCAQ034"
[86] "sCAQ035" "sCAQ036" "sCAQ037" "sCAQ038" "sCAQ039"
[91] "sCAQ040" "sCAQ041" "sCAQ042" "sCAQ043" "sCAQ044"
[96] "sCAQ045" "sCAQ046" "sCAQ047" "sCAQ048" "sCAQ049"
[101] "sCAQ050" "sCAQ051" "sCAQ052" "sCAQ053" "sCAQ054"
[106] "sCAQ055" "sCAQ056" "sCAQ057" "sCAQ058" "sCAQ059"
[111] "sCAQ060" "sCAQ061" "sCAQ062" "sCAQ063" "sCAQ064"
[116] "sCAQ065" "sCAQ066" "sCAQ067" "sCAQ068" "sCAQ069"
[121] "sCAQ070" "sCAQ071" "sCAQ072" "sCAQ073" "sCAQ074"
[126] "sCAQ075" "sCAQ076" "sCAQ077" "sCAQ078" "sCAQ079"
[131] "sCAQ080" "sCAQ081" "sCAQ082" "sCAQ083" "sCAQ084"
[136] "sCAQ085" "sCAQ086" "sCAQ087" "sCAQ088" "sCAQ089"
[141] "sCAQ090" "sCAQ091" "sCAQ092" "sCAQ093" "sCAQ094"
[146] "sCAQ095" "sCAQ096" "sCAQ097" "sCAQ098" "sCAQ099"
[151] "sCAQ100" "v2rbq001" "v2rbq002" "v2rbq003" "v2rbq004"
[156] "v2rbq005" "v2rbq006" "v2rbq007" "v2rbq008" "v2rbq009"
[161] "v2rbq010" "v2rbq011" "v2rbq012" "v2rbq013" "v2rbq014"
[166] "v2rbq015" "v2rbq016" "v2rbq017" "v2rbq018" "v2rbq019"
[171] "v2rbq020" "v2rbq021" "v2rbq022" "v2rbq023" "v2rbq024"
[176] "v2rbq025" "v2rbq026" "v2rbq027" "v2rbq028" "v2rbq029"
[181] "v2rbq030" "v2rbq031" "v2rbq032" "v2rbq033" "v2rbq034"
[186] "v2rbq035" "v2rbq036" "v2rbq037" "v2rbq038" "v2rbq039"
[191] "v2rbq040" "v2rbq041" "v2rbq042" "v2rbq043" "v2rbq044"
[196] "v2rbq045" "v2rbq046" "v2rbq047" "v2rbq048" "v2rbq049"
[201] "v2rbq050" "v2rbq051" "v2rbq052" "v2rbq053" "v2rbq054"
[206] "v2rbq055" "v2rbq056" "v2rbq057" "v2rbq058" "v2rbq059"
[211] "v2rbq060" "v2rbq061" "v2rbq062" "v2rbq063" "v2rbq064"
[216] "v2rbq065" "v2rbq066" "v2rbq067" "v3rbq001" "v3rbq002"
[221] "v3rbq003" "v3rbq004" "v3rbq005" "v3rbq006" "v3rbq007"
[226] "v3rbq008" "v3rbq009" "v3rbq010" "v3rbq011" "v3rbq012"
[231] "v3rbq013" "v3rbq014" "v3rbq015" "v3rbq016" "v3rbq017"
[236] "v3rbq018" "v3rbq019" "v3rbq020" "v3rbq021" "v3rbq022"
[241] "v3rbq023" "v3rbq024" "v3rbq025" "v3rbq026" "v3rbq027"
[246] "v3rbq028" "v3rbq029" "v3rbq030" "v3rbq031" "v3rbq032"
[251] "v3rbq033" "v3rbq034" "v3rbq035" "v3rbq036" "v3rbq037"
[256] "v3rbq038" "v3rbq039" "v3rbq040" "v3rbq041" "v3rbq042"
[261] "v3rbq043" "v3rbq044" "v3rbq045" "v3rbq046" "v3rbq047"
[266] "v3rbq048" "v3rbq049" "v3rbq050" "v3rbq051" "v3rbq052"
[271] "v3rbq053" "v3rbq054" "v3rbq055" "v3rbq056" "v3rbq057"
[276] "v3rbq058" "v3rbq059" "v3rbq060" "v3rbq061" "v3rbq062"
[281] "v3rbq063" "v3rbq064" "v3rbq065" "v3rbq066" "v3rbq067"
[286] "v4rbq001" "v4rbq002" "v4rbq003" "v4rbq004" "v4rbq005"
[291] "v4rbq006" "v4rbq007" "v4rbq008" "v4rbq009" "v4rbq010"
[296] "v4rbq011" "v4rbq012" "v4rbq013" "v4rbq014" "v4rbq015"
[301] "v4rbq016" "v4rbq017" "v4rbq018" "v4rbq019" "v4rbq020"
[306] "v4rbq021" "v4rbq022" "v4rbq023" "v4rbq024" "v4rbq025"
[311] "v4rbq026" "v4rbq027" "v4rbq028" "v4rbq029" "v4rbq030"
[316] "v4rbq031" "v4rbq032" "v4rbq033" "v4rbq034" "v4rbq035"
[321] "v4rbq036" "v4rbq037" "v4rbq038" "v4rbq039" "v4rbq040"
