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UJRC0256-85432150-2641Journal of Research in Childhood Education, Vol. 24, No. 2, February 2010: pp. 0–0Journal of Research in Childhood Education The Effects of Age at Kindergarten Entry on the Reading Proficiency of African American

and European American Students Age at Entry and Reading ProficiencyEaston-Brooks and Brown Donald Easton-Brooks

University of North Texas, Denton, Texas

Amber Brown University of Texas at Arlington, Arlington, Texas

European American students are more likely delayed entrance in kindergarten than African American students. This study examined whether age at kindergarten entry influences the reading proficiency skills of African American and European American students at the start of kindergarten, at the end of first grade, and at the end of third grade. Using a sample of 1,320 African Americans and 4,399 European American public school students from the Early Childhood Longitudinal Study- K-5, the study found that age at kindergarten entrance had a significant effect on the reading proficiency at the start of kindergarten and by the end of third grade. In addition, the study found that when entering kindergarten when age eligible, the reading proficiency skills of African American and European American students were not significantly different and neither was the reading proficiency growth between kindergarten and third grade.

Keywords: achievement gap, age entrance, ECLS-K, kindergarten achievement, reading proficiency, school readiness

Studies (Datar, 2003; Malone, West, Denton, & Park, 2006; West, Denton, & Germino-Hausken, 2000) have shown that a disparity exists in the age of kindergarten entry between minority and non- minority children. These findings propose that European American students are more likely than their minority counterparts to enter kindergarten a year after they are eligible to start kindergarten. Studies (Crone & Whitehurst, 1999; Datar, 2006; Graue & DiPerna, 2000; West, Denton, & Germino-Hausken, 2000) have demonstrated that older students perform academically higher than younger children. Given these findings, we have come to expect the academic skills of older kin- dergartners to be greater than those of younger kindergartners. Although many factors can contrib- ute to the difference in the academic skills between minority and nonminority students, age can play a critical role when assessing the difference in the academic skills between the two groups.

Submitted November 11, 2008; accepted April 15, 2009. Address correspondence to Donald Easton-Brooks, Assistant Professor, Department of Teacher Education

and Administration, University of North Texas, 115 Union Circle #310740, Denton, TX 76203-5017. E-mail: donald. [email protected]

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Further, Diamond, Reagan, and Bandyk (2000) found that though African American parents are more concerned than European American parents about whether their child is ready for kindergarten, African American parents are less likely than European American parents to delay their child’s entrance into kindergarten. However, research has yet to demonstrate if age difference, upon kindergarten entry, between African American and European American students influences the academic outcomes of students in early elementary grades. Based on this limited investigation, the purpose of the current study was to examine whether the age at which students enter kindergarten affects the reading proficiency difference for African American and European American students from kindergarten through third grade.

Although research (Good & Nichols, 2001; Nagelkerke, 1991) shows a gap in the reading skills of Spanish speaking and European American students, the current study did not include ethnicity minority groups in which a second language might play a significant role in their early reading development. The rationale is that speaking a language other than English at home can have varying effects on children’s English reading proficiency skills. In addition, students from families for whom English may not be their first language may benefit from additional educa- tional services not associated with African American or European American students.

LITERATURE REVIEW

Despite continued opposition from the National Association for the Education of Young Children (NAEYC; 2001), delaying a child’s entry into kindergarten continues to be a common practice, affecting many early elementary-age students. Over a 10-year span, Brent, May, and Kundson (1996) found that the percentage of students who entered school later than their age of eligibility increased nearly 10%. More recently, West, Meek, and Hurst (2000) reported that the number of students who enter school later than when eligible is increasing at the rate of nearly 9% per year.

A number of factors influence whether a child’s entrance into kindergarten is delayed. Some parents delay their children’s entrance into kindergarten because they feel their children are not yet ready to participate in, and benefit from, the rigors of formal schooling (Diamond et al., 2000; Marshall, 2003; Shepard & Smith, 1986). Among their concerns, parents cite social/emotional development, cognitive skills, academic expectations, and reading requirements as reasons why they delay their children’s kindergarten entrance (Hatcher, 2005). Economics also plays a role in the age at which children enter kindergarten, with children from higher income households start- ing kindergarten later (Brent et al., 1996; Meisels, 1992). Gender is another factor that contributes to delayed kindergarten entrance. Boys are more likely to be delayed entrance into kindergarten than girls (Bellisimo, Sacks, & Mergendoller, 1995; Malone et al., 2006; March, 2005). Finally, ethnicity is a contributing factor to delayed kindergarten entrance. In some cases, European American children were twice as likely as African American children to have their entrance into kindergarten delayed (Malone et al., 2006; West, Meek, & Hurst, 2000).

