PedsQLMFS {OmegaG}

PedsQL Multidimensional Fatigue Scale Factor Structure

Description

The data provide the information needed for estimating the CR coefficient Omega-generic of the PedsQL Multidimensional Fatigue Scale (Varni et al., 2002). The estimated parameter matrices (Lambda, Phi, and Psi) were obtained by fitting factor models with participants' respontses to the PedsQL Multidimensional Fatigue Scale. Two different factor structures, a three-correlated-factor model and a bi-factor model, were included in the example. Exploratory structural equation modeling (ESEM; Asparouhov, & Muthen, 2009; Morin, Arens, & Marsh, 2016) was employed to estimate the model. The sample included 87 young-adult cancer survivors. Sample data were collected by St. Jude LIFE Study (SJCRH., 2007-2021). Please refer to the publication (Mai, Srivastava, & Krull, 2021) for more information.

Usage

PedsQLMFS

Format

PedsQLMFS: A list including three sub-lists: ScaleStructure, ESEM, and biESEM.

1. PedsQLMFS$ScaleStructure: ScaleStructure is a list used to describe the subscale names and items within each subscale. It contains three vectors: GeneralFatigue, SleepFatigue and CognitiveFatigue.

GeneralFatigue

A vector of item varibale names that are in the subscale "General Fatigue"

SleepFatigue

A vector of item varibale names that are in the subscale "Sleep/rest Fatigue"

CognitiveFatigue

A vector of item varibale names that are in the subscale "Cognitive Fatigue"

2. PedsQLMFS$ESEM: ESEM is a list of parameter matrices of a three-correlated-factor model. It contains three matrices: Lambda, Phi, and Psi.

Lambda: The factor-loading matrix; A matrix with 18 rows and 3 columns, each row represent one scale item, each column represent one factor.

GeneralFatigue

Factor loadings on the sub-domain construct "Gneral Fatigue"

SleepFatigue

Factor loadings on the sub-domain construct "Sleep/rest Fatigue"

CognitiveFatigue

Factor loadings on the sub-domain construct "Cognitive Fatigue"

Phi: The factor variance-covariance matrix; A matrix with 3 rows and 3 columns. Each row represent one factor. So does each column.

GlobalFatigue

Factor loadings on the global (general factor) construct "Global Fatigue"

GeneralFatigue

Factor loadings on the specific (group factor) construct "General Fatigue"

SleepFatigue

Factor loadings on the specific (group factor) construct "Sleep/rest Fatigue"

CognitiveFatigue

Factor loadings on the specific (group factor) construct "Cognitive Fatigue"

Psi: The item-error variane-covariance matrix; A matrix with 18 rows and 18 columns. Each row represent one item. So does each column.

Y1

item GeneralFatigue1 measurement-error variance and covariance with other items

Y2

item GeneralFatigue2 measurement-error variance and covariance with other items

Y3

item GeneralFatigue3 measurement-error variance and covariance with other items

Y4

item GeneralFatigue4 measurement-error variance and covariance with other items

Y5

item GeneralFatigue5 measurement-error variance and covariance with other items

Y6

item GeneralFatigue6 measurement-error variance and covariance with other items

Y7

item SleepFatigue1 measurement-error variance and covariance with other items

Y8

item SleepFatigue2 measurement-error variance and covariance with other items

Y9

item SleepFatigue3 measurement-error variance and covariance with other items

Y10

item SleepFatigue4 measurement-error variance and covariance with other items

Y11

item SleepFatigue5 measurement-error variance and covariance with other items

Y12

item SleepFatigue6 measurement-error variance and covariance with other items

Y13

item CognitiveFatigue1 measurement-error variance and covariance with other items

Y14

item CognitiveFatigue2 measurement-error variance and covariance with other items

Y15

item CognitiveFatigue3 measurement-error variance and covariance with other items

Y16

item CognitiveFatigue4 measurement-error variance and covariance with other items

Y17

item CognitiveFatigue5 measurement-error variance and covariance with other items

Y18

item CognitiveFatigue6 measurement-error variance and covariance with other items

3. PedsQLMFS$biESEM: biESEM is a list of parameter matrices of a bi-factor model. It contains three matrices: Lambda, Phi, and Psi.

Lambda: The factor-loading matrix; A matrix with 18 rows and 4 columns, each row represent one scale item, each column represent one factor. The first factor is the global factor (also called general factor) of a bi-factor structure .

GlobalFatigue

Factor loadings on the global (general factor) construct "Global Fatigue"

GeneralFatigue

Factor loadings on the specific (group factor) construct "Gneral Fatigue"

SleepFatigue

Factor loadings on the specific (group factor) construct "Sleep/rest Fatigue"

CognitiveFatigue

Factor loadings on the specific (group factor) construct "Cognitive Fatigue"

Phi: The factor variance-covariance matrix; A matrix with 4 rows and 4 columns, each row represent one factor, each column represent one factor. The first factor is the global factor (also called general factor) of a bi-factor structure .