[326] "v4rbq041" "v4rbq042" "v4rbq043" "v4rbq044" "v4rbq045"
[331] "v4rbq046" "v4rbq047" "v4rbq048" "v4rbq049" "v4rbq050"
[336] "v4rbq051" "v4rbq052" "v4rbq053" "v4rbq054" "v4rbq055"
[341] "v4rbq056" "v4rbq057" "v4rbq058" "v4rbq059" "v4rbq060"
[346] "v4rbq061" "v4rbq062" "v4rbq063" "v4rbq064" "v4rbq065"
[351] "v4rbq066" "v4rbq067" "v5rbq001" "v5rbq002" "v5rbq003"
[356] "v5rbq004" "v5rbq005" "v5rbq006" "v5rbq007" "v5rbq008"
[361] "v5rbq009" "v5rbq010" "v5rbq011" "v5rbq012" "v5rbq013"
[366] "v5rbq014" "v5rbq015" "v5rbq016" "v5rbq017" "v5rbq018"
[371] "v5rbq019" "v5rbq020" "v5rbq021" "v5rbq022" "v5rbq023"
[376] "v5rbq024" "v5rbq025" "v5rbq026" "v5rbq027" "v5rbq028"
[381] "v5rbq029" "v5rbq030" "v5rbq031" "v5rbq032" "v5rbq033"
[386] "v5rbq034" "v5rbq035" "v5rbq036" "v5rbq037" "v5rbq038"
[391] "v5rbq039" "v5rbq040" "v5rbq041" "v5rbq042" "v5rbq043"
[396] "v5rbq044" "v5rbq045" "v5rbq046" "v5rbq047" "v5rbq048"
[401] "v5rbq049" "v5rbq050" "v5rbq051" "v5rbq052" "v5rbq053"
[406] "v5rbq054" "v5rbq055" "v5rbq056" "v5rbq057" "v5rbq058"
[411] "v5rbq059" "v5rbq060" "v5rbq061" "v5rbq062" "v5rbq063"
[416] "v5rbq064" "v5rbq065" "v5rbq066" "v5rbq067" "acq1CAQ001"
[421] "acq1CAQ002" "acq1CAQ003" "acq1CAQ004" "acq1CAQ005" "acq1CAQ006"
[426] "acq1CAQ007" "acq1CAQ008" "acq1CAQ009" "acq1CAQ010" "acq1CAQ011"
[431] "acq1CAQ012" "acq1CAQ013" "acq1CAQ014" "acq1CAQ015" "acq1CAQ016"
[436] "acq1CAQ017" "acq1CAQ018" "acq1CAQ019" "acq1CAQ020" "acq1CAQ021"
[441] "acq1CAQ022" "acq1CAQ023" "acq1CAQ024" "acq1CAQ025" "acq1CAQ026"
[446] "acq1CAQ027" "acq1CAQ028" "acq1CAQ029" "acq1CAQ030" "acq1CAQ031"
[451] "acq1CAQ032" "acq1CAQ033" "acq1CAQ034" "acq1CAQ035" "acq1CAQ036"
[456] "acq1CAQ037" "acq1CAQ038" "acq1CAQ039" "acq1CAQ040" "acq1CAQ041"
[461] "acq1CAQ042" "acq1CAQ043" "acq1CAQ044" "acq1CAQ045" "acq1CAQ046"
[466] "acq1CAQ047" "acq1CAQ048" "acq1CAQ049" "acq1CAQ050" "acq1CAQ051"
[471] "acq1CAQ052" "acq1CAQ053" "acq1CAQ054" "acq1CAQ055" "acq1CAQ056"
[476] "acq1CAQ057" "acq1CAQ058" "acq1CAQ059" "acq1CAQ060" "acq1CAQ061"
[481] "acq1CAQ062" "acq1CAQ063" "acq1CAQ064" "acq1CAQ065" "acq1CAQ066"
[486] "acq1CAQ067" "acq1CAQ068" "acq1CAQ069" "acq1CAQ070" "acq1CAQ071"
[491] "acq1CAQ072" "acq1CAQ073" "acq1CAQ074" "acq1CAQ075" "acq1CAQ076"
[496] "acq1CAQ077" "acq1CAQ078" "acq1CAQ079" "acq1CAQ080" "acq1CAQ081"
[501] "acq1CAQ082" "acq1CAQ083" "acq1CAQ084" "acq1CAQ085" "acq1CAQ086"
[506] "acq1CAQ087" "acq1CAQ088" "acq1CAQ089" "acq1CAQ090" "acq1CAQ091"
[511] "acq1CAQ092" "acq1CAQ093" "acq1CAQ094" "acq1CAQ095" "acq1CAQ096"
[516] "acq1CAQ097" "acq1CAQ098" "acq1CAQ099" "acq1CAQ100" "acq2CAQ001"
[521] "acq2CAQ002" "acq2CAQ003" "acq2CAQ004" "acq2CAQ005" "acq2CAQ006"
[526] "acq2CAQ007" "acq2CAQ008" "acq2CAQ009" "acq2CAQ010" "acq2CAQ011"
[531] "acq2CAQ012" "acq2CAQ013" "acq2CAQ014" "acq2CAQ015" "acq2CAQ016"