Race and Delayed Kindergarten Entrance

The role of race and kindergarten entrance is primarily connected to parents’ perceptions of school readiness and economic factors. Diamond et al. (2000) found that though African American and

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Spanish-speaking parents were significantly more likely to express concerns about school readiness, European Americans were more likely to delay their children’s kindergarten entrance. Economics can contribute to this decision. The income and wealth of minority parents, on average, is lower than that of nonminority families (Easton-Brooks & Davis, 2007). Access to higher income and wealth often provides nonminority parents a better opportunity to function as a one- parent-income family. The U.S. Department of Labor (2008) reported that 78% of African Ameri- cans mothers work outside of the home, whereas 70% of European American mothers work out- side of the home. This advantage allows nonminority parents more opportunities to stay home with their children, rather than paying for preschool (McAllister, Thomas, Wilson, & Green, 2009). On the other hand, minority parents are more likely than nonminority parents to send their children to preschool. In turn, the cost of child care or other economic stressors may force minority parents to enroll their children in public kindergarten sooner, even if a child is not ready for kindergarten.

Academic Outcomes of Delayed Kindergarten Entry

The effects of delayed kindergarten entry on academic achievement are mixed. Research on delayed kindergarten entry has so far failed to provide a clear picture of its short- and long-term effects. Some studies have found academic benefits for children who are delayed kindergarten entry, whereas others have found no benefits.

Those studies (Crosser, 1991; Kurdek & Sinclair, 2001; Malone et al., 2006; Oshima & Domalski, 2006; West, Meek, & Hurst, 2000) showing academic benefits for delayed kindergarten entry note that delayed students perform better academically. Delayed students showed a better understanding of the connection between letter sounds at the ends of words and understanding words in context (Malone et al., 2006) and score better on tests of academic achievement in math, reading, and general knowledge (West, Meek, & Hurst, 2000).

Other findings (Kurdek & Sinclair, 2001; Oshima & Domalski, 2006) show that, over time, delayed students continued to outperform students who entered school when eligible. In a study using the National Educational Longitudinal Survey (NELS), Lincove and Painter (2006) examined the long-term effects of age at kindergarten entry on a group of students who entered kindergarten in the late 1970s and early 1980s. Regression analysis confirmed that young children are more likely to repeat early grades, which does have negative academic conse- quences. Further, Oshima and Domalski (2006) found that race accounted for the highest percent of the variance in reading outcomes, yet their results showed that after controlling for ethnicity, age was a better predictor of achievement than gender at all grade levels.

In contrast, others (Crone & Whitehurst, 1999; March, 2005; Morrison, Griffith, & Alberts, 1997; Narahara, 1998) found no significant difference in academic outcomes between delayed students and students who entered school when eligible. Using data from the National Household Survey of 1993, West, Meek, and Hurst (2000) found that although children with delayed kinder- garten entry were more successful than their younger peers in kindergarten and first grade, these differences were of little practical significance and usually disappeared by third grade. Crone and Whitehurst (1999) found that any achievement gap between delayed students and students who entered school when eligible diminished as students moved through school.

In addition to academic effects, researchers have observed other effects of kindergarten delay within the first 3 years of elementary school, including children having a lower likelihood of receiving negative feedback from teachers about their academic performance or conduct in class

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(Cromwell, 1998; West, Meek, & Hurst, 2000). Some evidence also shows that first- through third graders who were delayed as kindergartners require greater use of special education services than their nondelayed and nonretained classmates (Graue & DiPerna, 2000; May, Kundert, & Brent, 1995).

Long-Term Effects of Delayed Kindergarten Entry

Byrd, Weitzman, and Auinger (1997) found that adolescents whose school entry had been delayed exhibited more behavioral problems than their classmates. In light of evidence that delayed youths use special education at a higher rate than classmates, there is a great deal of speculation that many individuals who were delayed as kindergartners may have had special needs that were misdiagnosed as immaturity and that should have been treated by some form of direct intervention other than delayed entry (Graue & DiPerna, 2000; May et al., 1995). Additionally, Lincove and Painter (2006) found that when controlling for grade retention and interactions with age at entry, starting school young does have modest advantages in terms of lifetime accumulation of human capital. Lincove and Painter also found that when using semi- parametric matching techniques to compare students who were delayed kindergarten entry to similar students who were not, the results showed that younger students performed at least as well as similar older students throughout high school and college.

Measuring Reading Skills

Many studies use standardized test scores in defining the achievement gap between African American and European American students. However, No Child Left Behind (NCLB; 2001) proposes a reduction in the gap in proficiency, not reduction of a test score gap. Although reading achievement is often interpreted as reading proficiency, there is a philosophical difference in the two concepts. Proficiency is theorized as promotion of the developmental learning process, whereas achievement tests focus on product testing (Hager & Gable, 1993; Kramsch, 1986). Further, national data sets, such as the Early Childhood Longitudinal Study-Kindergarten through Grade 5 (ECLS-K-5), make distinct differences between proficiency skills and achievement test scores. The ECLS-K suggested that though achievement tests measure students’ overall performance, proficiency measures specific skills within content areas of achievement tests.

Limited findings have been presented on the difference in proficiency skills between African American and European American students. On the other hand, the literature is replete with findings of test score gaps between African American and European American students, histori- cally (Brooks-Gunn, Rouse, & McLanahan, 2007; Fryer & Levitt, 2004; Hedges & Nowell, 1998; Jencks & Phillips, 1998; Madhabi, 2006; Rathbun, West, & Hausken, 2004).

