IBM SPSS Web Report - M2 variables 259 cases varimax rotated 2 factor.spv   


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Log
Log - Log - February 13, 2020

FACTOR
  /VARIABLES M2dTradAut M2eGVH M2fVH M2jPolice M2kTraders M2lTeacher M2mSchAdm M2nRelLead
  /MISSING LISTWISE
  /ANALYSIS M2dTradAut M2eGVH M2fVH M2jPolice M2kTraders M2lTeacher M2mSchAdm M2nRelLead
  /PRINT UNIVARIATE INITIAL EXTRACTION ROTATION
  /PLOT EIGEN ROTATION
  /CRITERIA MINEIGEN(1) ITERATE(25)
  /EXTRACTION PC
  /CRITERIA ITERATE(25)
  /ROTATION VARIMAX
  /METHOD=COVARIANCE.

Factor Analysis
Factor Analysis - Descriptive Statistics - February 13, 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
M2.d. Trust in Traditional Authorities 3.80 1.171 259
M2.e. Trust in group village headmen 3.69 1.207 259
M2.f. Trust in village headmen 3.69 1.216 259
M2.j. Trust in police 3.62 1.280 259
M2.k. Trust in traders 2.51 1.351 259
M2.l. Trust in teachers 3.86 1.096 259
M2.m.Trust in school administrators 3.71 1.164 259
M2.n. Trust in religious leaders 3.97 1.096 259
Factor Analysis
Factor Analysis - Communalities - February 13, 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
M2.d. Trust in Traditional Authorities 1.370 1.038 1.000 .757
M2.e. Trust in group village headmen 1.456 1.159 1.000 .796
M2.f. Trust in village headmen 1.479 1.154 1.000 .780
M2.j. Trust in police 1.639 .928 1.000 .566
M2.k. Trust in traders 1.824 1.494 1.000 .819
M2.l. Trust in teachers 1.200 .650 1.000 .542
M2.m.Trust in school administrators 1.354 .830 1.000 .613
M2.n. Trust in religious leaders 1.201 .448 1.000 .373
Extraction Method: Principal Component Analysis.
Factor Analysis
Factor Analysis - Total Variance Explained - February 13, 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 6.235 54.102 54.102 6.235 54.102 54.102 4.264 37.002 37.002
2 1.465 12.715 66.817 1.465 12.715 66.817 3.436 29.815 66.817
3 1.069 9.273 76.090            
4 .869 7.537 83.627            
5 .713 6.184 89.811            
6 .448 3.889 93.699            
7 .414 3.591 97.290            
8 .312 2.710 100.000            
Rescaled 1 6.235 54.102 54.102 4.341 54.261 54.261 3.041 38.015 38.015
2 1.465 12.715 66.817 .905 11.310 65.571 2.205 27.556 65.571
3 1.069 9.273 76.090            
4 .869 7.537 83.627            
5 .713 6.184 89.811            
6 .448 3.889 93.699            
7 .414 3.591 97.290            
8 .312 2.710 100.000            
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 13, 2020
Scree Plot Component Number: 8
Eigenvalue: 0.3123 Component Number: 7
Eigenvalue: 0.4138 Component Number: 6
Eigenvalue: 0.4481 Component Number: 5
Eigenvalue: 0.7126 Component Number: 4
Eigenvalue: 0.8685 Component Number: 3
Eigenvalue: 1.0686 Component Number: 2
Eigenvalue: 1.4653 Component Number: 1
Eigenvalue: 6.2349 Component Number: 7
Eigenvalue: 0.4138 Component Number: 6
Eigenvalue: 0.4481 Component Number: 5
Eigenvalue: 0.7126 Component Number: 4
Eigenvalue: 0.8685 Component Number: 3
Eigenvalue: 1.0686 Component Number: 2
Eigenvalue: 1.4653 Component Number: 1
Eigenvalue: 6.2349 0 2 4 6 1 2 3 4 5 6 7 8

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Factor Analysis
Factor Analysis - Component Matrix - February 13, 2020
Component MatrixaComponent Matrix, table, 3 levels of column headers and 1 levels of row headers, table with 5 columns and 14 rows
  Raw Rescaled
Component Component
1 2 1 2
M2.d. Trust in Traditional Authorities .903 -.472 .771 -.403
M2.e. Trust in group village headmen .964 -.480 .799 -.397
M2.f. Trust in village headmen 1.023 -.330 .841 -.271
M2.j. Trust in police .924 .272 .722 .212
M2.k. Trust in traders .847 .881 .627 .653
M2.l. Trust in teachers .802 .080 .732 .073
M2.m.Trust in school administrators .886 .213 .761 .183
M2.n. Trust in religious leaders .668 -.038 .610 -.034
Extraction Method: Principal Component Analysis.
a. 2 components extracted.
Factor Analysis
Factor Analysis - Rotated Component Matrix - February 13, 2020
Rotated Component MatrixaRotated Component Matrix, table, 3 levels of column headers and 1 levels of row headers, table with 5 columns and 14 rows
  Raw Rescaled
Component Component
1 2 1 2
M2.d. Trust in Traditional Authorities .995 .219 .850 .187
M2.e. Trust in group village headmen 1.047 .252 .867 .209
M2.f. Trust in village headmen .995 .405 .818 .333
M2.j. Trust in police .533 .802 .416 .627
M2.k. Trust in traders .082 1.219 .061 .903
M2.l. Trust in teachers .563 .577 .514 .527
M2.m.Trust in school administrators .542 .733 .466 .630
M2.n. Trust in religious leaders .536 .401 .489 .366
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
Factor Analysis
Factor Analysis - Component Transformation Matrix - February 13, 2020
Component Transformation MatrixComponent Transformation Matrix, table, 1 levels of column headers and 1 levels of row headers, table with 3 columns and 5 rows
Component 1 2
1 .766 .643
2 -.643 .766
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Factor Analysis
Factor Analysis - Component Plot of Factors 1, 2 - February 13, 2020
Component Plot of Factors 1, 2 Component 1: 0.4890
Component 2: 0.3656 Component 1: 0.4656
Component 2: 0.6296 Component 1: 0.5140
Component 2: 0.5268 Component 1: 0.0607
Component 2: 0.9028 Component 1: 0.4163
Component 2: 0.6266 Component 1: 0.8182
Component 2: 0.3328 Component 1: 0.8675
Component 2: 0.2091 Component 1: 0.8498
Component 2: 0.1868 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0

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