Various outputs from SPSS (version 27) FACTOR for the IDS-2 (Grob & Hagmann-von Arx, 2018), the WJIV (3 to 5 and 20 to 39 years; McGrew, LaForte, & Schrank, 2014), the DOSPERT (Frey et al., 2017; Weber, Blais, & Betz, 2002), and four simulated datasets (baseline, case_1a, case_6b, and case_11b, see test_models and population_models) used in Grieder and Steiner (2022).
Format
A list of 8 containing EFA results for each of the data sets mentioned above. Each of these eight entries is a list of 4, of the following structure:
- paf_load
(matrix) - F1 to FN = unrotated factor loadings obtained with the FACTOR algorithm with PAF. Rownames are the abbreviated subtest or item names, and generic variable labels (V1 to VN) for the simulated datasets.
- var_load
(matrix) - F1 to FN = varimax rotated factor loadings obtained with the FACTOR algorithm with PAF. Rownames are the abbreviated subtest or item names, and generic variable labels (V1 to VN) for the simulated datasets.
- pro_load
(matrix) - F1 to FN = promax rotated factor loadings obtained with the FACTOR algorithm with PAF. Rownames are the abbreviated subtest or item names, and generic variable labels (V1 to VN) for the simulated datasets.
- pro_phi
(matrix) - F1 to FN = intercorrelations of the promax rotated loadings.
Source
Grieder, S., & Steiner, M. D. (2022). Algorithmic jingle jungle: A comparison of implementations of principal axis factoring and promax rotation in R and SPSS. Behavior Research Methods, 54, 54–74. doi: 10.3758/s13428-021-01581-x
Grieder, S., & Grob, A. (2019). Exploratory factor analyses of the intelligence and development scales–2: Implications for theory and practice. Assessment. Advance online publication. doi:10.1177/1073191119845051
Grob, A., & Hagmann-von Arx, P. (2018). Intelligence and Development Scales–2 (IDS-2). Intelligenz- und Entwicklungsskalen für Kinder und Jugendliche. [Intelligence and Development Scales for Children and Adolescents.]. Bern, Switzerland: Hogrefe.
Frey, R., Pedroni, A., Mata, R., Rieskamp, J., & Hertwig, R. (2017). Risk preference shares the psychometric structure of major psychological traits. Science Advances, 3, e1701381.
McGrew, K. S., LaForte, E. M., & Schrank, F. A. (2014). Technical Manual. Woodcock-Johnson IV. Rolling Meadows, IL: Riverside.
Schrank, F. A., McGrew, K. S., & Mather, N. (2014). Woodcock-Johnson IV. Rolling Meadows, IL: Riverside.
Details
The principal axis factoring was run with the iteration limit raised above
SPSS's own default of 25, so reproducing these solutions requires the same: case_1a
needs 60 iterations and case_11b needs 33, and at max_iter = 25 both stop short of
convergence and differ from the stored loadings in the second decimal. Use
estimate_control(type = "SPSS", max_iter = 500) when checking a preset against these
references; the other two simulated cases converge in six iterations and are
unaffected.