Three recent studies (Denton & West, 2002; Douglas & Montiel, 2008; Rathbun et al., 2004) examined the proficiency skills of students using the ECLS-K data. Denton and West (2002) found that at the start of kindergarten, a lower percent of African American students were proficient in letter recognition, beginning sounds of words, ending sounds of word, and sight words than European American students. Nevertheless, by the end of first grade, almost all the African American and European American were proficient in these areas.

Douglas and Montiel (2008) found that children who live in poverty start kindergarten with an average reading proficiency 5 points lower than children not in poverty. The achievement gap

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widens through fifth grade, with children in poverty roughly 20 points lower. The findings showed that it takes children who live in poverty 3 months to reach the level of children not in poverty. The research found that 11% of African American students live in families at or above the poverty level, compared to 67% of the European American students.

Rathbun et al. (2004) also found that, by the end of first grade, a lower percentage of African American students than European American students were proficient in recognizing ending sounds of words, sight words, words in context, and the literal meaning. By the end of third grade, almost all of the African American and European American students were proficient in ending sounds of words and sight words. Although 90% of African Americans were proficient in literal inference, 97% of European Americans were proficient in this area. Additionally, 18% more European Americans were proficient in deriving meaning from a passage than were African Americans by third grade. Interestingly, the researchers showed that by the end of first grade, females performed slightly higher than males, and that trend continued through third grade.

The findings of the studies presented by Denton and West (2002) and Rathbun et al. (2004) demonstrate significant differences in the reading proficiency skills between African American and European Americans. The limitation in these studies is that the researchers do not examine whether a difference in reading proficiency occurs if both groups of students entered kindergarten when eligible.

METHOD

It is important to notice that the current study assessed the reading proficiency skills of students, rather than reading achievement test scores. The purpose of this approach was to understand if there was a difference in reading skills of African American and European American students as they started kindergarten and as they moved on to third grade. Further, techniques were used to eliminate biases that could affect the findings presented in the current study. The current study focused on only public school students, students who were first-time kindergartners, students who did not have an Individualized Education Plan (IEP), and students who remained in the same school throughout their elementary school years.

Sample

Data used in the current study came from the ECLS-K-5, funded by the U.S. Department of Education. The complete data set included approximately 22,000 children entering kindergarten in 1998 through fifth grade in 2003. Surveyors collected data on students, their families, teachers, and school performance during the fall and spring of their kindergarten, and the spring of the first, third, and fifth grade years. For the current study, the data from the fall of kindergarten, spring of first grade, and the spring of third grade were more consistent for examining reading proficiency skills.

The studies by Denton and West (2002) and Rathbun et al. (2004) examined public school students but did not examine the effects of race or gender, or of the interaction effects of these variables on proficiency skills. As mentioned, the students selected for the current study did not attend private school at any of the assessment points, did not repeat kindergarten, were not in special education, and did not transfer schools between kindergarten and third grade. These

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variables were eliminated to avoid confounding effects on reading proficiency. This approach is based on the Value added model (VAM) technique of examining how valuable schools and teachers are for students’ academic achievement of (Ballou, Sanders, & Wright, 2004; Callender, 2004; McCaffrey, Lockwood, Koretz, Louis, & Hamilton, 2004; Sanders, Saxton, & Horn, 1997; Webster & Mendro, 1997). In the current study, VAM was used to understand how much of the change in student performance over time can be attributed to differences in teachers, not to the difference in schools. After omitting students who attend private school, transfer between schools, repeat grades, and had an IEP, the current study examined the reading proficiency skills of 1,320 African Americans and 4,399 European American students.

All statistical outputs were weighted, using primary sampling unit weights (PSUs) based on geographic areas consisting of counties or groups of counties. The weights were designed to generate national estimates from the ECLS-K data. The weights also adjusted for unequal selection probability associated with the sampled populations and adjusted for nonresponses.

Independent Variable

The independent variable consists of age, ranging from 60 to 79 months. Cohort –1 = students between 60 and 71 months (5 years of age), and Cohort 1 = students greater than 72 months (6 years of age). The codes –1 and 1 were used to examine interaction effects (Raudenbush & Bryk, 2002).

Moderator Variables

Race was coded –1 for African Americans and 1 for European Americans. Gender was coded –1 = females and 1 = males. Socioeconomic status (SES) was coded –1 for families with below-average SES and 1 for families with at or above-average SES.

Dependent Variables

Reading proficiency scores, nested within students nested within teachers, were the dependent variable. Reading assessments, which measured specific reading skills, were administered to students at each grade level. At each grade level, the reading assessments consisted of 50 to 70 items (Pollack, Atkins-Burnett, Najarian, & Rock, 2005; Tourangeau, Nord, Lê, Pollack, & Atkins-Burnett, 2006). The items measured basic skills, vocabulary, initial understanding, personal reflections, and critical stance. Basic skills items made up a higher proportion of items at kindergarten, whereas more complicated items (e.g., critical stance) made up a lower proportion of items at kindergarten. As students moved from kindergarten to higher grades, the higher proportion complex concepts and lower proposition of basic skills were assessed.

