Page 209 - Jurnal Kurikulum BPK 2020

Table 5.
Standardised and unstandardized b-values from hierarchical analyses for variables predicting
reading comprehension

Variables Unstandardised b Std. Error Standardised b t
(β)
Constant -10.91 3.09 -3.54
VB 0.01 0.00 0.92*** 30.47
VD 0.03 0.04 0.03 0.93
Note. In both hierarchical analyses, there was a controlled variable included (either depth or
breadth) but are not shown in this table.
***P < 0.001; N = 221

In order to answer RQ 2 specifically, Table 5 displays that VB was statistically
significant where it recorded the highest beta value (β = 0.92, p < 0.001) compared to depth of
vocabulary knowledge (β =0.03, p > 0.01) which is not significant. As a result, the inclusion of
ESL learners’ VB and depth in the model of regression shows a large contribution of breadth
in comparison to the contribution of VD. In other words, for one standard deviation of change
in breadth of vocabulary knowledge, there will be 0.92 of standard deviation change in MUET
reading comprehension scores. Meanwhile, for depth of vocabulary knowledge, one standard
deviation of change in it will change 0.03 of a standard deviation in the MUET reading
comprehension scores or vice versa but that figure is insignificant.
This finding is in line with (Elmasry, 2012; Li & Kirby, 2014; Moinzadeh &
Moslehpour, 2012; Tengku Shahraniza Tengku Abdul Jalal et al., 2015; Wang, 2014) where
breadth has more predictive power on reading performance. Nonetheless, it does not indicate
that depth is unimportant for ESL learners for other skills. In addition, this finding is similar to
the finding of Moinzadeh and Moslehpour (2012) where the standardized regression coefficient
(β) showed that VB contributed significantly and more to reading comprehension. However,
Moinzadeh and Moslehpour (2012) found that VD also contributed significantly in reading
comprehension whereas this current study did not find depth as a significant predictor.
VD is hypothesized as a significant predictor in reading comprehension because it has
more items and points (160 points) compared to VB test (utilised in the study was Vocabulary
Level Test or VLT) (90 points) (Qian, 1999). Therefore, Qian’s studies (1998, 1999, 2002),
found that VD is more powerful than VB. However, the current study has found that VB is
more powerful as it was measured using VST which has more items or more points (140 points)
compared to VLT which has less points or items (90 points). It is conjectured that VB would
perform better in the regression analysis of this current study because VST measured words
from 1000 word-family to 14,000 word-family while VD test (WAT) was only based on words
at 2000, 3000 and 5000 levels. Otherwise, if WAT was joined with another instrument to gauge
VD, the contribution in predicting reading comprehension variance might be higher or
equivalent to the contribution of VB in reading comprehension. For example, in Choi's (2013)
study, when Vocabulary Knowledge Scale (VKS) was added to WAT, this VD was more highly
correlated with reading comprehension (0.790), exceeding that of VLT with reading
comprehension (0.765).

RQ 3: Is it possible to determine a VB threshold where learners are likely to
perform above average in the MUET reading comprehension? In order to answer RQ 3,
cross tabulation was performed (see Table 6). The possible VB threshold for reading can be
obtained through examining the results for MUET reading scores above or equivalent to 56
marks and below 56 marks (i.e. average score as 21/45 x 120) against VB. From Table 7,

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