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Abstract

Rapid automatized naming (RAN) is a powerful predictor of reading fluency, and many digitized dyslexia screeners include RAN as an essential component. However, the validity of digitized RAN has not been established. Using a sample of 174 second-graders, this study tested (1) the comparability between paper and digitized versions of RAN and (2) the validity of the digitized version. We found that paper and digital versions were highly correlated, and such correlation was consistent across students’ reading levels. Further, the digital RAN predicted children’s word reading proficiency as well as the paper version. Moreover, the constructs measured by paper and digital versions of RAN were comparable. We conclude that the digitized RAN is a valid alternative to the traditional paper version for this age group.

Keywords

rapid automatized naming dyslexia validity digital version

Introduction

Although not universally acknowledged, rapid automatized naming (RAN), how quickly an individual can name a visual array of alphanumeric or non-alphanumeric stimuli in a serial fashion, is one of the best predictors of dyslexia (e.g., McWeeny et al., 2022). RAN’s robust prediction capacity for reading fluency has made it an essential part of screening for reading challenges. Many states now mandate dyslexia screening, and most include the RAN. That said, many scholars underscore that RAN is considered as only one of several major predictors of later reading proficiency, including phonological awareness, vocabulary, and letter-sound correspondence knowledge, that should be used in any early prediction battery. Intervention studies have shown the crucial importance of early interventions that target weaknesses like RAN and PA, particularly when data from assessments including RAN assessments guide the instructional emphases (Gotlieb et al., 2022; Lovett et al., 2017; Morris et al., 2012).

Differences Between Paper and Digital Assessment Formats

Digital screeners are being deployed to allow for efficiency in administration and interpretation of test results. Digital assessment tools often offer automatic scoring; a dashboard that is quickly updated with student scores; and efficiency in administration for teachers. Many of these kinds of assessments allow children to complete tests with little to no adult supervision.

Several studies have shown the digital versions of various academic assessments to be equivalent to the paper and pencil versions. For instance, one study found that there were no significant differences between fourth grade students’ performance on a standardized assessment, Trends in International Mathematics and Science Study (TIMSS), when students took a paper and pencil version or a version on a tablet (Hamhuis et al., 2020).

The problem is that there are no known comparisons between children’s use of digitally administered RAN tests and the original paper-administered, classic RAN Tests (Denckla & Rudel, 1976; Wolf & Denckla, 2005). To date, comparisons of digital and paper versions of naming speed tasks have only been conducted with adult samples with varied versions of RAN tasks (Howe et al., 2006; Park et al., 2022). For instance, Park et al. (2022) found that the two mediums measuring naming speed showed high agreement (.92–.94) but they used a naming instrument specifically developed for concussion screening, not the classic RAN. Similarly, Howe and colleagues (2006) found a high agreement (.95) across mediums using researchers-created RAN tasks.

Given the proliferation of these screeners and the paucity of data on comparisons of print-based and digitized RAN in children, we investigate the assumption that these two mediums provide the same information with a newly digitized version of the classic RAN Letters and RAN Objects subsets. In other words, the overall aim of this study is to test whether a digitized version of the RAN test is a valid alternative to the original version. Therefore, we examined the following research questions:

1. Are the digital version and the paper version of the RAN test equivalent?

2. Is the degree of equivalency across the two mediums consistent across students’ levels of reading proficiency?

3. Is RAN’s predictive power of children’s reading proficiency equivalent across the two mediums?

4. Does the construct measured by the Letters and Objects subtest of the RAN, respectively, converge across the two mediums?

The implications of these results have critical importance for whether digitized RAN tasks can inform the emphases in instruction and intervention, particularly for struggling readers.

Methods

Participants and Setting

This study took place in a metropolitan city on the West Coast of the United States at three school sites: (1) a Title 1 school in a low-resourced neighborhood (hereinafter School 1, n = 55), (2) an inclusive charter school (School 2, n = 74), and (3) a private school (School 3, n = 45). The entire sample included a total of 174 students in Grade 2 (50.6% = female; M_age = 7.63, SD = .29). Of the sample, 39.7% were Latinx, and 32.8% were White (Table 1). Race and ethnicity compositions varied across the schools and such differences were statistically significant. Table 2 summarizes demographic characteristics by school. All parents consented to the study, and this study was approved by the Institutional Review Board (IRB #20-001008).

Table 1.

Race/Ethnicity Composition of the Sample.

Race/Ethnicity	N (%)
Latinx/Hispanic	69 (39.7%)
White	57 (32.8%)
Multiracial/multiethnic	25 (14.4%)
Asian, Pacific Islanders, Filipino	9 (5.2%)
Black/African American	7 (4%)
Unknown	5 (2.9%)
Native Americans	2 (1.1%)
Total	N = 174

Table 2.

