Table 2 Latent Growth Curve Model Fit Statistics

 Unconditional intercept-only

2986.286

3005.411

Χ² = 28.110, df = 8, p < .001

.119 [.073, .168]

.883

.143

 Unconditional linear

2981.816

3010.503

Χ² = 17.639, df = 5, p = .003

.119 [.062, .181]

.926

.095

 Unconditional quadratic

2978.536

3019.972

Χ² = 6.359, df = 1, p = .012

.173 [.065, .311]

.969

.039

 Conditional linear

2755.658

2879.652

Χ² = 40.325, df = 38, p = .368

.019 [.000, .059]

.987

.031

 Unconditional intercept-only

2688.499

2707.624

Χ² = 38.539, df = 8, p < .001

.146 [.102, .194]

.850

.162

 Unconditional linear

2676.845

2705.532

Χ² = 20.885, df = 5, p = .001

.133 [.077, .195]

.922

.133

 Unconditional quadratic

2665.156

2706.592

Χ² = 1.196, df = 1, p = .274

.033 [0, .204]

.999

.015

 Conditional linear

2525.150

2522.508

Χ² = 48.542, df = 38, p = .118

.041 [.000, .072]

.945

.036

 Unconditional intercept-only

4779.114

4798.238

Χ² = 43.931, df = 8, p < .001

.158 [.115, .206]

.809

.113

 Unconditional linear

4758.014

4786.700

Χ² = 16.831, df = 5, p = .005

.115 [.058, .178]

.937

.082

 Unconditional quadratic

4760.003

4801.439

Χ² = 10.820, df = 1, p = .001

.234 [.123, .369]

.948

.060

 Conditional linear

4193.475

4317.470

Χ² = 36.740, df = 38, p = .528

.000 [.000, .052]

1.00

.019