Gender Differences In Early Literacy: Analysis

School Psychology Review, 2010, Volume 39, No. 2, pp. 240 –257

Gender Differences in Early Literacy: Analysis of Kindergarten through Fifth-Grade Dynamic Indicators of Basic Early Literacy Skills Probes
Jaime L. Below Monroe Carell Jr. Children’s Hospital at Vanderbilt Christopher H. Skinner The University of Tennessee Jamie Y. Fearrington Appalachian State University Christy A. Sorrell Little Tennessee Valley Educational Cooperative
Abstract. Using a cross-sectional design and five Dynamic Indicators of Basic Early Literacy Skills measures, researchers tested for gender differences in reading skills for 1,218 kindergarten through fifth-grade students. A series of two-way repeated measures analyses of variance with time of year (fall, winter,
and spring) serving as the within-subjects variable and gender serving as the between-subjects variable showed girls scored significantly higher than boys on the four kindergarten measures; however, these differences were small. Firstgrade students were assessed on three of these four measures and there were no significant differences across boys and girls. For the oral reading fluency measure (Grades 1–5), a significant female advantage did not emerge until Grade 4 but was not significant in our Grade 5 sample. By the end of fifth grade, the difference in mean oral reading fluency scores was 1 word correct per minute. Discussion focuses on applied and theoretical implications of these findings, limitations of the study, and directions for future researchers.

Approximately a century ago, Ayers (1909) expressed concern over a male deficit in reading achievement. These findings have

been confirmed across researchers, populations, age/grade levels, and measures. For example, researchers compared reading scores

This research was supported by the Korn Learning, Assessment, and Social Skills Center at The University of Tennessee. Correspondence regarding this article should be addressed to Christopher H. Skinner, The University of Tennessee, BEC 525, Knoxville, TN 37996-3452; E-mail: [e-mail] Copyright 2010 by the National Association of School Psychologists, ISSN 0279-6015
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for 367,188 eighth-grade students taking the Minnesota Basic Skills Test from 1996 through 2001 (Davenport et al., 2002). Gender differences in reading that favored females were found for each year and the effect size (approximately 0.17) remained relatively constant across years. Klecker’s (2006) analysis of 4th-, 8th-, and 12th-grade students’ National Assessment of Educational Progress reading comprehension scores from 1992 to 2003 showed that females outperformed males every year at all three grade levels. In 4th grade, effect sizes ranged from 0.13 to 0.27 and were larger in the 8th (0.27– 0.43) and 12th (0.22– 0.44) grades. Similar discrepancies have been documented in international samples (Organization for Economic Cooperation and Development, 2001). In addition, researchers examining reading disabilities found clear evidence of male vulnerability with male-to-female ratios ranging from 2.04 to 6.78 across students 7–10 years old (Berger, Yule, & Rutter, 1975; Coutinho & Oswald, 2005; Flannery, Liederman, Daly, & Schultz, 2000; Lovell, Shapton, & Warren, 1964; Wehmeyer & Schwartz, 2001). Physiological-maturational and culturalsocietal factors may be related to male deficits in reading skills (Holbrook, 1988). Researchers investigating physiological-maturational theories have examined processing differences. Sequential processing refers to the ability to process information in sequence, and simultaneous processing is the ability to integrate parts of information into a meaningful whole. Perhaps because of increased levels of fetal testosterone delaying the development of the left-brain hemisphere (Geschwind & Behan, 1982; Waber, 1979), males tend to perform better on tasks requiring simultaneous (visual) processing and worse on tasks involving sequential (auditory) processing (Naglieri & Rojahn, 2001; Naour, 2001; Witelson, 1976). Although both types of processing may affect reading skill development (Das, Kirby, & Jarman, 1979; Naour, 2001; Witelson, 1976), deficits in sequential processing may affect early literacy skill development by impairing students’ ability to learn and perform

