| Characteristic |
English
|
Spanish
|
||||
|---|---|---|---|---|---|---|
| K N = 1,225 |
G1 N = 1,613 |
G2 N = 3,019 |
K N = 786 |
G1 N = 922 |
G2 N = 748 |
|
| Timepoint | ||||||
| Fall 2023 | 456 (48%) | 396 (30%) | 417 (15%) | 429 (69%) | 378 (54%) | 365 (49%) |
| Fall 2024 | 500 (52%) | 942 (70%) | 2,277 (85%) | 193 (31%) | 328 (46%) | 380 (51%) |
| Unknown | 269 | 275 | 325 | 164 | 216 | 3 |
| Administration Format | ||||||
| Not applicable | 1,225 (100%) | 1,613 (100%) | 3,019 (100%) | 786 (100%) | 922 (100%) | 748 (100%) |
| Race | ||||||
| American/Alaskan Native | 31 (2.6%) | 53 (3.4%) | 62 (2.2%) | 26 (3.3%) | 32 (3.5%) | 9 (1.2%) |
| Asian | 114 (9.5%) | 146 (9.3%) | 204 (7.4%) | 29 (3.7%) | 27 (3.0%) | 23 (3.1%) |
| Black/African American | 129 (11%) | 177 (11%) | 283 (10%) | 9 (1.2%) | 8 (0.9%) | 11 (1.5%) |
| Not reported | 193 (16%) | 267 (17%) | 310 (11%) | 354 (45%) | 409 (45%) | 271 (37%) |
| Other | 303 (25%) | 239 (15%) | 370 (13%) | 155 (20%) | 76 (8.3%) | 48 (6.5%) |
| White | 430 (36%) | 692 (44%) | 1,546 (56%) | 209 (27%) | 362 (40%) | 373 (51%) |
| Unknown | 25 | 39 | 244 | 4 | 8 | 13 |
| Ethnicity | ||||||
| Hispanic/Latin(o/a) | 679 (61%) | 993 (65%) | 1,929 (70%) | 699 (94%) | 858 (94%) | 680 (93%) |
| Intentional nonreport | 23 (2.1%) | 7 (0.5%) | 6 (0.2%) | 2 (0.3%) | 1 (0.1%) | 0 (0%) |
| Not Hispanic/Latin(o/a) | 403 (36%) | 536 (35%) | 815 (30%) | 40 (5.4%) | 49 (5.4%) | 50 (6.8%) |
| Unknown | 120 | 77 | 269 | 45 | 14 | 18 |
| Gender | ||||||
| Female | 568 (50%) | 790 (52%) | 1,344 (49%) | 393 (53%) | 480 (54%) | 362 (50%) |
| Male | 562 (50%) | 732 (48%) | 1,392 (51%) | 344 (47%) | 409 (46%) | 363 (50%) |
| Unknown | 95 | 91 | 283 | 49 | 33 | 23 |
| Home Language | ||||||
| English | 731 (62%) | 923 (61%) | 1,655 (64%) | 97 (13%) | 104 (11%) | 94 (13%) |
| Spanish | 365 (31%) | 524 (35%) | 813 (32%) | 671 (87%) | 804 (88%) | 604 (86%) |
| Other | 80 (6.8%) | 67 (4.4%) | 109 (4.2%) | 6 (0.8%) | 4 (0.4%) | 6 (0.9%) |
| Unknown | 49 | 99 | 442 | 12 | 10 | 44 |
| English Proficiency Label | ||||||
| (Re-)Classified Proficient | 63 (6.1%) | 109 (7.5%) | 270 (11%) | 82 (11%) | 124 (14%) | 83 (12%) |
| English Learner | 352 (34%) | 470 (33%) | 649 (25%) | 567 (79%) | 679 (76%) | 520 (75%) |
| English-only | 619 (60%) | 867 (60%) | 1,628 (64%) | 65 (9.1%) | 85 (9.6%) | 86 (12%) |
| Unknown | 191 | 167 | 472 | 72 | 34 | 59 |
| Ever IEP/504 | 64 (6.8%) | 129 (9.9%) | 224 (11%) | 48 (7.6%) | 69 (9.0%) | 61 (11%) |
| Unknown | 280 | 306 | 887 | 158 | 154 | 169 |
19 Rapid Automatized Naming of Letters
19.1 Task Description
Children are shown a page with 5 unique letters repeated randomly, arranged in five rows of ten. They are asked to name as many letters as quickly as possible.
19.2 Construct
The Rapid Automatized Naming - Letters task measures the construct of automatic processing and retrieval of letter names. It assesses how quickly and accurately children can name familiar letters, skills that are closely linked to automatic reading.
19.3 Item Development
19.3.1 English
For letter selection, we selected a mix of vowels and consonants, prioritizing letters that were typically acquired earlier in literacy development. Additionally, the selected letters fulfilled the following selection criteria:
- Letters could not be reversible: letter reversals are a common developmental mistake of early readers. To avoid eliciting a developmental error, we excluded the following letters: “b,” “d,” “p,” and “q.”
- Letters should be clearly visually distinguishable: depending on the font selected, some letters can be easily confused. Consequently, we excluded the letter “v” for its potential to confuse with the letter “u,” and we excluded the letter “a” for its potential to confuse with the letter “o.”
The final set of letters selected for the English measure was: c, e, o, s, u.
