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Large-scale Assessments in Education

An IERI – International Educational Research Institute Journal

Table 3 Parameter Estimates of the CT-LCM-SR for PISA Countries’ Mean Reading Literacy Scores Development

From: A primer on continuous-time modeling in educational research: an exemplary application of a continuous-time latent curve model with structured residuals (CT-LCM-SR) to PISA Data

Parameter (labelsa)

Estimate

SE

t

p

Fixed effects

 Intercept (int)

464.80

7.37

63.034

 < .001

 Linear growth (b)

0.43

0.20

2.148

.037

 CT auto-effect (a)

− 0.39

0.13

− 3.099

.003

Variance components (in SD metric)

 Intercept SD (int_SD)

53.50

5.57

9.597

 < .001

 Growth SD (b_SD)

1.06

0.32

3.360

.002

 Initial residual SD (T0dynSD)

9.95

0.86

11.527

 < .001

 Diffusion SD (q)

18.58

2.99

6.206

 < .001

  1. The random effects CT-LCM-SR contains an eighth parameter, the intercept-growth correlation, which was estimated to be − 0.61 (SE = 0.13, p < .001)
  2. a The labels refer to the parameter labels used in Eq. 10 and 11 as well as in the presentation of the R code (p. 23) and Fig. 2
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