sequentially oriented word attack skills (e.g., phonetic decoding), which are critical to prereading skills (Aaron, 1982). Others posit that gender differences in reading skills are influenced by environmental or cultural/societal causes. Differential response theory is based on the assumption that teacher behavior towards students is influenced by both the behavior of a particular student, as well as the teacher’s assumptions about what that student usually does or is likely to do. This hypothesis would suggest that teachers may hold higher expectations for females that turn into self-fulfilling prophecies (Bank, Biddle, & Good, 1980). Leinhardt, Seewald, and Engel (1979) found support for this hypothesis by coding teacher interactions with second-grade students during reading and mathematics instruction. Teachers made more academic contacts and spent more instructional time with girls during reading instruction and with boys during math instruction. Although there were no significant differences in initial achievement scores in either math or reading, differences favoring females were found in students’ end of the year reading achievement scores. Interest and/or motivation may also contribute to reading deficits in males (Brozo, 2002; Millard, 1997). Although boys prefer reading nonfiction and informational material that provides facts over fictional materials (Coles & Hall, 2001; Herz & Gallo, 1996), fictional reading is typically used during elementary school reading instruction (Brozo, 2002; Paris & Turner, 1994). Nonfiction reading in school is often limited to textbooks, which have been noted as a primary cause for dissatisfaction in reading among both boys and girls (Clary, 2001). This lack of motivation for reading may generalize to reading outside of school and explain why males are less likely to read for pleasure (Libsch & Breslow, 1996; Nippold, Duthie, & Larson, 2005). This is particularly alarming because reading achievement has been found to be a function of the amount of time and energy students invest in reading both in and out of school (Cipielewski & Stanovich, 1992).
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Male Deficits in Early Reading Skills Research supports that proficient reading is the result of a hierarchical process of skill development (Adams, 1990; Denton & West, 2002; Johnston, Anderson, & Holligan, 1996; National Institute of Child Health and Human Development, 2000; Pugh et al., 2001; Shanahan, 2005). After screening over 10,000 studies, the National Reading Panel published a report in 2000 citing findings in five sequential skills that may be necessary, but not sufficient, for developing strong readers including: (a) phonemic awareness, (b) phonics, (c) fluency, (d) vocabulary, and (e) comprehension (Shanahan, 2005). Orthographic processing, an area not addressed by the National Reading Panel, has been added to this discussion because there is evidence that it influences and predicts later reading achievement (Badian, 2001; Denton & West, 2002; Rego, 2006; West, Denton, & Germino-Hausken, 2000). Orthographic processing refers to the “process of identifying letters in isolation, in individual words, and in running text” (Schumm, 2006, p. 528). Researchers examining gender differences in early reading skill development have documented a female advantage. Gates (1961) measured the performance of 13,114 students in Grades 2– 8 on the three Gates reading survey tests: Speed of Reading, Reading Vocabulary, and Level of Comprehension. The seven grades were analyzed on each of the three reading measures, yielding a total of 21 comparisons. Gates found females performed significantly better on 18 of the 21 comparisons. Differences seemed to be the greatest in vocabulary, followed by speed, and then comprehension. Across grade levels, differences in fluency and vocabulary measures showed a slight increase as grade level increased, and differences in comprehension remained more constant as grade level increased. Chatterji (2006) conducted a study of 2,296 kindergarten (K) and first-grade students taken from the Early Childhood Longitudinal Study (ECLS) and found that males
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performed below females on tests of print familiarity, letter recognition, beginning and ending sounds, rhyming sounds, word recognition, receptive vocabulary, listening comprehension, and comprehension of words in context. The size of the male deficit increased from 0.17 SD units below females at K entry to 0.31 SD units below females at the end of first grade. These results provide evidence that gender differences are present when children enter school and become greater by the first grade. Camarata and Woodcock (2006) compared the performance of 1,102 females and 885 males, ages preschool through adulthood, on selected measures of cognitive ability and achievement using the Woodcock Johnson Psycho-Educational Battery—Revised (Woodcock & Johnson, 1989). They found that males scored significantly lower on tests measuring reading and writing fluency. These differences increased through adolescence and dropped off in young adulthood. Research findings suggest that, relative to girls, boys have weaker reading skill development when they enter K, and these differences either remain constant or increase during elementary school. The 2000 National Reading Panel report identified several prereading skills that are thought to be necessary for reading skill development. Because of the hierarchical nature of early reading skill development, identifying both when deficits emerge (student grade level) and the specific early reading skills that boys have more difficulty mastering has clear applied implications (e.g., alter procedures designed to enhance males’ specific skills at specific grade levels). However, researchers investigating early literacy skills have measured only one critical prereading skill identified by the 2000 NRP report (i.e., Camarata & Woodcock [2006] and Gates [1961] measured reading speed or fluency) or have tested for differences on broad reading scores as opposed to specific prereading skills across only a couple of grade levels (Chatterji, 2006). The current study extended this research by comparing boys’ and girls’ performance across four of the six early reading skills

Gender Differences in Early Literacy

thought critical in the development of reading (i.e., phonemic awareness, phonics, orthography, and fluency). As the analyses were conducted using Dynamic Indicators of Basic Early Literacy Skills (DIBELS) measures, which were collected to establish response to intervention benchmark scores prior to implementing response to intervention remediation procedures, the current study is the first investigation of gender differences in reading skill development using DIBELS data. Method Participants Participants included 1,218 general and special education students in Grades K–5 (606 [49.8%] males, and 612 females, 50.2%) from three elementary schools in a rural southeastern U.S. school district. School 1 enrolled students in Grades K–5 and accounted for 40% of the females and 35% of the males in the total sample. School 1 utilized the Scott Foresman (2004) reading curriculum. School 2 served students in Grades K– 4 and used Wilson Reading Fundations (2002) as their core reading curriculum. School 2 accounted for 25% of the females and 27% of the males in the total sample. School 3 was an intermediate school that served students in Grades 3–5. School 3 utilized the Scott Foresman (2004) reading curriculum and accounted for 35% of the females and 38% of the males in the total sample. All schools followed the same remedial procedures by using student assistance teams to implement prereferral interventions before special education evaluations were considered. Specifically, any concerned party (usually teacher or parent) could refer a struggling student to the student assistance team. Interventions in the suspected area of disability were developed by the team and implemented for a period of 6 weeks before a special education evaluation could be initiated. Table 1 displays the distribution of students by grade and gender. The data for the total population is a best-estimate, as enrollment figures varied throughout the school year

