SPSS_27: Various outputs from SPSS (version 27) FACTOR

In EFAtools: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

Various outputs from SPSS (version 27) FACTOR

Description

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).

Usage

SPSS_27

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.

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.

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.

EFAtools documentation built on Aug. 21, 2026, 5:16 p.m.