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


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Log
Log - Log - February 13, 2020

FACTOR
  /VARIABLES M2dTradBIN M2eGVHBIN M2fVHBIN M2jPoliceBIN M2kTradeBIN M2lTeachBIN M2mSchABIN
    M2nRelLeBIN
  /MISSING LISTWISE
  /ANALYSIS M2dTradBIN M2eGVHBIN M2fVHBIN M2jPoliceBIN M2kTradeBIN M2lTeachBIN M2mSchABIN
    M2nRelLeBIN
  /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 .60 .491 259
M2.e. Trust in group village headmen .56 .497 259
M2.f. Trust in village headmen .58 .495 259
M2.j. Trust in police .54 .500 259
M2.k. Trust in traders .19 .395 259
M2.l. Trust in teachers .61 .490 259
M2.m.Trust in school administrators .58 .494 259
M2.n. Trust in religious leaders .68 .468 259
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 .241 .178 1.000 .737
M2.e. Trust in group village headmen .247 .211 1.000 .853
M2.f. Trust in village headmen .245 .183 1.000 .746
M2.j. Trust in police .250 .125 1.000 .499
M2.k. Trust in traders .156 .045 1.000 .287
M2.l. Trust in teachers .240 .161 1.000 .671
M2.m.Trust in school administrators .244 .169 1.000 .693
M2.n. Trust in religious leaders .219 .095 1.000 .434
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 .926 50.260 50.260 .926 50.260 50.260 .583 31.639 31.639
2 .240 13.026 63.286 .240 13.026 63.286 .583 31.647 63.286
3 .159 8.643 71.929            
4 .146 7.917 79.846            
5 .118 6.403 86.249            
6 .101 5.497 91.746            
7 .094 5.099 96.845            
8 .058 3.155 100.000            
Rescaled 1 .926 50.260 50.260 3.913 48.915 48.915 2.517 31.467 31.467
2 .240 13.026 63.286 1.006 12.577 61.491 2.402 30.024 61.491
3 .159 8.643 71.929            
4 .146 7.917 79.846            
5 .118 6.403 86.249            
6 .101 5.497 91.746            
7 .094 5.099 96.845            
8 .058 3.155 100.000            
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: 0.0581 Component Number: 7
Eigenvalue: 0.0939 Component Number: 6
Eigenvalue: 0.1013 Component Number: 5
Eigenvalue: 0.1180 Component Number: 4
Eigenvalue: 0.1458 Component Number: 3
Eigenvalue: 0.1592 Component Number: 2
Eigenvalue: 0.2400 Component Number: 1
Eigenvalue: 0.9259 Component Number: 7
Eigenvalue: 0.0939 Component Number: 6
Eigenvalue: 0.1013 Component Number: 5
Eigenvalue: 0.1180 Component Number: 4
Eigenvalue: 0.1458 Component Number: 3
Eigenvalue: 0.1592 Component Number: 2
Eigenvalue: 0.2400 Component Number: 1
Eigenvalue: 0.9259 0.0 0.2 0.4 0.6 0.8 1.0 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 .371 -.200 .755 -.407
M2.e. Trust in group village headmen .393 -.237 .791 -.477
M2.f. Trust in village headmen .401 -.148 .811 -.298
M2.j. Trust in police .344 .081 .688 .162
M2.k. Trust in traders .196 .080 .496 .202
M2.l. Trust in teachers .339 .215 .691 .439
M2.m.Trust in school administrators .347 .220 .703 .445
M2.n. Trust in religious leaders .284 .120 .607 .257
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 .121 .404 .246 .822
M2.e. Trust in group village headmen .111 .446 .222 .896
M2.f. Trust in village headmen .180 .388 .363 .784
M2.j. Trust in police .300 .186 .601 .372
M2.k. Trust in traders .195 .082 .494 .208
M2.l. Trust in teachers .391 .087 .800 .178
M2.m.Trust in school administrators .401 .090 .812 .183
M2.n. Trust in religious leaders .285 .116 .610 .248
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 .707 .707
2 .707 -.707
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: 0.6104
Component 2: 0.2477 Component 1: 0.8120
Component 2: 0.1826 Component 1: 0.7997
Component 2: 0.1783 Component 1: 0.4939
Component 2: 0.2077 Component 1: 0.6005
Component 2: 0.3719 Component 1: 0.3625
Component 2: 0.7839 Component 1: 0.2222
Component 2: 0.8963 Component 1: 0.2459
Component 2: 0.8223 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0

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