as some students entered and others left. Within the school year, specific DIBELS measures were administered in the fall, winter, and spring, with some measures administered all three times, others administered twice, and one, Letter Naming Fluency, administered once (i.e., fall, to the first-grade sample). When a measure was administered more than one time and a student was assessed at least once with that measure, a multiple imputations procedure (five iterations) was used to replace missing data. As some educators were able to collect more DIBELS measures across more students than others, our numbers of participants in each grade level varies. Reasons for missing data include transiency, failure of examiners to record gender information, the exclusion of severely disabled students who lacked the ability to complete some or all DIBELS tasks (e.g., nonverbal students), absenteeism, spoiled tests, and scheduling conflicts or errors. Table 1 also provides a summary of the missing data by grade and the number of cases that the multiple imputations procedure was used to replace missing data (i.e., for each measure, cases where some scheduled measures were collected and others were not due to absenteeism, scheduling errors, and so on). Although researchers were not given permission to obtain data on disability or ethnicity, 95% of the students in the school district were White, 2.4% were Hispanic, 1.7% were African American, and 1% were Asian or Native American. Tables 2 and 3 provide a comparison of the current sample’s (combined boys and girls with replaced data) averages relative to the DIBELS averages for Grades K–3 reported in the DIBELS Technical Report 9 (Good, Wallin, Simmons, Kame’enui, & Kaminski, 2002). The DIBELS technical report only provides averages for Grades K–3. The current sample means appear commensurate with the standardization sample, with the exceptions of Nonsense Word Fluency in the spring of first grade and Oral Reading Fluency in the spring of second and third grade. At these points, the
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Table 1 Student Distribution by Grade and Measure
Total Enrolled Grade/Measure K-ISF K-LNF K-PSF K-NWF 1-LNF 1-PSF 1-NWF 1-ORF 2-ORF 3-ORF 4-ORF 5-ORF M 93 93 93 93 86 86 86 86 78 160 117 115 F 76 76 76 76 92 92 92 92 82 178 125 98 Missing Students M 14% 11% 15% 15% 7% 2% 2% 7% 4% 7% 7% 8% F 14% 12% 18% 18% 5% 2% 2% 3% 4% 7% 5% 7% Students w/ All Data M 77% 76% 77% 77% 93% 80% 80% 83% 87% 81% 77% 83% F 74% 72% 78% 78% 91% 87% 87% 91% 83% 78% 85% 89% Students w/ Replaced Data M 9% 13% 8% 8% NRP 17% 17% 10% 9% 12% 15% 9% F 12% 16% 4% 4% NRP 11% 11% 5% 13% 15% 10% 4%

Final Sample M 86% 89% 85% 85% 93% 97% 97% 93% 96% 93% 92% 92% F 86% 88% 82% 82% 91% 98% 98% 96% 96% 93% 95% 93%

Note. Percentages represent the percentage of Total Enrolled. Missing Students students who could not be included because of unrecorded gender or severe disability; Students w/All Data students w/data collected for each benchmark; Students w/Replaced Data students w/missing data replaced with multiple imputations procedure; Final Sample Students w/all data plus students with replaced data; ISF Initial Sound Fluency; LNF Letter Naming Fluency; PSF Phoneme Segmentation Fluency; NWF Nonsense Word Fluency; ORF Oral Reading Fluency; NRP No Replacement Procedures; K Kindergarten; M male; F female.

standardization sample means are much higher. Measures The school system collected five standardized, individually administered, DIBELS rate measures. Initial Sound Fluency (ISF) is a phonemic awareness measure, which assesses the ability to recognize the initial sound in words orally and visually presented by the examiner. ISF scores reflect the number of sounds correctly identified in 1 min. Letter Naming Fluency (LNF) measures orthographic processing speed. Students are presented with a page of upper- and lowercase letters arranged randomly and are asked to name as many letters as possible. LNF scores indicate how many letters are correctly identified in 1 min. Phoneme Segmentation Fluency (PSF) is another measure of phonemic awareness. PSF measures a student’s ability to
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fluently segment words into individual phonemes. The examiner reads words aloud, and the student is required to orally produce the individual phonemes. Elliott, Lee, and Tollefson (2001) examined the technical adequacy of the ISF, LNF, and PSF measures and found inter-rater reliabilities of .89, .94, and .97; test–retest reliabilities of .74, .90, and .85; and equivalent forms reliabilities of .64, .80, and .85, respectively. Concurrent validity scores with the Woodcock-Johnson Achievement Battery—Revised (WJ-R) Broad Reading Score (Woodcock & Johnson, 1989) were .42, .63, and .44, respectively. Nonsense Word Fluency (NWF) is a phonics assessment whereby students are given printed nonsense words and required to correctly state each individual letter sound or the entire nonsense word. The NWF score reflects the number of letter sounds correctly produced in 1 min. The DIBELS Web site