Demographic Characteristics of the Sample by Schools.

	School 1 (n = 55)	School 2 (n = 74)	School 3 (n = 45)	p
Sex	Male	24 (43.6%)	36 (51.4%)	25 (55.6%)	.471
Sex	Female	31 (56.4%)	34 (48.6)	20 (44.4%)	.471
Race/ethnicity	White	1 (1.8%)	50 (71.4%)	6 (13.6%)	<.001
	Black/African American	0 (.0%)	4 (5.7%)	3 (6.8%)
	Latinx/Hispanic	53 (96.4%)	13 (18.6%)	3 (6.8%)
	Asian, Pacific Islanders, Filipino	1 (1.8%)	1 (1.4%)	7 (15.9%)
	Native Americans	0 (.0%)	2 (2.9%)	0 (.0%)
	Multiracial	0 (.0%)	0 (.0%)	25 (56.8%)
Lunch status	Free	55 (100%)	7 (10.0%)	NA*	<.001
	Reduced	0 (.0%)	6 (8.6%)
	Full price	0 (.0%)	57 (81.4%)
Special education	In special education	10 (18.2%)	1 (1.4%)	NA*	<.001
Special education	Not in special education	45 (81.8%)	69 (98.6%)	NA*	<.001

*Special education services and free/reduced lunch are not provided at School 3 as it is a private school with no federal funding.

Measures

RAN Paper

The Letters and Objects subtests of the classic RAN test were administered in their original paper forms (Wolf & Denckla, 2005, Pro-Ed). Five rows of 10 letters or objects, a total of 50, were printed on a sheet of paper (17 × 11 inches). Students were asked to name an array of letters or objects as quickly and accurately as possible. A short practice task was provided before the actual test in order to confirm students’ understanding of the instruction and basic letter knowledge, so that only retrieval speed, not letter knowledge is measured. The reliability of the classic RAN test ranges from .84 to .92.

RAN Digital

The exact copy of the Letters and Objects subtests of the RAN test were digitized, and these digitized versions were accessed through an iPad app developed for this study that had a timer function embedded in it. The RAN tests were displayed in 8.5 × 6.5 inches screen on the iPad. As with the paper version, the testers controlled when the stimuli progressed from the practice image to the test image.

Test of Word Reading Efficiency—Second Edition (TOWRE-2)

The Real-Word subset of the TOWRE-2 was administered to measure decoding and word reading proficiency among students. The reliability of the TOWRE-2 ranges from .87 to .90.

Procedures

Each student completed both the paper and digitized versions of the Letters and Objects subtests of the RAN in one sitting. Testers used a handheld timer to record RAN completion time for the paper version and used either the handheld timer or the embedded timer for the digital version.

In order to control for possible practice effects, the four versions of the test (i.e., Paper-Letters, Digital-Letters, Paper-Objects, Digital-Objects) were counterbalanced using the Balanced Latin Square (BLS) design (Edwards, 1951). The resulting four randomization groups are illustrated in Table 3. Each student in the sample was randomly assigned to one of the four groups. No formal breaks or other activities were provided between each of the four administrations. No statistically significant differences in means were found in any of the four tests between the randomization groups. Therefore, we can infer that there were no statistically significant confounding order effects (Appendix A).

Table 3.

Randomization groups.

Group	Order
Group 1 (n = 46)	PL-DL-DO-PO
Group 2 (n = 43)	DL-PO-PL-DO
Group 3 (n = 45)	PO-DO-DL-PL
Group 4 (n = 40)	DO-PL-PO-DL

PL, Paper-Letters; DL, Digital-Letters; DO, Digital-Objects; PO, Paper-Objects.

On the same day, the TOWRE-2 Real-Word subset was administered to each student. Students were asked to read the list of the words as fast as they can in 45 seconds. The number of words read correctly within the given time was recorded.

Statistical Analysis

To measure the equivalency between the paper and digital versions of the RAN, a parallel form reliability test was conducted by calculating Pearson r between the two mediums for both Letters and Objects subtests.

To examine the consistency of the correlations (Pearson r) across students’ reading proficiency, Fisher’s z was calculated within reader groups. The reader group was determined by the students’ quartile scores in the TOWRE Real Word test. Students were identified as struggling readers (N = 52) if they scored below the 25^th percentile on the national norm in the TOWRE Real Word test (Torgesen et al., 2012). Those who scored above the 25^th percentile in the TOWRE Real Word test were identified as non-struggling readers (N = 122).

