Batch 1 - "MapIt: story". originality_raw, fluency_raw, divergency_raw MapIt: story-originality_raw: [0, 0, 100, 83, 0, 0, 1, 28, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0] ...
MapIt: story-fluency_raw: [1, 3, 3, 4, 1, 2, 4, 3, 1, 3, 1, 3, 1, 2, 1, 1, 4, 2, 1, 2, 1] ...
MapIt: story-divergency_raw: [0, 3, 3, 3, 0, 2, 3, 3, 0, 2, 0, 3, 0, 2, 0, 0, 2, 2, 0, 2, 0] ...
Analysis 2014-07-06 12:34:24 -0700
= Statsample::Factor::ParallelAnalysis
There are 3 real factors on data
== Principal Component Analysis
Number of factors: 1
Communalities
+------------------------------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+------------------------------+---------+------------+--------+
| MapIt: story-divergency_raw | 1.000 | 0.588 | 58.835 |
| MapIt: story-fluency_raw | 1.000 | 0.747 | 74.715 |
| MapIt: story-originality_raw | 1.000 | 0.377 | 37.654 |
+------------------------------+---------+------------+--------+
Total Variance Explained
+-------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+-------------+---------+---------+---------+
| Component 1 | 1.712 | 57.068% | 57.068 |
| Component 2 | 0.847 | 28.228% | 85.296 |
| Component 3 | 0.441 | 14.704% | 100.000 |
+-------------+---------+---------+---------+
Component matrix
+------------------------------+------+
| | PC_1 |
+------------------------------+------+
| MapIt: story-divergency_raw | .767 |
| MapIt: story-fluency_raw | .864 |
| MapIt: story-originality_raw | .614 |
+------------------------------+------+
Traditional Kaiser criterion (k>1) returns 1 factors
== Parallel Analysis
Bootstrap Method: random
Uses SMC: No
Correlation Matrix type : correlation_matrix
Number of variables: 3
Number of cases: 118
Number of iterations: 50
Number or factors to preserve: 1
Eigenvalues
+---+-----------------+----------------------+--------+-----------+
| n | data eigenvalue | generated eigenvalue | p.95 | preserve? |
+---+-----------------+----------------------+--------+-----------+
| 1 | 1.7120 | 1.1347 | 1.2223 | Yes |
| 2 | 0.8468 | 0.9934 | 1.0297 | |
| 3 | 0.4411 | 0.8719 | 0.9627 | |
+---+-----------------+----------------------+--------+-----------+
Parallel Analysis returns 1 factors to preserve
Batch 1 - "MapIt: laugh". originality_raw, fluency_raw, divergency_raw MapIt: laugh-originality_raw: [100, 63, 91, 0, 0, 91, 56, 0, 56, 0, 0, 91, 0, 56, 5, 0, 63, 0, 5, 19, 10] ...
MapIt: laugh-fluency_raw: [4, 3, 5, 1, 1, 6, 4, 1, 4, 1, 1, 5, 1, 2, 2, 1, 4, 1, 2, 3, 3] ...
MapIt: laugh-divergency_raw: [3, 2, 1, 0, 0, 1, 2, 0, 2, 0, 0, 1, 0, 2, 2, 0, 2, 0, 2, 3, 2] ...