[536] "acq2CAQ017" "acq2CAQ018" "acq2CAQ019" "acq2CAQ020" "acq2CAQ021"
[541] "acq2CAQ022" "acq2CAQ023" "acq2CAQ024" "acq2CAQ025" "acq2CAQ026"
[546] "acq2CAQ027" "acq2CAQ028" "acq2CAQ029" "acq2CAQ030" "acq2CAQ031"
[551] "acq2CAQ032" "acq2CAQ033" "acq2CAQ034" "acq2CAQ035" "acq2CAQ036"
[556] "acq2CAQ037" "acq2CAQ038" "acq2CAQ039" "acq2CAQ040" "acq2CAQ041"
[561] "acq2CAQ042" "acq2CAQ043" "acq2CAQ044" "acq2CAQ045" "acq2CAQ046"
[566] "acq2CAQ047" "acq2CAQ048" "acq2CAQ049" "acq2CAQ050" "acq2CAQ051"
[571] "acq2CAQ052" "acq2CAQ053" "acq2CAQ054" "acq2CAQ055" "acq2CAQ056"
[576] "acq2CAQ057" "acq2CAQ058" "acq2CAQ059" "acq2CAQ060" "acq2CAQ061"
[581] "acq2CAQ062" "acq2CAQ063" "acq2CAQ064" "acq2CAQ065" "acq2CAQ066"
[586] "acq2CAQ067" "acq2CAQ068" "acq2CAQ069" "acq2CAQ070" "acq2CAQ071"
[591] "acq2CAQ072" "acq2CAQ073" "acq2CAQ074" "acq2CAQ075" "acq2CAQ076"
[596] "acq2CAQ077" "acq2CAQ078" "acq2CAQ079" "acq2CAQ080" "acq2CAQ081"
[601] "acq2CAQ082" "acq2CAQ083" "acq2CAQ084" "acq2CAQ085" "acq2CAQ086"
[606] "acq2CAQ087" "acq2CAQ088" "acq2CAQ089" "acq2CAQ090" "acq2CAQ091"
[611] "acq2CAQ092" "acq2CAQ093" "acq2CAQ094" "acq2CAQ095" "acq2CAQ096"
[616] "acq2CAQ097" "acq2CAQ098" "acq2CAQ099" "acq2CAQ100"
RSPdata.sBFI1 RSPdata.sBFI6 RSPdata.sBFI11 RSPdata.sBFI16 RSPdata.sBFI21
1 5 1 4 4 1
2 4 3 5 5 4
3 3 4 2 3 4
4 4 3 4 4 3
5 3 2 4 4 3
6 5 3 4 4 2
RSPdata.sBFI26 RSPdata.sBFI31 RSPdata.sBFI36
1 4 1 5
2 3 5 4
3 3 4 2
4 3 4 3
5 4 4 4
6 4 2 5
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
0.8522702 0.8519561 0.8596228 0.4183829 5.754753 0.01539347 3.392073 0.6979583
median_r
0.4063875
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
4.625 3.375 2.375 3.250 3.500 4.125 3.000 4.375 1.875 1.875 3.500 2.375 4.125
X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26
3.375 2.500 3.375 2.750 2.375 3.500 2.500 2.875 2.375 1.750 3.500 3.250 3.375
X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38 X39
2.750 4.500 2.500 3.375 3.625 2.750 2.000 2.750 2.500 2.250 2.750 3.125 3.750
X40 X41 X42 X43 X44 X45 X46 X47 X48 X49 X50 X51 X52
3.000 4.125 3.500 3.875 2.375 3.125 4.000 2.875 2.375 3.500 3.500 2.750 3.875
X53 X54 X55 X56 X57 X58 X59 X60 X61 X62 X63 X64 X65
4.625 3.500 3.125 3.875 4.375 2.625 2.875 4.125 3.125 3.500 3.375 3.375 4.250
X66 X67 X68 X69 X70 X71 X72 X73 X74 X75 X76 X77 X78
3.375 4.000 3.500 3.875 4.250 3.375 3.750 3.000 3.625 4.500 3.875 2.500 2.500
X79 X80 X81 X82 X83 X84 X85 X86 X87 X88 X89 X90 X91
3.500 4.875 3.875 3.375 2.750 2.625 3.625 3.500 2.750 3.500 4.000 3.250 3.125
X92 X93 X94 X95 X96 X97 X98 X99 X100 X101 X102 X103 X104
2.875 1.875 3.125 3.125 4.125 3.375 4.875 3.750 4.000 3.375 4.375 3.625 3.375
X105 X106 X107 X108 X109 X110 X111 X112 X113 X114 X115 X116 X117
3.875 3.375 3.500 4.000 3.000 4.250 3.625 2.875 4.500 4.125 4.875 3.375 2.750
X118 X119 X120 X121 X122 X123 X124 X125 X126 X127 X128 X129 X130
