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Exploring the Relationship between National Board Certification and High School Student Achievement

Mr. Geary Don Crofford
Dean of Academics at Sequoyah Schools, Tahlequah, Oklahoma
Doctoral student at the University of Oklahoma

Dr. Jon E. Pedersen, Ph.D
Director of Science Education for the Center for Mathematics, Science and Computer Education at the University of Nebraska-Lincoln

Dr. Gregg Garn
Associate Dean of the Jeannine Rainbolt College of Education at the University of Oklahoma


Introduction


Numerous studies in the United States and abroad have confirmed that American high school students are lagging behind their international peers, particularly in the areas of science and mathematics. According to the National Science Board (2006), the fastest growing occupations are in fields that are dependent upon a knowledge base in science and mathematics. Furthermore, the National Science Board’s report indicated a widespread lack of student proficiency on national and international tests of mathematics and science as well as a decreased rate of U.S. students pursuing science and engineering degrees from universities nationwide as a cause for economic apprehension. These findings have alarmed the U.S. educational community as well as concerned stakeholders outside the educational sphere, including community leaders, business owners, and parents (Molfese et al., 2006). Accordingly, research that supports a relationship between teacher credentials and student and/or school achievement, in any form, is valuable to educators and policy makers.

Findings for a lack of student proficiency in science and mathematics present questions as to how and why many teachers are unable to sufficiently engage their students to achieve at acceptable levels. It is necessary to find ways to enable educators not only to enhance student proficiency but also to develop the relevance perceived by students in their learning. The enhancement of teacher education, including National Board Certification (NBC) and providing incentives for teachers to attain certification, may influence the academic achievement and proficiency of students. In 1987, after the Carnegie Forum on Education and the Economy’s Task Force on Teaching as a Profession released “A Nation Prepared: Teachers for the 21st Century,” the National Board for Professional Teaching Standards (NBPTS) was created. Immediately following its inception, NBPTS issued its first policy statement: “What Teachers Should Know and Be Able to Do.” This policy statement established the vision of the NBPTS for accomplished teaching. The NBPTS issued Five Core Propositions, which formed the framework for the knowledge, skills, dispositions, and beliefs that characterized National Board Certified Teachers (NBCTs). According to the NBPTS website,

The fundamental requirements for proficient teaching are relatively clear: a broad grounding in the liberal arts and sciences; knowledge of the subjects to be taught, of the skills to be developed, and of the curricular arrangements and materials that organize and embody that content; knowledge of general and subject-specific methods for teaching and for evaluating student learning; knowledge of students and human development; skills in effectively teaching students from racially, ethnically, and socioeconomically diverse backgrounds; and the skills, capacities and dispositions to employ such knowledge wisely in the interest of students.

National Board Certification is intended to complement (and not replace) state certification for teachers. Ideally, NBC contributes to producing and selecting the most effective teachers possible by enhancing positive aspects of all levels of certification. Data indicate that differences between effective and non-effective teachers have a remarkable influence on student achievement (Darling-Hammond, 2000). Similarly, research by Goldhaber (2006) indicates that National Board Certified Teachers (NBCTs) are significantly more effective teachers (Goldhaber, 2006). Teacher quality is inextricably linked with the degree of teachers’ preparation and experience in subject area matter and pedagogy. In other words, subject knowledge (the information) and pedagogy (the methods of instructional delivery) are both essential for student achievement.

Past research shows mixed results with regard to the positive impact of NBC in improving teacher practice, professional development, and other facets of school improvement that are presumed essential to elevating student achievement. However, the research on the effectiveness of initial teacher certification and professional development for in-service teachers has also yielded mixed findings. State certification and licensure, alternative certification, and NBC each represent differing levels of teacher preparation and professional development that have been and are being studied in terms of their impact on student and school achievement, with the relative effectiveness of each still in debate (Ballou & Podgursky, 2000; Darling-Hammond, Holtzman, Gatlin, & Heilig, 2005; Ferguson, 1991; Finn, 1999; Sanders, Ashton, & Wright, 2005).