Analysis 2014-07-06 12:35:58 -0700
= Statsample::Factor::ParallelAnalysis
There are 3 real factors on data
== Principal Component Analysis
Number of factors: 1
Communalities
+------------------------------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+------------------------------+---------+------------+--------+
| MapIt: laugh-divergency_raw | 1.000 | 0.519 | 51.928 |
| MapIt: laugh-fluency_raw | 1.000 | 0.831 | 83.077 |
| MapIt: laugh-originality_raw | 1.000 | 0.789 | 78.859 |
+------------------------------+---------+------------+--------+
Total Variance Explained
+-------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+-------------+---------+---------+---------+
| Component 1 | 2.139 | 71.288% | 71.288 |
| Component 2 | 0.639 | 21.298% | 92.586 |
| Component 3 | 0.222 | 7.414% | 100.000 |
+-------------+---------+---------+---------+
Component matrix
+------------------------------+------+
| | PC_1 |
+------------------------------+------+
| MapIt: laugh-divergency_raw | .721 |
| MapIt: laugh-fluency_raw | .911 |
| MapIt: laugh-originality_raw | .888 |
+------------------------------+------+
Traditional Kaiser criterion (k>1) returns 1 factors
== Parallel Analysis
Bootstrap Method: random
Uses SMC: No
Correlation Matrix type : correlation_matrix
Number of variables: 3
Number of cases: 117
Number of iterations: 50
Number or factors to preserve: 1
Eigenvalues
+---+-----------------+----------------------+--------+-----------+
| n | data eigenvalue | generated eigenvalue | p.95 | preserve? |
+---+-----------------+----------------------+--------+-----------+
| 1 | 2.1386 | 1.1429 | 1.2725 | Yes |
| 2 | 0.6389 | 0.9936 | 1.0471 | |
| 3 | 0.2224 | 0.8634 | 0.9332 | |
+---+-----------------+----------------------+--------+-----------+
Parallel Analysis returns 1 factors to preserve```
Batch 1 - "MapIt: place". originality_raw, fluency_raw, divergency_raw MapIt: place-originality_raw: [0, 0, 100, 8, 0, 0, 0, 44, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0] ...
MapIt: place-fluency_raw: [1, 1, 3, 3, 1, 2, 1, 5, 1, 2, 1, 4, 1, 1, 4, 1, 1, 4, 2, 2, 3] ...
MapIt: place-divergency_raw: [0, 0, 3, 3, 0, 2, 0, 3, 0, 2, 0, 3, 0, 0, 2, 0, 0, 3, 2, 2, 3] ...
Analysis 2014-07-06 12:37:20 -0700
= Statsample::Factor::ParallelAnalysis
There are 3 real factors on data
== Principal Component Analysis
Number of factors: 1
Communalities
+------------------------------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+------------------------------+---------+------------+--------+
| MapIt: place-divergency_raw | 1.000 | 0.635 | 63.545 |
| MapIt: place-fluency_raw | 1.000 | 0.726 | 72.586 |
| MapIt: place-originality_raw | 1.000 | 0.389 | 38.912 |
+------------------------------+---------+------------+--------+
Total Variance Explained
+-------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+-------------+---------+---------+---------+
| Component 1 | 1.750 | 58.348% | 58.348 |
| Component 2 | 0.802 | 26.747% | 85.095 |
| Component 3 | 0.447 | 14.905% | 100.000 |
+-------------+---------+---------+---------+
Component matrix
+------------------------------+------+
| | PC_1 |
+------------------------------+------+
| MapIt: place-divergency_raw | .797 |
| MapIt: place-fluency_raw | .852 |
| MapIt: place-originality_raw | .624 |
+------------------------------+------+
Traditional Kaiser criterion (k>1) returns 1 factors
== Parallel Analysis
Bootstrap Method: random
Uses SMC: No
Correlation Matrix type : correlation_matrix
Number of variables: 3
Number of cases: 125
Number of iterations: 50
Number or factors to preserve: 1
Eigenvalues
+---+-----------------+----------------------+--------+-----------+
| n | data eigenvalue | generated eigenvalue | p.95 | preserve? |
+---+-----------------+----------------------+--------+-----------+
| 1 | 1.7504 | 1.1464 | 1.2856 | Yes |
| 2 | 0.8024 | 0.9893 | 1.0292 | |
| 3 | 0.4471 | 0.8642 | 0.9490 | |
+---+-----------------+----------------------+--------+-----------+
Parallel Analysis returns 1 factors to preserve```
Batch 2 - "MapIt: creation". originality_raw, fluency_raw, divergency_raw MapIt: creation-originality_raw: [100, 86, 15, 100, 100, 7, 9, 18, 3, 4, 43, 46, 11, 17, 41, 13, 15, 14, 3, 2, 9] ...