Reading proficiency skills were taken from individual items on the overall reading assess- ment test at each grade level. The proficiency skills consisted of a cluster of four test questions with consistent content and level of difficulty along a 7-point scoring scale of the reading test (Pollack et al., 2005; Tourangeau et al., 2006). A student was assumed to be proficient in a particular area if at least three of the four items in the cluster were answered correctly. Tourangeau et al. (2006) found that the cluster of four questions was more reliable for measur- ing proficiency than a single question. This process reduced the probability of guessing.

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In the ECLS-K data set, reading proficiency-clustered items consisted of nine levels:

• Level 1. Letter recognition: identifying upper- and lowercase letters by name • Level 2. Beginning sounds: associating letters with sounds at the beginning of words • Level 3. Ending sounds: associating letters with sounds at the end of words • Level 4. Sight words: recognizing common “sight” words • Level 5. Comprehension of words in context: reading words in context • Level 6. Literal inference: making inferences using cues that are directly stated with key

words in text • Level 7. Extrapolation: identifying clues used to make inferences, and using background

knowledge combined with cues in a sentence to understand use of homonyms • Level 8. Evaluation: demonstrating understanding of an author’s craft, and making

connections between a problem in the narrative and similar life problems • Level 9. Evaluating nonfiction: critically evaluating, comparing, and contrasting, and

understanding the effect of features of expository and biographical texts.

The reading proficiency levels resemble a Guttman scale (1954). If a student was proficient at Level 3, it was understood that the student was also proficient at Levels 1 and 2. Only 7% of the students had a response pattern that did not follow a Guttman scale (Pollack et al., 2005). Further, O’Connell (2007) noted that these early reading categories are consistent with the skills identified in the building blocks of reading mastery presented by the Center for the Improve- ment of Early Reading Achievement (CIERA; 2003). The reliability of the scale ranged from .88 to .92 (Pollack et al., 2005; Tourangeau et al., 2006).

Analysis

Three separate two-level growth analyses with categorical outcome variables were used to test whether the independent and moderator variables were associated with change in the reading proficiency of students from the start of kindergarten to the end of third grade, using the MPLUS 4.2 software application (Muthén & Muthén, 2006). Level 1 represented the effect of time, and Level 2 represented the effects of the independent (age and race) on moderator (gender and SES) variables on proficiency scores. The three separate growth analyses consisted of three models. Model 1 was a fully unconditional analysis. The estimates in this model were representative of the residual reading proficiency growth curve through generalized least-squares (GLS) tech- niques and demonstrated whether the average relationship between the assessment points (time) and the residual proficiency scores was different from zero. The model also tested whether there were significant variances in the growth parameters to justify additional analysis. The findings from this model serve as a baseline model for comparing the findings of the two conditional models.

The variance in the unconditional intercept and slope parameters was compared to the two separate two-level conditional analyses (Models 2 & 3). Model 2 examined the effects of race, gender, SES, and the interaction of these variables with age on reading proficiency of all students over time. The model assessed the importance of age on children’s reading proficiency at the start of kindergarten and the growth of reading proficiency through third grade. Model 2 examined the effect of race, gender, SES, and the interaction of these variables with the reading proficiency scores of students who started kindergarten at 5 years of age. The model also

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examined the difference in reading proficiency based on students who started kindergarten when eligible.

The difference between the models is that the variability of the intercept (initial scores at the start of kindergarten) and slopes (growth in scores per year) was influenced by the regression coefficients of the predictor variables. If the variance of intercept or slope in the conditional models (Models 2 and 3) is lower than the fully unconditional (Model 1) intercept or slope, respectfully, then the predictor variables had a positive effect on the initial reading proficiency scores at kindergarten (intercept) and the growth rate (slope) (Raudenbush & Bryk, 2002). If the intercepts or slope in the conditional model is higher, the predictor variable had a negative effect. No change in the intercept or slope variance indicates no effect on the predictor variables. Level-2 predictor variables were regressed on the intercept and slope. The coefficients from these regressions indicated the level of the direct effect of the predictors on the initial reading proficiency scores at kindergarten, or the growth in reading proficiency between the beginning of kindergarten and the end of third grade.

RESULTS

Table 1 provides descriptive statistics for African American and European American students. Although others (Brooks-Gunn et al., 2007; Fryer & Levitt, 2004) have shown that European American children start kindergarten with significantly higher reading test scores than do African American children, Table 1 shows that the reading proficiency skills of the two groups are similar (1.15 vs. 1.23) at the start of kindergarten. The findings show that regardless of race the average student enters kindergarten being proficient in at least letter recognition. This trend continues as students progress through third grade. Further, similar to the findings of Denton and West (2002), the results in the current study show that a slightly higher percent of African American students (29.0%) are not as proficient in reading as European American students (28.1%) by the start of kindergarten. By the end of third grade, a 0.02% difference was found between the two groups, with European American students performing slightly better. It is also important to note that there is more variance in the scores of African Americans than in the scores of European Americans. These findings suggest a greater difference among the reading proficiency skills of African American students than among European American students.

