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Psychometrika

, Volume 81, Issue 2, pp 582–584 | Cite as

Erratum to: Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data

  • Kwanghee JungEmail author
  • Yoshio Takane
  • Heungsun Hwang
  • Todd S. Woodward
Erratum
  • 752 Downloads

1 Erratum to: PSYCHOMETRIKA DOI: 10.1007/s11336-015-9440-6

Correction 1:

On page 9. There are two places to be corrected in the paragraph regarding Tables 5 and 6.

“Table 5 shows that the variance estimate of each level-2 random loading \((\lambda _{j}{}^{\prime } s)\) across the subjects and its standard error obtained from bootstrap samples. Here, the random loading denotes the deviation between the fixed loading and individual subject’s estimate of the loading. Thus, the variance estimate of each level-2 random loading represents inter-subject variability in the loading. It was found that the variances of the random loadings for all observed variables turned out to be statistically significant. This suggests that there existed substantial subject-wise differences in each of the loadings. Table 6 exhibits the variance estimate of the Level-2 random path coefficient \((\theta _{ij}{}^{\prime }s)\) of each latent variable and its standard error obtained from bootstrap samples. About 87% of the variance estimates of the random path coefficients turned out to be statistically significant, suggesting substantial differences in each of the path coefficients across the subjects.”

The parts indicated above should have read as follows:

“Table 5 shows that the variance estimate of each level-2 random loading \((\lambda _{j}{}^{\prime } s)\) across the subjects and its standard error obtained from bootstrap samples. Here, the random loading denotes the deviation between the fixed loading and individual subject’s estimate of the loading. Thus, the variance estimate of each level-2 random loading represents inter-subject variability in the loading. It was found that the variances of the random loadings for about 71% of observed variables turned out to be statistically significant. This suggests that there existed substantial subject-wise differences in each of the loadings. Table 6 exhibits the variance estimate of the Level-2 random path coefficient \((\theta _{ij}{}^{\prime }s)\) of each latent variable and its standard error obtained from bootstrap samples. About 91% of the variance estimates of the random path coefficients turned out to be statistically significant, suggesting substantial differences in each of the path coefficients across the subjects.”

Correction 2: The values of C.R. in Tables 5 and 6 should be as follows:
Table 5

The variance estimates of the Level-2 random loading estimates and their standard errors.

The variance of \(\lambda _{j}{}^{\prime }s\)

Estimate

S.E.

C.R.

IPL_R

1

0.001*

0.000

2.92

2

0.002*

0.001

2.69

3

0.002*

0.001

2.00

4

0.001*

0.000

3.45

5

0.002*

0.001

2.43

PreCG_R

1

0.008*

0.002

3.34

2

0.003*

0.001

3.56

3

0.018

0.016

1.09

4

0.003*

0.001

2.93

5

0.010*

0.003

3.44

CL_R

1

0.001*

0.000

2.99

2

0.002

0.001

1.92

3

0.000*

0.000

2.23

4

0.001*

0.001

2.06

5

0.002

0.001

1.78

CL_L

1

0.003

0.002

1.50

2

0.001

0.001

1.48

3

0.004*

0.001

3.32

4

0.001

0.001

1.76

5

0.001*

0.000

2.83

IPL_L

1

0.003*

0.001

2.80

2

0.004

0.003

1.61

3

0.001*

0.000

3.18

4

0.001*

0.000

3.12

5

0.004*

0.002

2.34

PreCG_L

1

0.002*

0.001

4.06

2

0.002*

0.001

2.24

3

0.006*

0.001

4.21

4

0.006

0.005

1.28

5

0.003*

0.001

3.25

SMA

1

0.003

0.002

1.58

2

0.003*

0.001

3.30

3

0.003

0.002

1.98

4

0.007*

0.002

3.05

5

0.001*

0.001

2.39

Table 6

The variance estimates of the Level-2 random path coefficient estimates and their standard errors.

The variance of \(\theta _{lj}{}^{\prime }s\)

Estimate

S.E.

C.R.

IPL_R

\(\leftarrow \)

IPL_R

0.009*

0.004

2.15

PreCG_R

0.029*

0.013

2.27

CL_R

0.105*

0.030

3.55

CL_L

0.082*

0.034

2.38

IPL_L

0.043*

0.017

2.56

PreCG_L

0.043*

0.011

3.72

SMA

0.030*

0.011

2.79

PreCG_R

\(\leftarrow \)

IPL_R

0.025*

0.009

2.97

PreCG_R

0.006*

0.003

2.26

CL_R

0.028*

0.008

3.69

CL_L

0.075*

0.030

2.51

IPL_L

0.028*

0.007

3.76

PreCG_L

0.058*

0.017

3.45

SMA

0.047*

0.016

2.98

CL_R

\(\leftarrow \)

IPL_R

0.150*

0.050

2.98

PreCG_R

0.100*

0.045

2.21

CL_R

0.013*

0.003

4.00

CL_L

0.049*

0.019

2.59

IPL_L

0.050*

0.015

3.27

PreCG_L

0.030*

0.010

2.95

SMA

0.073*

0.025

2.95

CL_L

\(\leftarrow \)

IPL_R

0.044*

0.014

3.07

PreCG_R

0.096

0.077

1.25

CL_R

0.034*

0.013

2.61

CL_L

0.009*

0.003

3.49

IPL_L

0.035*

0.014

2.52

PreCG_L

0.020*

0.006

3.55

SMA

0.052*

0.024

2.13

IPL_L

\(\leftarrow \)

IPL_R

0.091*

0.033

2.78

PreCG_R

0.089*

0.024

3.80

CL_R

0.072*

0.026

2.80

CL_L

0.052*

0.016

3.17

IPL_L

0.012*

0.003

3.76

PreCG_L

0.053*

0.017

3.19

SMA

0.129*

0.038

3.40

PreCG_L

\(\leftarrow \)

IPL_R

0.154*

0.046

3.33

PreCG_R

0.304*

0.095

3.20

CL_R

0.066*

0.025

2.68

CL_L

0.097

0.051

1.92

IPL_L

0.053*

0.013

3.93

PreCG_L

0.012

0.006

1.89

SMA

0.156*

0.058

2.71

SMA

\(\leftarrow \)

IPL_R

0.025

0.013

1.95

PreCG_R

0.080*

0.023

3.48

CL_R

0.081*

0.030

2.66

CL_L

0.068*

0.029

2.34

IPL_L

0.081*

0.022

3.60

PreCG_L

0.054*

0.016

3.40

SMA

0.008*

0.002

3.87

Copyright information

© The Psychometric Society 2015

Authors and Affiliations

  • Kwanghee Jung
    • 1
    Email author
  • Yoshio Takane
    • 2
  • Heungsun Hwang
    • 3
  • Todd S. Woodward
    • 4
  1. 1.Department of Pediatrics, Children’s Learning InstituteThe University of Texas Health Science Center at HoustonHoustonUSA
  2. 2.University of VictoriaVictoriaCanada
  3. 3.McGill UniversityMontrealCanada
  4. 4.University of British ColumbiaVancouverCanada

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