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Table 2 Current Sample and DIBELS Normative Sample Means for K and First Grade
Benchmark Kindergarten Fall Winter Letter Naming Fluency Initial Sound Fluency Letter Naming Fluency Initial Sound Fluency Phoneme Segment Fluency Nonsense Word Fluency Letter Naming Fluency Phoneme Segment Fluency Nonsense Word Fluency 15.69 9.90 38.22 21.28 20.12 25.11 43.99 36.77 31.92 16.03 12.27 31.41 22.80 27.75 20.10 44.48 40.56 32.54 Measure Current Sample Normative Sample

Spring

First Grade Fall Letter Naming Fluency Phoneme Segment Fluency Nonsense Word Fluency Phoneme Segment Fluency Nonsense Word Fluency Oral Reading Fluency Phoneme Segment Fluency Nonsense Word Fluency Oral Reading Fluency
Dynamic Indicators of Basic Early Literacy Skills; K

Winter

Spring

46.08 35.74 33.90 46.84 52.28 41.73 48.25 56.39 56.80 kindergarten. 41.18 35.22 30.81 45.01 54.27 36.89 50.68 71.41 60.65

Note. DIBELS

reports that the 1-month, alternate form reliability for NWF is .83, and the predictive validity of NWF given in January of first grade with the WJ-R Broad Reading Cluster score is .66 (DIBELS Reliability Data, 2007). Oral Reading Fluency (ORF) is a measure of a student’s accurate aloud reading rates. Students are asked to read a graded passage out loud for 1 min as examiners score errors. ORF scores indicate the number of correct words read per minute. ORF is the DIBELS measure with the most psychometric support (Marston, 1989; Shaw & Shaw, 2002; Shinn, Good, Knutson, Tilly, & Collins, 1992). Procedures DIBELS probes were administered by a team of school psychologists, classroom

teachers, and graduate students in a local school psychology doctoral training program.
Training was provided to all administrators prior to testing. Assessments took place during the district’s scheduled fall, winter, and spring benchmark assessments throughout the 2005– 2006 school year. ISF was administered in the fall and winter of K. LNF was administered beginning in the fall of K and continued through the fall of first grade. The administration of both PSF and NWF began in the winter of K through the spring of first grade. ORF was administered from the winter of first grade through the spring of fifth grade. Assessments were conducted in a quiet area of the classroom or in the hallway outside the classroom. Each assessment was administered individually and took between 1 and 4 min to
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Table 3 Current Sample and DIBELS Normative Sample Means for Second and Third Grade
Benchmark Second Grade Fall Winter Spring Third Grade Fall Winter Spring
Note. DIBELS

Measure

Current Sample Mean

DIBELS Mean

Oral Reading Fluency Oral Reading Fluency Oral Reading Fluency

55.29 78.60 83.59

59.63 80.44 100.85

Oral Reading Fluency Oral Reading Fluency Oral Reading Fluency
Dynamic Indicators of Basic Early Literacy Skills.

75.56 90.85 98.38

87.69 100.95 118.53

plete. Student performance was then recorded into a database. Design and Data Analysis DIBELS scores served as the dependent variables. Absenteeism, transience, spoiled test, and scheduling errors caused some students to have data for some scheduled measures, but not others. In these cases, missing data were replaced using a multiple imputations replacement procedure. These data were combined with the cases for which all scheduled data were collected and analyzed (see Table 1). Because LNF was only administered in the fall to the first-grade sample, no replacement procedures were applied. An across subjects t test was used to test for significant difference across boys and girls on first-grade LNF data. All other analyses were conducted using a series of two-way repeated-measures analyses of variance (ANOVAs), one for each measure by grade level, with time of year (fall, winter, and spring) serving as the within-subjects variable and sex (boy or girl) serving as the between-subjects variable. Across all repeated measures analyses, Mauchly’s W indicated that assumptions regarding sphericity were met, but Greenhouse-Geisser corrections
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were still used to test for significant differences for time and time-by-gender interaction when a measure was collected three times per school year. When the measure was only administered two times per year, Phillai’s trace was used to test for significant difference. An alpha level of .05 was set for all analyses. Because previous research suggested that females would perform better, all tests were directional. Because each grade level was treated as a separate sample, and Grades 2–5 only received the ORF measure, no experiment-wise correction procedures were used for Grades 2–5. However, because hypotheses were directional, p values less than .10 were significant at the .05 alpha level (i.e., p value corrected for directional hypotheses). The K and first-grade samples were assessed with four DIBELS measures. Consequently, Bonferonni’s correction was used to control experiment-wise error rates (i.e., maintain an alpha of .05) and findings were considered significant at the p .025 level (i.e., .10/4 .025). When significant interactions were found, Bonferonni’s correction was used to test for all pairwise comparisons. Effect sizes were calculated for each significant difference using partial eta squared. The square root of

