IBM SPSS Web Report - L1 variables alone 3 factors 268 cases.spv   


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Log
Log - Log - February 27, 2020

GET
  FILE='E:\Arkiv\1 MLTSC\04 MLTSC papers and report\Berge2009 Trust games\Data SPSS files\MLTSC Trust data K-M78vars+BIN&9factors.sav'.
DATASET NAME DataSet1 WINDOW=FRONT.
FACTOR
  /VARIABLES L1a L1b L1c L1d L1e L1f L1g L1h
  /MISSING LISTWISE
  /ANALYSIS L1a L1b L1c L1d L1e L1f L1g L1h
  /PRINT UNIVARIATE INITIAL EXTRACTION ROTATION
  /PLOT EIGEN ROTATION
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION PC
  /CRITERIA ITERATE(25)
  /ROTATION VARIMAX
  /METHOD=COVARIANCE.

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Factor Analysis
Factor Analysis - Active Dataset - February 27, 2020


[DataSet1] E:\Arkiv\1 MLTSC\04 MLTSC papers and report\Berge2009 Trust games\Data SPSS files\MLTSC Trust data K-M78vars+BIN&9factors.sav

Factor Analysis
Factor Analysis - Descriptive Statistics - February 27, 2020
Descriptive StatisticsDescriptive Statistics, table, 1 levels of column headers and 1 levels of row headers, table with 4 columns and 10 rows
  Mean Std. Deviation Analysis N
L1a Member of local farmers groups .17 .378 268
L1b Member of NASFAM .04 .190 268
L1c Member of other farmers groups such as TAMA .09 .286 268
L1d Member of credit clubs, revolving funds, SACCOS .04 .190 268
L1e Member of water user associations .05 .223 268
L1f Member of dance, music and cultural groups .12 .320 268
L1g Member of religious groups .49 .501 268
L1h Member of home based care groups .10 .302 268
Factor Analysis
Factor Analysis - Communalities - February 27, 2020
CommunalitiesCommunalities, table, 2 levels of column headers and 1 levels of row headers, table with 5 columns and 12 rows
  Raw Rescaled
Initial Extraction Initial Extraction
L1a Member of local farmers groups .143 .140 1.000 .980
L1b Member of NASFAM .036 .003 1.000 .075
L1c Member of other farmers groups such as TAMA .082 .012 1.000 .141
L1d Member of credit clubs, revolving funds, SACCOS .036 .001 1.000 .014
L1e Member of water user associations .050 .001 1.000 .018
L1f Member of dance, music and cultural groups .103 .088 1.000 .859
L1g Member of religious groups .251 .251 1.000 .999
L1h Member of home based care groups .091 .009 1.000 .102
Extraction Method: Principal Component Analysis.
Factor Analysis
Factor Analysis - Total Variance Explained - February 27, 2020
Total Variance ExplainedTotal Variance Explained, table, 2 levels of column headers and 2 levels of row headers, table with 11 columns and 21 rows
  Component Initial Eigenvaluesa Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
  Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
Raw 1 .261 33.029 33.029 .261 33.029 33.029 .151 19.066 19.066
2 .143 18.078 51.107 .143 18.078 51.107 .105 13.251 32.317
3 .099 12.556 63.662 .099 12.556 63.662 .248 31.346 63.662
4 .095 11.952 75.614            
5 .079 10.026 85.640            
6 .046 5.868 91.507            
7 .042 5.359 96.867            
8 .025 3.133 100.000            
Rescaled 1 .261 33.029 33.029 1.134 14.170 14.170 1.121 14.011 14.011
2 .143 18.078 51.107 1.076 13.444 27.615 1.052 13.153 27.163
3 .099 12.556 63.662 .978 12.226 39.841 1.014 12.677 39.841
4 .095 11.952 75.614            
5 .079 10.026 85.640            
6 .046 5.868 91.507            
7 .042 5.359 96.867            
8 .025 3.133 100.000            
Extraction Method: Principal Component Analysis.
a. When analyzing a covariance matrix, the initial eigenvalues are the same across the raw and rescaled solution.
Factor Analysis
Factor Analysis - Scree Plot - February 27, 2020
Scree Plot Component Number: 8
Eigenvalue: 0.0248 Component Number: 7
Eigenvalue: 0.0424 Component Number: 6
Eigenvalue: 0.0464 Component Number: 5
Eigenvalue: 0.0793 Component Number: 4
Eigenvalue: 0.0945 Component Number: 3
Eigenvalue: 0.0993 Component Number: 2
Eigenvalue: 0.1430 Component Number: 1
Eigenvalue: 0.2612 Component Number: 7
Eigenvalue: 0.0424 Component Number: 6
Eigenvalue: 0.0464 Component Number: 5
Eigenvalue: 0.0793 Component Number: 4
Eigenvalue: 0.0945 Component Number: 3
Eigenvalue: 0.0993 Component Number: 2
Eigenvalue: 0.1430 Component Number: 1
Eigenvalue: 0.2612 0.00 0.05 0.10 0.15 0.20 0.25 0.30 1 2 3 4 5 6 7 8

