Output
The output of the program is stored in ASCII format in specified output file (for example, exop_output.txt). When the analysis finishes, this file is automatically loaded (except in some versions of the operating system). It can then be edited, saved or printed like any other text file.
We know show an abbreviated output of an analysis.
F A C T O R
Unrestricted Factor Analysis
Release Version 10.4.01 x32bits
April, 2016
Rovira i Virgili University
Tarragona, SPAIN
Programming:
Urbano Lorenzo-Seva
Mathematical Specification:
Urbano Lorenzo-Seva
Pere J. Ferrando
Date: Friday, October 21, 2016
Time: 9:37:58
--------------------------------------------------------------------------------
DETAILS OF ANALYSIS
Participants' scores data file : exop_with_missingdata.dat
Variable labels file : exop_labels.txt
Method to handle missing values : Hot-Deck Multiple Imputation in Exploratory Factor Analysis (Lorenzo-Seva & Van Ginkel,
2016)
Missing code value : 999
Number of participants : 500
Number of participants without missing data : 479
Number of variables : 14
Variables included in the analysis : ALL
Variables excluded in the analysis : NONE
Number of factors : 2
Number of second order factors : 0
Procedure for determining the number of dimensions : Optimal implementation of Parallel Analysis (PA) (Timmerman, & Lorenzo-Seva, 2011)
Dispersion matrix : Polychoric Correlations (Bayes modal estimation; Choi, Kim, Chen, & Dannels 2011)
Robust analyses : Bootstrap on the basis of Percentile Method
Number of bootstrap samples : 500
Asymptotic Covariance/Variance matrix : estimated using bootstrap sampling
Bootstrap confidence intervals : 95%
Method for factor extraction : Robust Diagonally Weighted Least Squares (RDWLS)
Correction for robust Chi square : Robust Mean-scaled
Rotation to user defined target : Semi-specified oblique Procrustes rotation (Browne, 1972b)
Number of random starts : 10
Maximum mumber of iterations : 100
Convergence value : 0.00001000
--------------------------------------------------------------------------------
UNIVARIATE DESCRIPTIVES
Variable Mean Confidence Interval Variance Skewness Kurtosis
(95%) (Zero centered)
1. Extraversion + 2.985 ( 2.88 3.09) 0.841 -0.118 -0.008
2. Extraversion + 3.793 ( 3.70 3.89) 0.627 -0.701 0.941
3. Extraversion - 2.326 ( 2.19 2.46) 1.259 0.551 -0.488
4. Extraversion + 3.614 ( 3.51 3.72) 0.834 -0.446 -0.054
5. Extraversion - 3.591 ( 3.48 3.70) 0.927 -0.217 -0.374
6. Extraversion - 3.106 ( 3.00 3.22) 0.905 -0.024 -0.381
7. Extraversion + 3.322 ( 3.22 3.42) 0.702 -0.236 0.331
8. Openness - 2.161 ( 2.03 2.29) 1.154 0.680 -0.259
9. Openness + 4.614 ( 4.55 4.68) 0.316 -1.267 1.392
10. Openness - 2.605 ( 2.47 2.75) 1.433 0.343 -0.800
11. Openness + 3.524 ( 3.41 3.64) 0.917 -0.411 -0.124
12. Openness + 4.497 ( 4.42 4.58) 0.484 -1.630 3.816
13. Openness + 4.418 ( 4.34 4.50) 0.444 -0.970 1.083
14. Openness - 1.868 ( 1.75 1.99) 1.004 1.102 0.659
Polychoric correlation is advised when the univariate distributions of ordinal items are
asymmetric or with excess of kurtosis. If both indices are lower than one in absolute value,
then Pearson correlation is advised. You can read more about this subject in:
Muthén, B., & Kaplan D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of
Mathematical and Statistical Psychology, 38, 171-189.
Muthén, B., & Kaplan D. (1992). A comparison of some methodologies for the factor analysis of non-normal Likert variables: A note on the size of
the model. British Journal of Mathematical and Statistical Psychology, 45, 19-30.
BAR CHARTS FOR ORDINAL VARIABLES
Variable 1
Value Freq
|
1 30 | *****
2 94 | ****************
3 229 | ****************************************
4 105 | ******************
5 21 | ***
+-----------+---------+---------+-----------+
0 57.3 114.5 171.8 229.0
Variable 2
Value Freq
|
1 5 |
2 23 | ***
3 111 | ****************
4 267 | ****************************************
5 73 | **********
+-----------+---------+---------+-----------+
0 66.8 133.5 200.3 267.0
Variable 3
Value Freq
|
1 132 | *********************************
2 156 | ****************************************
3 114 | *****************************
4 57 | **************
5 20 | *****
+-----------+---------+---------+-----------+
0 39.0 78.0 117.0 156.0
Variable 4
Value Freq
|
1 8 | *
2 46 | ********
3 142 | ***************************
4 210 | ****************************************
5 73 | *************
+-----------+---------+---------+-----------+
0 52.5 105.0 157.5 210.0
Variable 5
Value Freq
|
1 9 | **
2 43 | *********
3 177 | ****************************************
4 156 | ***********************************
5 94 | *********************
+-----------+---------+---------+-----------+
0 44.3 88.5 132.8 177.0
Variable 6
Value Freq
|
1 19 | ***
2 105 | *********************
3 193 | ****************************************
4 130 | **************************
5 32 | ******
+-----------+---------+---------+-----------+
0 48.3 96.5 144.8 193.0
Variable 7
Value Freq
|
1 12 | **
2 48 | ********
3 225 | ****************************************
4 162 | ****************************
5 32 | *****
+-----------+---------+---------+-----------+
0 56.3 112.5 168.8 225.0
Variable 8
Value Freq
|
1 159 | ****************************************
2 155 | **************************************
3 108 | ***************************
4 43 | **********
5 14 | ***
+-----------+---------+---------+-----------+
0 39.8 79.5 119.3 159.0
Variable 9
Value Freq
|
2 2 |
3 13 | *
4 153 | *******************
5 311 | ****************************************
+-----------+---------+---------+-----------+
0 77.8 155.5 233.3 311.0
Warning: Not all the categories are observed in variable 9
You should consider to remove this variable from the analysis
Variable 10
Value Freq
|
1 98 | ***************************
2 145 | ****************************************
3 120 | *********************************
4 80 | **********************
5 36 | *********
+-----------+---------+---------+-----------+
0 36.3 72.5 108.8 145.0
Variable 11
Value Freq
|
1 13 | **
2 52 | **********
3 154 | ********************************
4 191 | ****************************************
5 69 | **************
+-----------+---------+---------+-----------+
0 47.8 95.5 143.3 191.0
Variable 12
Value Freq
|
1 3 |
2 4 |
3 26 | ***
4 165 | ***********************
5 281 | ****************************************
+-----------+---------+---------+-----------+
0 70.3 140.5 210.8 281.0
Variable 13
Value Freq
|
1 1 |
2 2 |
3 36 | *****
4 197 | ********************************
5 243 | ****************************************
+-----------+---------+---------+-----------+
0 60.8 121.5 182.3 243.0
Variable 14
Value Freq
|
1 219 | ****************************************
2 151 | ***************************
3 72 | *************
4 27 | ****
5 10 | *
+-----------+---------+---------+-----------+
0 54.8 109.5 164.3 219.0
--------------------------------------------------------------------------------
MULTIVARIATE DESCRIPTIVES
Analysis of the Mardia's (1970) multivariate asymmetry skewness and kurtosis.
Coefficient Statistic df P
Skewness 18.469 1474.447 560 1.0000
SKewness corrected for small sample 18.469 1484.919 560 1.0000
Kurtosis 257.130 17.128 0.0000**
** Significant at 0.05
--------------------------------------------------------------------------------
STANDARIZED VARIANCE / COVARIANCE MATRIX (POLYCHORIC CORRELATION)
(Polychoric algorithm: Bayes modal estimation; Choi, Kim, Chen, & Dannels, 2011)
Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14
V 1 1.000
V 2 0.394 1.000
V 3 -0.404 -0.478 1.000
V 4 0.432 0.615 -0.474 1.000
V 5 -0.423 -0.282 0.381 -0.123 1.000
V 6 -0.361 -0.403 0.425 -0.280 0.619 1.000
V 7 0.382 0.424 -0.339 0.455 -0.266 -0.359 1.000
V 8 0.037 -0.043 0.015 0.026 0.084 0.057 -0.104 1.000
V 9 -0.044 0.129 -0.023 0.120 0.071 0.016 0.058 -0.253 1.000
V 10 -0.003 -0.087 0.073 -0.036 0.176 0.165 -0.141 0.393 -0.193 1.000
V 11 0.004 0.068 -0.043 0.063 -0.133 -0.079 0.129 -0.527 0.333 -0.260 1.000
V 12 0.094 0.230 -0.113 0.197 -0.067 -0.047 0.061 -0.182 0.281 -0.168 0.247 1.000
V 13 0.053 0.221 -0.103 0.186 -0.036 -0.049 0.132 -0.295 0.444 -0.289 0.328 0.485 1.000
V 14 -0.066 -0.161 0.115 -0.065 0.016 0.021 -0.171 0.480 -0.317 0.297 -0.406 -0.337 -0.354 1.000
--------------------------------------------------------------------------------
BOOTSTRAP 95% CONFIDENCE INTERVALS FOR CORRELATIONS BETWEEN VARIABLES
Variables Value Confidence Interval
v 1 -- v 2 0.394* ( 0.299 0.479)
v 1 -- v 3 -0.404* ( -0.484 -0.314)
v 1 -- v 4 0.432* ( 0.332 0.511)
v 1 -- v 5 -0.423* ( -0.501 -0.346)
v 1 -- v 6 -0.361* ( -0.421 -0.240)
v 1 -- v 7 0.382* ( 0.278 0.472)
v 1 -- v 8 0.037 ( -0.058 0.152)
v 1 -- v 9 -0.044 ( -0.131 0.054)
v 1 -- v 10 -0.003 ( -0.106 0.096)
v 1 -- v 11 0.004 ( -0.094 0.094)
v 1 -- v 12 0.094 ( -0.014 0.189)
v 1 -- v 13 0.053 ( -0.042 0.163)