From the above cited research, questions arise as to whether more effective teaching is the result of specialized education in how to teach, more general academic ability and knowledge of subject matter, or some combination of these components and others, including school locale, size, Title I status, SES, and parental levels of education and involvement. National Board Certification represents the synthesis of all of these teacher education variables coupled with invaluable classroom experience and reflection. In this paper, we focus on the impact of NBCTs on student academic achievement. Therefore, the scope and purpose of this study is to determine if NBCTs produce measurable and significant increases in high school student achievement in their schools. This study is unique in that it incorporated secondary, school-level data in a particular state, and that it attempted to find relationships between the presence of NBCTs, student academic achievement, and distinctions of locale, size, and Title I status. We hope to build on existing research as well as point out new factors and combinations of factors affecting student achievement that may lead to further research.

In her comprehensive review of state policy, Darling-Hammond (2000) argued, “Quantitative analyses indicate that measures of teacher preparation and certification are the strongest correlates of student achievement in reading and mathematics, both before and after controlling for student poverty and language status” (pp. 1-2). Studies in a variety of states have examined NBC specifically, and many have found that NBCTs contribute to a significantly measurable difference in student and school achievement. For example, Vandevoort, Beardsley, and Berliner (2004) discovered that student proficiency was higher in 75% of the NBCTs’ classrooms when compared with students in non-NBCT classrooms. According to Goldhaber and Anthony (2004), the NBPTS is successfully identifying more effective teachers among applicants, and that NBPTS-certified teachers were more effective than were their non-certified counterparts at increasing student achievement, especially among minority students. The statistical significance and relevance of this NBPTS effect, however, tends to differ significantly by grade level and student type. Smith, Gordon, Colby, and Wang (2005) indicated that the relationship between student learning outcomes and teacher certification status was highly statistically significant on 6 of the 7 student outcomes measured, with the results in favor of NBCTs over non-NBCTS.

Although quantitative approaches have dominated the research on NBCTs and student academic achievement, qualitative research also provides insight into this issue. A case study of three high school teachers suggested that NBC affected teachers’ pedagogical content knowledge (PCK) positively through their reflection on teaching practices, implementation of new and/or innovative teaching strategies, inquiry-oriented instruction, assessments of students’ learning, and understanding of students (Park & Oliver, 2007). In spite of the limitations imposed by analyzing only three cases, such research may give us some idea as to how NBC enhances teacher effectiveness. Other researchers have investigated several aspects of teacher certification and found links to student achievement; studies have also found that when student demographic variables are controlled, schools with a larger proportion of NBCTs demonstrate moderately higher test scores (Bundy, 2006; Cavalluzzo, 2004; Clotfelter, Ladd, & Vigdon, 2007). In addition, research has found that a larger proportion of NBCTs in a given school coincides with a small increase in teacher empowerment, but these gains are unrelated to the improvement in student test scores. It is possible and probable that increased proportions of NBCTs on school campuses produce increases in student achievement across academic subjects, regardless of other factors such as student population diversity, locales, and size of schools. Despite mixed results and a variety of methodologies employed in prior studies, NBC appears to show a positive impact on academic achievement (McColskey & Stronge, 2005; Sanders et al., 2005). Our research question is whether the presence of NBCTs in high schools is related to campus-wide performance on standardized state examinations, independently of other factors, or in conjunction with other factors.


Methodology


The purpose of this study was to investigate whether the number of NBCTs to which students in a given high school are exposed correlated to the school’s outcomes on standardized state examinations. Data sets used in the analysis consisted of End-of-Instruction tests (EOIs) in Biology, Mathematics, and Reading, which were obtained from the state’s Department of Education through an Open Records Request for the test data of all high schools (including those with grades 9-12 and 10-12) in the state. Data on the total number of faculty with NBC for each high school in the state was also obtained through the state’s Department of Education.