TABLE 1 Descriptive Statistics

African Americans (n = 1,320)

European Americans (n = 4,399)

M SD M SD

Reading proficiency scores at kindergarten (Start) 1.15 1.13 1.23 1.14 Reading proficiency scores at first grade 2.79 1.51 2.98 1.29 Reading proficiency scores at third grade 6.64 1.23 6.54 1.22 Age at kindergarten entry !0.97 0.26 !0.88 0.47 Gender !0.02 1.00 0.02 1.00 Socioeconomic status 0.38 0.92 0.43 0.90

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Table 1 also shows that, on average, students started kindergarten at 5 years of age. Similar to the findings of Diamond et al. (2000), the current study found that a higher percent of European American students (6.0%, n = 326) than African American students (1.7%, n = 22) started kindergarten at 6 years of age. Additionally, 69% of the African American students entering kindergarten were from families with an above-average SES. In comparison, 71% of the European American students were from families with an above-average SES. The selection of the sampled group may have contributed to very little variation in the families’ SES (0.90 and 0.92, respectively).

The findings of the fully unconditional (Model 1) and the conditional models are presented in Table 2.

TABLE 2 Growth Models

Fully Unconditional

Conditional All Students

Conditional for Kindergarten Entry at 5

Model 1 (n = 5,719) Model 2 (n = 5,719) Model 3 (n = 5,141)

Est. SE Est. SE Est. SE

Intercept Intercept 1.20*** 0.05 1.28*** 0.07 1.17*** 0.17 Age — — 0.35 0.23 — — Race — — –0.10** 0.04 –0.04 0.13 Gender — — 0.01 0.03 0.07 0.11 SES — — –0.02 0.52 0.10 0.14 Age " Race — — 0.02 0.11 — — Age " Gender — — –0.04 0.10 — — Age " SES — — 0.07 0.11 — — Race " Gender — — — — –0.03 0.08 Race " SES — — — — –0.04 0.08 Gender " SES — — — — –0.05 0.07 Random error 1.14*** 0.46 1.12*** 0.05 1.11*** 0.05

Slope Intercept 1.07*** 0.01 1.03*** 0.01 1.02*** 0.04 Age –0.02 0.05 — — Race — — 0.03*** 0.01 0.04 0.03 Gender — — 0.00 0.01 0.01 0.03 SES — — 0.00 0.01 –0.02 0.04 Age " Race — — –0.00 0.03 — — Age " Gender — — –0.00 0.03 — — Age " SES — — 0.00 0.03 — — Race " Gender — — — — –0.01 0.02 Race " SES — — — — 0.01 0.02 Gender " SES — — — — 0.00 0.02 Random error 0.05*** 0.01 0.05*** 0.01 0.05*** 0.01

SES = socioeconomic status. *p < .05, **p < .01, ***p < .001.

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Fully Unconditional Model

The intercept of the unconditional analysis (Model 1) illustrates that the average initial reading proficiency scores (1.20) of students at the kindergarten level were significantly different, and that the variance between individual scores (1.14) also was significant. The unconditional model also shows a significant difference in the growth in reading scores over time (1.07), and a small but significant difference in variance of scores over time (0.05). The significant difference in the intercept and the slope suggests that other variables may explain the difference between students’ reading proficiency scores as students start kindergarten and as students continue through third grade.

The findings from the unconditional model also suggest that the proportion of the unexplained variance is accounted for by time or the growth in reading proficiency scores per year. The residual variance for the intercept (1.14) decreases as the model changes over time (0.05 per year). This difference represents the amount of within-person variance, with a pseudo R2 (Singer & Willett, 2003) of 0.96. These findings imply that 96% of the variance in the uncon- ditional model is explained by time. These findings simply suggest that only 4% of variances in reading proficiency can be accounted for by some external factor. Please reference Singer and Willett (2003) for a detailed explanation of pseudo R2.

Conditional Models

The unconditional model demonstrates that very little of the variance (4%) in reading proficiency can be accounted for by external factors. Others (Crone & Whitehurst, 1999; West, Meek, & Hurst, 2000) showed that age had a significant effect on academic outcomes, whereas Model 2 (see Table 2) showed that variable age did not have a direct effect on reading profi- ciency outcomes. However, Model 2 shows that when examining students who enter kindergarten (at either age 5 or 6) and accounting for gender, SES, and interaction effects, African American students had significantly higher reading proficiency skills at the beginning of kindergarten than European American students. Similar to other findings (Brooks-Gunn et al., 2007; Fryer & Levitt, 2004), the findings from Model 2 did show that the reading proficiency growth of European American students was significantly 0.33 points higher per year than that for African American students. These findings suggest that though African American students start kindergarten with higher reading proficiency scores than European American students, the gap is reduced by the end of third grade.

The difference between the findings from the current study and those from others (Brooks- Gunn et al., 2007; Crone & Whitehurst, 1999; Fryer & Levitt, 2004; West, Meek, & Hurst, 2000) is that the current study assessed reading proficiency scores, not standardized reading scores.

Model 3 examined the effects of race, gender, and SES on the reading proficiency of African American and European American students who started kindergarten when age eligible (see Table 2). Although others (Brooks-Gunn et al., 2007; Fryer & Levitt, 2004) found a significant difference in the achievement test scores between African American and European American students, the current study found no significant difference in reading proficiency skills between the two groups when both sets of students start kindergarten when eligible.

