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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
  Component
  1 2 3 4 5 6
D1S1Q2 0.031 − 0.084 0.858 − 0.019 0.063 0.054
D1S2Q2 0.004 − 0.013 0.868 − 0.060 0.161 − 0.071
D1S3Q2 0.023 0.109 0.777 0.031 − 0.070 − 0.013
D2S1Q2 − 0.073 − 0.057 0.054 0.041 0.678 0.219
D2S2Q2 − 0.052 0.115 0.052 0.180 0.750 − 0.092
D2S3Q2 0.059 0.149 0.109 − 0.142 0.695 0.125
D2S4Q1 0.555 0.098 0.114 0.261 − 0.083 − 0.056
D3S1Q2 − 0.058 0.804 0.060 0.047 0.108 − 0.117
D3S2Q2 − 0.036 0.725 0.008 0.053 0.264 − 0.143
D3S3Q2 0.120 0.906 − 0.070 − 0.203 − 0.049 0.132
D4S1Q2 − 0.172 0.220 0.056 0.463 − 0.242 0.286
D4S2Q1 0.891 − 0.092 − 0.049 − 0.055 0.031 − 0.042
D4S3Q1 0.624 0.069 − 0.008 0.248 − 0.127 0.021
D4S4Q2 − 0.052 − 0.091 0.042 0.924 − 0.043 − 0.009
D4S5Q1 0.760 − 0.015 0.108 0.066 − 0.110 0.034
D5S1Q2 0.146 − 0.048 0.036 0.650 0.152 − 0.054
D5S2Q1 0.800 0.075 − 0.031 − 0.190 0.120 0.100
D5S3Q2 0.110 − 0.026 − 0.270 0.630 0.297 − 0.023
D6S1Q2 − 0.053 − 0.229 0.121 0.269 0.130 0.615
D6S2Q2 0.125 − 0.032 − 0.048 − 0.180 0.068 0.905
D6S3Q2 − 0.055 0.149 − 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)