v 1 -- v 14 -0.066 ( -0.170 0.036)
v 2 -- v 3 -0.478* ( -0.570 -0.395)
v 2 -- v 4 0.615* ( 0.540 0.700)
v 2 -- v 5 -0.282* ( -0.378 -0.185)
v 2 -- v 6 -0.403* ( -0.488 -0.323)
v 2 -- v 7 0.424* ( 0.343 0.518)
v 2 -- v 8 -0.043 ( -0.154 0.054)
v 2 -- v 9 0.129* ( 0.031 0.228)
v 2 -- v 10 -0.087 ( -0.186 0.003)
v 2 -- v 11 0.068 ( -0.024 0.169)
v 2 -- v 12 0.230* ( 0.127 0.337)
v 2 -- v 13 0.221* ( 0.115 0.313)
v 2 -- v 14 -0.161* ( -0.256 -0.059)
v 3 -- v 4 -0.474* ( -0.549 -0.374)
v 3 -- v 5 0.381* ( 0.290 0.466)
v 3 -- v 6 0.425* ( 0.342 0.508)
v 3 -- v 7 -0.339* ( -0.429 -0.239)
v 3 -- v 8 0.015 ( -0.089 0.126)
v 3 -- v 9 -0.023 ( -0.117 0.087)
v 3 -- v 10 0.073 ( -0.022 0.181)
v 3 -- v 11 -0.043 ( -0.136 0.072)
v 3 -- v 12 -0.113* ( -0.215 -0.011)
v 3 -- v 13 -0.103 ( -0.205 0.004)
v 3 -- v 14 0.115* ( 0.014 0.225)
v 4 -- v 5 -0.123* ( -0.224 -0.023)
v 4 -- v 6 -0.280* ( -0.378 -0.184)
v 4 -- v 7 0.455* ( 0.373 0.542)
v 4 -- v 8 0.026 ( -0.103 0.115)
v 4 -- v 9 0.120* ( 0.028 0.214)
v 4 -- v 10 -0.036 ( -0.144 0.054)
v 4 -- v 11 0.063 ( -0.045 0.171)
v 4 -- v 12 0.197* ( 0.095 0.305)
v 4 -- v 13 0.186* ( 0.086 0.281)
v 4 -- v 14 -0.065 ( -0.169 0.040)
v 5 -- v 6 0.619* ( 0.560 0.692)
v 5 -- v 7 -0.266* ( -0.356 -0.171)
v 5 -- v 8 0.084 ( -0.005 0.180)
v 5 -- v 9 0.071 ( -0.024 0.176)
v 5 -- v 10 0.176* ( 0.070 0.275)
v 5 -- v 11 -0.133* ( -0.224 -0.027)
v 5 -- v 12 -0.067 ( -0.155 0.040)
v 5 -- v 13 -0.036 ( -0.122 0.073)
v 5 -- v 14 0.016 ( -0.075 0.118)
v 6 -- v 7 -0.359* ( -0.451 -0.261)
v 6 -- v 8 0.057 ( -0.051 0.153)
v 6 -- v 9 0.016 ( -0.066 0.127)
v 6 -- v 10 0.165* ( 0.043 0.262)
v 6 -- v 11 -0.079 ( -0.174 0.025)
v 6 -- v 12 -0.047 ( -0.158 0.051)
v 6 -- v 13 -0.049 ( -0.142 0.051)
v 6 -- v 14 0.021 ( -0.078 0.121)
v 7 -- v 8 -0.104* ( -0.203 -0.002)
v 7 -- v 9 0.058 ( -0.026 0.153)
v 7 -- v 10 -0.141* ( -0.241 -0.027)
v 7 -- v 11 0.129* ( 0.048 0.230)
v 7 -- v 12 0.061 ( -0.044 0.162)
v 7 -- v 13 0.132* ( 0.047 0.238)
v 7 -- v 14 -0.171* ( -0.272 -0.071)
v 8 -- v 9 -0.253* ( -0.353 -0.147)
v 8 -- v 10 0.393* ( 0.293 0.471)
v 8 -- v 11 -0.527* ( -0.591 -0.430)
v 8 -- v 12 -0.182* ( -0.272 -0.088)
v 8 -- v 13 -0.295* ( -0.378 -0.206)
v 8 -- v 14 0.480* ( 0.382 0.554)
v 9 -- v 10 -0.193* ( -0.280 -0.100)
v 9 -- v 11 0.333* ( 0.240 0.421)
v 9 -- v 12 0.281* ( 0.178 0.379)
v 9 -- v 13 0.444* ( 0.356 0.534)
v 9 -- v 14 -0.317* ( -0.417 -0.213)
v 10 -- v 11 -0.260* ( -0.359 -0.172)
v 10 -- v 12 -0.168* ( -0.279 -0.070)
v 10 -- v 13 -0.289* ( -0.385 -0.199)
v 10 -- v 14 0.297* ( 0.195 0.406)
v 11 -- v 12 0.247* ( 0.158 0.333)
v 11 -- v 13 0.328* ( 0.249 0.426)
v 11 -- v 14 -0.406* ( -0.496 -0.320)
v 12 -- v 13 0.485* ( 0.410 0.566)
v 12 -- v 14 -0.337* ( -0.428 -0.239)
v 13 -- v 14 -0.354* ( -0.446 -0.232)
* Significantly different from zero at population
--------------------------------------------------------------------------------
ADEQUACY OF THE CORRELATION MATRIX
Determinant of the matrix = 0.030805628460148
Bartlett's statistic = 1644.3 (df = 91; P = 0.000010)
Kaiser-Meyer-Olkin (KMO) test = 0.78437 (fair)
Bootstrap 95% confidence interval of KMO = ( 0.730 0.802)
--------------------------------------------------------------------------------
EXPLAINED VARIANCE BASED ON EIGENVALUES
Variable Eigenvalue Proportion of Cumulative Proportion
Variance of Variance
1 3.78605 0.27043 0.27043
2 2.63675 0.18834 0.45877
3 1.36472 0.09748
4 0.92263 0.06590
5 0.77285 0.05520
6 0.73259 0.05233
7 0.64574 0.04612
8 0.59479 0.04248
9 0.55818 0.03987
10 0.49111 0.03508
11 0.45710 0.03265
12 0.39830 0.02845
13 0.35307 0.02522
14 0.28612 0.02044
--------------------------------------------------------------------------------
PARALLEL ANALYSIS (PA) BASED ON MINIMUM RANK FACTOR ANALYSIS
(Timmerman & Lorenzo-Seva, 2011)
Implementation details:
Correlation matrices analized: Polychoric correlation matrices
Number of random correlation matrices: 500
Method to obtain random correlation matrices: Permutation of the raw data (Buja & Eyuboglu, 1992)
Variable Real-data Mean of random 95 percentile of random
% of variance % of variance % of variance
1 31.4* 14.4 16.6
2 21.6* 13.1 15.0
3 10.2 11.9 13.4
4 6.7 10.8 12.0
5 6.1 9.8 10.8
6 5.2 8.7 9.7
7 4.5 7.6 8.6
8 3.8 6.6 7.7
9 3.3 5.6 6.7
10 2.8 4.5 5.7
11 2.2 3.4 4.6
12 1.4 2.3 3.5
13 0.8 1.3 2.3
14 0.0 0.0 0.0
* Advised number of dimensions: 2
--------------------------------------------------------------------------------
ROBUST GOODNESS OF FIT STATISTICS
Root Mean Square Error of Approximation (RMSEA) = 0.112; Bootstrap 95% confidence interval = ( 0.103 0.137)
Estimated Non-Centrality Parameter (NCP) = 76.480
Degrees of Freedom = 64
Test of Approximate Fit
H0 : RMSEA < 0.05; P = 1.000
Minimum Fit Function Chi Square with 64 degrees of freedom = 226.170 (P = 0.000010)
Robust Mean-Scaled Chi Square with 64 degrees of freedom = 449.484 (P = 0.000010)
Chi-Square for independence model with 91 degrees of freedom = 3360.642
Non-Normed Fit Index (NNFI; Tucker & Lewis) = 0.832; Bootstrap 95% confidence interval = ( 0.734 0.874)
Comparative Fit Index (CFI) = 0.882; Bootstrap 95% confidence interval = ( 0.813 0.912)
Schwarz’s Bayesian Information Criterion (BIC) = 708.695; Bootstrap 95% confidence interval = (645.789 900.509)
Goodness of Fit Index (GFI) = 1.000; Bootstrap 95% confidence interval = ( 0.986 1.000)
Adjusted Goodness of Fit Index (AGFI) = 1.000; Bootstrap 95% confidence interval = ( 0.980 1.009)
Goodness of Fit Index without diagonal values (GFI) = 1.000; Bootstrap 95% confidence interval = ( 0.970 1.000)
Adjusted Goodness of Fit Index without diagonal values(AGFI) = 1.000; Bootstrap 95% confidence interval = ( 0.958 1.017)
EIGENVALUES OF THE REDUCED CORRELATION MATRIX
Variable Eigenvalue
1 3.191689508
2 2.025701391
3 0.758072051
4 0.291424832
5 0.106437825
6 0.059364930
7 0.042258424
8 -0.039806738
9 -0.048211520
10 -0.105387518
11 -0.172879323
12 -0.190037553
13 -0.235361805
14 -0.299768291
--------------------------------------------------------------------------------
UNROTATED LOADING MATRIX
Variable F 1 F 2 Communality
1. Extraversion + -0.519 -0.359 0.398
2. Extraversion + -0.669 -0.220 0.496
3. Extraversion - 0.586 0.302 0.434
4. Extraversion + -0.610 -0.244 0.431
5. Extraversion - 0.542 0.316 0.394
6. Extraversion - 0.592 0.357 0.478
7. Extraversion + -0.547 -0.173 0.329
8. Openness - 0.350 -0.560 0.436
9. Openness + -0.264 0.458 0.279
10. Openness - 0.326 -0.324 0.211
11. Openness + -0.381 0.511 0.406
12. Openness + -0.386 0.334 0.260
13. Openness + -0.430 0.473 0.409
14. Openness - 0.395 -0.514 0.421
--------------------------------------------------------------------------------
SEMI-SPECIFIED TARGET LOADING MATRIX
User defined semi-specified target matrix
Variable
1. Extraversion + --- 0.000
2. Extraversion + --- 0.000
3. Extraversion - --- 0.000
4. Extraversion + --- 0.000
5. Extraversion - --- 0.000
6. Extraversion - --- 0.000
7. Extraversion + --- 0.000
8. Openness - 0.000 ---
9. Openness + 0.000 ---
10. Openness - 0.000 ---
11. Openness + 0.000 ---
12. Openness + 0.000 ---
13. Openness + 0.000 ---
14. Openness - 0.000 ---
--------------------------------------------------------------------------------
ROTATED LOADING MATRIX
Variable F 1 F 2
1. Extraversion + 0.651 -0.114
2. Extraversion + 0.679 0.082
3. Extraversion - -0.666 0.031
4. Extraversion + 0.647 0.033
5. Extraversion - -0.641 0.063
6. Extraversion - -0.708 0.080
7. Extraversion + 0.550 0.074
8. Openness - 0.088 -0.678
9. Openness + -0.090 0.544
10. Openness - -0.047 -0.445
11. Openness + -0.032 0.644
12. Openness + 0.088 0.481
13. Openness + 0.032 0.630
14. Openness - 0.022 -0.654
ROTATED LOADING MATRIX
(loadings lower than absolute 0.300 omitted)
Variable F 1 F 2
1. Extraversion + 0.651
2. Extraversion + 0.679
3. Extraversion - -0.666
4. Extraversion + 0.647
5. Extraversion - -0.641
6. Extraversion - -0.708
7. Extraversion + 0.550
8. Openness - -0.678
9. Openness + 0.544
10. Openness - -0.445
11. Openness + 0.644
12. Openness + 0.481
13. Openness + 0.630
14. Openness - -0.654
EXPLAINED VARIANCE OF ROTATED FACTORS AND RELIABILITY OF PHI-INFORMATION OBLIQUE EAP SCORES
Ferrando & Lorenzo-Seva (2016)
Factor Variance ORION (Overall Reliability of fully-Informative prior Oblique N-EAP scores)
1 2.955 0.917
2 2.428 0.874
The appropriate implementation of EAP score estimation in factor model involves to obtain
point estimates that make use of the full prior information (in particular the inter-factor
correlation matrix), and to complement the point estimates with measures of the reliability of
these estimates. In order to achieve it, FACTOR computes: (1) the EAP score estimation named
'Fully-Informative Prior Oblique EAP scores'; and (2) the reliability estimates named ORION
(acronim for 'Overall Reliability of fully-Informative prior Oblique N-EAP scores').
See Ferrando & Lorenzo-Seva (2016) for further details.
