In the present article, I demonstrate that processing speed (using ASVAB speeded subtests) has a modest predictive validity over the g factor extracted from the ASVAB (non-speeded subtests) in predicting overall GPA in the NLSY97, within black, hispanic and the white sample. Next, I investigate the mediation of speed in the black-white difference in IQ (g). For both analyses, processing speed accounts for a modest portion of these associations. Nonetheless, some issues related with such ‘psychometric speed’ measures need to be clarified.


Processing speed, as Rindermann et al. (2011) describe it, is assumed to have a biological basis. This may explain why ECTs are less amenable to learning and personality factors (Rindermann & Neubauer, 2001; Jensen, 2006, pp. 175-178), to the Flynn effects (Nettelbeck & Wilson, 2004; Woodley et al., 2013), education gains (Ritchie & Bates, 2013), and also why repeated retesting effects on ECTs, due to their nature of being knowledge-free, do not show any improvement while the conventional IQ tests are much more (positively) impacted by retesting even if the g component is not affected. This could also explain why speed mediates the relationship between IQ and death while on the other hand, smoking, education, and social class had a small contribution in comparison (Deary & Der, 2005). Still related with the biological question, Penke et al. (2012) noted that the correlation between indicators of white matter tract and g was mediated by processing speed. This clearly draws some pictures on the relationship between speed and g. But curiously, Johnson & Deary (2011) suggest that speed can correlate with g through specific cognitive abilities, to the extent that speed may not be related with specific abilities through g, which seems coherent with models assuming g as an emergent construct, such as mutualism (van der Maas, 2006). This result is very ambiguous due to the difficulty of determining a clear winner between the models. But if true, this may rise some concerns about the views of g as a latent construct (or factor).

But given this, anyway, it would be interesting to investigate all possible mediations due to processing speed, and better, to investigate if the causal link starting from speed of processing to IQ to achievement is produced (or not) by genetic effects. This has been answered directly by Luo et al. (2003a, 2003b) SEM-based study. There was evidence that processing speed (represented by the Cognitive Abilities Test (CAT-g) chronometric measures) mediates g (WISC-g) in predicting scholastic performance (on the MAT). Some tests of the CAT were measuring components unrelated with mental speed. When these so-called ‘percent-correct’ (non-chronometric) variables have been removed from the CAT-g, becoming thus a purely chronometric measure, the strength of its mediation shows only a slight decline. They also discovered that processing speed relates to scholastic achievement mainly through genetic pathways.

Rohde & Thompson (2007) use multiple regression to assess the independent contribution of processing speed (as independent var.) on achievement tests such as GPA, WRAT-III, SAT combined, SAT-math, SAT-verbal (as dependent var.) when controlling for Raven and Mill Hill Vocabulary Scales (independent var.) as measures of cognitive abilities. In predicting these achievement measures, processing speed accounts for a small increase in R². Its contribution was only strong for SAT-math (R²=0.132). Other research (Vock et al., 2011; Rindermann & Neubauer, 2004) arrived at a similar conclusion. Speed partially mediated IQ in predicting achievement, leaving not much room for an independent link. Still, speed mediates intelligence but it also has a non trivial predictive validity above the effect of crystallized IQ in predicting scholastic achievement, as noted by Luo et al. (2006). Finally, Dodonova & Dodonov (2013) found a different result, with hypothesis of IQ and speed (each) having unique contribution (on school achievement) which seems to better explain their data.

With regard to the contribution of processing speed in the black-white IQ difference, only the Pesta & Poznanski (2008) study comes to mind. They administered RT and IT tests, yielding four variables, RT and IT means, as well as RTSD and ITSD, or the standard deviation of RT and IT scores, also called intra-individual variability. They factor analyzed these four variables, yielding an ECT factor score. This ECT partially mediates the BW difference in the Wonderlic Personnel Test by an amount of 49%. The ECT however did not mediate BW difference in GPA.


Of use here is the multiple regression method, with age and gender controlled (always used in Model 1, or Step 1 in SPSS). Speed is included in model 1, with g added in model 2. In this way, we could see how much the independent, unique contribution of speed had diminished. Subsequently, g is included in model 1 and speed in model 2. In this way, we could see how much the independent contribution of g had changed from model 1 to model 2. The adjusted R² (because it is less biased) is also displayed; it shows the increment in variance explained by model 2 compared to model 1.

Speed is measured here by standardizing and then by averaging Numerical Operations and Coding Speed, the two speeded tests of the ASVAB. The remaining subtests are factor analyzed to give a g factor. As always, outliers have been removed, i.e., cases having a z-score equal or less than -3 SD.

The other variables of use are PIAT-math (the ‘updated’ standard score variable), SAT-verbal, SAT-math, overall_GPA, and the ASVAB.


Data and syntax below :

On the partial mediating role of processing speed between black-white differences, IQ and GPA (EXCEL)
On the partial mediating role of processing speed between black-white differences, IQ and GPA (NLSY79 syntax)
On the partial mediating role of processing speed between black-white differences, IQ and GPA (NLSY97 syntax)

In the NLSY97, the BW (beta) coefficient was 0.491 in model 1, decreasing at 0.369 when the speed factor is added in model 2. This accounts for only 1-(0.369/0.491) =0.248 of the initial gap. The respective number for GPA as dependent variable was 0.310. On the other hand, in the NLSY79, the speed factor accounted for a much larger BW gap in g : 1-(0.281/0.495) =0.432. This number is somewhat close to what Pesta & Poznanski found. In model (step) 2, the increment in the (adjusted) R² is about 27% in NLSY79 and 25% in NLSY97 for the inclusion of speed. For overall GPA (in NSY97) the increment in R² amounts only to 12%.

