scholarly journals New resilience instrument for family caregivers in cancer: a multidimensional item response theory analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Mu Zi Liang ◽  
Ying Tang ◽  
Peng Chen ◽  
Jian Liang ◽  
Zhe Sun ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Family Caregivers ◽  
Item Response ◽  
Latent Profile Analysis ◽  
Short Form ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Sf 36 ◽  
Non Linear

Abstract Objective Resilience instruments specific to family caregivers (FCs) in cancer are limited. This study was designed to validate the 10-item Resilience Scale Specific to Cancer (RS-SC-10) in FCs using multidimensional item response theory (MIRT) analysis. Methods 382 FCs were enrolled from Be Resilient to Cancer Program (BRCP) and administered with RS-SC-10 and 36-item Short Form Health Survey (SF-36). MIRT was performed to evaluate item parameters while Generalized Additive Model (GAM) and Latent Profile Analysis (LPA) were performed to test the non-linear relationship between resilience (RS-SC-10) and Quality of Life (QoL, SF-36). Results RS-SC-10 retained 10 items with high multidimensional discrimination, monotonous thresholds and its original two-factor structure (Generic and Shift-Persist). Four latent resilience subgroups were identified and a non-linear dose–response pattern between resilience and QoL was confirmed (per-SD increase OR = 1.62, 95% CI 1.16–2.13, p = 0.0019). Conclusion RS-SC-10 is a brief and suitable resilience instrument for FCs in cancer. The resilience screening of patients and FCs can be performed simultaneously in clinical practice.

scholarly journals New Resilience Instrument for Family Caregivers in Cancer: A Multidimensional Item Response Theory Analysis

2021 ◽  
Author(s):  
MU ZI LIANG ◽  
YING TANG ◽  
PENG CHEN ◽  
JIAN LIANG ◽  
ZHE SUN ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Family Caregivers ◽  
Item Response ◽  
Latent Profile Analysis ◽  
Short Form ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Sf 36 ◽  
Non Linear

Abstract Objective: Resilience instruments specific to family caregivers (FCs) in cancer are limited. This study was designed to validate the 10-item Resilience Scale Specific to Cancer (RS-SC-10) in FCs using multidimensional item response theory (MIRT) analysis.Methods: 382 FCs were enrolled from Be Resilient to Cancer Program (BRCP) and administered with RS-SC-10 and 36-item Short Form Health Survey (SF-36). MIRT was performed to evaluate item parameters while Generalized Additive Model (GAM) and Latent Profile Analysis (LPA) were performed to test the non-linear relationship between resilience (RS-SC-10) and Quality of Life (QoL, SF-36).Results: RS-SC-10 retained 10 items with high multidimensional discrimination, monotonous thresholds and its original two-factor structure (Generic and Shift-Persist). Four latent resilience subgroups were identified and a non-linear dose-response pattern between resilience and QoL was confirmed (per-SD increase OR= 1.62, 95%CI, 1.16-2.13, p=0.0019).Conclusion: RS-SC-10 is a brief and suitable resilience instrument for FCs in cancer. The resilience screening of patients and FCs can be performed simultaneously in clinical practice.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

2011 ◽  
Vol 35 (8) ◽  
pp. 604-622 ◽  
Author(s):  
Hirotaka Fukuhara ◽  
Akihito Kamata
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Estimation Method ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Data Set ◽  
Detection Rates ◽  
Item Functioning

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

Sign in / Sign up

Export Citation Format

Share Document