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Table 4 Male and Female Means, Standard Deviations, and N by Grade, Measure, and Time of Year
Fall G K K K K 1 1 1 1 2 3 4 5 Measure ISF LNF PSF NWF LNF PSF NWF ORF ORF ORF ORF ORF M(n) 80 83 79 79 80 84 84 80 75 149 109 106 F(n) 65 67 62 62 84 90 90 89 79 166 119 91 M 8.68 (6.65) 13.61 (12.40) — — 45.31 (14.92) 33.33 (16.21) 32.45 (21.31) — 53.26 (32.21) 72.51 (31.95) 84.47 (33.67) 105.24 (40.32) F 11.40 (7.87) 18.26 (15.27) — — 46.85 (14.39) 37.98 (16.11) 35.25 (18.31) — 57.21 (27.66) 78.30 (31.16) 96.70 (37.66) 109.66 (40.03) M 19.31 (10.74) 36.44 (14.70) 16.62 (13.25) 22.13 (14.98) — 44.63 (12.79) 52.63 (23.01) 37.49 (30.21) 76.77 (36.14) 87.54 (34.45) 97.94 (35.72) 111.21 (40.66) Winter F 23.72 (13.02) 40.43 (15.70) 24.57 (14.53) 28.90 (16.71) — 48.91 (143.12) 51.95 (17.32) 45.54 (31.12) 80.34 (33.01) 93.82 (33.88) 110.06 (36.74) 115.69 (41.22) M — 40.63 (14.81) 33.18 (16.45) 27.53 (14.06) — 47.32 (12.11) 55.89 (24.87) 51.09 (33.21) 81.23 (35.65) 95.45 (33.74) 105.34 (40.66) 119.80 (40.51) Spring F — 48.14 (16.82) 41.34 (15.05) 37.51 (19.75) — 49.12 (10.86) 56.86 (24.45) 61.93 (34.34) 85.84 933.12) 101.02 (31.49) 120.10 (42.48) 120.25 (38.42)

Note. Standard deviations are denoted in parentheses. ISF Initial Sound Fluency; LNF Letter Naming Fluency; PSF Phoneme Segmentation Fluency; NWF Nonsense Word Fluency; ORF Oral Reading Fluency; G grade; K kindergarten; M male; F female.

partial eta squared was calculated and then compared to the qualitative categories defined by Cohen (1988): .2 small, .5 medium, and .8 large. Results Table 4 displays the mean and standard deviations for boys and girls across all grades and DIBELS measures after the multiple imputation procedure was used to replace missing data. Figures 1–5 display the data for each measure. Although these figures include data across multiple grade levels, readers are cau-

tioned against interpreting these comparisons as they are across different cohorts. Table 5 summarizes the main effects from our statistical analyses examining differences between girls and boys and effect size analyses when significant differences were found. No statistical tests were run across grade levels. Initial Sound Fluency Figure 1 displays the average fall and winter ISF data for male and female K students. Boys and girls entered K with average ISF scores of 8.68 and 11.40, respectively.
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Table 5 Mean Gender Difference and Effect Size for Each Measure Taken at Each Grade Level
Initial Sound Letter Naming Phoneme Nonsense Fluency Fluencya Segmentation Fluency Word Fluency Kindergarten M Difference Effect Size First Grade M Difference Second Grade M Difference Third Grade M Difference Effect Size Fourth Grade M Difference Effect Size Fifth Grade M Difference Oral Reading Fluency

3.57* 0.21

5.38* 0.20 1.54a

8.06* 0.30 3.58

8.37* 0.26 1.03 9.44 4.05 5.88

13.04* 0.18 3.12

Note. All significant differences indicate a female advantage. An alpha level of .05 was used for all analyses with Bonferroni’s correction used to control experiment-wise error rates. * Denotes a significant gender difference. a Letter Naming Fluency was administered only once in first grade. Therefore, an independent t-test was run for this analysis.

Both groups showed improvement when assessed again in the winter (an increase of 10.63 for boys and 12.32 for girls). A two-way repeated-measures ANOVA revealed a significant main effect for time, F(1,143) 172.25, p .001. The main effect for time was moderate, ES 0.74. The main effect for gender was significant, F(1,143) 6.65, p .011, and the effect size was small, ES 0.21. The Time Gender interaction was not sig0.941, p .334. These nificant F(1,143) results show that females score higher than males (M difference 3.57), but the difference was small (ES 0.21, see Table 5), and that both groups made significant gains from fall to winter, but that these gains did not differ significantly. Letter Naming Fluency Figure 2 displays the average LNF data for male and female K and first-grade students.
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Entering K, boys’ and girls’ average LNF scores were 13.61 and 18.26, respectively. Both groups showed improvement when assessed again in the winter (an increase of 22.83 for boys and 22.17 for girls) and smaller improvements when assessed in the spring (an increase of 4.19 for boys and 7.71 for girls). A two-way repeated-measures ANOVA for the K sample revealed a significant main effect for gender, F(1,148) 6.13, p .014, and a significant main effect for time, F(2,147) 475.26, p .001. The effect size was small for gender (ES 0.20) and large for time (ES 0.87). The Time Gender interaction was not significant, F(2,147) 1.85, p .162. In the first-grade sample, students were only assessed during the fall benchmark. Average LNF scores for girls and boys were 46.85 and 45.31, respectively. An independent t test of the fall LNF scores revealed a nonsignificant difference across boys and girls, t(162) 0.651, p .258.

