Table 4.
Description of population models used for simulation analyses
| Pattern matrices | Number of factors | Number of indicators per factor | Size of pattern coefficients | Notes |
| a) Pattern matrices | ||||
| Case 18|3|6 | 3 | 6 | .60 | Same baseline model as used in (De Winter & Dodou, 2012) |
| Case 6|3|6 | 3 | 2 | .60 | |
| Case 9|3|6 | 3 | 3 | .60 | Case 5 in (De Winter & Dodou, 2012) |
| Case 12|3|6 | 3 | 4 | .60 | |
| Case 15|3|6 | 3 | 5 | .60 | |
| Case 18|3|3 | 3 | 6 | .30 | |
| Case 18|3|9 | 3 | 6 | .90 | |
| Case 18|3|369b | 3 | 6 | .30, .60, .90 | Different pattern coefficients between factors. Case 7 in (De Winter & Dodou, 2012) |
| Case 18|3|369w | 3 | 6 | .30, .60, .90 | Different pattern coefficients within factors (each factor two each). Similar to cases 8/9 in (De Winter & Dodou, 2012) |
| Case 18|3|46|1c | 3 | 6 | .60 | One cross-loading of .40. Similar to case 10 in (De Winter & Dodou, 2012) |
| Pattern matrices | Number of factors | Number of indicators per factor | Size of pattern coefficients | Notes |
| Case 18|3|46|3c | 3 | 6 | .60 | Three cross-loadings of .40 (One factor with 2 and one with 1 cross-loading). Similar to case 10 in (De Winter & Dodou, 2012) |
| Case 12|3m|6 | 3 | 2, 4, 6 | .60 | Similar to cases 11/ 12 in (De Winter & Dodou, 2012) |
| Case 18|3|6n | 3 | 6 | .60 | Random variation in pattern coefficients added, drawn from a uniform distribution [-.2, .2]. Case 13 in (De Winter & Dodou, 2012) |
| Case 6|3|369wb | 3 | 2 | .30, .60, .90 | Different pattern coefficients within one of the factors |
| Case 9|3|369wb | 3 | 3 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 12|3|369wb | 3 | 4 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 15|3|369wb | 3 | 5 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 12|6|6 | 6 | 2 | .60 | |
| Case 18|6|6 | 6 | 3 | .60 | |
| Case 24|6|6 | 6 | 4 | .60 | |
| Case 30|6|6 | 6 | 5 | .60 | |
| Case 36|6|6 | 6 | 6 | .60 | |
| Case 12|6|369wb | 6 | 2 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 18|6|369wb | 6 | 3 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 24|6|369wb | 6 | 4 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 30|6|369wb | 6 | 5 | .30, .60, .90 | Different pattern coefficients within and between factors |
| Case 36|6|369w | 6 | 6 | .30, .60, .90 | Different pattern coefficients within factors |
| Factor Inter- correlations | Size of Inter- correlations | Notes | ||
| b) Factor intercorrelations | ||||
| Zero | .00 | Same intercorrelations as used in (De Winter & Dodou, 2012) | ||
| Moderate | .30 | |||
| Mixed | .30, .50, .70 | |||
| Strong | .70 | Same intercorrelations as used in (De Winter & Dodou, 2012) | ||
A population model is always a combination of a population pattern matrix and a population factor intercorrelation matrix. All population models are available in the EFAtools package