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Contrary to the Douglas and Montiel (2008) study, the current study found no significant effects of SES on the reading proficiency of a general population of public school students. Nor did the current study find an interaction effect between race and SES on reading proficiency.

Variance Explained by the Models

Table 3 explained the difference in the effects of race, gender, and SES on the reading proficiency of African American and European American students. Model 3 (0.03) explained slightly more of the variances in reading proficiency at the start of kindergarten than Model 2 (0.02). The conditional models did not explain any of the variance in the growth in reading proficiency between kindergarten and third grade. These findings suggest that age, race, and SES explain some of the variance in reading proficiency scores between students, but not in the growth in the reading proficiency scores over time.

DISCUSSION

The purpose of the current study was to examine whether the age at which students enter kindergarten affects the reading proficiency difference for African American and European American students from kindergarten through third grade. The unconditional model showed that time accounted for 96% of the variances in reading proficiency scores over time. However, it is important to note that the population examined in the current study consisted of children with no private school experience, no kindergarten repeaters, and no students with special needs. Therefore, those elements (repeating kindergarten, special education, etc.) that may nor- mally affect the difference in reading proficiency skills were eliminated. The findings in the unconditional model show that for a general public school population of students, time or natu- ral progress accounts for 96% of the growth in reading proficiency without much external influence.

The two conditional models examined the effects of the independent variable (age) and moderator variables (gender and SES) on the reading proficiency skills of African American and European American students as these students entered kindergarten and moved through third grade. Unlike those from other students (Brooks-Gunn et al., 2007; Crone & Whitehurst, 1999; Fryer & Levitt, 2004; West, Meek, & Hurst, 2000), the findings in the current study showed that

TABLE 3 Variance Explained in Initial and Slope for Reading

Proficiency Scores

Models Intercept Slope

Model 2 vs. Model 1 0.02 0.00 Model 3 vs. Model 1 0.03 0.00

Note: Proportion of variance explained = unconditional – conditional/unconditional.

Source: Raudenbush & Bryk, 2002.

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though the reading proficiency scores for African American students were higher at the start of kindergarten than for European American students, the gap between the groups decreased over time. Further studies should continue to examine the relationship between reading proficiency and African American students. Such studies should examine factors (e.g., preschool experience, family, etc.) that may influence the reading proficiency skills of a general public school, African American population. Findings from these types of studies can lead to an understanding of what factors aid in enhancing the reading proficiency of other African American public school students.

Model 3 showed that when African American and European American students enter kinder- garten when age eligible, no significant difference emerged in the reading proficiency scores of the two groups. Other studies (Brooks-Gunn et al., 2007; Crone & Whitehurst, 1999; Fryer & Levitt, 2004) demonstrated, when assessing standardized test scores, that a significant academic gap exists between African American and European American students. Denton and West (2002) and Rathbun et al. (2004) also found a difference in the reading proficiency of the two groups. The researchers did consider age differences at kindergarten entrance as a factor that can contribute to the difference in the scores of these two populations. The current study did not show a significant difference in the reading proficiency between African American and European American students based on age, and when both groups start kindergarten when age eligible. Morrison et al. (1997) further argued that age is not a good predictor of learning or academic risk. Again, it is important to take into consideration the population used in the current study in comparison to others.

Given that the two conditional models explained very little of the variances in reading proficiency scores, the findings in the current study conclude that 96% of the variance in the reading proficiency of students can be explained by time. This important finding suggests that when assessing a general population of public school students, as mentioned, it can be expected that a natural positive progression will occur in the reading proficiency of students from the time they start kindergarten to the end of third grade. In turn, such variables as age at kindergarten entry, race, gender, and SES do little to explain how reading proficiency may vary between African American and European American students during their early elementary school years.

Further, many parents and policy makers are concerned with whether students are equipped to perform well on standardized tests. One approach taken by parents to help prevent low test performance is to delay a child’s entrance into kindergarten. This approach leads parents to ignore whether a child has lasting skills that will enhance his or her reading ability over his or her lifetime. Related to age at kindergarten entrance, the findings in the current study did not show that the reading proficiency skills of children who were delayed entrance into kindergarten were greater than the reading proficiency skills of students who entered school when eligible, particularly by the end of third grade.

One of the important elements of the current study was examining a sample group that excluded outliers, such as students who attended private school, transfer students, students who repeated kindergarten, and students in special education. This approach led to the examination of a somewhat homogeneous group. Encouraged by VAM, the approach of the current study was to understand the value added by age at school entrance on reading proficiency. As a result, the current study not could discern a difference in reading proficiency of African American and European American students when the groups entered kindergarten when first eligible. To further examine the influence of outliers, other researchers should conduct parallel analyses, examining the effects of the aforementioned outliers on academic outcomes.

AGE AT ENTRY AND READING PROFICIENCY 109

The current study concludes that age at kindergarten entry can play a significant role in comparing the reading skills of young children. Therefore, researchers should take into account the age-appropriate level of students and develop strategies to account for age differences when assessing younger students. The current study further concludes that though standardized reading tests provide an understanding of students’ ability to test, understanding the reading proficiency skills provides teachers, policy makers, and parents a benchmark to help students develop the skills needed to become successful readers.