MapIt: creation-fluency_raw: [22, 6, 4, 7, 2, 10, 5, 6, 8, 5, 4, 9, 9, 6, 11, 5, 6, 21, 6, 2, 5] ...
MapIt: creation-divergency_raw: [1, 2, 3, 5, 2, 1, 2, 2, 1, 3, 2, 2, 2, 2, 2, 3, 1, 1, 3, 2, 2] ...
Analysis 2014-07-06 12:37:57 -0700
= Statsample::Factor::ParallelAnalysis
There are 3 real factors on data
== Principal Component Analysis
Number of factors: 2
Communalities
+---------------------------------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+---------------------------------+---------+------------+--------+
| MapIt: creation-divergency_raw | 1.000 | 0.764 | 76.404 |
| MapIt: creation-fluency_raw | 1.000 | 0.837 | 83.734 |
| MapIt: creation-originality_raw | 1.000 | 0.871 | 87.118 |
+---------------------------------+---------+------------+--------+
Total Variance Explained
+-------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+-------------+---------+---------+---------+
| Component 1 | 1.383 | 46.113% | 46.113 |
| Component 2 | 1.089 | 36.306% | 82.419 |
| Component 3 | 0.527 | 17.581% | 100.000 |
+-------------+---------+---------+---------+
Component matrix
+---------------------------------+-------+------+
| | PC_1 | PC_2 |
+---------------------------------+-------+------+
| MapIt: creation-divergency_raw | -.873 | .042 |
| MapIt: creation-fluency_raw | .629 | .665 |
| MapIt: creation-originality_raw | -.475 | .803 |
+---------------------------------+-------+------+
Traditional Kaiser criterion (k>1) returns 2 factors
== Parallel Analysis
Bootstrap Method: random
Uses SMC: No
Correlation Matrix type : correlation_matrix
Number of variables: 3
Number of cases: 108
Number of iterations: 50
Number or factors to preserve: 2
Eigenvalues
+---+-----------------+----------------------+--------+-----------+
| n | data eigenvalue | generated eigenvalue | p.95 | preserve? |
+---+-----------------+----------------------+--------+-----------+
| 1 | 1.3834 | 1.1367 | 1.2312 | Yes |
| 2 | 1.0892 | 0.9962 | 1.0446 | Yes |
| 3 | 0.5274 | 0.8671 | 0.9461 | |
+---+-----------------+----------------------+--------+-----------+
Parallel Analysis returns 2 factors to preserve
Batch 2 - "MapIt: song". originality_raw, fluency_raw, divergency_raw MapIt: song-originality_raw: [2, 100, 3, 4, 3, 7, 1, 25, 12, 1, 87, 3, 1, 4, 31, 58, 22, 1, 0, 5, 6] ...
MapIt: song-fluency_raw: [7, 9, 9, 7, 4, 11, 9, 8, 15, 4, 4, 5, 8, 7, 5, 5, 15, 2, 3, 6, 7] ...
MapIt: song-divergency_raw: [2, 6, 1, 1, 3, 2, 2, 2, 1, 3, 3, 1, 1, 3, 2, 2, 1, 2, 3, 1, 2] ...