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These results show that girls performed significantly better than boys on LNF scores in the K sample (M difference 5.38), but these differences were small (ES 0.20, see Table 5). During K, both groups made significant gains in LNF, but these gains did not differ significantly. In the first-grade sample no significant differences were found during the fall, the last time LNF data were collected. Phoneme Segmentation Fluency Figure 3 displays the average PSF data for male and female K and first-grade students. In the winter of K, boys’ and girls’ average PSF scores were 16.62 and 24.57, respectively. Both boys and girls showed similar levels of improvement when assessed again in the spring (an increase of 16.56 for boys and 16.77 for girls). A two-way repeatedmeasures ANOVA revealed a significant main effect for gender, F(1,139) 13.19, p .001, and for time, F(1,139) 191.10, p .001. The effect size was small for gender (ES 0.30) and moderate for time (ES 0.76). The Time Gender interaction was not significant, F(1,139) 0.007, p .935. Boys and girls entered first grade with PSF scores of 33.33 and 37.98, respectively.

Both groups made improvements when assessed again in the winter (an increase of 11.30 for boys and 10.93 for girls) and smaller improvements in the spring (an increase of 2.69 for boys and 0.21 for girls). ANOVA revealed a significant, F(2,171) 72.71, p .001, and moderate (ES 0.55) main effect for time. Neither the main effect for gender, F(1,172) 4.79, p .030, nor the Time Gender interaction, F(2,171) 0.924, p .389, was significant. These results show that girls performed significantly better than boys on PSF scores in the K sample (M difference 8.06), but not the first-grade sample. During K and first grade, both groups made significant PSF gains, and within each grade these gains did not differ significantly. Nonsense Word Fluency Figure 4 displays the average NWF data for male and female K and first-grade students. In winter, K boys and girls average NWF scores were 22.13 and 28.90, respectively, and both groups improved when assessed again in the spring (an increase of 5.40 for boys and 8.61 for girls). A two-way repeated-measures ANOVA for the K sample revealed a significant main effect for

Figure 1. Average Initial Sound Fluency scores for males and females at fall (FK) and winter (WK) benchmarks in kindergarten.
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gender, F(1,139) 10.28, p .002, and for time, F(1,139) 59.72, p .001. The effect size was small for gender (ES .26) and moderate for time (ES .55). The interaction of Time Gender was not significant, F(1,139) 3.13, p .079. Boys and girls entered first grade with average NWF scores of 32.45 and 35.25, respectively. Both groups improved when assessed again in the winter (an increase of 20.18 for boys and 16.70 for girls) and less so in the spring (an increase of 3.26 for boys and 4.19 for girls). For the first-grade sample, ANOVA revealed a significant, F(2,171) 153.99, p .001, and moderate (ES 0.69) main effect for time. The main effect for gender was not significant, F(1,172) .128, p .721, and the Time Gender interaction was not significant, F(2,171) .806, p .446. During K and first grade, boys and girls from both samples made significant gains in NWF scores, and within each grade growth rates did not differ. In the K sample, girls performed significantly better than boys on NWF, but these differences were small (M difference 8.37). In the first-grade sample,

these differences were smaller (M difference 1.03) and not significant. Oral Reading Fluency Figure 5 displays the average ORF data for male and female first- through fifth-grade students, and Table 5 provides mean gender difference data and effect size calculations for significant differences. A series of two-way repeated-measures ANOVAs revealed a significant main effect for time for Grade 1, F(1,167) 300.20, p .001; Grade 2, F(2,151) 339.92, p .001; Grade 3, 481.59, p .001; Grade 4, F(2,312) F(2,225) 219.09, p .001; and Grade 5, F(2, 194) 87.32, p .001. The effect sizes were large for Grades 1, 2, and 3 (ES 0.80, 0.83, and 0.81, respectively) and moderate for Grades 4 and 5 (ES 0.70 and 0.56, respectively). The main effect of gender was not significant for Grade 1, F(1,167) 3.72, p .056; Grade 2, F(1,152) 0.615, p .434; or Grade 3, F(1,313) 2.70, p .101. In addition, the Time Gender interaction was not

Figure 2. Average Letter Naming Fluency scores for males and females during fall (FK), winter (WK), and spring (SK) benchmarks in kindergarten and fall (F1) benchmarks in first grade.
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significant for Grade 1, F(1,167) 2.59, p .109; Grade 2, F(2, 151) 0.10, p .883; or Grade 3, F(2,312) 0.119, p .880. With the fourth-grade sample the main effect for gender was significant, F(1,226) 7.12, p .008, but very small (ES .18), and the Time Gender interaction was not significant, F(2,225) 1.67, p .176. For the fifth-grade sample, the main effect for gender was not significant, F(1,195) 0.31, p .581. However, the Time Gender interaction was significant, F(2,194) 2.94, p .055. The effect size for this interaction was very small (ES .12) and occurred from the winter to spring when boys had larger growth rates than girls (8.593 increase for boys and 4.564 increase for girls, see Figure 5). Each post hoc comparison showed that both boys and girls made significant (all ps .001) gains across all three measures (fall, winter, and spring). These results showed no significant gender differences in ORF scores in Grades 1–3. In the fourth-grade sample, girls’ ORF scores were significantly higher than boys’ (M difference 13.04), but the difference was very small. For each grade between 1 and 4, both girls and boys showed significant increases in