To compare the predictability of RAN for children’s word reading fluency between the two mediums, the correlation between the TOWRE score and the paper versions of RAN was compared to the correlation between the TOWRE score and the digital versions. Such a comparison was made for both the Letters subset and the Objects subset using Fisher’s z.

Lastly, to measure if the validity of the Letters and Objects subsets converges across mediums, the correlation between the Letters subset score and the Objects subset score of the paper RAN was compared to the correlation between the Letters subset score and the Objects subset score of the digital RAN. This comparison was also made using Fisher’s z.

All statistical analyses were conducted using SPSS version 28.

Results

Parallel Form Reliability

Descriptive statistics (i.e., mean, SD) for each of the four types of tests are summarized in Table 4. The correlations of both the Letters and Objects subset across the paper and digital versions were high and positive, and both were statistically significant (p < .05) (Mukaka, 2012) (Table 5).

Table 4.

Descriptive Statistics.

	Mean	SD	Kurtosis	Skewness
Paper-letters	35.69	9.68	2.46	1.29
Digital-letters	37.70	11.16	6.94	2.00
Paper-objects	56.42	14.84	2.26	1.29
Digital-objects	58.30	15.65	2.46	1.32

N = 174; unit: seconds.

Table 5.

Parallel Form Reliability.

	Pearson r	p
Letters subset	.805	<.001
Objects subset	.760	<.001

Consistency Across Reading Proficiency Groups

The Pearson r between versions of the Letters subset was moderately to highly positive among both struggling and non-struggling readers. These two correlations were statistically significant (p < .05). Fisher’s z for these two correlations were non-significant (p > .05), indicating a non-significant difference in the correlations across the two groups.

The Pearson r between versions of the Objects subset was highly positive for both the struggling readers group and non-struggling readers. These relationships were statistically significant (p < .05). Fisher’s z for these two correlations were non-significant (p > .05), indicating a non-significant difference in the correlations across the two groups (Table 6).

Table 6.

Correlation Comparison Between Reading Proficiency Groups Across RAN Versions.

Subtest	Group	Pearson r	Fisher’s z	p
RAN letters	Struggling readers (N = 52)	.776**	1.612	.053
RAN letters	Non-struggling readers (N = 122)	.642**	1.612	.053
RAN objects	Struggling readers (N = 52)	.727**	.135	.446
RAN objects	Non-struggling readers (N = 122)	.716**	.135	.446

**p < .001.

Association Between RAN on Word Reading Proficiency

The correlations between the Letter subset of the RAN and the TOWRE score were moderately negative for both paper and digital versions. These relationships were statistically significant (p < .05). Fisher’s z for these two correlations was not significant (p > .05), indicating a non-significant difference in the correlations across the mediums.

The correlations between the Objects subset of the RAN and the TOWRE score were also moderately negative for both paper and digital versions. These relationships were statistically significant (p < .05). Fisher’s z for these two correlations was not significant (p > .05), indicating a non-significant difference in the correlations across the mediums (Table 7).

Table 7.

Correlation Comparison Between RAN and TOWRE Across Mediums.

Subtest	Correlations	Pearson r	Fisher’s z	p
RAN letters	Paper RAN versus TOWRE	−.683 **	−.371	.355
RAN letters	Digital RAN versus TOWRE	−.661**	−.371	.355
RAN objects	Paper RAN versus TOWRE	−.499**	.049	.480
RAN objects	Digital RAN versus TOWRE	−.495**	.049	.480

**p < .001.

Convergent Validity and Effects of Medium

The correlations between the Letters and Objects subsets of the RAN were moderately positive for both the paper and the digital version. These correlations were statistically significant (p < .05). Fisher’s z for these two correlations was not significant (p > .05), indicating a non-significant difference in the correlations across the mediums (Table 8).

Table 8.

Correlation Comparison Between RAN Letters and RAN Objects Across Mediums.

Correlations	Pearson r	Fisher’s z	p
Paper letters versus Paper objects	.564 **	−1.231	.109
Digital letters versus Digital objects	.648**	−1.231	.109

**p < .001.

Discussion

Most states in the U.S. currently require some form of early literacy screening for students (National Center on Improving Literacy, 2022), and increasingly screening may take place using digital devices. Many of these screeners include naming speed, ideally using RAN. Although RAN is one of the best early predictors of reading challenges (McWeeny et al., 2022), previous research has not demonstrated the efficacy of a digital RAN and its comparability to the traditional paper version of RAN in children. Here, we demonstrate that a diverse sample of second grade students’ performance on a digitized version of the Letters and Objects subsets of RAN administered via an iPad and on the traditional Letters and Objects RAN test is equivalent. More specifically, we found that the digitized version and the original paper version of the RAN were highly correlated, and such correlations were consistent across students’ levels of reading proficiency. In addition, we found that the digital version of the RAN was just as powerful a predictor of word reading proficiency as the traditional version. The digital version of the RAN measures the equivalent construct as the paper version does.