Analysis 2014-07-06 12:38:37 -0700
= Statsample::Factor::ParallelAnalysis
There are 3 real factors on data
== Principal Component Analysis
Number of factors: 1
Communalities
+-----------------------------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+-----------------------------+---------+------------+--------+
| MapIt: song-divergency_raw | 1.000 | 0.719 | 71.880 |
| MapIt: song-fluency_raw | 1.000 | 0.317 | 31.699 |
| MapIt: song-originality_raw | 1.000 | 0.457 | 45.671 |
+-----------------------------+---------+------------+--------+
Total Variance Explained
+-------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+-------------+---------+---------+---------+
| Component 1 | 1.492 | 49.750% | 49.750 |
| Component 2 | 0.965 | 32.175% | 81.925 |
| Component 3 | 0.542 | 18.075% | 100.000 |
+-------------+---------+---------+---------+
Component matrix
+-----------------------------+-------+
| | PC_1 |
+-----------------------------+-------+
| MapIt: song-divergency_raw | .848 |
| MapIt: song-fluency_raw | -.563 |
| MapIt: song-originality_raw | .676 |
+-----------------------------+-------+
Traditional Kaiser criterion (k>1) returns 1 factors
== Parallel Analysis
Bootstrap Method: random
Uses SMC: No
Correlation Matrix type : correlation_matrix
Number of variables: 3
Number of cases: 104
Number of iterations: 50
Number or factors to preserve: 1
Eigenvalues
+---+-----------------+----------------------+--------+-----------+
| n | data eigenvalue | generated eigenvalue | p.95 | preserve? |
+---+-----------------+----------------------+--------+-----------+
| 1 | 1.4925 | 1.1537 | 1.2847 | Yes |
| 2 | 0.9653 | 1.0008 | 1.0643 | |
| 3 | 0.5422 | 0.8455 | 0.9503 | |
+---+-----------------+----------------------+--------+-----------+
Parallel Analysis returns 1 factors to preserve```
Batch 2 - "MapIt: stop". originality_raw, fluency_raw, divergency_raw MapIt: stop-originality_raw: [14, 100, 100, 45, 14, 6, 15, 53, 31, 4, 60, 1, 4, 29, 1, 12, 12, 1, 1, 1, 11] ...
MapIt: stop-fluency_raw: [6, 9, 5, 5, 3, 7, 4, 7, 11, 3, 6, 3, 5, 6, 6, 4, 11, 4, 2, 5, 5] ...
MapIt: stop-divergency_raw: [2, 3, 3, 2, 3, 1, 3, 2, 1, 2, 2, 3, 2, 2, 2, 3, 2, 1, 2, 2, 3] ...
Analysis 2014-07-06 12:39:00 -0700
= Statsample::Factor::ParallelAnalysis
There are 3 real factors on data
== Principal Component Analysis
Number of factors: 2
Communalities
+-----------------------------+---------+------------+--------+
| Variable | Initial | Extraction | % |
+-----------------------------+---------+------------+--------+
| MapIt: stop-divergency_raw | 1.000 | 0.728 | 72.831 |
| MapIt: stop-fluency_raw | 1.000 | 0.804 | 80.412 |
| MapIt: stop-originality_raw | 1.000 | 0.907 | 90.653 |
+-----------------------------+---------+------------+--------+
Total Variance Explained
+-------------+---------+---------+---------+
| Component | E.Total | % | Cum. % |
+-------------+---------+---------+---------+
| Component 1 | 1.413 | 47.090% | 47.090 |
| Component 2 | 1.026 | 34.208% | 81.298 |
| Component 3 | 0.561 | 18.702% | 100.000 |
+-------------+---------+---------+---------+
Component matrix
+-----------------------------+-------+------+
| | PC_1 | PC_2 |
+-----------------------------+-------+------+
| MapIt: stop-divergency_raw | -.853 | .027 |
| MapIt: stop-fluency_raw | .706 | .553 |
| MapIt: stop-originality_raw | -.433 | .848 |
+-----------------------------+-------+------+
Traditional Kaiser criterion (k>1) returns 2 factors
== Parallel Analysis
Bootstrap Method: random
Uses SMC: No
Correlation Matrix type : correlation_matrix
Number of variables: 3
Number of cases: 103
Number of iterations: 50
Number or factors to preserve: 1
Eigenvalues
+---+-----------------+----------------------+--------+-----------+
| n | data eigenvalue | generated eigenvalue | p.95 | preserve? |
+---+-----------------+----------------------+--------+-----------+
| 1 | 1.4127 | 1.1629 | 1.2716 | Yes |
| 2 | 1.0262 | 0.9970 | 1.0623 | |
| 3 | 0.5610 | 0.8401 | 0.9332 | |
+---+-----------------+----------------------+--------+-----------+
Parallel Analysis returns 1 factors to preserve
