Research confirms early RAN screening is recommended for establishing reading risk

By Helen Dimond, Read3 Consultant Speech Pathologist


We rabbit on about Rapid Automatised Naming (RAN) a fair bit here at Read3 so you’ll understand why I was particularly interested in a recent podcast from The Literacy View which talked about findings from a RAN meta-analysis. If you aren’t sure what RAN is check out our previous blog post.

Ensuring the Read3 program stays solidly nestled in the latest research is a priority for us, so I decided to pop the aforementioned RAN article on ‘the list’ for discussion at our Journal Club.

The 2022 article was Rapid Automatized Naming (RAN) as a Kindergarten Predictor of Future Reading in English: A Systematic Review and Meta-analysis by McWeeny et al.

Being a systematic review and meta-analysis, the aim is to identify and analyse all relevant studies in the area and look for patterns. This particular systematic review and meta-analysis (with N = 60 samples; k = 373 effect sizes; n = 10,513 participants), was the first to test the extent to which measures of RAN assessed before primary grade school predict future reading performance in English-speaking children.

So what patterns were they looking for and what did they find?

Question 1: To what extent do RAN measures taken before school predict later reading scores?

Findings showed a strong relationship between preschool (4 - 5 years) RAN scores and later reading skills (r= - 0.38). Furthermore, when the child’s scores related to phonological awareness were taken out of the data (known as ‘partialling out’), RAN was still found to be a significant predictor (rsp = −0.25). Essentially, RAN scores correlate with later reading skills even when the child doesn’t have phonological awareness concerns.

Question 2: Do types of RAN tasks affect reading scores (and how?)

RAN tasks involving letters and numbers are much stronger at predicting later reading than other RAN tasks, like colour or object naming. Researchers made the important caveat that for children in kindergarten or preschool who do not yet know the names of letters or digits automatically, a RAN task using colours or objects would be a better choice. Then, once letters or digits are automatic, the alpha-numeric tests would be a better choice for later reading prediction.

Question 3: Do types of reading measures affect the relationship with RAN?

The investigators found that although RAN significantly predicts all types of reading measures, RAN tasks more strongly predict real word reading than nonword reading, as nonwords are not stored in long term memory for retrieval but rather have to be decoded via the phonological route. However, when PA was partialled out, real word and nonword reading did not have a differential relationship with RAN. There were also differences in reading accuracy measures versus reading fluency measures, with RAN scores being a sensitive predictor of reading fluency difficulties.

Question 4: Does the demographics of the sample affect the relationship?

The demographic factor the study looked at was dyslexia risk. They classified risk by low, medium and high and tested whether the RAN-reading relationship was affected by dyslexia risk. It turns out it wasn’t. Researchers feel this may indicate that children at risk and children not at risk are using similar cognitive processes, even if these processes are impaired in children at risk.

The authors’ final conclusion was “These results support shared cognitive resource models in which the similarity between RAN and reading tasks accounts for their correlation.” In other words, reading is very similar to a RAN task.

Practical implications from the findings

All in all there was some really interesting information in the article. But the question is, what do we need to do for the beginner readers in our care?

Our Journal Club group came up with a few suggestions:

  • Screen RAN when working with preschool or early school aged children. Phonological awareness (PA) screening, while important, does not give you the full picture of reading risk. Interestingly, the research identifies that a normed RAN screener is not essential, so our free CHIP Screener is still a great choice if you don’t have any standardised measures on hand.
  • Use alphanumeric RAN tests if the child can automatically recall letters or numbers as these are a stronger predictor of reading risk. If they can’t, then use colour or object RAN tests. The important thing to note is that the child must be able to name the items automatically – otherwise there can be no rapid naming!
  • Talk about the importance of RAN screening in the early years! In the USA, population-level screening is now considered best practice and we need to be advocating for something similar here in Australia. It would be great to see RAN screening undertaken as part of the school entry screening and interviews. I haven’t seen RAN in any preschool screeners as yet, but wouldn't it be great!

Last but not least, RAN should always be assessed as part of a battery of screening measures, as RAN alone only predicts 14% of variance in future reading scores.

When it comes to literacy instruction, Australia is really just getting started with evidence-based practice. However, our ability to advocate for comprehensive early screening, which includes RAN, can have an impact in narrowing the research-to-practise gap.

Happy screening everyone!



Journal Article: Sean McWeeny, Soujin Choi, June Choe, Alexander LaTourrette, Megan Y. Roberts, Elizabeth S. Norton (2022) Rapid Automatized Naming (RAN) as a Kindergarten Predictor of Future Reading in English: A Systematic Review and Meta-analysis. Reading Research Quarterly, 57(4) pp. 1187–1211 | doi:10.1002/rrq.467

1 comment

  • Hi, this is useful – thanks! How is automaticity defined, in terms of being able to name the relevant items automatically for the RAN test to be valid? For example, should the child be able to name the items within two seconds each? I can’t find any reference to this in the linked studies, and can’t access the full text for Catts et al 2009.

    Michelle Chadwick

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