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Factor Analysis
Factor Analysis - Component Matrix - February 27, 2020
Component MatrixaComponent Matrix, table, 3 levels of column headers and 1 levels of row headers, table with 7 columns and 14 rows
  Raw Rescaled
Component Component
1 2 3 1 2 3
L1a Member of local farmers groups -.091 .360 -.040 -.242 .954 -.107
L1b Member of NASFAM -.012 .047 .018 -.064 .249 .093
L1c Member of other farmers groups such as TAMA -.012 .069 .082 -.042 .240 .285
L1d Member of credit clubs, revolving funds, SACCOS -.003 .018 -.013 -.013 .095 -.068
L1e Member of water user associations .027 .013 -.006 .120 .058 -.025
L1f Member of dance, music and cultural groups .090 .044 .280 .282 .136 .872
L1g Member of religious groups .493 .061 -.060 .984 .122 -.121
L1h Member of home based care groups .023 -.001 .093 .078 -.004 .310
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Factor Analysis
Factor Analysis - Rotated Component Matrix - February 27, 2020
Rotated Component MatrixaRotated Component Matrix, table, 3 levels of column headers and 1 levels of row headers, table with 7 columns and 14 rows
  Raw Rescaled
Component Component
1 2 3 1 2 3
L1a Member of local farmers groups .374 -.017 .009 .989 -.045 .023
L1b Member of NASFAM .048 .020 -.003 .251 .107 -.018
L1c Member of other farmers groups such as TAMA .065 .085 -.011 .226 .297 -.038
L1d Member of credit clubs, revolving funds, SACCOS .019 -.011 .005 .099 -.059 .024
L1e Member of water user associations .006 .001 .030 .026 .003 .133
L1f Member of dance, music and cultural groups .002 .294 .041 .007 .918 .129
L1g Member of religious groups -.065 .034 .495 -.131 .068 .988
L1h Member of home based care groups -.013 .095 .004 -.043 .316 .012
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
Factor Analysis
Factor Analysis - Component Transformation Matrix - February 27, 2020
Component Transformation MatrixComponent Transformation Matrix, table, 1 levels of column headers and 1 levels of row headers, table with 4 columns and 6 rows
Component 1 2 3
1 -.259 .176 .950
2 .964 .107 .244
3 -.058 .979 -.197
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Factor Analysis
Factor Analysis - Component Plot of Factors 1, 2, 3 - February 27, 2020
Component Plot of Factors 1, 2, 3 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 Component 1: -0.1306
Component 2: 0.0680
Component 3: 0.9884 Component 1: 0.9887
Component 2: -0.0452
Component 3: 0.0235 Component 1: 0.0072
Component 2: 0.9177
Component 3: 0.1287 Component 1: 0.2255
Component 2: 0.2974
Component 3: -0.0376 Component 1: 0.2510
Component 2: 0.1068
Component 3: -0.0182 Component 1: 0.0261
Component 2: 0.0032
Component 3: 0.1333 Component 1: 0.0995
Component 2: -0.0586
Component 3: 0.0239 Component 1: -0.0426
Component 2: 0.3162
Component 3: 0.0119