Not shown in the spreadsheed, in NLSY97, the results for hispanic-white (HW) gap is similar concerning g with 1-(0.239/0.350) = 0.320 of the gap accounted for by speed. The number for black-hispanic (BH) gap is smaller, with 1-(0.157/0.172) = 0.09. For the NLSY79, the HW gap diminished by 1-(0.200/0.339) = 0.410, and the BH gap by 1-(0.060/0.147) = 0.408.

Concerning the independent contribution of speed in predicting GPA when controlling for g, this unique effect is greatly reduced in the white, black, and hispanic sample as well. Speed has a modest predictive validity above g. The increment in (adjusted) R² is generally about 1-2% with g controlled for all groups. The increment in the same R² is about 2%, 7%, and 4% or blacks, hispanics and whites, respectively, when controlling for SAT-verbal. The respective numbers for SAT-math are 1%, 3% and 1%. And the respective numbers for PIAT-math are 3%, 3% and 4%. The increment in R² when SAT (verbal or math) is entered in Model 2 instead yielded a stronger incremental validity. However, R² increment for PIAT-math is similar to that obtained for speed.


Unlike path analysis, multiple regression cannot decompose the correlations into direct and indirect effects to give a clear picture on how these relationships are actually working by examining all the possible indirect paths. But this is the least of the problems.

One would question whether psychometric processing speed is comparable to chronometric processing speed as assessed by RT and IT. Coyle (2011) uses these subtests as a measure of processing speed and was able to find that the speed factor mediates the effect of age on g. That is, the model with no mediation fitted much worse than a model positing speed as a mediator between age and g. This appears curious because NO and CS consistently have one the lowest g-loadings together with Auto & Shop Info among the ASVAB subtests (Jensen, 1985, table 5; Hu, Sept.21.2013). Besides, Jensen (1998, pp. 224-225, 236; 2006, pp. 157, 178) informs us the following about the ASVAB :

Some psychometricians have mistakenly believed that RT measures the same speed factor that is measured by highly speeded psychometric tests, such as clerical checking, number series comparisons, and simple arithmetic. In fact, such tests have lower correlations with RT than do nonspeeded power tests. The two most speeded subtests out of the ten subtests of the Armed Services Vocational Aptitude Battery (ASVAB), for example, have repeatedly shown the lowest correlations with RT, yet these tests are typically identified with the speed factor that appears in factor analyses of various speeded and nonspeeded psychometric tests.

The fact is that psychometric speed – better called test-taking speed – is something entirely different from the speed of information processing measured by RT or IT. RT and IT have their highest correlations with pure power tests. The explanation for this seeming paradox is that the speed of information processing is a large part of g, whereas test-taking speed is not – it is more a personality factor than a cognitive factor. One of my studies found that the time taken by university students to complete the Raven’s matrices, when instructed to take all the time they need and to attempt every item, was not significantly correlated with their Raven scores (number right), nor was test-taking time significantly correlated with RT, but it was significantly correlated (r = -.45) with Extraversion as measured by the Eysenck Personality Inventory, which was not significantly correlated with RT. The personality trait of “conscientiousness” is probably also related to test-taking speed, but this has not yet been investigated. In all such correlations involving time, the variable of age must be controlled, as both test-taking speed and RT gradually change for the “worse” with increasing age beyond early adulthood. [31] There is, of course, a wide range of individual differences in the rates of this change with aging, which has the effect of increasing the correlations between all speeded tests in elderly people.

If true, the above finding may not be valid. Unfortunately, I don’t have much information on this issue that would give the definitive answer, apart from the small study by Larson (1988, Table 4) where Numerical Operations and Coding Speed subtests did not correlate with IT or RT and poorly with some other speed measures, in comparison to the other ASVAB subtests. According to Danthiir et al. (2005, 2012) however, psychometric speed tests seem to be strongly related (near unity) to a general mental speed factor derived from several ECTs.

On the other hand, if we think these speeded tests are quite comparable to what RT and IT actually measure, there is a good chance that the strength of the mediation accounted for by speed is under-estimated here. More tests involved in the construction of a (latent) factor would decrease error variance. For example Grandy et al. (2013) made this specific point to explain why the studies on the correlation of intelligence and individual alpha peak frequency (IAF) yielded so much confusions and conflicts. A better example is illustrated by Betjemann et al. (2010). Contra Leeuwen et al. (2009), they found a positive relationship between processing speed and brain volume. The difference is that the former computed a speed factor with 4 measures while the latter uses only 2 measures.

(One last thing : it must be noted that RT is devoid of information or culture content unlike most of paper-pencil IQ tests, and the ASVAB is not an exception. Even if speed partially mediates BW difference, it is unlikely to be relevant to Spearman’s hypothesis.)


There is a need to distinguish between psychometric and chronometric speed. Coyle (2011) is a good example of this whole problem, because if we accept that distinction, one would wonder why Kail (2007), Nettelbeck (2010) and some earlier studies (e.g., Fry & Hale, 1996; Jensen, 2006, pp. 91-94, 104), found the same results as that reported by Coyle, with the difference that they have also used chronometric speed tests.

Generally speaking, studies investigating the questions surrounding processing speed may have sometimes yielded heterogeneous results, depending on the specific topic, probably because the latent speed factor is poorly and differently constructed among studies, or the use of different procedures such as using individual measures as such versus latent factors created from these individual measures, but perhaps more generally because they use different measures which may have different properties.