### Table 3. Six LSA indices for the low and high-cohesion text versions. Cohesion LSA Index Low High

### Table 3: Correlations of Latent Constructs

"... In PAGE 10: ... This is assessed by checking the square root of the average variance extracted (AVE) for each construct. Table3 presents the inter-correlations among the constructs, AVE and the square root of AVE for the extended ERP success measurement model (Figure 3). Fornell and Larcker (1981) recommends that AVE val- ues should be at least 0.... ..."

### Table 2. Correlations of Latent Variables and Evidence for Discriminant Validity

"... In PAGE 7: ... For satisfactory discriminant validity, the square root of AVE from the construct should be greater than the variance shared between the construct and other constructs in the model [4]. These items also demonstrated satisfactory convergent and discriminant validity (see Table2 ). Having validated the measurement modeling, the next step was testing the hypothesized relationships among various latent constructs in the PLS structural model.... ..."

### Table 1: Speaker identification errors for the Gaussian mixture model (GMM), the probabilistic latent semantic analysis model (PLSA) and the regularized probabilistic latent semantic analysis model (RPLSA). Test Data

2005

"... In PAGE 8: ... Specifically, three pieces of test speech from each speaker that have the lengths of 2, 3 or 5 seconds were used in each experiment. The results are shown in Table1 . Clearly, both PLSA and RPLSA are more effective than the GMM in all cases.... ..."

Cited by 3

### Table 1: Speaker identification errors for the Gaussian mixture model (GMM), the probabilistic latent semantic analysis model (PLSA) and the regularized probabilis- tic latent semantic analysis model (RPLSA). Test Data

2005

"... In PAGE 8: ... To compare the algorithms in a wide range we tried various lengths of test data. The results are shown in Table1 . Clearly, both PLSA and RPLSA are more effective than the GMM in all cases.... ..."

Cited by 3

### Table 7. Data from Corpus Analysis

1990

Cited by 8

### TABLE 2 Latent Semantic Analysis Matrix for Topmost Comparison Self-Descriptors Self or Other Representations

"... In PAGE 13: ...etween variables or cases in a standard data matrix (Hair et al., 1999). In this in- stance, HCA was used to outline discrete clusters of self or other representations based on conceptual similarities as found in the LSA matrix. Cluster analysis was conducted using average linkage (between-group) with the squared Euclidean dis- MORAL IDENTITY IN ADOLESCENCE 241 TABLE2 (Continued) Self or Other Representations Self-Descriptor Actual Self Temporal Self Ideal Self Despised Self Social Self Expected Self Mother Father Friend Admired Adult 28. Conceited .... ..."

### TABLE 1 Latent Semantic Analysis Matrix for Topmost Care Exemplar Self-Descriptors Self or Other Representations

"... In PAGE 11: ... TABLE1 (continued) Self or Other Representations Self-Descriptor Actual Self Temporal Self Ideal Self Despised Self Social Self Expected Self Mother Father Friend Admired Adult 15.Trytobe something better .... ..."

### Table 4. Factor loadings for two latent factors extracted from the original six logistic variables.

"... In PAGE 8: ... We then apply our clustering technique on these latent factors. Table4 displays the results of the Principal Component Analysis. Table 4.... ..."

### Table 4. Factor loadings for two latent factors extracted from the original six logistic variables.

"... In PAGE 8: ... We then apply our clustering technique on these latent factors. Table4 displays the results of the Principal Component Analysis. Table 4.... ..."