1.750 2.500 3.625 3.750 4.500 3.000 2.625 3.625 2.875 3.750 2.875 3.625 3.625
X131 X132 X133 X134 X135 X136 X137 X138 X139 X140 X141 X142 X143
3.500 2.875 4.000 3.750 3.500 3.500 3.250 2.375 3.250 3.750 3.125 2.750 4.875
X144 X145 X146 X147 X148 X149 X150 X151 X152 X153 X154 X155 X156
2.875 4.125 3.125 3.000 3.125 3.750 3.750 3.000 3.250 3.375 3.250 2.625 3.750
X157 X158 X159 X160 X161 X162 X163 X164 X165 X166 X167 X168 X169
2.500 3.000 3.625 4.250 4.125 4.125 2.375 2.000 4.125 2.375 3.500 3.875 3.750
X170 X171 X172 X173 X174 X175 X176 X177 X178 X179 X180 X181 X182
4.000 2.875 4.250 3.750 3.750 4.625 3.875 3.125 3.000 3.125 4.125 3.750 4.000
X183 X184 X185 X186 X187 X188 X189 X190 X191 X192 X193 X194 X195
4.875 3.000 1.375 3.125 3.125 3.875 4.250 3.750 2.500 4.250 3.375 4.375 2.750
X196 X197 X198 X199 X200 X201 X202 X203 X204 X205
4.000 2.500 4.750 3.625 4.375 3.375 4.125 4.000 2.750 2.750
raw_alpha std.alpha G6(smc) average_r S/N ase mean
0.8746749 0.8746749 0.8867729 0.4367695 6.979247 0.01319109 2.289676e-17
sd median_r
0.7066475 0.4160115
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 205 0 0.71 0.06 0.02 0.76 -2.11 1.45 3.56 -0.27 -0.35 0.05
multicon documentation built on May 2, 2019, 3:18 a.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Returns a mean composite for each observation (row) in the set
Usage
1 |
Arguments
set |
A matrix or data.frame of the scores in the columns to be averaged |
R |
A numeric vector specifying the columns in set that should be reverse scored prior to averaging. |
Zitems |
A logical indicating whether the items should be standardized (Z-scored) before creating a composite. This is probably most useful when items have been measured on different scales. |
maxScore |
A numeric element indicating the maximum possible score on each scale. If R = NULL then this is not needed. If not provided, composite will try to find the maximum possible score on its own. |
rel |
A logical indicating whether the reliability information (alpha, avg r, etc.) for the composite should be printed (not returned however). |
nomiss |
A numeric vector specifying the proporiton of valid cases in set (i.e. data that must not be NA) for the mean to be returned |
tr |
Amount of trimming to be done before calculating the mean |
Details
This function is used to create a unit-weighted composite of the variables listed in the columns of the matrix or data.frame "set" for each row. The nomiss option lets one specify the proportion of valid cases required for the composite mean to be computed. By default, the mean is computed if at least 80 precent of the data in the the row are valid, the mean results otherwise NA results.
Value
Returns a list of composite scores corresponding to each row of 'set'
Author(s)
Ryne A. Sherman
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data(RSPdata)
names(RSPdata)
