PedsQLMFS: 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

Arguments

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

References

Asparouhov, T., & Muthen, B. (2009). Exploratory structural equation modeling. Structural equation modeling: a multidisciplinary journal, 16(3), 397<U+2013>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<U+2013>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

Run this code

# NOT RUN {
 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


# }

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