--------------------------------------------------------------------------------
INTER-FACTORS CORRELATION MATRIX
Factor F 1 F 2
F 1 1.000
F 2 0.256 1.000
--------------------------------------------------------------------------------
STRUCTURE MATRIX
Variable F 1 F 2
1. Extraversion + 0.621 0.052
2. Extraversion + 0.700 0.255
3. Extraversion - -0.658 -0.140
4. Extraversion + 0.656 0.199
5. Extraversion - -0.624 -0.101
6. Extraversion - -0.687 -0.101
7. Extraversion + 0.569 0.214
8. Openness - -0.085 -0.655
9. Openness + 0.049 0.521
10. Openness - -0.161 -0.457
11. Openness + 0.133 0.636
12. Openness + 0.211 0.503
13. Openness + 0.194 0.639
14. Openness - -0.145 -0.648
--------------------------------------------------------------------------------
BOOTSTRAP 95% CONFIDENCE INTERVALS FOR LOADING VALUES
Variable F 1 Confidence Interval
1. Extraversion + 0.651 ( 0.559 0.711)
2. Extraversion + 0.679 ( 0.599 0.754)
3. Extraversion - -0.666 ( -0.735 -0.583)
4. Extraversion + 0.647 ( 0.559 0.723)
5. Extraversion - -0.641 ( -0.725 -0.552)
6. Extraversion - -0.708 ( -0.777 -0.617)
7. Extraversion + 0.550 ( 0.470 0.634)
8. Openness - 0.088 ( -0.010 0.167)
9. Openness + -0.090 ( -0.176 0.007)
10. Openness - -0.047 ( -0.145 0.040)
11. Openness + -0.032 ( -0.108 0.055)
12. Openness + 0.088 ( -0.027 0.204)
13. Openness + 0.032 ( -0.057 0.116)
14. Openness - 0.022 ( -0.067 0.094)
Variable F 2 Confidence Interval
1. Extraversion + -0.114 ( -0.227 -0.003)
2. Extraversion + 0.082 ( -0.003 0.186)
3. Extraversion - 0.031 ( -0.046 0.104)
4. Extraversion + 0.033 ( -0.064 0.156)
5. Extraversion - 0.063 ( -0.052 0.187)
6. Extraversion - 0.080 ( -0.022 0.186)
7. Extraversion + 0.074 ( -0.018 0.175)
8. Openness - -0.678 ( -0.749 -0.563)
9. Openness + 0.544 ( 0.461 0.631)
10. Openness - -0.445 ( -0.547 -0.340)
11. Openness + 0.644 ( 0.562 0.721)
12. Openness + 0.481 ( 0.371 0.578)
13. Openness + 0.630 ( 0.546 0.726)
14. Openness - -0.654 ( -0.729 -0.544)
--------------------------------------------------------------------------------
BOOTSTRAP 95% CONFIDENCE INTERVALS FOR INTER-FACTORS CORRELATION VALUES
1 -- 2 0.256* ( 0.138 0.339)
* Significantly different from zero at population
--------------------------------------------------------------------------------
CONGRUENCE BETWEEN ROTATED LOADING MATRIX AND TARGET MATRIX
Tucker (1951)
CONGRUENCE OF VARIABLES
Variable Congruence Bootstrap 95% Confidence intervals
1. Extraversion + 0.985 ( 0.948 1.000)
2. Extraversion + 0.993 ( 0.959 1.000)
3. Extraversion - 0.999 ( 0.989 1.000)
4. Extraversion + 0.999 ( 0.967 1.000)
5. Extraversion - 0.995 ( 0.965 1.000)
6. Extraversion - 0.994 ( 0.969 1.000)
7. Extraversion + 0.991 ( 0.951 1.000)
8. Openness - 0.992 ( 0.970 1.000)
9. Openness + 0.987 ( 0.954 1.000)
10. Openness - 0.994 ( 0.933 1.000)
11. Openness + 0.999 ( 0.987 1.000)
12. Openness + 0.984 ( 0.894 1.000)
13. Openness + 0.999 ( 0.979 1.000)
14. Openness - 0.999 ( 0.990 1.000)
F 1 0.993 ( 0.979 0.996)
F 2 0.982 ( 0.957 0.988)
OVERALL CONGRUENCE = 0.986
BOOTSTRAP 95% CONFIDENCE INTERVALS = ( 0.972 0.990)
GUIDELINEs TO INTERPRET CONGRUENCE INDEX
Lorenzo-Seva & ten Berge (2006)
A congruence value in the range .85-.94 corresponds to a fair similarity,
while a value higher than .95 implies that the two factors (or components)
compared can be considered equal.
Note: non-specified values in the target matrix were set to 1 (or -1)
to compute congruences.
--------------------------------------------------------------------------------
ITEM RESPONSE THEORY PARAMETERIZATION: MULTIDIMENSIONAL NORMAL-OGIVE GRADED RESPONSE MODEL
Reckase's parameterization (Reckase, 1985)
PATTERN OF ITEM DISCRIMINATIONS
Item a 1 a 2 MDISC
1. Extraversion + 0.839 -0.147 0.852
2. Extraversion + 0.956 0.115 0.963
3. Extraversion - -0.886 0.041 0.887
4. Extraversion + 0.858 0.044 0.859
5. Extraversion - -0.823 0.081 0.827
6. Extraversion - -0.980 0.111 0.986
7. Extraversion + 0.672 0.090 0.678
8. Openness - 0.117 -0.902 0.910
9. Openness + -0.106 0.641 0.650
10. Openness - -0.053 -0.501 0.504
11. Openness + -0.041 0.836 0.837
12. Openness + 0.103 0.559 0.568
13. Openness + 0.042 0.820 0.821
14. Openness - 0.029 -0.859 0.860
a: item discrimination in each dimension
MDISC: item multidimensional discrimination
CATEGORY INTERCEPTS
Item d 1 d 2 d 3 d 4
1. Extraversion + -1.969 -0.835 0.806 2.149
2. Extraversion + -3.205 -2.199 -0.780 1.425
3. Extraversion - -0.794 0.334 1.300 2.245
4. Extraversion + -2.791 -1.604 -0.308 1.342
5. Extraversion - -2.645 -1.582 -0.075 1.084
6. Extraversion - -2.416 -0.897 0.569 2.037
7. Extraversion + -2.373 -1.400 0.287 1.796
8. Openness - -0.581 0.525 1.548 2.444
9. Openness + -3.627 -3.019 -2.178 -0.454
10. Openness - -0.929 0.016 0.777 1.590
11. Openness + -2.478 -1.425 -0.144 1.359
12. Openness + -2.842 -2.506 -1.720 -0.258
13. Openness + -3.558 -3.179 -1.810 -0.029
14. Openness - -0.146 0.969 1.837 2.577
--------------------------------------------------------------------------------
DISTRIBUTION OF RESIDUALS
Number of Residuals = 91
Summary Statistics for Fitted Residuals
Smallest Fitted Residual = -0.1081
Median Fitted Residual = 0.0266
Largest Fitted Residual = 0.2850
Mean Fitted Residual = 0.0304
Variance Fitted Residual = 0.0043
Root Mean Square of Residuals (RMSR) = 0.0723
Bootstrap 95% confidence interval of RMSR = ( 0.068 0.087)
Expected mean value of RMSR for an acceptable model = 0.0457 (Kelley's criterion) (Kelley, 1935,page 146; see also Harman, 1962, page 21 of the
2nd edition)
Note: if the value of RMSR is much larger than Kelley's criterion value the model cannot be considered as good
Histogram for fitted residuals
Value Freq
|
-0.1081 2 | ***
-0.0688 4 | ******
-0.0295 18 | *******************************
0.0098 23 | ****************************************
0.0491 22 | **************************************
0.0885 13 | **********************
0.1278 3 | *****
0.1671 5 | ********
0.2064 0 |
0.2457 0 |
0.2850 1 | *
+-----------+---------+---------+-----------+
0 5.8 11.5 17.3 23.0
Summary Statistics for Standardized Residuals
Smallest Standardized Residual = -2.36
Median Standardized Residual = 0.58
Largest Standardized Residual = 6.23
Mean Standardized Residual = 0.66
Stemleaf Plot for Standardized Residuals
-2 | 40
-1 | 9766000
-0 | 98877766555444322221
0 | 0001112223345566667788888999
1 | 011112233445555667899
2 | 112256
3 | 13457
4 | 1
5 |
6 | 2
Largest Positive Standardized Residuals
Residual for Var 4 and Var 2 3.37
Residual for Var 5 and Var 2 3.29
Residual for Var 5 and Var 4 6.23
Residual for Var 6 and Var 4 3.67
Residual for Var 6 and Var 5 4.06
Residual for Var 12 and Var 8 3.07
Residual for Var 13 and Var 8 2.65
Residual for Var 13 and Var 12 3.52
--------------------------------------------------------------------------------
DESCRIPTIVES RELATED TO MISSING DATA
Missing value code : 999
Total number of missing data : 30
Method to handle missing data: Hot-Deck Multiple Imputation in Exploratory Factor Analysis (Lorenzo-Seva & Van Ginkel, 2016)
Number of imputated datasets : Five
NUMBER OF VARIABLES MISSING PER CASE
Number of variables Frequency
missing
0 | 479 ( 95.8%)
1 | 15 ( 3.0%)
2 | 4 ( 0.8%)
3 | 1 ( 0.2%)
4 | 1 ( 0.2%)
NUMBER OF CASES MISSING PER VARIABLE
Variable Frequency of cases missing
1. Extraversion + 2
2. Extraversion + 2
3. Extraversion - 3
4. Extraversion + 1
5. Extraversion - 3
6. Extraversion - 2
7. Extraversion + 3
8. Openness - 3
9. Openness + 4
10. Openness - 3
11. Openness + 0
12. Openness + 1
13. Openness + 0
14. Openness - 3
CONSENSUS LOADING MATRIX AMONG MULTIPLE IMPUTATED DATASETS
Variable F 1 F 2
1. Extraversion + 0.637 -0.100
2. Extraversion + 0.692 0.082
3. Extraversion - -0.678 0.039
4. Extraversion + 0.658 0.033
5. Extraversion - -0.635 0.061
6. Extraversion - -0.722 0.068
7. Extraversion + 0.561 0.064
8. Openness - 0.100 -0.681
9. Openness + -0.071 0.532
10. Openness - -0.049 -0.433
11. Openness + -0.051 0.657
12. Openness + 0.098 0.495
13. Openness + 0.041 0.636
14. Openness - 0.038 -0.648
CONSENSUS INTER-FACTOR CORRELATION MATRIX AMONG MULTIPLE IMPUTATED DATASETS
Factor F 1 F 2
F 1 1.000
F 2 0.264 1.000
--------------------------------------------------------------------------------
PARTICIPANTS' SCORES ON FACTORS: PHI-Information Oblique EAP scores
Ferrando & Lorenzo-Seva (2016)
Method to handle missing data: Hot-Deck Multiple Imputation in Exploratory Factor Analysis (Lorenzo-Seva & Van Ginkel, 2016)