REFERENCES

Ballou, D., Sanders, W., & Wright, P. (2004). Controlling for student background in value-added assessment of teachers. Journal of Educational and Behavioral Statistics, 29(1), 37–65.

Bellisimo, Y., Sacks, C. H., & Mergendoller, J. R. (1995). Changes over time in kindergarten holding out: Parent and school contexts. Early Childhood Research Quarterly, 10, 205–222.

Brent, D., May, D. C., & Knudson, B. W. (1996). The incidence of delayed school entry: A twenty-year review. Early Education and Development, 7, 121–135.

Brooks-Gunn, J., Rouse, C. E., & McLanahan, S. (2007). Racial and ethnic gaps in school readiness. In R. Pianta, M. Cox, & K. Snow (Eds.), School readiness and the transition to kindergarten in the era of accountability (pp. 283–306). Baltimore: Paul H. Brookes.

Byrd, R. S., Weitzman, M., & Auinger, P. (1997). Increased behavior problems associated with delayed school entry and delayed school progress. Pediatrics, 100(4), 654–661.

Callender, J. (2004). Value-added assessment. Journal of Educational and Behavioral Statistics, 29(1), 5. Center for the Improvement of Early Reading Achievement. (2003). Put reading first: The research building blocks for

teaching children to read, kindergarten through 3rd grade. Washington, DC: Government Printing Office. Cromwell, S. (1998). Starting kindergarten late: How does it affect school performance? Education World. Retrieved

March 2, 2009, from www.educationworld.com/a_admin/admin/admin045.shtml. Crone, D. A., & Whitehurst, G. J. (1999). Age and schooling effects on emergent literacy and early reading skills.

Journal of Educational Psychology, 91(4), 604–613. Crosser, S. (1991). Summer birth date children: Kindergarten entrance age and academic achievement. Journal of

Educational Research, 84(3), 140–146. Datar, A. (2003). The impact of changes in kindergarten entrance age policies on children’s academic achievement and

the child care needs of families. Santa Monica, CA: RAND. Datar, A. (2006). Does delaying kindergarten entrance give children a head start? Economics of Education Review, 25,

43–62. Denton, K., & West, J. (2002). Children’s reading and mathematics achievement in kindergarten and first grade (NCES

2002-125). Washington, DC: Office of Educational Research and Improvement. Diamond, K., Reagan, A., & Bandyk, J. (2000). Parents’ conceptions of kindergarten readiness: Relationships with race,

ethnicity, and development. Journal of Educational Research, 94(2), 93–100. Douglas, K., & Montiel, E. (2008). Learning to read in American elementary school classrooms: Poverty and the acqui-

sition of reading skills. Retrieved April 30, 2008, from www.reading.org/Libraries/SRII/ECLS-K_SES_Report. sflb.ashx.

Easton-Brooks, D., & Davis, A. (2007). Wealth, traditional socioeconomic indicators, and the achievement debt. Journal of Negro Education, 76(4), 530–542.

Fryer, R. G., & Levitt, S. D. (2004). Understanding the black-white test score gap in the first two years of school. Review of Economics and Statistics, 86(2), 447–464.

Good, T. L., & Nichols, S. L. (2001). Expectancy effects in the classroom: A special focus on first-grade classrooms. Educational Psychologist, 36(2), 113–126.

Graue, M. E., & DiPerna, J. (2000). Redshirting and early retention: Who gets the “gift of time” and what are its outcomes? American Educational Research Journal, 37(2), 509–534.

Guttman, L. A. (1954). A new approach to factor analysis: The radix. In P. F. Lazarsfeld (Ed.), Mathematical thinking in the social sciences (pp. 258–348). New York: Columbia University Press.

110 EASTON-BROOKS AND BROWN

Hager, J. M., & Gable, R. A. (1993). Content reading assessment: A rethinking of methodology. The Clearing House, 66(5), 269–272.

Hatcher, V. B. G. (2005). Readiness beliefs of parents of kindergarten age-eligible children enrolled in NAEYC- accredited programs. Unpublished doctoral dissertation, Texas Woman’s University, Denton, TX. Retrieved March 30, 2008, from Dissertations & Theses: A&I database (Publication No. AAT 3188016). http://proquest.umi.com/ pqdlink?Ver=18Exp=02-21-2015&FMT=78DID=9942359218RQT=3098attempt-1

Hedges, L., & Nowell, A. (1998). Black-white test score convergence since 1965. In C. Jencks & M. Phillips (Eds.), The black-white test score gap (pp. 149–181). Washington, DC: Brookings Institution Press.

Jencks, C., & Phillips, M. (Eds.). (1998). The black-white test score gap. Washington, DC: Brookings Institution Press. Kramsch, C. J. (1986). Proficiency versus achievement: reflections on the proficiency movement. Association of

Departments of Foreign Languages, 18(1), 22–24. Kurdek, L. A., & Sinclair, R. J. (2001). Predicting reading and mathematics achievement in fourth-grade children from

kindergarten readiness scores. Journal of Educational Psychology, 93, 451–455. Lincove, J. A., & Painter, G. (2006). Does the age that children start kindergarten matter? Evidence of long-term educa-

tional and social outcomes. Educational Evaluation and Policy Analysis, 28(2), 153–179. Madhabi, C. (2006). Reading achievement gap, correlations, and moderators of early reading achievement: Evidence

from the early childhood longitudinal study (ECLS) kindergarten to first grade sample. Journal of Educational Psychology, 98(3), 489–507.

