A meta-analysis of reading span research

3.3.2 A meta-analysis of reading span research

  Daneman and Merikle (1996) performed a meta-analysis on all studies that applied the DC RST to some measure of reading comprehension in normal populations. Its purpose was to go beyond inconsistent data (such as those identified in the above paragraphs) and illustrate trends. For example, a glance at the literature reveals that some have found the DC RST to be a good predictor of comprehension (e.g. Dixon, LeFevre Twiley, 1988; Masson Miller, 1983) whereas others have not (e.g. Light Anderson, 1985; Morrow, Leirer Altieri, 1992). Some have found the DC RST superior in its prediction of reading comprehension in comparison with traditional span measures (e.g. Dixon et al., 1988; Masson Miller, 1983; Turner Engle, 1989) whereas others have not (e.g. Calvo, Ramos Estevez, 1992; La Pointe Engle, 1990). Some have found verbal working memory measures superior to mathematical working memory measures for the prediction of language comprehension performance (e.g. Baddeley, Logie, Nimmo-Smith Bereton, 1985; Daneman Tardiff, 1987) whereas others have not (e.g. Norman, Kemper Kynette, 1992; Turner Engle, 1989). Daneman and Merikle’s (1996) meta-analysis incorporated papers from 1980 to 1995 (plus a 1939 Daneman and Merikle (1996) performed a meta-analysis on all studies that applied the DC RST to some measure of reading comprehension in normal populations. Its purpose was to go beyond inconsistent data (such as those identified in the above paragraphs) and illustrate trends. For example, a glance at the literature reveals that some have found the DC RST to be a good predictor of comprehension (e.g. Dixon, LeFevre Twiley, 1988; Masson Miller, 1983) whereas others have not (e.g. Light Anderson, 1985; Morrow, Leirer Altieri, 1992). Some have found the DC RST superior in its prediction of reading comprehension in comparison with traditional span measures (e.g. Dixon et al., 1988; Masson Miller, 1983; Turner Engle, 1989) whereas others have not (e.g. Calvo, Ramos Estevez, 1992; La Pointe Engle, 1990). Some have found verbal working memory measures superior to mathematical working memory measures for the prediction of language comprehension performance (e.g. Baddeley, Logie, Nimmo-Smith Bereton, 1985; Daneman Tardiff, 1987) whereas others have not (e.g. Norman, Kemper Kynette, 1992; Turner Engle, 1989). Daneman and Merikle’s (1996) meta-analysis incorporated papers from 1980 to 1995 (plus a 1939

  by Baddeley et al. (1985); mathematical storage-and-processing included math span 1 ,

  counting span and backward digit span.

  Measures of comprehension were classified as ‘global’ and ‘specific’. The former include the Verbal SAT, the Nelson-Denny Reading Test, the Mill Hill Vocabulary test, WAIS Vocabulary, and others. These are standardized tests. The ‘specific’ measurement generally addressed sentence-level processing: assigning pronominal reference, making inferences, detecting ambiguity, monitoring and revising inconsistencies, abstracting the main theme, following verbal directions, and recalling propositional content. These tasks are nonstandardized. Note that these measures fall into postinterpretive category of SSIR Theory: verbally-mediated, consciously- controlled tasks that are (hypothesized to be) unrelated to the interpretive module responsible for initial sentence interpretation.

  Table 1: Average weighted effect size (product moment correlation coefficient, r) and 95 confidence interval (CI) for each working-memorycomprehension association (Reproduced from Daneman Merikle, 1996, p. 427)

  Global Comprehension Specific Comprehension r

  CI

  CI r

  Process and storage

  Storage alone

  1 Approximately, this is the RST with equations (instead of sentences) that take the form: (93) + 4 = ?

  Table 1 (reproduced from Daneman Merikle, 1996) shows a breakdown of the effect sizes for working memory on comprehension. The presence of non-overlapping

  95 confidence intervals for each correlation indicates a difference at the 5 level of significance. The first point to note is that verbal processing-storage measures are good predictors of comprehension. This is consistent with the accounts of Carpenter and colleagues (including Daneman) and MacDonald and colleagues. However, it is also compatible with SSIR theory, which holds that the DC RST taps the pool used by postinterpretive processes and, thus, will find a relationship with tasks that are postinterpretive. The second point to note is that processing-storage (CE) tasks are generally better predictors of comprehension than storage-alone (STS) tasks. To rule out the possibility that this is due to theoretically-trivial reliability differences, the authors conducted a small-scale meta-analysis on the same papers and found that reliability did not differ significantly between the two. A similar analysis was conducted to rule out an effect of age. Thirdly, such processing-storage tasks are better predictors of specific comprehension, with r = 0.52, than global comprehension, where r = 0.41. This means that in 36 studies, comprising 2745 participants, the DC RST was a significant predictor of various sentence processing tasks. This is also consistent with the idea that working memory tests and sentence processing share a common resource pool.

  As we have already seen, the assertion that a correlation between two tasks implies a common resource pool is not the only explanation for the correlation; more trivially, the correlation may arise from task overlap (see 3.2.4 Interpreting correlations between resource measures and task measures, p. 61). Daneman and Merikle respond to this criticism (e.g. Baddeley, Logie, Nimmo-Smith Bereton, 1985) with the observation that non-verbal mathematical storage-and-processing tasks correlate almost as well (r =

  0.48 in specific comprehension tests), which, they suggest, indicates that both these tasks and the DC RST tap a general resource that underlies reading comprehension. This may indeed be the case – unless the mathematical task superificially overlaps with the DC RST because the two are verbally-mediated, which may be supported by the same data (e.g. Logie, Gilhooly Wynn, 1994).

  Daneman and Merikle’s observation is consistent with the theory of Carpenter and colleagues, which would suggest that all three tasks – verbal processing-and-storage, mathematical processing-and-storage and specific comprehension – draw upon the same resource pool. It is not clear that the theory of MacDonald and colleagues would predict such a correlation. If not, the authors might take the same view as those of SSIR

  Theory: mental arithmetic may be consciously, verbally-mediated. In this case, the mathematical processing-and-storage task taps the resources used in postinterpretive processing.

  The finding does, at the very least, add credence to the claim that the DC RST prediction of specific comprehension is based on more than the trivial explanation (e.g. Baddeley et al., 1985) that sentence comprehension correlates with paragraph comprehension; it would appear that an individual’s ability to process and store representations is related to their comprehension ability. The fifth, and final, point is that while mathematical storage-processing is a good predictor of specific comprehension, verbal storage-processing is a better one. Processing domain is therefore important (as is storage domain, indicated by the bottom half of Table 1).

  Overall, the Daneman and Merikle meta-analysis is consistent with each of the three resource accounts under review. However, it does not provide direct evidence for the role of resources in language comprehension. Moreover, its measures of specific comprehension – assigning pronominal reference, making inferences, detecting ambiguity, monitoring and revising inconsistencies, abstracting the main theme, following verbal directions, recalling propositional content – provide data for processes that are nonpsycholinguistic in the conception of SSIR Theory. So if Daneman and Merikle had found evidence for resource effects in these tasks, it would not serve to demarcate between the three resource theories. We need to review measures that are more obviously related to initial parsing.