data.lsem: Datasets for Local Structural Equation Models / Moderated...

In sirt: Supplementary Item Response Theory Models

Datasets for Local Structural Equation Models / Moderated Factor Analysis

Description

Datasets for local structural equation models or moderated factor analysis.

Usage

data(data.lsem01)
data(data.lsem02)
data(data.lsem03)

Format

• The dataset data.lsem01 has the following structure

  'data.frame': 989 obs. of 6 variables:
$ age: num 4 4 4 4 4 4 4 4 4 4 ...
$ v1 : num 1.83 2.38 1.85 4.53 -0.04 4.35 2.38 1.83 4.81 2.82 ...
$ v2 : num 6.06 9.08 7.41 8.24 6.18 7.4 6.54 4.28 6.43 7.6 ...
$ v3 : num 1.42 3.05 6.42 -1.05 -1.79 4.06 -0.17 -2.64 0.84 6.42 ...
$ v4 : num 3.84 4.24 3.24 3.36 2.31 6.07 4 5.93 4.4 3.49 ...
$ v5 : num 7.84 7.51 6.62 8.02 7.12 7.99 7.25 7.62 7.66 7.03 ...
  

• The dataset data.lsem02 is a slightly perturbed dataset of the Woodcock-Johnson III (WJ-III) Tests of Cognitive Abilities used in Hildebrandt et al. (2016) and has the following structure

  'data.frame': 1129 obs. of 8 variables:
$ age : int 4 4 4 4 4 4 4 4 4 4 ...
$ gcw : num -3.53 -3.73 -3.77 -3.84 -4.26 -4.6 -3.66 -4.31 -4.46 -3.64 ...
$ gvw : num -1.98 -1.35 -1.66 -3.24 -1.17 -2.78 -2.97 -3.88 -3.22 -0.68 ...
$ gfw : num -2.49 -2.41 -4.48 -4.17 -4.43 -5.06 -3.94 -3.66 -3.7 -2.74 ...
$ gsw : num -4.85 -5.05 -5.66 -4.3 -5.23 -5.63 -4.91 -5.75 -6.29 -5.47 ...
$ gsmw: num -2.99 -1.13 -4.21 -3.59 -3.79 -4.77 -2.98 -4.48 -2.99 -3.83 ...
$ glrw: num -2.49 -2.91 -3.45 -2.91 -3.31 -3.78 -3.5 -3.96 -2.97 -3.14 ...
$ gaw : num -3.22 -3.77 -3.54 -3.6 -3.22 -3.5 -1.27 -2.08 -2.23 -3.25 ...
  

• The dataset data.lsem03 is a synthetic dataset of the SON-R application used in Hueluer et al. (2011) has the following structure

  'data.frame': 1027 obs. of 10 variables:
$ id : num 10001 10002 10003 10004 10005 ...
$ female : int 0 0 0 0 0 0 0 0 0 0 ...
$ age : num 2.62 2.65 2.66 2.67 2.68 2.68 2.68 2.69 2.71 2.71 ...
$ age_group: int 1 1 1 1 1 1 1 1 1 1 ...
$ p1 : num -1.98 -1.98 -1.67 -2.29 -1.67 -1.98 -2.29 -1.98 -2.6 -1.67 ...
$ p2 : num -1.51 -1.51 -0.55 -1.84 -1.51 -1.84 -2.16 -1.84 -2.48 -1.84 ...
$ p3 : num -1.4 -2.31 -1.1 -2 -1.4 -1.7 -2.31 -1.4 -2.31 -0.79 ...
$ r1 : num -1.46 -1.14 -0.49 -2.11 -1.46 -1.46 -2.11 -1.46 -2.75 -1.78 ...
$ r2 : num -2.67 -1.74 0.74 -1.74 -0.81 -1.43 -2.05 -1.43 -1.74 -1.12 ...
$ r3 : num -1.64 -1.64 -1.64 -0.9 -1.27 -3.11 -2.74 -1.64 -2.37 -1.27 ...
  

  The subtests Mosaics (p1), Puzzles (p1), and Patterns (p3) constitute the performance subscale; the subtests Categories (r1), Analogies (r2), and Situations (r3) constitute the reasoning subscale.

References

Hildebrandt, A., Luedtke, O., Robitzsch, A., Sommer, C., & Wilhelm, O. (2016). Exploring factor model parameters across continuous variables with local structural equation models. Multivariate Behavioral Research, 51(2-3), 257-278. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2016.1142856")}

Hueluer, G., Wilhelm, O., & Robitzsch, A. (2011). Intelligence differentiation in early childhood. Journal of Individual Differences, 32(3), 170-179. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1027/1614-0001/a000049")}

sirt documentation built on May 29, 2024, 8:43 a.m.