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An IERI – International Educational Research Institute Journal

Table 1 Bayesian growth curve modeling results for boys and girls

From: Bayesian probabilistic forecasting with large-scale educational trend data: a case study using NAEP

 

Estimate

Post.SD

HPD.025

HPD.975

PSRF

Prior

Boys growth params.

 Intercept

274.782

1.300

272.116

277.226

1.000

dnorm(260,.1)

 Slope

0.931

0.108

0.726

1.149

1.000

dnorm(0,1e−2)

 Pre(intercept)

88.460

19.415

55.216

127.007

1.000

dwish(iden,3)

 Pre(slope)

0.246

0.067

0.132

0.385

1.000

dwish(iden,3)

Girls growth params.

 Intercept

273.707

1.308

271.113

276.219

1.000

dnorm(260,.1)

 Slope

0.902

0.122

0.669

1.142

1.002

dnorm(0,1e−2)

 Pre(intercept)

85.114

19.065

51.593

122.738

1.000

dwish(iden,3)

 Pre(slope)

0.269

0.084

0.126

0.43

1.000

dwish(iden,3)

  1. pre() refers to the precision of the parameter, where precision = 1/variance. dnorm is the normal \(N(\mu ,\tau ^2)\) distribution, where \(\tau ^2\) is the precision. dwish is the Wishart \((R,\nu )\) distribution for the precision matrix with shape matrix, R and degrees-of-freedom, \(\nu\)