population_models: population_models

In mdsteiner/EFAdiff: Fast and Flexible Implementations of Exploratory Factor Analysis Tools

population_models

Description

Population factor models, some of which (baseline to case_11e) used for the simulation analyses reported in Grieder and Steiner (2019). All combinations of the pattern matrices and the factor intercorrelations were used in the simulations. Many models are based on cases used in de Winter and Dodou (2012).

Usage

population_models

Format

A list of 3 lists "loadings", "phis_3", and "phis_6".

loadings contains the following matrices of pattern coefficients:

baseline

(matrix) - The pattern coefficients of the baseline model. Three factors with six indicators each, all with pattern coefficients of .6. Same baseline model as used in de Winter and Dodou (2012).

case_1a

(matrix) - Three factors with 2 indicators per factor.

case_1b

(matrix) - Three factors with 3 indicators per factor. Case 5 in de Winter and Dodou (2012).

case_1c

(matrix) - Three factors with 4 indicators per factor.

case_1d

(matrix) - Three factors with 5 indicators per factor.

case_2

(matrix) - Same as baseline model but with low pattern coefficients of .3.

case_3

(matrix) - Same as baseline model but with high pattern coefficients of .9.

case_4

(matrix) - Three factors with different pattern coefficients between factors (one factor with .9, one with .6, and one with .3, respectively). Case 7 in de Winter and Dodou (2012).

case_5

(matrix) - Three factors with different pattern coefficients within factors (each factor has two pattern coefficients of each .9, .6, and .3). Similar to cases 8/ 9 in de Winter and Dodou (2012).

case_6a

(matrix) - Same as baseline model but with one cross loading of .4. Similar to case 10 in de Winter and Dodou (2012).

case_6b

(matrix) - Same as baseline model but with three cross loading of .4 (One factor with 2 and one with 1 crossloading). Similar to case 10 in de Winter and Dodou (2012).

case_7

(matrix) - Three factors with different number of indicators per factor (2, 4, and 6 respectively). Similar to cases 11/ 12 in de Winter and Dodou (2012).

case_8

(matrix) - Three factors with random variation in pattern coefficients added, drawn from a uniform distribution between [-.2, .2]. Case 13 in de Winter and Dodou (2012).

case_9a

(matrix) - Three factors with 2 indicators per factor, with different pattern coefficients within one of the factors.

case_9b

(matrix) - Three factors with 3 indicators per factor, with different pattern coefficients.

case_9c

(matrix) - Three factors with 4 indicators per factor, with different pattern coefficients.

case_9d

(matrix) - Three factors with 5 indicators per factor, with different pattern coefficients.

case_10a

(matrix) - Six factors with 2 indicators per factor, all with pattern coefficients of .6.

case_10b

(matrix) - Six factors with 3 indicators per factor, all with pattern coefficients of .6.

case_10c

(matrix) - Six factors with 4 indicators per factor, all with pattern coefficients of .6.

case_10d

(matrix) - Six factors with 5 indicators per factor, all with pattern coefficients of .6.

case_10e

(matrix) - Six factors with 6 indicators per factor, all with pattern coefficients of .6.

case_11a

(matrix) - Six factors with 2 indicators per factor, with different pattern coefficients within and between factors (.3, .6, and .9).

case_11b

(matrix) - Six factors with 3 indicators per factor, with different pattern coefficients within and between factors (.3, .6, and .9).

case_11c

(matrix) - Six factors with 4 indicators per factor, with different pattern coefficients within and between factors (.3, .6, and .9).

case_11d

(matrix) - Six factors with 5 indicators per factor, with different pattern coefficients within and between factors (.3, .6, and .9).

case_11e

(matrix) - Six factors with 6 indicators per factor, with different pattern coefficients within and between factors (.3, .6, and .9).

case_12a

(matrix) - One factor, with 2 equal pattern coefficients (.6).

case_12b

(matrix) - One factor, with 3 equal pattern coefficients (.6).

case_12c

(matrix) - One factor, with 6 equal pattern coefficients (.6).

case_12d

(matrix) - One factor, with 10 equal pattern coefficients (.6).

case_12e

(matrix) - One factor, with 15 equal pattern coefficients (.6).

case_13a

(matrix) - One factor, with 2 different pattern coefficients (.3, and .6).

case_13b

(matrix) - One factor, with 3 different pattern coefficients (.3, .6, and .9).

case_13c

(matrix) - One factor, with 6 different pattern coefficients (.3, .6, and .9).

case_13d

(matrix) - One factor, with 10 different pattern coefficients (.3, .6, and .9).

case_13e

(matrix) - One factor, with 15 different pattern coefficients (.3, .6, and .9).

case_14a

(matrix) - No factor, 2 variables (0).

case_14b

(matrix) - No factor, 3 variables (0).

case_14c

(matrix) - No factor, 6 variables (0).

case_14d

(matrix) - No factor, 10 variables (0).

case_14e

(matrix) - No factor, 15 variables (0).

phis_3 contains the following 3x3 matrices:

zero

(matrix) - Matrix of factor intercorrelations of 0. Same intercorrelations as used in de Winter and Dodou (2012).

moderate

(matrix) - Matrix of moderate factor intercorrelations of .3.

mixed

(matrix) - Matrix of mixed (.3, .5, and .7) factor intercorrelations.

strong

(matrix) - Matrix of strong factor intercorrelations of .7. Same intercorrelations as used in de Winter and Dodou (2012).

phis_6 contains the following 6x6 matrices:

zero

(matrix) - Matrix of factor intercorrelations of 0. Same intercorrelations as used in de Winter and Dodou (2012).

moderate

(matrix) - Matrix of moderate factor intercorrelations of .3.

mixed

(matrix) - Matrix of mixed (around .3, .5, and .7; smoothing was necessary for the matrix to be positive definite) factor intercorrelations.

strong

(matrix) - Matrix of strong factor intercorrelations of .7. Same intercorrelations as used in de Winter and Dodou (2012).

Source

Grieder, S., & Steiner, M.D. (2020). Algorithmic Jingle Jungle: A Comparison of Implementations of Principal Axis Factoring and Promax Rotation in R and SPSS. Manuscript in Preparation.

de Winter, J.C.F., & Dodou, D. (2012). Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics. 39.

mdsteiner/EFAdiff documentation built on Jan. 10, 2023, 8:54 a.m.