Case Factor
1 2
1 82.440 57.421
2 56.355 54.268
3 50.053 46.905
4 49.199 34.086
5 47.521 39.617
6 26.275 44.579
7 51.850 49.699
8 57.155 56.090
9 52.509 55.125
10 54.564 46.216
11 30.245 62.670
12 59.826 53.456
13 75.770 60.847
14 52.925 50.863
15 65.778 56.760
16 46.578 45.231
17 51.930 47.250
18 43.204 44.278
19 37.742 49.588
20 35.898 46.496
21 47.953 27.123
22 47.338 43.623
23 29.245 41.985
24 61.554 70.209
25 41.999 40.085
26 39.043 41.359
27 82.153 74.652
28 47.140 33.274
29 61.154 46.617
30 54.032 54.816
31 41.111 41.550
32 49.870 40.596
33 39.469 30.044
34 32.094 39.928
35 47.459 42.344
36 53.003 59.308
37 45.826 44.885
38 49.092 55.216
39 38.578 55.855
40 52.673 61.202
41 56.639 41.113
42 37.165 55.148
43 50.449 50.494
44 55.947 69.696
45 59.277 42.630
46 42.481 53.869
47 39.284 43.609
48 38.191 55.413
49 45.994 46.191
50 48.223 51.503
51 42.227 59.332
52 42.858 38.213
53 38.494 45.088
54 52.758 40.381
55 64.409 54.533
56 51.331 55.009
57 62.366 40.041
58 49.698 22.430
59 45.488 49.716
60 42.222 56.845
61 45.612 33.672
62 30.089 50.129
63 62.331 53.681
64 76.171 61.238
65 48.386 45.904
66 45.871 51.327
67 51.617 54.835
68 40.752 57.274
69 52.606 49.148
70 53.105 70.684
71 51.601 69.893
72 45.457 35.278
73 51.211 38.412
74 67.549 41.929
75 55.595 71.699
76 42.995 54.660
77 38.264 42.437
78 47.209 56.135
79 46.607 57.903
80 57.486 60.301
81 55.327 37.727
82 36.794 60.122
83 24.600 38.826
84 56.242 54.101
85 53.899 43.199
86 46.997 46.239
87 59.364 44.080
88 46.442 46.129
89 49.774 48.002
90 51.979 34.244
91 64.177 61.049
92 37.793 60.428
93 41.341 55.122
94 53.460 52.801
95 55.140 70.720
96 51.975 44.461
97 55.189 68.992
98 65.028 52.029
99 44.588 39.853
100 44.985 34.148
101 45.530 48.302
102 52.820 63.292
103 52.087 53.499
104 43.626 60.049
105 48.249 49.621
106 45.103 53.114
107 42.831 56.658
108 68.118 56.252
109 48.526 40.487
110 41.899 44.288
111 49.927 49.566
112 52.616 67.748
113 48.294 42.716
114 44.942 53.204
115 44.700 39.672
116 56.644 52.280
117 50.254 49.367
118 48.458 52.618
119 67.957 54.482
120 45.631 44.378
121 44.875 51.609
122 46.534 35.471
123 53.489 40.248
124 59.430 55.385
125 30.524 47.101
126 47.374 52.454
127 50.884 33.648
128 47.639 44.637
129 54.664 54.096
130 43.180 38.731
131 44.239 38.158
132 43.609 50.047
133 42.134 41.430
134 55.011 59.752
135 60.669 55.586
136 55.236 38.675
137 52.921 48.369
138 44.008 46.742
139 57.331 43.304
140 36.718 53.836
141 73.420 49.922
142 41.574 45.509
143 37.240 43.416
144 55.872 48.659
145 43.211 37.675
146 57.755 54.684
147 55.850 39.247
148 51.843 44.922
149 59.170 49.185
150 42.548 43.738
151 49.768 68.271
152 65.472 48.175
153 62.279 49.457
154 60.199 51.575
155 50.728 58.430
156 55.815 55.307
157 60.013 60.106
158 49.164 46.558
159 55.176 54.138
160 59.065 55.579
161 60.316 55.002
162 50.527 48.690
163 44.449 38.123
164 46.852 51.010
165 56.993 59.089
166 45.396 49.059
167 50.436 53.159
168 54.126 41.475
169 49.660 45.209
170 48.344 53.603
171 51.081 65.883
172 58.979 26.576
173 49.776 71.615
174 54.624 35.589
175 41.664 48.286
176 63.912 47.133
177 41.387 53.234
178 63.170 43.039
179 57.387 40.791
180 42.965 48.180
181 35.116 47.973
182 58.533 59.130
183 47.560 49.714
184 47.871 52.305
185 44.192 44.049
186 44.377 42.862
187 44.398 51.948
188 42.715 37.882
189 58.480 54.997
190 60.379 39.631
191 59.476 52.758
192 48.852 62.865
193 30.141 50.808
194 48.792 60.491
195 63.495 58.478
196 51.835 42.155
197 50.057 47.435
198 40.171 42.935
199 44.642 54.038
200 40.570 47.200
201 36.681 55.091
202 51.934 57.615
203 45.367 48.303
204 70.005 54.006
205 42.972 52.493
206 51.619 49.668
207 47.539 56.116
208 57.660 69.767
209 56.313 59.712
210 68.266 54.262
211 48.307 61.725
212 59.511 44.372
213 52.528 48.371
214 41.938 36.429
215 50.140 51.582
216 40.790 40.273
217 41.247 38.844
218 48.720 43.389
219 56.563 65.101
220 30.696 34.738
221 52.818 56.177
222 59.863 40.095
223 61.271 53.664
224 52.977 56.337
225 51.401 40.347
226 60.639 44.493
227 46.967 46.912
228 52.616 41.558
229 51.271 45.603
230 45.000 40.083
231 81.581 48.394
232 38.243 47.169
233 58.080 45.707
234 42.722 59.843
235 45.497 48.973
236 51.644 66.690
237 39.220 35.641
238 43.581 67.614
239 42.939 44.787
240 55.737 31.990
241 50.435 50.961
242 52.094 59.101
243 48.524 43.565
244 42.374 52.035
245 38.251 44.231
246 64.269 63.553
247 50.303 42.099
248 43.438 45.931
249 50.448 42.178
250 54.635 57.334
251 33.420 46.523
252 33.418 43.922
253 73.324 54.436
254 47.844 41.337
255 59.537 44.970
256 51.901 45.818
257 39.773 43.491
258 50.030 49.658
259 51.129 52.750
260 65.465 52.459
261 39.292 30.847
262 52.316 45.121
263 42.884 50.865
264 43.307 68.195
265 56.692 42.032
266 57.731 57.668
267 44.360 45.374
268 49.016 58.735
269 44.968 47.309
270 39.123 45.548
271 50.760 66.487
272 58.769 45.486
273 57.148 56.610
274 46.973 40.729
275 31.876 40.928
276 54.630 60.816
277 42.803 54.863
278 42.084 31.288
279 54.306 44.965
280 41.172 39.575
281 50.166 58.537
282 48.671 44.614
283 49.313 50.151
284 45.669 43.303
285 31.402 44.462
286 61.260 37.616
287 36.046 68.246
288 57.037 38.370
289 47.140 60.785
290 59.809 65.913
291 52.953 51.021
292 44.170 44.691
293 37.542 68.091
294 40.530 38.571
295 64.536 41.964
296 83.462 61.059
297 49.402 45.521
298 60.031 60.422
299 50.612 52.668
300 40.008 65.085
301 61.261 69.964
302 66.674 46.210
303 51.246 46.490
304 59.957 55.313
305 52.480 48.409
306 37.367 39.587
307 64.887 30.708
308 45.924 55.514
309 47.531 55.525
310 55.689 71.151
311 48.248 70.191
312 36.435 52.164
313 59.402 56.899
314 56.308 50.211
315 49.405 45.226
316 47.947 36.250
317 54.171 49.399
318 56.491 53.870
319 38.456 72.189
320 45.035 31.956
321 54.884 58.106
322 41.687 45.292
323 54.694 62.778
324 40.966 31.709
325 54.861 49.234
326 43.801 32.598
327 55.669 58.525
328 59.633 42.871
329 39.697 58.954
330 53.222 76.060
331 75.509 44.523
332 49.989 48.391
333 46.703 49.378
334 52.296 57.515
335 53.619 56.594
336 57.687 65.179
337 39.036 61.029
338 70.290 66.786
339 34.481 67.089
340 54.628 56.868
341 50.020 31.461
342 43.040 31.436
343 50.211 45.572
344 53.450 57.710
345 59.338 47.694
346 52.114 50.266
347 49.175 52.530
348 59.364 59.989
349 64.953 46.612
350 39.746 38.049
351 43.678 44.179
352 55.959 46.440
353 45.862 53.935
354 56.561 51.764
355 49.270 40.220
356 44.102 52.145
357 59.764 39.021
358 29.987 42.558
359 27.591 50.283
360 46.290 61.134
361 43.300 52.442
362 43.762 55.086
363 63.822 68.303
364 59.509 62.981
365 54.711 53.955
366 34.244 46.476
367 48.869 49.876
368 46.427 37.848
369 46.713 55.180
370 55.117 53.156
371 42.275 26.926
372 50.362 43.764
373 55.511 39.636
374 41.569 55.429
375 55.599 39.466
376 63.822 68.303
377 72.047 50.655
378 41.874 30.809
379 60.820 58.700
380 54.584 56.658
381 46.335 45.623
382 44.227 58.629
383 50.509 70.288
384 51.665 42.524
385 67.531 72.212
386 46.732 50.513
387 54.923 57.988
388 60.039 68.723
389 56.467 59.250
390 48.983 40.716
391 48.983 40.716
392 58.931 54.784
393 60.148 58.604
394 57.000 42.199
395 54.639 32.948
396 44.146 36.658
397 47.158 66.587
398 47.993 67.385
399 36.984 60.634
400 56.618 43.296
401 63.356 57.415
402 65.066 63.387
403 46.175 47.370
404 42.448 61.604
405 42.021 40.993
406 34.862 43.225
407 43.015 45.753
408 42.584 63.514
409 63.206 62.126
410 70.360 45.133
411 41.048 67.819
412 59.178 58.265
413 42.889 74.484
414 38.042 50.299
415 57.860 45.511
416 62.222 76.016
417 66.344 37.055
418 47.198 55.626
419 38.372 43.356
420 48.381 57.100
421 52.401 54.124
422 35.842 48.442
423 52.217 42.125
424 30.644 53.977
425 44.521 37.183
426 35.501 60.286
427 67.708 52.717
428 43.295 52.748
429 17.254 36.451
430 44.228 52.803
431 24.630 51.122
432 57.917 55.955
433 75.063 68.622
434 59.000 60.969
435 25.596 45.059
436 39.731 42.542
437 59.131 70.036
438 34.436 44.548
439 34.049 35.971
440 55.321 72.111
441 55.942 40.567
442 52.368 46.508
443 54.314 51.884
444 54.624 59.667
445 54.355 39.890
446 51.793 32.977
447 43.626 39.291
448 48.572 46.376
449 49.716 44.959
450 51.688 45.759
451 60.951 60.105
452 47.304 47.997
453 30.637 40.977
454 58.827 53.439
455 39.666 35.185
456 41.499 36.020
457 44.961 49.357
458 39.323 42.951
459 54.999 44.478
460 47.984 47.690
461 52.328 43.972
462 60.181 55.351
463 50.985 42.340
464 37.164 45.835
465 56.121 77.270
466 44.334 40.334
467 45.879 49.402
468 69.575 42.467
469 56.757 46.553
470 56.407 52.649
