Skip to main content

Table 7 Factor analysis

From: Towards an educational data literacy framework: enhancing the profiles of instructional designers and e-tutors of online and blended courses with new competences

Pattern matrixa

Ā 

Component

Ā 

1

2

3

4

5

6

D1S1Q2

0.031

āˆ’ā€‰0.084

0.858

āˆ’ā€‰0.019

0.063

0.054

D1S2Q2

0.004

āˆ’ā€‰0.013

0.868

āˆ’ā€‰0.060

0.161

āˆ’ā€‰0.071

D1S3Q2

0.023

0.109

0.777

0.031

āˆ’ā€‰0.070

āˆ’ā€‰0.013

D2S1Q2

āˆ’ā€‰0.073

āˆ’ā€‰0.057

0.054

0.041

0.678

0.219

D2S2Q2

āˆ’ā€‰0.052

0.115

0.052

0.180

0.750

āˆ’ā€‰0.092

D2S3Q2

0.059

0.149

0.109

āˆ’ā€‰0.142

0.695

0.125

D2S4Q1

0.555

0.098

0.114

0.261

āˆ’ā€‰0.083

āˆ’ā€‰0.056

D3S1Q2

āˆ’ā€‰0.058

0.804

0.060

0.047

0.108

āˆ’ā€‰0.117

D3S2Q2

āˆ’ā€‰0.036

0.725

0.008

0.053

0.264

āˆ’ā€‰0.143

D3S3Q2

0.120

0.906

āˆ’ā€‰0.070

āˆ’ā€‰0.203

āˆ’ā€‰0.049

0.132

D4S1Q2

āˆ’ā€‰0.172

0.220

0.056

0.463

āˆ’ā€‰0.242

0.286

D4S2Q1

0.891

āˆ’ā€‰0.092

āˆ’ā€‰0.049

āˆ’ā€‰0.055

0.031

āˆ’ā€‰0.042

D4S3Q1

0.624

0.069

āˆ’ā€‰0.008

0.248

āˆ’ā€‰0.127

0.021

D4S4Q2

āˆ’ā€‰0.052

āˆ’ā€‰0.091

0.042

0.924

āˆ’ā€‰0.043

āˆ’ā€‰0.009

D4S5Q1

0.760

āˆ’ā€‰0.015

0.108

0.066

āˆ’ā€‰0.110

0.034

D5S1Q2

0.146

āˆ’ā€‰0.048

0.036

0.650

0.152

āˆ’ā€‰0.054

D5S2Q1

0.800

0.075

āˆ’ā€‰0.031

āˆ’ā€‰0.190

0.120

0.100

D5S3Q2

0.110

āˆ’ā€‰0.026

āˆ’ā€‰0.270

0.630

0.297

āˆ’ā€‰0.023

D6S1Q2

āˆ’ā€‰0.053

āˆ’ā€‰0.229

0.121

0.269

0.130

0.615

D6S2Q2

0.125

āˆ’ā€‰0.032

āˆ’ā€‰0.048

āˆ’ā€‰0.180

0.068

0.905

D6S3Q2

āˆ’ā€‰0.055

0.149

āˆ’ā€‰0.052

0.071

0.054

0.765

  1. Extraction method: Principal Component Analysis
  2. Rotation method: Promax with Kaiser Normalization
  3. aRotation converged in 7 iterations
  4. In this table, the values in bold show which items (rows) loads on the respecive factor (columns)