Although no previous research to our knowledge has compared the traditional and digitized RAN in children, our findings accord with previous research showing comparability of the RAN across mediums in healthy adults (Howe et al., 2006; Park et al., 2022). In addition, the high correlation found across mediums (.805) in the Letters subset of the classic RAN in our study aligns with the similarly high correlation (.85) found in the alphanumeric naming tasks between the classic RAN and an alternative RAN test developed by Decker (1989) (Compton et al., 2002). Together, findings from the current study suggest that the digitized version is a valid alternative to the original in this age group.

Wide-spread use of a digitized RAN requires that the test is a fair instrument across students with different levels of reading proficiency and that differences as a function of RAN test medium are not related to reading proficiency. Our findings suggest that the digitized RAN is equivalent to the traditional RAN for students who are poor readers and for average or above readers. Results offer preliminary assurances of its usefulness.

It is important to note that children in our sample performed better in the Letters subset of the RAN compared to the Objects subset. This in fact supports the previous finding by Norton and Wolf (2012) that RAN latencies are related to how automatized the naming process was, not how early the children learned the names of the stimuli. For this reason, young children ages 5–6 years often name non-alphanumeric stimuli (e.g., objects) faster than alphanumeric stimuli (e.g., letters). However, automatization increases with more exposure and practice with letters in the school setting, and by first grade, alphanumeric stimuli become much more automized than non-alphanumeric stimuli. In our sample of second-graders, the alphabet appears to be much more automatized than object names as we would expect. Such a difference in the level of automatization between letters and objects explains the relatively low correlations between the Letters and Objects subset across both mediums.

Aside from reducing the size of the stimulus to fit on an iPad, we underscore that we kept the same layout as the classic RAN test, only varying the medium. Many existing naming-speed tests have varied the number and type of stimuli. Emerging readers, particularly struggling readers, require a substantial number of stimuli before their system is taxed sufficiently so as to expose challenges to automaticity in their retrieval processes. The fifty-item structure has proven more efficacious in discriminating children with fluent retrieval weaknesses than measures with fewer stimuli (O’Rourke et al., 2001). We urge, therefore, caution in generalizing the present results to naming-speed tests that have reduced the number of stimuli, despite the advantage of efficiency in administration.

We used a moderate-sized sample from three substantially different schools and demonstrated equivalence across RAN mediums. As complete demographic data were not available, we plan future research to test the equivalence of the traditional and digitized RAN for a larger sample with other sub-populations of students. Further, future research should investigate affordances and challenges of digital RAN screening as a function of various socio-cognitive aspects of the student, and investigate various other reading measures, in addition to the TOWRE used here, to provide a more comprehensive understanding of individual differences in students’ reading competencies.

Summary

RAN represents one of the most universal predictors of reading and is a component in many dyslexia screeners. As digital screeners for dyslexia become increasingly common, the data presented here indicate that a digitized RAN screener that adheres to basic principles around assessing retrieval can be an effective alternative to the traditional, print-based RAN for most young children, including struggling readers.

Footnotes

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: One of the authors in this report is a co-author of the RAN test, which was studied in the current manuscript.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Office of Special Education Programs, Office of Special Education and Rehabilitative Services, (H326M190005).

ORCID iD

Sohyun An Kim

Appendix

Table 1.

One-Way ANOVA to Test Randomization Group Differences.

Tests		Sum of squares	df	Mean square	F	p
Paper-letters	Between groups	93.55	3	31.183	.329	.805
	Within groups	16,127.692	170	94.869
	Total	16,221.241	173
Paper-objects	Between groups	1169.56	3	389.853	1.796	.15
	Within groups	36,910.814	170	217.122
	Total	38,080.374	173
Digital-letters	Between groups	401.285	3	133.762	1.076	.361
	Within groups	21,129.175	170	124.289
	Total	21,530.46	173
Digital-objects	Between groups	881.646	3	293.882	1.203	.310
	Within groups	41,512.814	170	244.193
	Total	42,394.46	173

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A Validity Study of the Digitized Version of the Rapid Automatized Naming Test

Abstract

Keywords

Introduction

Differences Between Paper and Digital Assessment Formats

Methods

Participants and Setting

Measures

RAN Paper

RAN Digital

Test of Word Reading Efficiency—Second Edition (TOWRE-2)

Procedures

Statistical Analysis

Results

Parallel Form Reliability

Consistency Across Reading Proficiency Groups

Association Between RAN on Word Reading Proficiency

Convergent Validity and Effects of Medium

Discussion

Summary

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

Appendix

References