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The influence of the place-value structure of the Arabic number ...

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236) = 417.38, p < .001] and incorporated <strong>the</strong> three predictors: problem size, difference<br />

between <strong>the</strong> logarithmic magnitudes <strong>of</strong> <strong>the</strong> two to-be-compared <strong>number</strong>s, and logarithm <strong>of</strong><br />

problem size (see Table 1). Evaluation <strong>of</strong> <strong>the</strong> beta weights revealed that response latencies<br />

increased as (i) distance between <strong>the</strong> logarithms <strong>of</strong> <strong>the</strong> <strong>number</strong>s decreased, (ii) absolute<br />

problem size as well as (iii) logarithmic problem size decreased. Thus, comparable to <strong>the</strong><br />

results <strong>of</strong> <strong>the</strong> ANOVA no indication <strong>of</strong> an <strong>influence</strong> <strong>of</strong> unit-decade compatibility was<br />

observed in <strong>the</strong> RT data modelled by <strong>the</strong> holistic model.<br />

Please note <strong>the</strong> unexpected direction <strong>of</strong> <strong>the</strong> <strong>influence</strong> <strong>of</strong> <strong>the</strong> predictors absolute and<br />

logarithmic problem size in <strong>the</strong> final model. However, <strong>the</strong> reversed problem size effect does<br />

not necessarily disprove <strong>the</strong> holistic model as it is probably driven by <strong>the</strong> interrelation <strong>of</strong><br />

problem size and <strong>the</strong> difference between <strong>the</strong> logarithms <strong>of</strong> <strong>the</strong> two <strong>number</strong>s. As explained in<br />

greater detail in Appendix C this interrelation is not linear as assumed in <strong>the</strong> regression<br />

analysis, but curvilinear instead meaning that up to a problem size <strong>of</strong> about 65, problem size<br />

is positively correlated with distance, which in turn drives <strong>the</strong> reversed problem size effect.<br />

Table 1: Predictors included in <strong>the</strong> final regression model for <strong>the</strong> holistic data<br />

Predictor B b t sign.<br />

Change in<br />

R 2<br />

Raw<br />

correlation<br />

Partial<br />

correlation<br />

Constant 1226.38 - 10.16 < .001 - - -<br />

Inclusion <strong>of</strong> absolute overall distance .50<br />

Problem size -1.53 - .39 2.08 < .05 .33 - .55 - .13<br />

Diff Log distance - 216.83 - .74 23.91 < .001 .01 - .55 - .84<br />

Exclusion <strong>of</strong> absolute distance - .001<br />

Log problem size - 185.89 - .37 1.99 < .05 .003 - .61 - .13<br />

Finally, <strong>the</strong> multiple regression including <strong>the</strong> four reliable predictors <strong>of</strong> item RT<br />

identified by Nuerk et al. (2001) produced a model predicting modelled RTs reliably [adj. R 2<br />

= .81, R = .90, F(5, 235) = 254.29, p < .001] and without a substantial loss <strong>of</strong> descriptive<br />

adequacy. However, when looking at <strong>the</strong> beta weights <strong>of</strong> <strong>the</strong> individual predictors it was<br />

253

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