471 56.761 48.262
472 49.208 51.255
473 41.190 50.240
474 66.302 59.278
475 43.175 32.879
476 35.314 46.564
477 42.484 42.081
478 43.572 45.231
479 46.494 53.750
480 74.159 76.187
481 29.235 33.409
482 64.497 38.380
483 74.523 68.642
484 37.072 37.358
485 46.282 51.425
486 34.877 43.492
487 60.084 41.691
488 21.761 55.804
489 33.546 35.438
490 54.602 64.955
491 68.838 49.563
492 41.147 38.875
493 44.153 53.412
494 48.879 45.270
495 42.720 60.956
496 53.658 23.509
497 37.695 53.546
498 61.726 45.578
499 48.652 47.050
500 56.021 43.380
PRECISION OF FACTOR SCORES
FACTOR: 1
Case Approximate 95% Posterior Reliability
confidence interval SE
1 ( 76.326 88.555) 3.717 0.862
2 ( 51.204 61.507) 3.132 0.902
3 ( 45.822 54.285) 2.572 0.934
4 ( 44.767 53.632) 2.695 0.927
5 ( 43.266 51.775) 2.586 0.933
6 ( 21.690 30.861) 2.788 0.922
7 ( 47.496 56.203) 2.647 0.930
8 ( 52.786 61.525) 2.656 0.929
9 ( 48.012 57.007) 2.734 0.925
10 ( 50.098 59.030) 2.715 0.926
11 ( 25.381 35.108) 2.957 0.913
12 ( 55.093 64.559) 2.877 0.917
13 ( 69.741 81.799) 3.670 0.865
14 ( 48.560 57.290) 2.654 0.930
15 ( 61.245 70.310) 2.756 0.924
16 ( 41.966 51.190) 2.804 0.921
17 ( 47.151 56.709) 2.905 0.916
18 ( 37.344 49.064) 3.563 0.873
19 ( 32.093 43.391) 3.434 0.882
20 ( 30.958 40.838) 3.005 0.910
21 ( 43.521 52.385) 2.695 0.927
22 ( 43.048 51.629) 2.609 0.932
23 ( 24.566 33.924) 2.845 0.919
24 ( 57.135 65.973) 2.687 0.928
25 ( 37.931 46.067) 2.473 0.939
26 ( 34.455 43.631) 2.789 0.922
27 ( 75.794 88.512) 3.866 0.851
28 ( 42.857 51.423) 2.604 0.932
29 ( 56.710 65.599) 2.702 0.927
30 ( 49.363 58.702) 2.839 0.919
31 ( 36.765 45.457) 2.642 0.930
32 ( 45.473 54.267) 2.673 0.929
33 ( 34.967 43.972) 2.737 0.925
34 ( 27.595 36.594) 2.735 0.925
35 ( 43.096 51.823) 2.654 0.930
36 ( 48.642 57.363) 2.651 0.930
37 ( 41.476 50.175) 2.644 0.930
38 ( 44.372 53.812) 2.870 0.918
39 ( 34.404 42.753) 2.538 0.936
40 ( 48.186 57.160) 2.728 0.926
41 ( 52.192 61.086) 2.704 0.927
42 ( 33.062 41.268) 2.495 0.938
43 ( 46.012 54.885) 2.697 0.927
44 ( 51.445 60.450) 2.737 0.925
45 ( 54.809 63.744) 2.716 0.926
46 ( 38.428 46.535) 2.464 0.939
47 ( 35.263 43.305) 2.445 0.940
48 ( 34.022 42.361) 2.535 0.936
49 ( 41.240 50.747) 2.890 0.916
50 ( 43.540 52.907) 2.847 0.919
51 ( 38.025 46.429) 2.555 0.935
52 ( 38.775 46.940) 2.482 0.938
53 ( 34.083 42.904) 2.682 0.928
54 ( 48.232 57.284) 2.751 0.924
55 ( 59.603 69.216) 2.922 0.915
56 ( 47.069 55.592) 2.591 0.933
57 ( 57.204 67.527) 3.138 0.902
58 ( 44.712 54.685) 3.032 0.908
59 ( 40.540 50.437) 3.009 0.909
60 ( 37.739 46.706) 2.726 0.926
61 ( 40.674 50.550) 3.002 0.910
62 ( 25.042 35.136) 3.068 0.906
63 ( 57.574 67.088) 2.892 0.916
64 ( 70.330 82.012) 3.551 0.874
65 ( 44.151 52.622) 2.575 0.934
66 ( 41.503 50.239) 2.656 0.929
67 ( 46.696 56.539) 2.992 0.910
68 ( 36.608 44.896) 2.520 0.937
69 ( 48.232 56.980) 2.659 0.929
70 ( 48.754 57.456) 2.645 0.930
71 ( 47.105 56.098) 2.734 0.925
72 ( 41.259 49.656) 2.553 0.935
73 ( 46.739 55.683) 2.719 0.926
74 ( 62.798 72.300) 2.888 0.917
75 ( 51.189 60.001) 2.678 0.928
76 ( 38.557 47.433) 2.699 0.927
77 ( 34.072 42.456) 2.549 0.935
78 ( 42.791 51.628) 2.686 0.928
79 ( 41.979 51.235) 2.813 0.921
80 ( 53.068 61.904) 2.686 0.928
81 ( 50.458 60.196) 2.960 0.912
82 ( 32.344 41.244) 2.705 0.927
83 ( 19.180 30.021) 3.295 0.891
84 ( 51.574 60.909) 2.837 0.919
85 ( 49.561 58.237) 2.637 0.930
86 ( 42.690 51.303) 2.618 0.931
87 ( 54.945 63.782) 2.687 0.928
88 ( 41.756 51.128) 2.849 0.919
89 ( 45.092 54.455) 2.846 0.919
90 ( 47.023 56.935) 3.013 0.909
91 ( 59.468 68.885) 2.863 0.918
92 ( 33.449 42.137) 2.641 0.930
93 ( 36.838 45.843) 2.737 0.925
94 ( 49.126 57.794) 2.635 0.931
95 ( 50.257 60.022) 2.969 0.912
96 ( 46.917 57.032) 3.075 0.905
97 ( 50.504 59.875) 2.849 0.919
98 ( 60.576 69.480) 2.707 0.927
99 ( 40.139 49.036) 2.705 0.927
100 ( 40.733 49.237) 2.585 0.933
101 ( 40.760 50.299) 2.900 0.916
102 ( 48.562 57.078) 2.589 0.933
103 ( 47.494 56.681) 2.793 0.922
104 ( 39.426 47.827) 2.554 0.935
105 ( 43.699 52.799) 2.766 0.923
106 ( 40.051 50.156) 3.072 0.906
107 ( 38.654 47.008) 2.539 0.936
108 ( 63.250 72.986) 2.960 0.912
109 ( 44.100 52.952) 2.691 0.928
110 ( 37.337 46.460) 2.773 0.923
111 ( 45.480 54.373) 2.703 0.927
112 ( 48.290 56.943) 2.631 0.931
113 ( 43.684 52.905) 2.803 0.921
114 ( 39.748 50.136) 3.158 0.900
115 ( 40.295 49.104) 2.678 0.928
116 ( 52.122 61.167) 2.750 0.924
117 ( 45.989 54.519) 2.593 0.933
118 ( 43.974 52.942) 2.726 0.926
119 ( 63.162 72.751) 2.915 0.915
120 ( 41.425 49.837) 2.557 0.935
121 ( 40.234 49.515) 2.821 0.920
122 ( 42.042 51.025) 2.731 0.925
123 ( 48.955 58.023) 2.756 0.924
124 ( 54.904 63.955) 2.751 0.924
125 ( 25.583 35.464) 3.004 0.910
126 ( 42.972 51.775) 2.676 0.928
127 ( 45.645 56.123) 3.185 0.899
128 ( 43.280 51.998) 2.650 0.930
129 ( 50.135 59.193) 2.753 0.924
130 ( 38.883 47.477) 2.612 0.932
131 ( 39.752 48.726) 2.728 0.926
132 ( 38.964 48.254) 2.824 0.920
133 ( 38.040 46.229) 2.489 0.938
134 ( 49.828 60.193) 3.150 0.901
135 ( 56.243 65.094) 2.691 0.928
136 ( 50.705 59.767) 2.755 0.924
137 ( 48.553 57.289) 2.655 0.929
138 ( 39.558 48.458) 2.705 0.927
139 ( 52.812 61.850) 2.747 0.925
140 ( 32.517 40.919) 2.554 0.935
141 ( 67.242 79.597) 3.756 0.859
142 ( 36.833 46.315) 2.883 0.917
143 ( 32.900 41.580) 2.638 0.930
144 ( 51.023 60.722) 2.948 0.913
145 ( 39.096 47.326) 2.502 0.937
146 ( 53.077 62.434) 2.844 0.919
147 ( 51.402 60.297) 2.704 0.927
148 ( 47.460 56.227) 2.665 0.929
149 ( 54.643 63.697) 2.752 0.924
150 ( 37.146 47.951) 3.285 0.892
151 ( 45.084 54.453) 2.848 0.919
152 ( 59.882 71.062) 3.402 0.884
153 ( 57.506 67.051) 2.902 0.916
154 ( 55.585 64.814) 2.806 0.921
155 ( 46.055 55.400) 2.841 0.919
156 ( 51.458 60.172) 2.649 0.930
157 ( 55.109 64.917) 2.981 0.911
158 ( 44.564 53.763) 2.796 0.922
159 ( 50.518 59.834) 2.832 0.920
160 ( 54.641 63.488) 2.689 0.928
161 ( 55.821 64.811) 2.733 0.925
162 ( 45.906 55.148) 2.809 0.921
163 ( 40.165 48.732) 2.604 0.932
164 ( 42.518 51.185) 2.635 0.931
165 ( 52.722 61.264) 2.596 0.933
166 ( 41.145 49.647) 2.585 0.933
167 ( 45.443 55.430) 3.036 0.908
168 ( 49.766 58.487) 2.651 0.930
169 ( 44.949 54.370) 2.864 0.918
170 ( 43.951 52.738) 2.671 0.929
171 ( 46.530 55.632) 2.767 0.923
172 ( 54.081 63.878) 2.978 0.911
173 ( 44.986 54.566) 2.912 0.915
174 ( 49.583 59.664) 3.064 0.906
175 ( 37.642 45.687) 2.446 0.940
176 ( 59.394 68.431) 2.747 0.925
177 ( 36.988 45.785) 2.674 0.928
178 ( 58.469 67.872) 2.859 0.918
179 ( 52.525 62.249) 2.956 0.913
180 ( 38.844 47.086) 2.505 0.937
181 ( 30.553 39.678) 2.774 0.923
182 ( 53.881 63.185) 2.828 0.920
183 ( 42.744 52.377) 2.928 0.914
184 ( 43.267 52.476) 2.800 0.922
185 ( 39.935 48.450) 2.588 0.933
186 ( 39.910 48.845) 2.716 0.926
187 ( 40.057 48.738) 2.639 0.930
188 ( 38.642 46.788) 2.476 0.939
189 ( 53.518 63.441) 3.016 0.909
190 ( 55.793 64.965) 2.788 0.922
191 ( 54.699 64.253) 2.904 0.916
192 ( 44.336 53.368) 2.745 0.925
193 ( 24.472 35.810) 3.447 0.881
194 ( 44.440 53.143) 2.646 0.930
195 ( 57.738 69.252) 3.500 0.877
196 ( 46.502 57.168) 3.242 0.895
197 ( 45.569 54.544) 2.728 0.926
198 ( 35.704 44.637) 2.715 0.926
199 ( 39.069 50.214) 3.388 0.885
200 ( 36.512 44.628) 2.467 0.939
201 ( 31.782 41.579) 2.978 0.911
202 ( 47.401 56.467) 2.756 0.924
203 ( 40.581 50.153) 2.909 0.915
204 ( 64.081 75.928) 3.619 0.869
205 ( 38.558 47.385) 2.683 0.928
206 ( 47.318 55.920) 2.615 0.932
207 ( 42.883 52.195) 2.831 0.920
208 ( 53.252 62.067) 2.680 0.928
209 ( 50.616 62.010) 3.464 0.880
210 ( 63.609 72.922) 2.831 0.920
211 ( 43.286 53.329) 3.053 0.907
212 ( 54.588 64.434) 2.993 0.910
213 ( 47.811 57.244) 2.867 0.918
214 ( 37.785 46.092) 2.525 0.936
215 ( 45.536 54.744) 2.799 0.922
216 ( 36.556 45.025) 2.575 0.934
217 ( 36.705 45.788) 2.761 0.924
218 ( 44.141 53.300) 2.784 0.922
219 ( 52.154 60.972) 2.681 0.928
220 ( 26.001 35.392) 2.855 0.919
221 ( 48.215 57.422) 2.799 0.922
222 ( 55.415 64.311) 2.704 0.927
223 ( 56.758 65.784) 2.744 0.925
224 ( 48.349 57.605) 2.813 0.921
225 ( 46.876 55.927) 2.752 0.924
226 ( 56.189 65.088) 2.705 0.927
227 ( 42.189 51.746) 2.905 0.916
228 ( 48.028 57.203) 2.789 0.922
229 ( 46.639 55.902) 2.816 0.921
230 ( 40.424 49.575) 2.782 0.923
231 ( 75.341 87.821) 3.794 0.856
232 ( 34.190 42.295) 2.464 0.939
233 ( 53.502 62.658) 2.783 0.923
234 ( 38.490 46.954) 2.573 0.934
235 ( 41.193 49.800) 2.616 0.932
236 ( 46.910 56.377) 2.879 0.917
237 ( 35.077 43.362) 2.518 0.937
238 ( 38.800 48.361) 2.906 0.916
239 ( 38.678 47.200) 2.590 0.933