Malone, L. M., West, J., Denton, K. F., & Park, J. (2006). The early reading and mathematics achievement of children who repeated kindergarten or who began school a year late (NCES 2006–064). Washington, DC: National Center for Education Statistics, Institute of Education Sciences.

March, C. (2005). Academic delayed kindergarten entry: Does withholding a child from school entrance for one year increase academic success? Issues in Educational Research, 15(1), 69–85.

Marshall, H. H. (2003). Opportunity deferred or opportunity taken? An updated look at delaying kindergarten entry. Beyond the Journal, Young Children on the Web. Retrieved November 15, 2007, from www.journal.naeyc.org/btj/ 200309/DelayingKEntry.pdf.

May, D. C., Kundert, D. K., & Brent, D. (1995). Does delayed school entry reduce later grade retentions and use of special education services? Remedial and Special Education, 16(5), 288–294.

McAllister, C., Thomas, T., Wilson, P., & Green, B. (2009). Root shock revisited: Perspectives of Early Head Start mothers on community and policy environments and their effects on child health, development, and school readiness. American Journal of Public Health, 99(2), 205–210.

McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling for teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 37–65.

Meisels, S. J. (1992). Doing harm by doing good: Iatrogenic effects of early childhood enrollment and promotion policies. Early Childhood Research Quarterly, 7(2), 155–174.

Morrison, F. J., Griffith, E. M., & Alberts, D. M. (1997). Nature-nurture in the classroom: Entrance age, school readiness, and learning in children. Developmental Psychology, 33(2), 254–262.

Muthén, L. K., & Muthén, B. O. (2006). Mplus: Statistical analysis with latent variables. Los Angeles: Muthén and Muthén. Nagelkerke, J. D. (1991). A note on the general definition of the coefficient of determination. Biometrika, 78, 691–692. Narahara, M. (1998). Kindergarten entrance age and academic achievement. Retrieved from ERIC Document Repro-

duction Service (ED 421218). National Association for the Education of Young Children. (2001). Still unacceptable trends in kindergarten entry and

placement: A position statement developed by the National Association of Early Childhood Specialists in State Departments of Education, 2000 revision and update. Retrieved February 28, 2007, from www.naeyc.org/about/ positions/PsUnacc.asp.

No Child Left Behind Act of 2001, Pub. L. No. 107-110, 115 Stat. 1425 (2002). O’Connell, A. A. (2007). Logistic regression models doe ordinal response variables. Thousand Oaks, CA: Sage. Oshima, T. C., & Domalski, C. S. (2006). Academic performance gap between summer-birthday and fall-birthday

children in grades K-8. Journal of Educational Research, 99(4), 212–217. Pollack, J. M., Atkins-Burnett, S., Najarian, M., & Rock, D. A. (2005). Early childhood longitudinal study, kindergarten

class 1998-99 (ECLS-K), psychometric report for the fifth grade (NCES 2006-036). Washington, DC: National Center for Education Statistics.

Rathbun, A., West, J., & Hausken, E. G. (2004). From kindergarten through third grade. Children’s beginning school experiences (NCES 2004–007). Washington, DC: U.S. Department of Education.

AGE AT ENTRY AND READING PROFICIENCY 111

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.

Sanders, W. L., Saxton, A., & Horn, S. (1997). The Tennessee value-added assessment system: A quantitative, outcomes-based approach to educational assessment. In J. Millman (Ed.), Grading teachers, grading schools: Is student achievement a valid measure? (pp. 138–162). Thousand Oaks, CA: Corwin Press.

Shepard, L. A., & Smith, M. L. (1986). Synthesis of research on school readiness and kindergarten retention. Educational Leadership, 44, 78–86.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and even occurrence. New York: Oxford University Press.

Tourangeau, K., Nord, C., Lê, T., Pollack, J. M., & Atkins-Burnett, S. (2006). ECLS-K: Combined user’s manual for the ECLS-K fifth-grade data files and electronic codebooks (NCES 2006-032). Washington, DC: National Center for Education Statistics.

U.S. Department of Labor. (2008). A visual essay: Blacks, Asians, and Hispanics in the civilian labor force. Retrieved September 14, 2008, from www.bls.gov/opub/mlr/2004/06/ressum.pdf.

Webster, W. J., & Mendro, R. L. (1997). The Dallas value-added accountability system. In J. Millman (Ed.), Grading teachers, grading schools: Is student achievement a valid measure? (pp. 81–99). Thousand Oaks, CA: Corwin Press.

West, J., Denton, K., & Germino-Hausken, E. (2000). America’s kindergartners: Findings from the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99, Fall 1998 (NES 2000-070). Washington, DC: U.S. Department of Education.

West, J., Meek, A., & Hurst, D. (2000). Children who enter kindergarten late or repeat kindergarten: Their characteristics and later school performance (NCES No. 2000-039). Washington, DC: U.S. Department of Education.

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