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Heritage Science

Table 4 Rotated component matrixa table and factors extracted

From: The implication of spatial-dispersion theory in housing genotypes of Boushehri historic and contemporary houses

 

Component

1

2

3

4

5

6

var038

.923

.243

.129

.012

.003

.004

var033

.919

.298

.124

.010

− .005

− .002

var035

.901

.243

.226

− .065

− .030

.008

var039

.900

.282

.108

− .046

− .022

− .017

var043

.893

.332

.116

− .009

− .003

.022

var042

.886

.282

.113

− .009

− .031

.001

var034

.881

.137

.054

− .081

.004

− .030

var032

.879

.272

.076

.053

.013

.002

var044

.875

.306

.198

− .043

− .044

.018

var045

.875

.307

.198

− .043

− .044

.019

var037

.867

.358

.141

− .005

− .010

.031

var031

.816

.402

.234

.001

− .024

.057

var040

.815

.403

.108

.046

.013

.021

var030

.790

.492

.068

.085

.016

− .021

var029

.757

.500

− .009

.023

.028

− .017

var017

.666

.660

.153

.021

.015

− .031

var041

.342

.121

.034

.002

.003

− .037

var028

.345

.843

.245

− .032

− .024

.036

var026

.417

.837

.219

.008

.010

− .037

var022

.347

.833

.201

.090

− .027

.039

var027

.289

.831

.200

.041

− .045

.082

var020

.433

.829

.210

.005

.009

− .036

var018

.341

.827

.095

− .089

.026

− .093

var019

.349

.826

.275

− .029

− .010

− .042

var016

.378

.826

.132

− .024

.010

− .056

var023

.345

.807

.248

.069

− .039

.103

var025

.390

.805

.319

.031

− .022

.010

var024

.458

.712

.044

− .171

.025

− .072

var021

.481

.593

.148

.018

− .057

.065

var009

.034

.014

.866

.162

.139

− .047

var005

.163

.238

.859

.014

− .114

.028

var011

.150

.191

.850

.069

− .154

− .015

var015

.070

.107

.846

.073

− .004

− .010

var014

.051

.104

.819

.068

.154

− .073

var001

.129

.222

.818

.004

.107

.000

var013

.196

.200

.797

− .085

.088

.066

var003

.133

.099

.795

.136

.020

.013

var008

.023

.058

.793

.241

.027

− .054

var007

.083

.106

.780

.304

− .096

− .054

var004

.280

.351

.755

− .094

− .119

.011

var012

.223

.378

.700

− .019

− .110

.127

var010

− .064

− .037

.366

.844

− .036

− .012

var002

− .010

− .003

.489

.788

.053

.001

var006

− .030

− .035

.056

.001

.962

.017

var036

− .018

− .010

− .049

− .012

.017

.958

  1. Extraction method: Principal component analysis
  2. Rotation method: Varimax with Kaiser normalizationa
  3. aRotation converged in 6 iterations
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