240 ( 51.077 60.398) 2.833 0.920
241 ( 46.166 54.704) 2.595 0.933
242 ( 47.564 56.624) 2.754 0.924
243 ( 44.168 52.881) 2.649 0.930
244 ( 38.148 46.600) 2.569 0.934
245 ( 34.198 42.303) 2.464 0.939
246 ( 59.609 68.929) 2.833 0.920
247 ( 45.813 54.793) 2.730 0.925
248 ( 39.298 47.579) 2.517 0.937
249 ( 46.161 54.735) 2.606 0.932
250 ( 50.102 59.168) 2.756 0.924
251 ( 28.978 37.862) 2.701 0.927
252 ( 28.709 38.127) 2.863 0.918
253 ( 67.705 78.943) 3.416 0.883
254 ( 43.481 52.207) 2.652 0.930
255 ( 55.131 63.942) 2.678 0.928
256 ( 47.550 56.252) 2.645 0.930
257 ( 35.291 44.255) 2.725 0.926
258 ( 45.433 54.628) 2.795 0.922
259 ( 46.079 56.180) 3.070 0.906
260 ( 60.949 69.981) 2.746 0.925
261 ( 35.043 43.541) 2.583 0.933
262 ( 47.997 56.636) 2.626 0.931
263 ( 38.716 47.051) 2.534 0.936
264 ( 39.089 47.525) 2.564 0.934
265 ( 52.286 61.098) 2.679 0.928
266 ( 53.377 62.085) 2.647 0.930
267 ( 40.102 48.619) 2.589 0.933
268 ( 44.578 53.454) 2.698 0.927
269 ( 40.207 49.728) 2.894 0.916
270 ( 34.515 43.730) 2.801 0.922
271 ( 45.707 55.814) 3.072 0.906
272 ( 54.345 63.193) 2.690 0.928
273 ( 52.502 61.795) 2.825 0.920
274 ( 42.520 51.426) 2.707 0.927
275 ( 27.415 36.337) 2.712 0.926
276 ( 50.090 59.169) 2.760 0.924
277 ( 37.953 47.653) 2.949 0.913
278 ( 37.152 47.015) 2.998 0.910
279 ( 49.781 58.832) 2.751 0.924
280 ( 36.698 45.646) 2.720 0.926
281 ( 45.909 54.423) 2.588 0.933
282 ( 44.331 53.011) 2.639 0.930
283 ( 44.727 53.898) 2.788 0.922
284 ( 41.199 50.138) 2.717 0.926
285 ( 26.878 35.926) 2.750 0.924
286 ( 56.387 66.132) 2.962 0.912
287 ( 30.957 41.134) 3.094 0.904
288 ( 52.211 61.863) 2.934 0.914
289 ( 42.399 51.882) 2.882 0.917
290 ( 55.391 64.227) 2.686 0.928
291 ( 48.375 57.532) 2.783 0.923
292 ( 39.580 48.760) 2.790 0.922
293 ( 33.387 41.698) 2.526 0.936
294 ( 36.109 44.951) 2.688 0.928
295 ( 59.954 69.119) 2.786 0.922
296 ( 77.506 89.419) 3.621 0.869
297 ( 45.064 53.741) 2.638 0.930
298 ( 55.078 64.983) 3.011 0.909
299 ( 46.192 55.031) 2.687 0.928
300 ( 35.108 44.909) 2.979 0.911
301 ( 56.517 66.006) 2.884 0.917
302 ( 61.869 71.479) 2.921 0.915
303 ( 46.882 55.610) 2.653 0.930
304 ( 55.565 64.350) 2.671 0.929
305 ( 48.032 56.927) 2.704 0.927
306 ( 33.172 41.562) 2.551 0.935
307 ( 60.284 69.489) 2.798 0.922
308 ( 41.129 50.719) 2.915 0.915
309 ( 42.990 52.073) 2.761 0.924
310 ( 51.290 60.089) 2.675 0.928
311 ( 43.797 52.699) 2.706 0.927
312 ( 31.649 41.222) 2.910 0.915
313 ( 55.022 63.782) 2.663 0.929
314 ( 51.837 60.779) 2.718 0.926
315 ( 44.966 53.844) 2.699 0.927
316 ( 43.421 52.472) 2.752 0.924
317 ( 49.487 58.854) 2.848 0.919
318 ( 51.903 61.079) 2.789 0.922
319 ( 34.060 42.852) 2.672 0.929
320 ( 39.723 50.346) 3.229 0.896
321 ( 50.121 59.647) 2.896 0.916
322 ( 37.270 46.104) 2.685 0.928
323 ( 49.869 59.518) 2.933 0.914
324 ( 35.964 45.968) 3.041 0.908
325 ( 49.975 59.747) 2.970 0.912
326 ( 39.263 48.338) 2.758 0.924
327 ( 51.175 60.164) 2.732 0.925
328 ( 55.083 64.182) 2.766 0.923
329 ( 35.358 44.036) 2.638 0.930
330 ( 48.153 58.290) 3.082 0.905
331 ( 69.839 81.179) 3.447 0.881
332 ( 45.535 54.444) 2.708 0.927
333 ( 42.160 51.246) 2.762 0.924
334 ( 47.771 56.821) 2.751 0.924
335 ( 48.803 58.435) 2.928 0.914
336 ( 52.899 62.475) 2.911 0.915
337 ( 34.358 43.714) 2.844 0.919
338 ( 64.975 75.606) 3.232 0.896
339 ( 28.668 40.294) 3.534 0.875
340 ( 49.686 59.570) 3.004 0.910
341 ( 45.723 54.317) 2.612 0.932
342 ( 38.740 47.340) 2.614 0.932
343 ( 45.570 54.851) 2.821 0.920
344 ( 48.649 58.251) 2.919 0.915
345 ( 54.697 63.980) 2.822 0.920
346 ( 47.571 56.657) 2.762 0.924
347 ( 44.689 53.660) 2.727 0.926
348 ( 54.913 63.815) 2.706 0.927
349 ( 60.460 69.446) 2.732 0.925
350 ( 35.668 43.823) 2.479 0.939
351 ( 39.149 48.207) 2.754 0.924
352 ( 51.290 60.629) 2.839 0.919
353 ( 41.668 50.055) 2.550 0.935
354 ( 51.740 61.382) 2.931 0.914
355 ( 44.681 53.859) 2.790 0.922
356 ( 39.345 48.859) 2.892 0.916
357 ( 54.599 64.928) 3.140 0.901
358 ( 25.628 34.345) 2.650 0.930
359 ( 22.805 32.376) 2.909 0.915
360 ( 42.149 50.432) 2.518 0.937
361 ( 39.053 47.548) 2.582 0.933
362 ( 39.495 48.028) 2.594 0.933
363 ( 58.911 68.734) 2.986 0.911
364 ( 54.623 64.396) 2.971 0.912
365 ( 50.035 59.387) 2.843 0.919
366 ( 29.994 38.494) 2.584 0.933
367 ( 44.072 53.666) 2.916 0.915
368 ( 41.587 51.266) 2.942 0.913
369 ( 42.388 51.038) 2.630 0.931
370 ( 50.473 59.761) 2.823 0.920
371 ( 37.135 47.415) 3.125 0.902
372 ( 45.951 54.772) 2.681 0.928
373 ( 51.026 59.997) 2.727 0.926
374 ( 36.686 46.452) 2.969 0.912
375 ( 51.114 60.084) 2.727 0.926
376 ( 58.911 68.734) 2.986 0.911
377 ( 66.776 77.318) 3.205 0.897
378 ( 35.993 47.755) 3.575 0.872
379 ( 56.322 65.318) 2.735 0.925
380 ( 50.269 58.898) 2.623 0.931
381 ( 42.179 50.492) 2.527 0.936
382 ( 39.193 49.260) 3.060 0.906
383 ( 45.157 55.861) 3.254 0.894
384 ( 46.123 57.207) 3.369 0.886
385 ( 62.869 72.193) 2.835 0.920
386 ( 41.662 51.802) 3.082 0.905
387 ( 50.607 59.239) 2.624 0.931
388 ( 55.552 64.526) 2.728 0.926
389 ( 51.639 61.295) 2.935 0.914
390 ( 44.738 53.228) 2.581 0.933
391 ( 44.738 53.228) 2.581 0.933
392 ( 54.223 63.638) 2.862 0.918
393 ( 55.695 64.601) 2.707 0.927
394 ( 52.568 61.432) 2.695 0.927
395 ( 49.854 59.425) 2.909 0.915
396 ( 39.597 48.695) 2.766 0.924
397 ( 42.777 51.539) 2.664 0.929
398 ( 43.499 52.486) 2.733 0.925
399 ( 32.340 41.629) 2.824 0.920
400 ( 52.217 61.019) 2.675 0.928
401 ( 58.720 67.993) 2.819 0.921
402 ( 59.617 70.516) 3.313 0.890
403 ( 41.140 51.211) 3.061 0.906
404 ( 38.058 46.838) 2.669 0.929
405 ( 37.817 46.224) 2.556 0.935
406 ( 30.173 39.551) 2.851 0.919
407 ( 38.046 47.984) 3.021 0.909
408 ( 38.543 46.624) 2.457 0.940
409 ( 58.248 68.164) 3.014 0.909
410 ( 65.102 75.617) 3.196 0.898
411 ( 36.742 45.354) 2.618 0.931
412 ( 53.310 65.046) 3.568 0.873
413 ( 38.486 47.291) 2.677 0.928
414 ( 33.883 42.202) 2.529 0.936
415 ( 52.874 62.846) 3.031 0.908
416 ( 57.016 67.429) 3.165 0.900
417 ( 61.222 71.466) 3.114 0.903
418 ( 42.893 51.503) 2.617 0.931
419 ( 34.180 42.565) 2.549 0.935
420 ( 43.556 53.206) 2.933 0.914
421 ( 48.024 56.778) 2.661 0.929
422 ( 31.335 40.348) 2.740 0.925
423 ( 47.504 56.930) 2.865 0.918
424 ( 26.208 35.081) 2.697 0.927
425 ( 39.823 49.218) 2.856 0.918
426 ( 31.373 39.629) 2.510 0.937
427 ( 63.089 72.327) 2.808 0.921
428 ( 39.142 47.449) 2.525 0.936
429 ( 11.473 23.035) 3.515 0.876
430 ( 39.936 48.520) 2.609 0.932
431 ( 19.581 29.678) 3.069 0.906
432 ( 53.562 62.273) 2.648 0.930
433 ( 69.188 80.937) 3.571 0.872
434 ( 53.236 64.764) 3.504 0.877
435 ( 20.186 31.007) 3.289 0.892
436 ( 35.388 44.074) 2.640 0.930
437 ( 54.433 63.828) 2.856 0.918
438 ( 29.793 39.079) 2.823 0.920
439 ( 29.662 38.437) 2.667 0.929
440 ( 49.978 60.664) 3.248 0.894
441 ( 50.740 61.144) 3.162 0.900
442 ( 47.989 56.747) 2.662 0.929
443 ( 49.258 59.369) 3.074 0.906
444 ( 49.317 59.930) 3.226 0.896
445 ( 49.889 58.820) 2.715 0.926
446 ( 47.250 56.337) 2.762 0.924
447 ( 39.021 48.231) 2.800 0.922
448 ( 43.935 53.210) 2.819 0.921
449 ( 43.958 55.475) 3.501 0.877
450 ( 46.726 56.650) 3.017 0.909
451 ( 56.453 65.449) 2.735 0.925
452 ( 43.081 51.528) 2.568 0.934
453 ( 26.221 35.053) 2.685 0.928
454 ( 53.966 63.688) 2.955 0.913
455 ( 35.073 44.259) 2.792 0.922
456 ( 37.003 45.996) 2.734 0.925
457 ( 40.492 49.429) 2.716 0.926
458 ( 34.803 43.843) 2.748 0.924
459 ( 49.206 60.793) 3.522 0.876
460 ( 43.535 52.432) 2.705 0.927
461 ( 47.959 56.698) 2.657 0.929
462 ( 55.738 64.624) 2.701 0.927
463 ( 46.692 55.279) 2.610 0.932
464 ( 33.003 41.325) 2.530 0.936
465 ( 51.293 60.950) 2.936 0.914
466 ( 39.685 48.983) 2.826 0.920
467 ( 41.533 50.226) 2.642 0.930
468 ( 64.856 74.293) 2.869 0.918
469 ( 51.634 61.881) 3.115 0.903
470 ( 51.604 61.210) 2.920 0.915
471 ( 51.588 61.935) 3.145 0.901
472 ( 44.586 53.831) 2.810 0.921
473 ( 36.710 45.671) 2.724 0.926
474 ( 61.687 70.918) 2.806 0.921
475 ( 39.065 47.285) 2.499 0.938
476 ( 30.414 40.213) 2.979 0.911
477 ( 37.759 47.209) 2.873 0.917
478 ( 39.118 48.026) 2.708 0.927
479 ( 41.956 51.032) 2.759 0.924
480 ( 68.054 80.264) 3.711 0.862
481 ( 24.727 33.744) 2.741 0.925
482 ( 59.916 69.077) 2.785 0.922
483 ( 68.982 80.065) 3.369 0.886
484 ( 31.671 42.474) 3.284 0.892
485 ( 41.429 51.134) 2.950 0.913
486 ( 30.017 39.737) 2.955 0.913
487 ( 55.584 64.584) 2.736 0.925
488 ( 16.312 27.210) 3.313 0.890
489 ( 28.566 38.527) 3.028 0.908
490 ( 50.059 59.146) 2.762 0.924
491 ( 64.163 73.512) 2.842 0.919
492 ( 36.987 45.308) 2.530 0.936
493 ( 39.631 48.675) 2.749 0.924