# Forming a composite:
# We will form a composite extraversion variable using BFI scores.
# First put the variables into one data.frame
ext.vars <- data.frame(RSPdata$sBFI1, RSPdata$sBFI6, RSPdata$sBFI11,
RSPdata$sBFI16, RSPdata$sBFI21, RSPdata$sBFI26, RSPdata$sBFI31, RSPdata$sBFI36)
head(ext.vars) # Looks good
# Three items need to be reverse scored
ext.comp <- composite(ext.vars, R = c(2,5,7), rel = TRUE)
ext.comp
# Let's say we want to include the CAQ item "04 - Is a talkative Individual" in our
# extraversion composite. But is is measured on a 1 to 9 scale while the BFI variables
# are measured on a 1 to 5 scale. We should set Zitems=TRUE to Z-score all of the
# items before compositing.
ext.comp2 <- composite(data.frame(ext.vars, RSPdata$sCAQ004),
R = c(2,5,7), rel= TRUE, Zitems = TRUE)
describe(ext.comp2) # mean is zero
|
Example output
Loading required package: psych
Loading required package: abind
Loading required package: foreach
[1] "SID" "ssex" "sBFI1" "sBFI2" "sBFI3"
[6] "sBFI4" "sBFI5" "sBFI6" "sBFI7" "sBFI8"
[11] "sBFI9" "sBFI10" "sBFI11" "sBFI12" "sBFI13"
[16] "sBFI14" "sBFI15" "sBFI16" "sBFI17" "sBFI18"
[21] "sBFI19" "sBFI20" "sBFI21" "sBFI22" "sBFI23"
[26] "sBFI24" "sBFI25" "sBFI26" "sBFI27" "sBFI28"
[31] "sBFI29" "sBFI30" "sBFI31" "sBFI32" "sBFI33"
[36] "sBFI34" "sBFI35" "sBFI36" "sBFI37" "sBFI38"
[41] "sBFI39" "sBFI40" "sBFI41" "sBFI42" "sBFI43"
[46] "sBFI44" "sEXT" "sAGR" "sCON" "sNEUR"
[51] "sOPEN" "sCAQ001" "sCAQ002" "sCAQ003" "sCAQ004"
[56] "sCAQ005" "sCAQ006" "sCAQ007" "sCAQ008" "sCAQ009"
[61] "sCAQ010" "sCAQ011" "sCAQ012" "sCAQ013" "sCAQ014"
[66] "sCAQ015" "sCAQ016" "sCAQ017" "sCAQ018" "sCAQ019"
[71] "sCAQ020" "sCAQ021" "sCAQ022" "sCAQ023" "sCAQ024"
[76] "sCAQ025" "sCAQ026" "sCAQ027" "sCAQ028" "sCAQ029"
[81] "sCAQ030" "sCAQ031" "sCAQ032" "sCAQ033" "sCAQ034"
[86] "sCAQ035" "sCAQ036" "sCAQ037" "sCAQ038" "sCAQ039"
[91] "sCAQ040" "sCAQ041" "sCAQ042" "sCAQ043" "sCAQ044"
[96] "sCAQ045" "sCAQ046" "sCAQ047" "sCAQ048" "sCAQ049"
[101] "sCAQ050" "sCAQ051" "sCAQ052" "sCAQ053" "sCAQ054"
[106] "sCAQ055" "sCAQ056" "sCAQ057" "sCAQ058" "sCAQ059"
[111] "sCAQ060" "sCAQ061" "sCAQ062" "sCAQ063" "sCAQ064"
[116] "sCAQ065" "sCAQ066" "sCAQ067" "sCAQ068" "sCAQ069"
[121] "sCAQ070" "sCAQ071" "sCAQ072" "sCAQ073" "sCAQ074"
[126] "sCAQ075" "sCAQ076" "sCAQ077" "sCAQ078" "sCAQ079"
[131] "sCAQ080" "sCAQ081" "sCAQ082" "sCAQ083" "sCAQ084"
[136] "sCAQ085" "sCAQ086" "sCAQ087" "sCAQ088" "sCAQ089"
[141] "sCAQ090" "sCAQ091" "sCAQ092" "sCAQ093" "sCAQ094"
[146] "sCAQ095" "sCAQ096" "sCAQ097" "sCAQ098" "sCAQ099"
[151] "sCAQ100" "v2rbq001" "v2rbq002" "v2rbq003" "v2rbq004"
[156] "v2rbq005" "v2rbq006" "v2rbq007" "v2rbq008" "v2rbq009"
[161] "v2rbq010" "v2rbq011" "v2rbq012" "v2rbq013" "v2rbq014"
[166] "v2rbq015" "v2rbq016" "v2rbq017" "v2rbq018" "v2rbq019"
[171] "v2rbq020" "v2rbq021" "v2rbq022" "v2rbq023" "v2rbq024"
[176] "v2rbq025" "v2rbq026" "v2rbq027" "v2rbq028" "v2rbq029"