ORF scores, but neither showed significantly greater growth rates.
Fifth-grade results showed no significant gender differences in ORF. In the fifth-grade sample, both boys and girls made significant gains throughout the year, with boys showing greater gains than girls from winter to spring. The last time ORF was administered (spring, Grade 5) differences between girls’ and boys’ ORF scores was 1.0 word correct per minute. Discussion Research on gender differences in reading skill development was extended by analyzing repeated measures of multiple preliteracy skills across Grades K–5. Because girls scored significantly higher on all four K preliteracy skills, these findings support previous researchers who found evidence that girls enter school with stronger literacy skills. However, our results failed to support findings that these differences grow larger as students’ progress through school (e.g., Camarata & Woodcock, 2006; Chatterji, 2006). Rather than growing larger, significant gender
differ-

Figure 3. Average Phoneme Segmentation Fluency scores for males and females during winter (WK) and spring (SK) benchmarks in kindergarten and fall (F1), winter (W1), and spring (S1) benchmarks in first grade.
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ences on LNF, PSF, and NWF in the K sample were not significant in the first-grade sample. Also, analysis of ORF data from Grades 1–5 contradicts earlier research (e.g., Gates, 1961; Klecker, 2006). Differences in ORF scores were not significant in the first-, second-, and third-grade samples. Although a small (ES 0.176) but significant female advantage was found in the fourth-grade sample, in the fifthgrade sample these differences were not significant. Fifth-grade boys showed greater increases in ORF from winter to spring, and by the end of the academic year, boys’ and girls’ ORF scores were almost identical. Thus, analyses of the K and Grade 1 measures and Grades 1–5 ORF measures failed to support previous researchers who found that differences in early literacy skill development grow larger over time. There are several theoretical implications associated with the current findings. Significant differences favoring females in K on early literacy measures may lend support to physiological-maturational theories suggesting that innate differences between males and females cause differences in early literacy skill development (e.g., Aaron, 1982; Das et al.,

1979; Geschwind & Behan, 1982; Naglieri & Rojahn, 2001; Naour, 2001; Witelson, 1976). Because the significant differences found in the current study were not found at every grade level, the current study does not support theories suggesting that the school environment, curricula, teachers, or other educational factors favoring girls contribute to sex differences in reading skill development. However, because significant differences in ORF scores emerged in the fourth-grade samples, the current results provide some support for culturalsocietal theories contributing to gender differences in reading skill development. These findings suggest that researchers should consider investigating differential response to school-based instruction and/or differential interest/motivation to assignments at these grade levels (Bank et al., 1980; Brozo, 2002; Clary, 2001; Coles & Hall, 2001; Herz & Gallo, 1996; Leinhardt et al., 1979; Millard, 1997). For example, researchers should determine if gender-by-assignment interactions occur in these grades. Perhaps in fourth grade, males begin to develop stronger interests in reading material (e.g., factual information) that are different from the type of reading (e.g., stories

Figure 4. Average Nonsense Word Fluency scores for males and females during winter (WK) and spring (SK) benchmarks in kindergarten and fall (F1), winter (W1), and spring (S1) benchmarks in first grade.
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and fiction) they encounter during early literacy instruction (Brozo, 2002). Limitations and Directions for Future Research Perhaps the greatest limitation of the current study was the use of a cross-sectional design. This design was chosen because longitudinal data were not available at the time of the study and would have been compromised by remediation procedures that were applied (i.e., response to intervention remediation procedures) in the subsequent years to remedy poor performing students’ skill deficits. Regardless, this design did not control for variables that may affect differences in reading performance across cohorts (e.g., differences in curriculum, teachers). Because different findings across grade levels may have been caused by developmental differences or other cohort effects, readers are cautioned against making cross-grade comparisons. Sampling procedures compound these threats to internal

validity. Although the school district planned and attempted to obtain scores on all students, this was the district’s initial application of DIBELS assessments, and difficulties in obtaining scores for each student resulted in variable numbers of students being eliminated from our sample and variable numbers of cases requiring multiple imputation procedures to replace missing data. Future researchers could address these concerns by using longitudinal designs. Relative to previous national and international samples, the current sample was small and taken from a single geographic location. Thus, the current results cannot be generalized to other populations. As current results contradicted earlier findings, which suggested that reading skill differences grow larger as reading skills develop (e.g., Camarata & Woodcock, 2006; Chatterji, 2006; Gates, 1961; Klecker, 2006), future researchers should address external validity limitations using larger, nationally representative samples of students.