494 ( 44.036 53.723) 2.945 0.913
495 ( 38.393 47.048) 2.631 0.931
496 ( 48.541 58.774) 3.111 0.903
497 ( 33.249 42.140) 2.702 0.927
498 ( 57.208 66.245) 2.749 0.924
499 ( 43.968 53.337) 2.848 0.919
500 ( 51.375 60.666) 2.824 0.920
PRECISION OF FACTOR SCORES
FACTOR: 2
Case Approximate 95% Posterior Reliability
confidence interval SE
1 ( 51.387 63.455) 3.668 0.865
2 ( 48.371 60.164) 3.585 0.871
3 ( 41.674 52.136) 3.180 0.899
4 ( 28.915 39.256) 3.144 0.901
5 ( 34.748 44.486) 2.960 0.912
6 ( 39.281 49.878) 3.221 0.896
7 ( 44.211 55.186) 3.336 0.889
8 ( 50.188 61.991) 3.588 0.871
9 ( 49.600 60.650) 3.359 0.887
10 ( 40.690 51.742) 3.359 0.887
11 ( 55.763 69.578) 4.200 0.824
12 ( 47.658 59.253) 3.525 0.876
13 ( 54.409 67.284) 3.916 0.847
14 ( 45.560 56.166) 3.224 0.896
15 ( 50.975 62.545) 3.517 0.876
16 ( 39.539 50.923) 3.461 0.880
17 ( 42.146 52.353) 3.103 0.904
18 ( 39.175 49.380) 3.102 0.904
19 ( 43.971 55.205) 3.415 0.883
20 ( 40.979 52.012) 3.354 0.888
21 ( 21.477 32.769) 3.433 0.882
22 ( 38.762 48.483) 2.955 0.913
23 ( 36.667 47.304) 3.234 0.895
24 ( 62.165 78.252) 4.890 0.761
25 ( 33.999 46.170) 3.700 0.863
26 ( 36.432 46.286) 2.996 0.910
27 ( 65.766 83.538) 5.402 0.708
28 ( 28.420 38.127) 2.951 0.913
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491 ( 43.818 55.309) 3.493 0.878
492 ( 34.189 43.561) 2.849 0.919
493 ( 47.549 59.275) 3.564 0.873
494 ( 40.023 50.517) 3.190 0.898
495 ( 54.674 67.238) 3.819 0.854
496 ( 18.449 28.568) 3.076 0.905
497 ( 48.014 59.077) 3.363 0.887
498 ( 39.887 51.269) 3.462 0.880
499 ( 41.224 52.875) 3.542 0.875
500 ( 38.399 48.361) 3.028 0.908
--------------------------------------------------------------------------------
PERSON-FIT INDICES FOR CONTINUOUS MODELS
Ferrando, Vigil-Colet, & Lorenzo-Seva (2017)
Summary Statistics for Person Fit Indices
Indices computed
Weighted Mean-Squared Index (WMSI)
Personal Correlation (rp)
WMSI rp
Smallest 0.1443 -0.4560
Largest 3.9343 0.9550
Mean 1.0109 0.6894
Variance 0.3922 0.0501
Cases with high WMSI (value larger than 1.53) and/or low rp (value lower than 0.65)
Case WMSI rp
4 1.459 0.439
5 0.638 0.619
6 0.717 0.459
11 2.277 0.564
16 1.207 0.594
19 2.439 0.541
20 1.701 0.385
21 2.861 0.132
23 1.245 0.478
25 0.963 0.606
26 0.592 0.530
28 1.342 0.378
31 0.916 0.588
33 1.925 0.012
34 1.351 0.118
35 0.997 0.588
41 0.902 0.598
45 0.629 0.618
49 1.548 0.811
52 0.514 0.594
57 2.342 0.388
58 3.934 -0.109
61 2.327 0.186
62 1.646 0.376
64 1.841 0.628
72 0.850 0.464
73 0.964 0.590
74 0.618 0.636
77 0.975 0.609
81 1.207 0.644
83 1.381 0.058
90 1.962 0.409
91 1.556 0.706
99 1.073 0.582
100 1.372 0.289
109 0.899 0.641
110 1.189 0.593
113 0.851 0.624
115 2.035 0.438
122 1.428 0.297
125 1.817 0.234
127 1.829 0.389
131 0.757 0.579
133 0.359 0.637
136 1.786 0.374
138 0.927 0.544
141 2.726 0.577
142 0.947 0.642
143 0.929 0.588
145 0.681 0.542
147 0.758 0.650
150 2.891 0.367
152 2.307 0.530
169 1.057 0.601
172 3.683 -0.070
174 1.398 0.452
179 1.328 0.547
181 0.728 0.571
184 1.795 0.667
186 0.931 0.611
188 0.678 0.434
190 1.997 0.573
193 2.080 0.417
195 2.562 0.704
196 1.406 0.449
198 1.155 0.545
199 2.626 0.624
214 0.615 0.396
217 1.389 0.180
220 1.557 0.066
222 1.182 0.533
231 2.612 0.247
237 0.641 0.206
240 3.246 0.237
251 0.704 0.563
257 1.481 0.509
261 1.386 0.312
263 1.146 0.610
270 1.173 0.521
275 0.924 0.235
277 1.730 0.692
278 1.516 -0.025
280 0.885 0.521
285 0.746 0.463
286 1.652 0.516
287 1.824 0.646
288 1.283 0.577
292 1.153 0.388
294 0.850 0.503
295 1.556 0.450
300 2.431 0.688
302 1.502 0.572
303 1.271 0.638
306 0.582 0.490
307 3.583 -0.110
316 1.430 0.629
319 1.538 0.815
320 2.204 0.307
322 1.954 0.384
324 1.412 0.146
326 1.148 0.304
330 2.299 0.754
331 0.750 0.620
337 1.603 0.674
339 2.098 0.712
341 2.031 0.100
342 1.522 0.346
350 1.239 0.281
351 1.323 0.585
355 1.460 0.454
357 1.427 0.370
358 1.042 0.144
359 1.726 0.507
366 0.538 0.424
367 2.072 0.480
368 1.710 0.488
371 3.543 0.053
373 0.941 0.604
375 1.082 0.546
377 0.867 0.571
378 2.236 0.089
384 1.430 0.541
389 1.946 0.717
390 0.900 0.602
391 0.900 0.602
394 1.282 0.516
395 3.013 0.329
396 1.130 0.411
399 2.123 0.659
400 0.863 0.644
402 2.144 0.736
403 1.624 0.666
405 1.201 0.397
406 1.053 0.549
407 1.237 0.649
413 1.945 0.781
416 1.877 0.856
417 3.914 0.272
418 1.885 0.471
419 0.949 0.515
423 1.593 0.516
424 1.827 0.464
425 1.751 0.496
429 0.989 -0.079
431 0.727 0.413
434 1.632 0.723
435 1.657 0.141
436 2.001 0.269
438 1.613 0.341
439 1.212 0.016
440 1.585 0.750
441 2.417 0.271
445 1.427 0.488
446 1.460 0.301
447 1.285 0.469
448 1.646 0.596
449 1.834 0.611
453 0.963 0.379
455 1.421 0.279
456 2.014 -0.018
458 0.682 0.615
459 2.851 0.424
460 1.989 0.433
463 0.907 0.614
466 1.078 0.376
468 1.346 0.488
469 2.418 0.596
470 2.188 0.523
475 0.973 0.352
476 1.279 0.532
477 0.735 0.621
481 1.426 -0.092
482 1.853 0.475
484 1.416 0.405
486 1.612 0.656
487 0.882 0.624
488 1.026 0.481
489 1.212 0.246
492 0.556 0.501
496 3.933 -0.456
498 1.460 0.628
499 1.692 0.672
500 0.831 0.638
Person-Fit Indices for individuals
Case WMSI rp
1 0.276 0.737
2 1.239 0.752
3 0.561 0.796
4** 1.459 0.439
5** 0.638 0.619
6** 0.717 0.459
7 0.487 0.899
8 0.536 0.895
9 0.521 0.880
10 0.455 0.891
11** 2.277 0.564
12 0.821 0.834
13 0.483 0.756
14 0.287 0.906
15 0.520 0.761
16** 1.207 0.594
17 0.915 0.754
18 1.496 0.801
19** 2.439 0.541
20** 1.701 0.385
21** 2.861 0.132
22 0.300 0.870
23** 1.245 0.478
24 0.497 0.898
25** 0.963 0.606
26** 0.592 0.530
27 0.721 0.717
28** 1.342 0.378
29 0.506 0.786
30 0.823 0.920
31** 0.916 0.588
32 0.566 0.758
33** 1.925 0.012
34** 1.351 0.118
35** 0.997 0.588
36 0.437 0.947
37 0.262 0.890
38 0.810 0.877
39 1.354 0.722
40 0.629 0.940
41** 0.902 0.598
42 0.641 0.775
43 0.371 0.919
44 0.753 0.908
45** 0.629 0.618
46 0.637 0.815
47 0.646 0.686
48 1.377 0.752
49** 1.548 0.811
50 0.566 0.842
51 1.101 0.857
52** 0.514 0.594
53 0.911 0.840
54 0.839 0.692
55 0.482 0.761
56 0.307 0.936
57** 2.342 0.388
58** 3.934 -0.109
59 0.918 0.672
60 0.911 0.762
61** 2.327 0.186
62** 1.646 0.376
63 0.749 0.833
64** 1.841 0.628
65 0.754 0.842
66 0.342 0.843
67 1.262 0.804
68 0.558 0.792
69 0.267 0.928
70 0.740 0.927
71 1.015 0.891
72** 0.850 0.464
73** 0.964 0.590
74** 0.618 0.636
75 0.684 0.926
76 0.788 0.843
77** 0.975 0.609
78 0.811 0.804
79 1.383 0.737
80 0.585 0.881
81** 1.207 0.644
82 1.120 0.809
83** 1.381 0.058
84 0.458 0.853
85 0.382 0.801
86 0.468 0.812
87 0.651 0.740
88 1.206 0.819
89 0.650 0.889
90** 1.962 0.409
91** 1.556 0.706
92 1.231 0.804
93 1.365 0.747
94 0.303 0.893
95 0.819 0.914
96 0.829 0.811
97 0.570 0.935
98 0.517 0.805
99** 1.073 0.582
100** 1.372 0.289
101 1.340 0.698
102 1.314 0.756
103 0.437 0.875
104 0.997 0.824
105 0.605 0.831
106 0.980 0.715
107 0.354 0.878
108 0.724 0.761
109** 0.899 0.641
110** 1.189 0.593
111 0.386 0.881
112 0.666 0.905
113** 0.851 0.624
114 1.354 0.762
115** 2.035 0.438
116 0.761 0.831
117 0.673 0.808
118 0.436 0.891
119 0.594 0.760
120 0.440 0.764
121 0.789 0.760
122** 1.428 0.297
123 0.553 0.765
124 0.352 0.892
125** 1.817 0.234
126 0.765 0.807
127** 1.829 0.389
128 0.405 0.851
129 0.255 0.931
130 0.582 0.653
131** 0.757 0.579
132 0.967 0.650
133** 0.359 0.637
134 1.467 0.849
135 0.521 0.902
136** 1.786 0.374
137 0.679 0.763
138** 0.927 0.544
139 0.479 0.776
140 0.847 0.680
141** 2.726 0.577
142** 0.947 0.642
143** 0.929 0.588
144 0.468 0.865
145** 0.681 0.542
146 0.411 0.858
147** 0.758 0.650
148 0.420 0.890
149 0.390 0.840
150** 2.891 0.367
151 0.761 0.902
152** 2.307 0.530
153 0.948 0.747
154 0.524 0.856
155 0.489 0.939
156 0.434 0.880
157 0.632 0.895
158 0.577 0.812
159 0.397 0.931
160 0.310 0.896
161 0.439 0.899
162 0.439 0.875
163 0.399 0.740
164 0.796 0.794
165 0.355 0.926
166 0.729 0.747
167 1.127 0.855
168 0.535 0.742
169** 1.057 0.601
170 0.519 0.911
171 0.825 0.854
172** 3.683 -0.070
173 1.322 0.852
174** 1.398 0.452
175 0.357 0.751
176 0.632 0.701
177 1.074 0.843
178 0.654 0.809
179** 1.328 0.547
180 0.480 0.849
181** 0.728 0.571
182 0.282 0.903
183 1.103 0.782
184** 1.795 0.667
185 0.519 0.804
186** 0.931 0.611
187 0.544 0.844
188** 0.678 0.434
189 0.908 0.828
190** 1.997 0.573
191 1.028 0.786
192 0.716 0.866
193** 2.080 0.417
194 0.684 0.918
195** 2.562 0.704
196** 1.406 0.449
197 0.907 0.676
198** 1.155 0.545
199** 2.626 0.624
200 0.172 0.812
201 1.208 0.815
202 0.717 0.898
203 0.647 0.870
204 1.275 0.673
205 0.884 0.825
206 0.351 0.902
207 0.768 0.886
208 0.628 0.916
209 0.932 0.870
210 0.672 0.773