[181] "v2rbq030" "v2rbq031" "v2rbq032" "v2rbq033" "v2rbq034"
[186] "v2rbq035" "v2rbq036" "v2rbq037" "v2rbq038" "v2rbq039"
[191] "v2rbq040" "v2rbq041" "v2rbq042" "v2rbq043" "v2rbq044"
[196] "v2rbq045" "v2rbq046" "v2rbq047" "v2rbq048" "v2rbq049"
[201] "v2rbq050" "v2rbq051" "v2rbq052" "v2rbq053" "v2rbq054"
[206] "v2rbq055" "v2rbq056" "v2rbq057" "v2rbq058" "v2rbq059"
[211] "v2rbq060" "v2rbq061" "v2rbq062" "v2rbq063" "v2rbq064"
[216] "v2rbq065" "v2rbq066" "v2rbq067" "v3rbq001" "v3rbq002"
[221] "v3rbq003" "v3rbq004" "v3rbq005" "v3rbq006" "v3rbq007"
[226] "v3rbq008" "v3rbq009" "v3rbq010" "v3rbq011" "v3rbq012"
[231] "v3rbq013" "v3rbq014" "v3rbq015" "v3rbq016" "v3rbq017"
[236] "v3rbq018" "v3rbq019" "v3rbq020" "v3rbq021" "v3rbq022"
[241] "v3rbq023" "v3rbq024" "v3rbq025" "v3rbq026" "v3rbq027"
[246] "v3rbq028" "v3rbq029" "v3rbq030" "v3rbq031" "v3rbq032"
[251] "v3rbq033" "v3rbq034" "v3rbq035" "v3rbq036" "v3rbq037"
[256] "v3rbq038" "v3rbq039" "v3rbq040" "v3rbq041" "v3rbq042"
[261] "v3rbq043" "v3rbq044" "v3rbq045" "v3rbq046" "v3rbq047"
[266] "v3rbq048" "v3rbq049" "v3rbq050" "v3rbq051" "v3rbq052"
[271] "v3rbq053" "v3rbq054" "v3rbq055" "v3rbq056" "v3rbq057"
[276] "v3rbq058" "v3rbq059" "v3rbq060" "v3rbq061" "v3rbq062"
[281] "v3rbq063" "v3rbq064" "v3rbq065" "v3rbq066" "v3rbq067"
[286] "v4rbq001" "v4rbq002" "v4rbq003" "v4rbq004" "v4rbq005"
[291] "v4rbq006" "v4rbq007" "v4rbq008" "v4rbq009" "v4rbq010"
[296] "v4rbq011" "v4rbq012" "v4rbq013" "v4rbq014" "v4rbq015"
[301] "v4rbq016" "v4rbq017" "v4rbq018" "v4rbq019" "v4rbq020"
[306] "v4rbq021" "v4rbq022" "v4rbq023" "v4rbq024" "v4rbq025"
[311] "v4rbq026" "v4rbq027" "v4rbq028" "v4rbq029" "v4rbq030"
[316] "v4rbq031" "v4rbq032" "v4rbq033" "v4rbq034" "v4rbq035"
[321] "v4rbq036" "v4rbq037" "v4rbq038" "v4rbq039" "v4rbq040"
[326] "v4rbq041" "v4rbq042" "v4rbq043" "v4rbq044" "v4rbq045"
[331] "v4rbq046" "v4rbq047" "v4rbq048" "v4rbq049" "v4rbq050"
[336] "v4rbq051" "v4rbq052" "v4rbq053" "v4rbq054" "v4rbq055"
[341] "v4rbq056" "v4rbq057" "v4rbq058" "v4rbq059" "v4rbq060"
[346] "v4rbq061" "v4rbq062" "v4rbq063" "v4rbq064" "v4rbq065"
[351] "v4rbq066" "v4rbq067" "v5rbq001" "v5rbq002" "v5rbq003"
[356] "v5rbq004" "v5rbq005" "v5rbq006" "v5rbq007" "v5rbq008"
[361] "v5rbq009" "v5rbq010" "v5rbq011" "v5rbq012" "v5rbq013"
[366] "v5rbq014" "v5rbq015" "v5rbq016" "v5rbq017" "v5rbq018"
[371] "v5rbq019" "v5rbq020" "v5rbq021" "v5rbq022" "v5rbq023"
[376] "v5rbq024" "v5rbq025" "v5rbq026" "v5rbq027" "v5rbq028"
[381] "v5rbq029" "v5rbq030" "v5rbq031" "v5rbq032" "v5rbq033"
[386] "v5rbq034" "v5rbq035" "v5rbq036" "v5rbq037" "v5rbq038"
[391] "v5rbq039" "v5rbq040" "v5rbq041" "v5rbq042" "v5rbq043"
[396] "v5rbq044" "v5rbq045" "v5rbq046" "v5rbq047" "v5rbq048"
[401] "v5rbq049" "v5rbq050" "v5rbq051" "v5rbq052" "v5rbq053"
[406] "v5rbq054" "v5rbq055" "v5rbq056" "v5rbq057" "v5rbq058"
[411] "v5rbq059" "v5rbq060" "v5rbq061" "v5rbq062" "v5rbq063"