GlobalFatigue

Factor loadings on the global (general factor) construct "Global Fatigue"

GeneralFatigue

Factor loadings on the specific (group factor) construct "General Fatigue"

SleepFatigue

Factor loadings on the specific (group factor) construct "Sleep/rest Fatigue"

CognitiveFatigue

Factor loadings on the specific (group factor) construct "Cognitive Fatigue"

Psi: The item-error variane-covariance matrix; A matrix with 18 rows and 18 columns. Each row represent one item. So does each column.

Y1

item GeneralFatigue1 measurement-error variance and covariance with other items

Y2

item GeneralFatigue2 measurement-error variance and covariance with other items

Y3

item GeneralFatigue3 measurement-error variance and covariance with other items

Y4

item GeneralFatigue4 measurement-error variance and covariance with other items

Y5

item GeneralFatigue5 measurement-error variance and covariance with other items

Y6

item GeneralFatigue6 measurement-error variance and covariance with other items

Y7

item SleepFatigue1 measurement-error variance and covariance with other items

Y8

item SleepFatigue2 measurement-error variance and covariance with other items

Y9

item SleepFatigue3 measurement-error variance and covariance with other items

Y10

item SleepFatigue4 measurement-error variance and covariance with other items

Y11

item SleepFatigue5 measurement-error variance and covariance with other items

Y12

item SleepFatigue6 measurement-error variance and covariance with other items

Y13

item CognitiveFatigue1 measurement-error variance and covariance with other items

Y14

item CognitiveFatigue2 measurement-error variance and covariance with other items

Y15

item CognitiveFatigue3 measurement-error variance and covariance with other items

Y16

item CognitiveFatigue4 measurement-error variance and covariance with other items

Y17

item CognitiveFatigue5 measurement-error variance and covariance with other items

Y18

item CognitiveFatigue6 measurement-error variance and covariance with other items

Author(s)

Yujiao Mai, Deo Kumar Srivastava, and Kevin R Krull

References

Asparouhov, T., & Muthen, B. (2009). Exploratory structural equation modeling. Structural equation modeling: a multidisciplinary journal, 16(3), 397–438.

Mai, Y., Srivastava, D.K., & Krull, K.R. (2021). Estimating Composite reliability of Multidimensional Measurement with Overlapping Items. Present at the 2021 Eastern North American Region (ENAR) Spring Virtual Meeting.

Morin, A. J. S., Arens, A. K., & Marsh, H. W. (2016). A Bifactor Exploratory Structural Equation Modeling Framework for the Identification of Distinct Sources of Construct-Relevant Psychometric Multidimensionality. Structural equation modeling, 23(1), 116–139. doi: 10.1080/10705511.2014.961800

Varni, J. W., Burwinkle, T. M., Katz, E. R., Meeske, K., & Dickinson, P. (2002). The PedsQL in pediatric cancer: Reliability and validity of the Pediatric Quality of Life Inventory Generic Core Scales, Multidimensional Fatigue Scale, and Cancer Module. Cancer, 94(7), 2090.

St. Jude Children's Research Hospital. SJCRH. (2007-2021). St. Jude LIFE Study.

Examples

 OmegaG::PedsQLMFS$ScaleStructure
#  $GeneralFatigue
#  [1] "Y1" "Y2" "Y3" "Y4" "Y5" "Y6"
#
#  $SleepFatigue
#  [1] "Y7"  "Y8"  "Y9"  "Y10" "Y11" "Y12"
#
#  $CognitiveFatigue
#  [1] "Y13" "Y14" "Y15" "Y16" "Y17" "Y18"

 OmegaG::PedsQLMFS$ESEM$Lambda
#           GeneralFatigue  SleepFatigue  CognitiveFatigue
#  Y1           0.582        0.134           -0.093
#  Y2           0.640        0.161            0.109
#  Y3           0.779        0.180            0.110
#  Y4           0.728        0.039            0.097
#  Y5           0.283        0.109            0.431
#  Y6           0.412       -0.011            0.365
#  Y7           0.010        0.597           -0.150
#  Y8           0.516        0.009            0.195
#  Y9           0.578        0.092            0.057
#  Y10          0.010        0.820           -0.108
#  Y11         -0.043        0.696            0.119
#  Y12          0.024        0.652            0.222
#  Y13          0.376        0.123            0.350
#  Y14          0.073        0.194            0.639
#  Y15          0.052        0.183            0.693
#  Y16         -0.026        0.161            0.445
#  Y17          0.042        0.025            0.696
#  Y18         -0.019        0.175            0.607