211 1.096 0.920
212 0.967 0.742
213 0.900 0.808
214** 0.615 0.396
215 1.299 0.718
216 0.314 0.680
217** 1.389 0.180
218 0.543 0.772
219 0.421 0.900
220** 1.557 0.066
221 0.613 0.913
222** 1.182 0.533
223 0.514 0.829
224 0.736 0.899
225 0.415 0.803
226 0.905 0.691
227 1.098 0.794
228 0.441 0.824
229 0.759 0.784
230 0.955 0.721
231** 2.612 0.247
232 0.215 0.731
233 0.996 0.868
234 0.671 0.892
235 0.371 0.827
236 0.841 0.897
237** 0.641 0.206
238 1.157 0.833
239 0.539 0.752
240** 3.246 0.237
241 0.357 0.916
242 0.477 0.950
243 0.541 0.770
244 0.600 0.838
245 0.155 0.731
246 0.649 0.850
247 0.593 0.702
248 0.348 0.782
249 0.864 0.740
250 0.217 0.934
251** 0.704 0.563
252 0.998 0.688
253 0.653 0.794
254 0.262 0.891
255 0.390 0.825
256 0.375 0.853
257** 1.481 0.509
258 0.642 0.888
259 0.834 0.801
260 0.393 0.800
261** 1.386 0.312
262 0.537 0.806
263** 1.146 0.610
264 1.023 0.847
265 0.753 0.757
266 0.279 0.915
267 0.342 0.879
268 0.558 0.906
269 0.622 0.771
270** 1.173 0.521
271 1.148 0.832
272 0.310 0.824
273 0.569 0.900
274 0.593 0.748
275** 0.924 0.235
276 0.566 0.951
277** 1.730 0.692
278** 1.516 -0.025
279 0.371 0.893
280** 0.885 0.521
281 0.610 0.909
282 0.628 0.820
283 0.452 0.832
284 0.651 0.732
285** 0.746 0.463
286** 1.652 0.516
287** 1.824 0.646
288** 1.283 0.577
289 1.150 0.877
290 0.553 0.853
291 0.364 0.861
292** 1.153 0.388
293 1.337 0.741
294** 0.850 0.503
295** 1.556 0.450
296 0.365 0.698
297 0.441 0.863
298 1.331 0.777
299 0.565 0.864
300** 2.431 0.688
301 0.997 0.840
302** 1.502 0.572
303** 1.271 0.638
304 0.304 0.890
305 0.783 0.869
306** 0.582 0.490
307** 3.583 -0.110
308 0.867 0.805
309 1.330 0.808
310 0.797 0.911
311 0.878 0.932
312 1.399 0.752
313 0.501 0.899
314 0.591 0.821
315 0.583 0.888
316** 1.430 0.629
317 0.697 0.860
318 0.627 0.853
319** 1.538 0.815
320** 2.204 0.307
321 1.120 0.845
322** 1.954 0.384
323 1.066 0.921
324** 1.412 0.146
325 0.813 0.797
326** 1.148 0.304
327 0.459 0.925
328 0.406 0.794
329 0.887 0.819
330** 2.299 0.754
331** 0.750 0.620
332 0.664 0.728
333 0.636 0.805
334 0.280 0.944
335 0.821 0.781
336 1.137 0.739
337** 1.603 0.674
338 0.645 0.835
339** 2.098 0.712
340 1.009 0.757
341** 2.031 0.100
342** 1.522 0.346
343 0.761 0.782
344 1.109 0.782
345 0.665 0.731
346 0.488 0.881
347 1.281 0.732
348 0.528 0.870
349 0.686 0.739
350** 1.239 0.281
351** 1.323 0.585
352 0.725 0.868
353 0.470 0.873
354 0.745 0.818
355** 1.460 0.454
356 0.826 0.844
357** 1.427 0.370
358** 1.042 0.144
359** 1.726 0.507
360 0.881 0.828
361 0.660 0.815
362 0.697 0.841
363 0.795 0.796
364 1.439 0.823
365 0.616 0.943
366** 0.538 0.424
367** 2.072 0.480
368** 1.710 0.488
369 0.676 0.892
370 0.928 0.810
371** 3.543 0.053
372 0.736 0.723
373** 0.941 0.604
374 1.202 0.784
375** 1.082 0.546
376 0.795 0.796
377** 0.867 0.571
378** 2.236 0.089
379 0.372 0.869
380 0.532 0.883
381 0.317 0.850
382 1.412 0.686
383 1.411 0.888
384** 1.430 0.541
385 0.414 0.881
386 1.426 0.673
387 0.144 0.955
388 0.501 0.858
389** 1.946 0.717
390** 0.900 0.602
391** 0.900 0.602
392 0.714 0.898
393 0.561 0.884
394** 1.282 0.516
395** 3.013 0.329
396** 1.130 0.411
397 0.667 0.923
398 0.884 0.909
399** 2.123 0.659
400** 0.863 0.644
401 0.742 0.848
402** 2.144 0.736
403** 1.624 0.666
404 0.947 0.867
405** 1.201 0.397
406** 1.053 0.549
407** 1.237 0.649
408 1.165 0.833
409 0.704 0.744
410 0.695 0.696
411 1.131 0.782
412 1.321 0.817
413** 1.945 0.781
414 0.882 0.735
415 0.810 0.659
416** 1.877 0.856
417** 3.914 0.272
418** 1.885 0.471
419** 0.949 0.515
420 1.061 0.764
421 0.570 0.939
422 0.838 0.706
423** 1.593 0.516
424** 1.827 0.464
425** 1.751 0.496
426 1.055 0.762
427 0.274 0.844
428 1.148 0.676
429** 0.989 -0.079
430 1.058 0.837
431** 0.727 0.413
432 0.462 0.905
433 0.627 0.746
434** 1.632 0.723
435** 1.657 0.141
436** 2.001 0.269
437 1.531 0.848
438** 1.613 0.341
439** 1.212 0.016
440** 1.585 0.750
441** 2.417 0.271
442 0.747 0.710
443 1.020 0.886
444 1.154 0.863
445** 1.427 0.488
446** 1.460 0.301
447** 1.285 0.469
448** 1.646 0.596
449** 1.834 0.611
450 0.710 0.791
451 0.285 0.891
452 1.043 0.710
453** 0.963 0.379
454 1.090 0.812
455** 1.421 0.279
456** 2.014 -0.018
457 0.756 0.744
458** 0.682 0.615
459** 2.851 0.424
460** 1.989 0.433
461 0.450 0.816
462 0.460 0.774
463** 0.907 0.614
464 0.229 0.708
465 1.464 0.894
466** 1.078 0.376
467 0.417 0.924
468** 1.346 0.488
469** 2.418 0.596
470** 2.188 0.523
471 0.705 0.715
472 0.955 0.703
473 1.354 0.708
474 0.223 0.882
475** 0.973 0.352
476** 1.279 0.532
477** 0.735 0.621
478 0.537 0.863
479 0.837 0.719
480 0.549 0.825
481** 1.426 -0.092
482** 1.853 0.475
483 0.469 0.796
484** 1.416 0.405
485 1.335 0.664
486** 1.612 0.656
487** 0.882 0.624
488** 1.026 0.481
489** 1.212 0.246
490 0.448 0.897
491 0.735 0.757
492** 0.556 0.501
493 0.988 0.715
494 0.727 0.845
495 1.245 0.810
496** 3.933 -0.456
497 0.897 0.712
498** 1.460 0.628
499** 1.692 0.672
500** 0.831 0.638
**: Individual with a large Person-Fit Index value
--------------------------------------------------------------------------------
References
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Buja, A., & Eyuboglu, N. (1992). Remarks on parallel analysis. Multivariate Behavioral Research, 27(4), 509-540.
Choi, J., Kim, S., Chen, J., & Dannels, S. (2011). A comparison of maximum likelihood and Bayesian estimation for polychoric correlation using
Monte Carlo simulation. Journal of Educational and Behavioral Statistics, 36, 523–549. doi:10.3102/1076998610381398
Ferrando, P. J., & Lorenzo-Seva U. (2016). A note on improving EAP trait estimation in oblique factor-analytic and item response theory models.
Psicologica, 37, 235-247.
Ferrando, P. J. (2009). Multidimensional Factor-Analysis-Based Procedures for Assessing Scalability in Personality Measurement. Structural
Equation Modeling, 16, 10-133.
Harman, H. H. (1962). Modern Factor Analysis, 2nd Edition. University of Chicago Press, Chicago.
Kelley, T. L. (1935). Essential Traits of Mental Life, Harvard Studies in Education, vol. 26. Harvard University Press, Cambridge.
Lorenzo-Seva, U., & Van Ginkel, J. R. (2016). Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales:
estimating latent trait scores. Anales de Psicología/Annals of Psychology, 32(2), 596-608.
Lorenzo-Seva, U. & ten Berge, J.M.F. (2006). Tucker's Congruence Coefficient as a Meaningful Index of Factor Similarity. Methodology, 2, 57-64.
McDonald, R.P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum.
Mardia, K. V. (1970), Measures of multivariate skewnees and kurtosis with applications. Biometrika, 57, 519-530.
Muraki, E. (1990). Fitting a polytomous item response model to Likert-type data. Applied Psychological Measurement, 14, 59-71.
Mislevy, R.J., & Bock, R.D. (1990). BILOG 3 Item analysis and test scoring with binary logistic models. Mooresville: Scientific Software.
Samejima F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometric Monograph, No. 17.
Reckase, M. D. (1985). The difficulty of test items that measure more than one ability. Applied Psychological Measurement, 9, 401-412.
Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality Assessment of Ordered Polytomous Items with Parallel Analysis. Psychological
Methods, 16, 209-220.
Tucker, L. R. (1951). A method for synthesis of factor analysis studies. Personnel Research Section Report, 984. Washington, D. C.: Department
of the Army.
FACTOR is based on CLAPACK.
Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Greenbaum, A., Hammarling, S., McKenney, A., &
Sorensen, D. (1999). LAPACK Users' Guide. Society for Industrial and Applied Mathematics. Philadelphia, PA
FACTOR can be refered as:
Lorenzo-Seva, U., & Ferrando, P.J. (2013). FACTOR 9.2 A Comprehensive Program for Fitting Exploratory and Semiconfirmatory Factor Analysis and
IRT Models. Applied Psychological Measurement, 37(6), 497-498.
Lorenzo-Seva, U., & Ferrando, P.J. (2006). FACTOR: A computer program to fit the exploratory factor analysis model.Behavioral Research Methods,
Instruments and Computers, 38(1), 88-91.
For furhter information and new releases go to:
psico.fcep.urv.cat/utilitats/factor
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FACTOR completed
Computing time : 7.78 minutes.
Matrices generated : 110703233
Our last advice: Distrust 5% of statistics, and 95% of statisticians. (Cal desconfiar un 5% de l'estadística, i un 95% de l'estadístic.)
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