[416] "v5rbq064" "v5rbq065" "v5rbq066" "v5rbq067" "acq1CAQ001"
[421] "acq1CAQ002" "acq1CAQ003" "acq1CAQ004" "acq1CAQ005" "acq1CAQ006"
[426] "acq1CAQ007" "acq1CAQ008" "acq1CAQ009" "acq1CAQ010" "acq1CAQ011"
[431] "acq1CAQ012" "acq1CAQ013" "acq1CAQ014" "acq1CAQ015" "acq1CAQ016"
[436] "acq1CAQ017" "acq1CAQ018" "acq1CAQ019" "acq1CAQ020" "acq1CAQ021"
[441] "acq1CAQ022" "acq1CAQ023" "acq1CAQ024" "acq1CAQ025" "acq1CAQ026"
[446] "acq1CAQ027" "acq1CAQ028" "acq1CAQ029" "acq1CAQ030" "acq1CAQ031"
[451] "acq1CAQ032" "acq1CAQ033" "acq1CAQ034" "acq1CAQ035" "acq1CAQ036"
[456] "acq1CAQ037" "acq1CAQ038" "acq1CAQ039" "acq1CAQ040" "acq1CAQ041"
[461] "acq1CAQ042" "acq1CAQ043" "acq1CAQ044" "acq1CAQ045" "acq1CAQ046"
[466] "acq1CAQ047" "acq1CAQ048" "acq1CAQ049" "acq1CAQ050" "acq1CAQ051"
[471] "acq1CAQ052" "acq1CAQ053" "acq1CAQ054" "acq1CAQ055" "acq1CAQ056"
[476] "acq1CAQ057" "acq1CAQ058" "acq1CAQ059" "acq1CAQ060" "acq1CAQ061"
[481] "acq1CAQ062" "acq1CAQ063" "acq1CAQ064" "acq1CAQ065" "acq1CAQ066"
[486] "acq1CAQ067" "acq1CAQ068" "acq1CAQ069" "acq1CAQ070" "acq1CAQ071"
[491] "acq1CAQ072" "acq1CAQ073" "acq1CAQ074" "acq1CAQ075" "acq1CAQ076"
[496] "acq1CAQ077" "acq1CAQ078" "acq1CAQ079" "acq1CAQ080" "acq1CAQ081"
[501] "acq1CAQ082" "acq1CAQ083" "acq1CAQ084" "acq1CAQ085" "acq1CAQ086"
[506] "acq1CAQ087" "acq1CAQ088" "acq1CAQ089" "acq1CAQ090" "acq1CAQ091"
[511] "acq1CAQ092" "acq1CAQ093" "acq1CAQ094" "acq1CAQ095" "acq1CAQ096"
[516] "acq1CAQ097" "acq1CAQ098" "acq1CAQ099" "acq1CAQ100" "acq2CAQ001"
[521] "acq2CAQ002" "acq2CAQ003" "acq2CAQ004" "acq2CAQ005" "acq2CAQ006"
[526] "acq2CAQ007" "acq2CAQ008" "acq2CAQ009" "acq2CAQ010" "acq2CAQ011"
[531] "acq2CAQ012" "acq2CAQ013" "acq2CAQ014" "acq2CAQ015" "acq2CAQ016"
[536] "acq2CAQ017" "acq2CAQ018" "acq2CAQ019" "acq2CAQ020" "acq2CAQ021"
[541] "acq2CAQ022" "acq2CAQ023" "acq2CAQ024" "acq2CAQ025" "acq2CAQ026"
[546] "acq2CAQ027" "acq2CAQ028" "acq2CAQ029" "acq2CAQ030" "acq2CAQ031"
[551] "acq2CAQ032" "acq2CAQ033" "acq2CAQ034" "acq2CAQ035" "acq2CAQ036"
[556] "acq2CAQ037" "acq2CAQ038" "acq2CAQ039" "acq2CAQ040" "acq2CAQ041"
[561] "acq2CAQ042" "acq2CAQ043" "acq2CAQ044" "acq2CAQ045" "acq2CAQ046"
[566] "acq2CAQ047" "acq2CAQ048" "acq2CAQ049" "acq2CAQ050" "acq2CAQ051"
[571] "acq2CAQ052" "acq2CAQ053" "acq2CAQ054" "acq2CAQ055" "acq2CAQ056"
[576] "acq2CAQ057" "acq2CAQ058" "acq2CAQ059" "acq2CAQ060" "acq2CAQ061"
[581] "acq2CAQ062" "acq2CAQ063" "acq2CAQ064" "acq2CAQ065" "acq2CAQ066"
[586] "acq2CAQ067" "acq2CAQ068" "acq2CAQ069" "acq2CAQ070" "acq2CAQ071"
[591] "acq2CAQ072" "acq2CAQ073" "acq2CAQ074" "acq2CAQ075" "acq2CAQ076"
[596] "acq2CAQ077" "acq2CAQ078" "acq2CAQ079" "acq2CAQ080" "acq2CAQ081"
[601] "acq2CAQ082" "acq2CAQ083" "acq2CAQ084" "acq2CAQ085" "acq2CAQ086"
[606] "acq2CAQ087" "acq2CAQ088" "acq2CAQ089" "acq2CAQ090" "acq2CAQ091"
[611] "acq2CAQ092" "acq2CAQ093" "acq2CAQ094" "acq2CAQ095" "acq2CAQ096"
[616] "acq2CAQ097" "acq2CAQ098" "acq2CAQ099" "acq2CAQ100"
