#' Various outputs from SPSS (version 27) FACTOR
#'
#' 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:
#' \describe{
#'   \item{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.}
#'   \item{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.}
#'   \item{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.}
#'   \item{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
#' @source 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
#' @source 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.
#' @source 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.
#' @source McGrew, K. S., LaForte, E. M., & Schrank, F. A. (2014). Technical
#'  Manual. Woodcock-Johnson IV. Rolling Meadows, IL: Riverside.
#' @source Schrank, F. A., McGrew, K. S., & Mather, N. (2014). Woodcock-Johnson IV.
#' Rolling Meadows, IL: Riverside.
"SPSS_27"
