Multi-group analyses for measurement invariance parameter estimates and model fit (ML)
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1 LBP-TBQ: Supplementary digital content 8 Multi-group analyses for measurement invariance parameter estimates and model fit (ML) Medication data Multi-group CFA analyses were performed with the 16-item LBP-TBQ to examine measurement invariance (MI) for medication data between: o Participants with nerve compression likely or not o Participants with sciatica diagnosis reported or not o Participants with pain duration less than 3 years versus more than 3 years o Treatment-experienced versus treatment-naïve participants o Across time (wave 1 versus wave 2) Results are presented below and include model fit summaries, nested models comparisons, and graphical representation of the most appropriate models. For these analyses, multivariate outliers were first excluded from the sample to exclude this source of model misspecification; sensitivity analyses were performed selectively with the total samples, with similar results. Models reported here were estimated using maximum likelihood (ML). A. Nerve compression likely (N= 144 cases -8 outliers = 136) or not (N=170 cases -10 outliers=160) Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Baseline Comparisons Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Unconstrained Measurement weights Measurement intercepts Structural covariances
2 Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Measurement residuals Saturated model Independence model RMSEA Model RMSEA LO 90 HI 90 PCLOSE Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Independence model AIC Model AIC BCC BIC CAIC Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Nested Model Comparisons Assuming model Unconstrained to be correct: Measurement weights Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement weights to be correct: Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement intercepts to be correct: Structural covariances Measurement residuals Assuming model Structural covariances to be correct: Measurement residuals
3 Unconstrained models: 3
4 B. Sciatica diagnosis reported (N=192 cases-13 outliers=179) or not (N=237 cases-17 outliers= 220) Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Baseline Comparisons Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model
5 RMSEA Model RMSEA LO 90 HI 90 PCLOSE Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Independence model AIC Model AIC BCC BIC CAIC Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Nested Model Comparisons Assuming model Unconstrained to be correct: Measurement weights Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement weights to be correct: Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement intercepts to be correct: Structural covariances Measurement residuals Assuming model Structural covariances to be correct: Measurement residuals Measurement intercepts models: 5
6 C. Pain duration less than 3 years (N = 151cases 10 outliers=141) vs more than 3 years (N = 278 cases - 20 outliers=258) 6
7 Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Baseline Comparisons Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model RMSEA Model RMSEA LO 90 HI 90 PCLOSE Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Independence model AIC Model AIC BCC BIC CAIC Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Nested Model Comparisons Assuming model Unconstrained to be correct: NFI IFI RFI Delta-1 Delta-2 rho-1 TLI rho2 Measurement weights Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement weights to be correct: 7
8 Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement intercepts to be correct: Structural covariances Measurement residuals Assuming model Structural covariances to be correct: Measurement residuals Measurement intercepts models: 8
9 D. Treatment-experienced (N = 389cases 29 outliers=360) or not (N = 39 cases 1 case with all data missing=38) Sample size for no treatment experience too low - Model presented only for treatment experienced subsample: 9
10 E. Measurement invariance across time: wave 1 (N= 429 cases - 30 outliers=399) versus wave 2 (N=115 cases -4 outliers=111) Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Baseline Comparisons Model NFI RFI IFI TLI Delta1 rho1 Delta2 rho2 CFI Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model RMSEA Model RMSEA LO 90 HI 90 PCLOSE Unconstrained Measurement weights
11 Model RMSEA LO 90 HI 90 PCLOSE Measurement intercepts Structural covariances Measurement residuals Independence model AIC Model AIC BCC BIC CAIC Unconstrained Measurement weights Measurement intercepts Structural covariances Measurement residuals Saturated model Independence model Nested Model Comparisons Assuming model Unconstrained to be correct: Measurement weights Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement weights to be correct: Measurement intercepts Structural covariances Measurement residuals Assuming model Measurement intercepts to be correct: Structural covariances Measurement residuals Assuming model Structural covariances to be correct: Measurement residuals
12 Structural covariances models: 12
C:\Users\Rex Kline\AppData\Local\Temp\AmosTemp\AmosScratch.amw. Date: Friday, December 5, 2014 Time: 11:20:30 AM
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