RSPdata.sBFI1 RSPdata.sBFI6 RSPdata.sBFI11 RSPdata.sBFI16 RSPdata.sBFI21
1 5 1 4 4 1
2 4 3 5 5 4
3 3 4 2 3 4
4 4 3 4 4 3
5 3 2 4 4 3
6 5 3 4 4 2
RSPdata.sBFI26 RSPdata.sBFI31 RSPdata.sBFI36
1 4 1 5
2 3 5 4
3 3 4 2
4 3 4 3
5 4 4 4
6 4 2 5
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
0.8522702 0.8519561 0.8596228 0.4183829 5.754753 0.01539347 3.392073 0.6979583
median_r
0.4063875
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13
4.625 3.375 2.375 3.250 3.500 4.125 3.000 4.375 1.875 1.875 3.500 2.375 4.125
X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26
3.375 2.500 3.375 2.750 2.375 3.500 2.500 2.875 2.375 1.750 3.500 3.250 3.375
X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38 X39
2.750 4.500 2.500 3.375 3.625 2.750 2.000 2.750 2.500 2.250 2.750 3.125 3.750
X40 X41 X42 X43 X44 X45 X46 X47 X48 X49 X50 X51 X52
3.000 4.125 3.500 3.875 2.375 3.125 4.000 2.875 2.375 3.500 3.500 2.750 3.875
X53 X54 X55 X56 X57 X58 X59 X60 X61 X62 X63 X64 X65
4.625 3.500 3.125 3.875 4.375 2.625 2.875 4.125 3.125 3.500 3.375 3.375 4.250
X66 X67 X68 X69 X70 X71 X72 X73 X74 X75 X76 X77 X78
3.375 4.000 3.500 3.875 4.250 3.375 3.750 3.000 3.625 4.500 3.875 2.500 2.500
X79 X80 X81 X82 X83 X84 X85 X86 X87 X88 X89 X90 X91
3.500 4.875 3.875 3.375 2.750 2.625 3.625 3.500 2.750 3.500 4.000 3.250 3.125
X92 X93 X94 X95 X96 X97 X98 X99 X100 X101 X102 X103 X104
2.875 1.875 3.125 3.125 4.125 3.375 4.875 3.750 4.000 3.375 4.375 3.625 3.375
X105 X106 X107 X108 X109 X110 X111 X112 X113 X114 X115 X116 X117
3.875 3.375 3.500 4.000 3.000 4.250 3.625 2.875 4.500 4.125 4.875 3.375 2.750
X118 X119 X120 X121 X122 X123 X124 X125 X126 X127 X128 X129 X130
1.750 2.500 3.625 3.750 4.500 3.000 2.625 3.625 2.875 3.750 2.875 3.625 3.625
X131 X132 X133 X134 X135 X136 X137 X138 X139 X140 X141 X142 X143
3.500 2.875 4.000 3.750 3.500 3.500 3.250 2.375 3.250 3.750 3.125 2.750 4.875
X144 X145 X146 X147 X148 X149 X150 X151 X152 X153 X154 X155 X156
2.875 4.125 3.125 3.000 3.125 3.750 3.750 3.000 3.250 3.375 3.250 2.625 3.750
X157 X158 X159 X160 X161 X162 X163 X164 X165 X166 X167 X168 X169
2.500 3.000 3.625 4.250 4.125 4.125 2.375 2.000 4.125 2.375 3.500 3.875 3.750
X170 X171 X172 X173 X174 X175 X176 X177 X178 X179 X180 X181 X182
4.000 2.875 4.250 3.750 3.750 4.625 3.875 3.125 3.000 3.125 4.125 3.750 4.000
X183 X184 X185 X186 X187 X188 X189 X190 X191 X192 X193 X194 X195
4.875 3.000 1.375 3.125 3.125 3.875 4.250 3.750 2.500 4.250 3.375 4.375 2.750
X196 X197 X198 X199 X200 X201 X202 X203 X204 X205
4.000 2.500 4.750 3.625 4.375 3.375 4.125 4.000 2.750 2.750
raw_alpha std.alpha G6(smc) average_r S/N ase mean
0.8746749 0.8746749 0.8867729 0.4367695 6.979247 0.01319109 2.289676e-17
sd median_r
0.7066475 0.4160115
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 205 0 0.71 0.06 0.02 0.76 -2.11 1.45 3.56 -0.27 -0.35 0.05
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