Contents lists available at ScienceDirect Intelligence journal homepage: www.elsevier.com/locate/intell PASS theory of intelligence and academic achievement: A meta-analytic review George K. Georgiou a,⁎ , Kan Guo b,⁎⁎ , Nithya Naveenkumar a , Ana Paula Alves Vieira c , J.P. Das a a University of Alberta, Canada b Beijing Normal University, China c State University of Maringá, Brazil ARTICLE INFO Keywords: Intelligence Mathematics Meta-analysis PASS processes Reading ABSTRACT Although Planning, Attention, Simultaneous and Successive (PASS) processing theory of intelligence has been argued to offer an alternative look at intelligence and PASS processes – operationalized with the Cognitive Assessment System – have been used in several studies, it remains unclear how well the PASS processes relate to academic achievement. Thus, this study aimed to determine their association by conducting a meta-analysis. A random-effects model analysis of data from 62 studies with 93 independent samples revealed a moderate-to- strong relation between PASS processes and reading, r = 0.409, 95% CI = [0.363, 0.454]), and mathematics, r = 0.461, CI = [0.405, 0.517]. Moderator analyses further showed that (1) PASS processes were more strongly related with reading and math in English than in other languages, (2) Simultaneous processing was more strongly related to math accuracy and problem solving than math fluency, (3) Simultaneous processing was more strongly related to problem solving than Attention, and (4) Planning was more strongly related to math fluency than Simultaneous processing. Age, grade level, and sample characteristics did not influence the size of the correlations. Taken together, these findings suggest that PASS cognitive processes are significant correlates of academic achievement, but their relation may be affected by the language in which the study is conducted and the type of mathematics outcome. They further support the use of intervention programs that stem from PASS theory for the enhancement of reading and mathematics skills. 1. Introduction A plethora of studies has established that intelligence (oper- ationalized with IQ tests) is related to school achievement (e.g., Barton, Dielman, & Cattell, 1972; Deary, Strand, Smith, & Fernandes, 2007; Mayes, Calhoun, Bixler, & Zimmerman, 2009; Naglieri & Bornstein, 2003; Soares, Lemos, Primi, & Almeida, 2015; see also Peng, Wang, Wang, & Lin, 2019; Roth et al., 2015, for meta-analyses). For example, Roth et al. (2015) estimated the average correlation between IQ (op- erationalized with different IQ measures) and school grades to be 0.44. In general, those with higher IQ outperform others with lower IQ in important school subjects such as reading and mathematics. Although this is well established, some researchers have argued that the most popular IQ batteries (e.g., WISC) include tests (e.g., Vocabulary, Ar- ithmetic) that are very similar to achievement tests and thus assess more “knowing” than “thinking” (which should be the target of in- telligence testing) (e.g., Das, 2002; Gardner, 1993; Naglieri & Otero, 2018). To bypass this problem as well as to broaden the scope of abilities measured, Das, Naglieri, and Kirby (1994) proposed a neurocognitive theory of intelligence called PASS (for Planning, Attention, Simulta- neous, and Successive processing) and a way of measuring it (Cognitive Assessment System [CAS]; Naglieri & Das, 1997). Although PASS theory is more than 20 years old and several studies have examined the rela- tion of CAS measures with academic achievement, we are still lacking a quantitative synthesis of this line of research. Thus, the purpose of this meta-analysis was to estimate the size of the relation between PASS processes and reading/mathematics and if their relation is influenced by different factors (e.g., the type of reading and mathematics outcome, the age of participants, the sample characteristics, and the language in which the study was conducted). https://doi.org/10.1016/j.intell.2020.101431 Received 10 September 2019; Received in revised form 8 December 2019; Accepted 11 January 2020 ⁎ Correspondence to: G. K. Georgiou, Department of Educational Psychology, University of Alberta, 6-102 Educational North, Edmonton, AB T6G2G5, Canada. ⁎⁎ Correspondence to: K. Guo, School of Mathematical Sciences, Beijing Normal University, Beijing 100875, P.R. China. E-mail addresses: georgiou@ualberta.ca (G.K. Georgiou), guokan@bnu.edu.cn (K. Guo). Intelligence 79 (2020) 101431 0160-2896/ © 2020 Elsevier Inc. All rights reserved. T