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Table 2 Model fit statistics for factor analyses and measurement invariance testing

From: A bifactor representation of the Center for Epidemiological Studies Depression Scale for children: gender and age invariance and implications for adolescents’ social and academic adjustment

Models

χ2

df

CFI

TLI

RMSEA

[90% CI]

ΔCFI

ΔRMSEA

Unidimensional Model

1374.495

170

0.834

0.814

0.118

[0.112-0.123]

  

CFA

        

 Correlated traits

545.109

164

0.947

0.939

0.067

[0.061-0.074]

0.113

− 0.051

 Higher-order CFA

538.294

166

0.949

0.941

0.066

[0.060-0.072]

0.002

− 0.001

 Bi-CFA

399.587

151

0.966

0.957

0.057

[0.050-0.063]

0.016

− 0.009

ESEM

        

 Correlated traits

332.135

116

0.970

0.951

0.060

[0.053-0.068]

  

 Higher-order ESEM

328.117

118

0.971

0.953

0.059

[0.051-0.067]

0.002

− 0.001

 Bi-ESEM

254.136

100

0.979

0.960

0.055

[0.047-0.063]

0.007

− 0.004

Models

χ2

df

CFI

TLI

RMSEA

[90% CI]

ΔCFI

ΔRMSEA

Invariance across gender

       

 Configural

344.536

215

0.980

0.965

0.049

[0.039-0.058]

  

 Scalar invariance

447.846

310

0.979

0.975

0.042

[0.033-0.050]

− 0.001

− 0.007

 Strict invariance

462.385

330

0.980

0.977

0.040

[0.031-0.048]

0.001

− 0.002

 Latent means invariance

584.676

335

0.962

0.957

0.054

[0.047-0.061]

0.018

0.014

Invariance across age (12–14, 15–16, 17–18)

    

 Configural

462.981

330

0.981

0.967

0.049

[0.038-0.059]

  

 Scalar invariance

661.315

520

0.979

0.978

0.040

[0.030-0.049]

0.002

− 0.009

 Strict invariance

718.436

560

0.977

0.977

0.041

[0.031-0.049]

− 0.002

0.001

 Latent means invariance

787.315

570

0.968

0.968

0.047

[0.039-0.055]

0.009

0.006

  1. Note. χ2 = chi square (weighted least square estimator was used); df = degrees of freedom; CFI = Comparative fit index; TLI = Tucker-Levis Index; RMSEA = root mean square error of approximation; 90% CI = 90% confidence interval for the RMSEA; CFA = confirmatory factor analysis; ESEM = exploratory structural equation model; Bi = bifactor