19.3.2 Spanish
For letter selection, we wanted to select a mix of vowels and consonants, prioritizing letters that were typically acquired earlier in literacy development. Additionally, the selected letters needed to fulfill the following selection criteria:
- Letters had to be monosyllabic: to be able to compare the performance in English and Spanish, letters needed to have similar phonological length. While most of the letters in English are monosyllabic, that is not the case in Spanish. Most of the letters acquired earlier in literacy development, because they are simpler and consistent, tend to be disyllabic (e.g., m: e-me, n: e-ne, l: e-le, s: e-se, f: e-fe). These letters were excluded.
- Letters could not be reversible: letter reversals are a common, developmentally expected mistake that early readers commit. To avoid over-penalizing children for making mistakes due to letter reversal, we excluded the following letters: “b,” “d,” “p,” and “q.”
- Letters also need to be clearly visually identifiable: depending on the font selected, some letters can be easily confused. Consequently, we excluded the letter “v” for its potential to confuse with the letter “u,” and we excluded the letter “a” for its potential to confuse with the letter “o.”
The final set of letters selected for the Spanish measure was: c, e, o, t, u.
19.4 Scoring
Participating children were presented with a 5x10 grid containing 50 letters and were asked to name as fast as they could, and the time taken by the participant to name all letters was recorded. The final score consisted of a rate between the number of accurately named letters (i.e., the total number of letters minus the incorrectly named letters) divided by the total time it took the child to complete the grid.
19.5 Samples
19.6 Score distribution
19.7 Criterion Validity Evidence
19.7.1 Sample
| Characteristic |
English
|
Spanish
|
||
|---|---|---|---|---|
| K N = 190 |
G1 N = 209 |
K N = 129 |
G1 N = 175 |
|
| Timepoint | ||||
| Spring 2024 | 190 (100%) | 209 (100%) | 129 (100%) | 175 (100%) |
| Race | ||||
| American/Alaskan Native | 2 (1.1%) | 4 (1.9%) | 4 (3.1%) | 4 (2.3%) |
| Asian | 29 (15%) | 26 (12%) | 9 (7.0%) | 5 (2.9%) |
| Black/African American | 23 (12%) | 25 (12%) | 2 (1.6%) | 1 (0.6%) |
| Not reported | 29 (15%) | 52 (25%) | 48 (37%) | 78 (45%) |
| Other | 43 (23%) | 27 (13%) | 22 (17%) | 7 (4.0%) |
| White | 64 (34%) | 75 (36%) | 44 (34%) | 79 (45%) |
| Ethnicity | ||||
| Hispanic/Latin(o/a) | 84 (44%) | 89 (43%) | 115 (89%) | 163 (93%) |
| Intentional nonreport | 10 (5.3%) | 2 (1.0%) | 1 (0.8%) | 0 (0%) |
| Not Hispanic/Latin(o/a) | 96 (51%) | 118 (56%) | 13 (10%) | 12 (6.9%) |
| Gender | ||||
| Female | 101 (53%) | 105 (50%) | 73 (57%) | 99 (57%) |
| Male | 89 (47%) | 104 (50%) | 56 (43%) | 76 (43%) |
| Home Language | ||||
| English | 137 (72%) | 154 (74%) | 28 (22%) | 28 (16%) |
| Spanish | 34 (18%) | 28 (14%) | 101 (78%) | 144 (83%) |
| Other | 18 (9.5%) | 25 (12%) | 0 (0%) | 2 (1.1%) |
| Unknown | 1 | 2 | 0 | 1 |
| English Proficiency Label | ||||
| (Re-)Classified Proficient | 14 (8.4%) | 25 (12%) | 25 (20%) | 32 (18%) |
| English Learner | 31 (19%) | 38 (18%) | 81 (65%) | 119 (69%) |
| English-only | 121 (73%) | 143 (69%) | 18 (15%) | 22 (13%) |
| Unknown | 24 | 3 | 5 | 2 |
| Ever IEP/504 | 8 (5.9%) | 20 (11%) | 6 (5.4%) | 12 (8.3%) |
| Unknown | 55 | 19 | 18 | 30 |
| Unknown | 0 | 1 | ||
English Rapid Automatized Naming of Letters was correlated with two measures: the Letters subtest from the Acadience Learning RAN (Powell-Smith et al. 2020) in kindergarten and first grade, and the RAN Letters subtest from the Rapid Automatized Naming and Rapid Alternating Stimulus Tests (Wolf and Denckla 2003) in second grade.
Spanish Rapid Automatized Naming of Letters was correlated with the Letters subtest from the Acadience Learning RAN (Powell-Smith et al. 2020) in kindergarten and first grade. No rapid naming of letters task could be found in Spanish that was normed on second-grade-aged children in the United States.
These analyses are problematic because of differences in how the measures are scored. The external assessments are scored based on total time, with RAN Objects and RAN Letters being completed in tandem. The Multitudes RANL measure is scored as the number correct divided by the total time and is distinct from RANO. Given these scoring differences, the correlations are difficult to interpret.
| Grade | n | r [CI] | n | r [CI] | n | r [CI] |
|---|---|---|---|---|---|---|
| K | 190 | -0.66 [-0.73, -0.57] | 31 | -0.74 [-0.87, -0.53] | 129 | -0.69 [-0.77, -0.59] |
| G1 | 207 | -0.71 [-0.77, -0.63] | 38 | -0.80 [-0.89, -0.65] | 175 | -0.61 [-0.69, -0.50] |