Figure 5. Average Oral Reading Fluency scores for males and females during winter (W1) and spring (S1) benchmarks in first grade and fall, winter, and spring benchmarks in Grades 2–5.
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Comparisons of scores were limited by the DIBELS measures and administration schedules. Many students may begin receiving early literacy instruction in preschool, but no data were collected for this sample. Although the current study enhanced the research base by assessing more specific early literacy skill measures, vocabulary, and comprehension, two of the five critical areas of reading identified by the National Reading Panel (National Institute of Child Health and Human Development, 2000) were not analyzed. Thus, researchers should consider conducting additional studies assessing reading skill development in preschool and secondary school students. Many researchers, who found evidence that across gender reading skill differences grow larger over time, used more comprehensive measures of reading skill development. This is the first study to use DIBELS measures. Given that each of these measures is a rate measure, future researchers should determine if the different findings across studies are attributed to different measurement procedures (e.g., relative to girls, do boys score higher on rates measures than accuracy measures?). Rate measures may be more sensitive to changes in skill development (Skinner, Neddenriep, Bradley-Klug, & Ziemann, 2002). Although DIBELS measures may be more sensitive, because each measure only assesses one skill, each measure may be restrained by ceiling effects (Paris, 2005). Howell and Nolet (2000) reported average ORF scores for fifth-grade students exceeded 140 words correct per minute, suggesting that ceiling effects did not restrain the current findings. However, researchers studying ORF found that the validity of ORF begins to decrease in fifth or sixth grade, which may be at least partially caused by ceiling effects (Hintze & Shapiro, 1997; Jenkins & Jewell, 1993). Therefore, researchers should determine if ceiling effects contributed to our finding of boys’ superior ORF growth rates in the fifthgrade sample (i.e., girls’ growth rates were depressed by ceiling effects). Similarly, in the current study, girls’ performance increases on
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K and first-grade measures may have been constrained by ceiling effects, confounding the current results. Summary and Applied Implications Some have suggested that previous studies on gender differences in reading may create self-fulfilling prophecies that exacerbate these differences by causing them to increase (Bank et al., 1980; Leinhardt et al., 1979). The current results, which suggest that significant gender differences in prereading skills found in earlier grade levels were not significant in later grade levels, may mitigate these selffulfilling prophecy effects and prevent educators from assuming that boys will never be as strong as girls with respect to reading skills. Thus, although the current results suggest that K teachers should be aware of male deficits in early reading skill development, they also suggest that these differences are small, temporary, and surmountable. With respect to ORF, the current results suggest that educators may want to implement procedures in fourth grade, and perhaps as early as third grade, that enhance reading fluency in males. Given that fluency may be affected by the amount one reads, educators may want to use procedures such as altering reading material, providing choices on what is read, and providing more reinforcement for reading to enhance boys’ motivation to read during these grades (Daly, Chafouleas, & Skinner, 2005; Skinner, Logan, Robinson, & Robinson, 1997). However, before such applied recommendations can be made, longitudinal experiments are needed where these procedures are manipulated and learning rates are assessed across males and females. Researchers in the future should determine if ceiling effects account for our findings that contradict many large-scale studies showing that reading skill differences between boys and girls grow larger over time. For example, researchers could use longitudinal designs with more frequent repeated measures of other reading skills in more advanced readers (e.g., vocabulary and comprehension in Grades

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5–12) to determine if a similar pattern (i.e., females being stronger but this difference decreasing over time) emerges with these reading skills. If this is the case, it may be that females who are constrained by ceiling effects on currently used measures are actually improving their skills over males on other reading skills that are not being assessed. Thus, while boys may be catching up on the measured skills (e.g., the measure that females show small increases is from ceiling effects), they may be falling behind on nonmeasured skills. The possibility that ceiling effects influenced the current findings may provide important information for educators who use DIBELS to make educational decisions based on students’ growth rates. Specifically, if educators know when a specific measure begins to become constrained, they can apply and/or develop other measures to assess students’ growth on subsequent reading skills. For example, a better understanding of when LNF becomes constrained by ceiling effects may allow educators to decide when to begin using PSF to measure reading skill development. Similarly, if constrained ORF scores caused girls to cease increasing their ORF scores relative to boys, then researchers may need to develop alternative DIBELS measures that are more sensitive to reading skill growth for more advanced readers (e.g., reading comprehension rates, see Skinner et al., 2002).

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Date Received: January 22, 2009 Date Accepted: January 30, 2010 Action Editor: James DiPerna

Jaime L. Below is Assistant Professor of Pediatrics in the Division of Developmental Medicine at Monroe Carell Jr. Children’s Hospital at Vanderbilt. Her research interests include improving the effectiveness of behavioral and academic interventions for children with developmental disabilities, learning disabilities, and social/emotional difficulties. She received her PhD from The University of Tennessee in 2008. Christopher H. Skinner is Professor and Coordinator of School Psychology Programs at The University of Tennessee. His research interests include prevention and remediation of academic and social problems within educational settings, single-subject design research, learning theories, and applied behavioral analysis. He received his PhD from Lehigh University in 1989. Jamie Y. Fearrington is Assistant Professor in the Department of Psychology at Appalachian State University. Her research interests include identifying procedures to enhance student learning and prevention of academic failure. She received her PhD in School Psychology from the University of Tennessee in 2004. Christy A. Sorrell is a licensed psychologist who provides psychoeducational services to area rural school systems. She currently is the owner of APPLE Psychological Consultants, LLC, a behavioral health agency serving the Maryville, Tennessee, area. Her research interests include supervision, rural psychology, and system issues. She received her PhD from the University of Tennessee in 2003.

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