In 2005, Senate Bill (SB) 982, known as the Achieving Classroom Excellence (ACE) Act, changed the curriculum, testing, and graduation requirements for students in all public schools in the state. Students entering 9th grade in the 2008-2009 school year must demonstrate mastery of state academic content standards. Mastery is defined as a satisfactory or advanced score on four of seven criterion-referenced EOI exams in core content areas:

Secondary ACE Testing Requirements:

SUBJECTACE REQUIREMENT
Algebra IAll students
English II All students
Algebra II, or Geometry, or Biology I, or English III, or US HistoryAll students must take EOI exams in all areas and pass EOI exams in at least two of the five subject areas
(See OS §70-1210.523)


All secondary students enrolled in a “core” course are required to take an EOI exam as a part of the course. Students have three retesting opportunities each year to take and pass any exam on which they do not score satisfactory or above. The State Board of Education and the Department of Education developed and field-tested EOI exams in Algebra II, Geometry, and English III in the 2006-2007 school year. These examinations were fully implemented in 2007-2008, and a student’s highest performance levels (that are satisfactory and above) will become a part of his/her permanent transcript in 2008-2009 (OS §70-1210.508). The ACE District Remediation Plan is also in place in an attempt to provide intervention activities for students who fail to gain a satisfactory score. ACE provides remediation for any ninth-grade student on any EOI test. The EOI secondary-level tests are aligned to the state-mandated curriculum, the Priority Academic Student Skills (PASS), which has been adopted by the State Board of Education and is the foundation for curriculum development in all public schools. In order to assure the content validity of the state EOI assessments, commercial content developers, who analyzed a pool of items that existed previously to determine the status and viability of each item, studied the PASS objectives. State content area specialists and teachers worked with the commercial developers to develop a pool of items that measured PASS in each content area. The Content and Bias/Sensitivity Review committees carefully reviewed and discussed these items to ensure content validity and the quality and appropriateness of the items. These committees were comprised of state teachers and State Departments of Education staff.

Criteria for Aligning the Test with the PASS Standards and Objectives

  1. Categorical Concurrence
    The test is constructed so that there are at least six items measuring each PASS standard with the content category consistent with the related standard. The number of items, six, is based on estimating the number of items that could produce a reasonably reliable estimate of a student’s mastery of the content measured.
  2. Depth-of-Knowledge Consistency
    The test is constructed so that at least 50% of the test items measuring a PASS objective are at or above the depth of knowledge of the related PASS objective.
  3. Range-of-Knowledge Correspondence
    The test is constructed so that at least 50% of the objectives for a PASS standard have at least four corresponding assessment items.
  4. Balance-of-Representation
    The test is constructed according to the Alignment Blueprint which reflects the degree of representation given on the test to each PASS standard and objective in terms of the percent of total test items measuring each standard and the number of test items measuring each objective.
  5. Source-of-Challenge
    Each test item is constructed in such a way that the major cognitive demand comes directly from the targeted PASS skill or concept being assessed, not from specialized knowledge or cultural background that the test-taker may bring to the testing situation. (State EOI Technical Report 2001-02)

All data used in this study relied strictly on secondary data sets from the 2006-2007 school year. The study excluded private schools and the 72 smallest schools in the state, primarily because of concerns about effect size, as few of these schools had any NBCTs on campus. The dependent variables included EOI scores in biology, math, and reading, and the independent variables included Title I or non-Title I, School Locale and School Size, and the number of NBCTs in each school. Excluding the number of NBCTs, the independent variables were categorical or nominal. The 30 largest schools from each of the six size classifications were included in the sample for convenience, for a total sample of N = 175 schools. The results of the study are constrained from three limitations: (a) we used school-level data, not student-level data, (b) we assumed PASS and EOIs are aligned, (c) and convenience sampling. Results were disaggregated by Locale (urban, suburban, town, and rural high school settings), Size, and Title I status. Pearson’s correlation, basic regression, and ANOVA techniques were used to examine this large data set to see if any relationship existed between the independent and dependent variables. A significance value of .05 was established a priori.

Results

The results show that the number of NBCTs on a high school campus correlates positively and significantly with student achievement (in terms of EOI test scores) when the data were analyzed in aggregate for math, reading, and biology EOI mean scores (see Table 1). About 3% of teachers were NBCTs throughout all high schools in the state, and the mean scores for math and reading were higher than were those for biology on the EOIs.

Table 1
Pearson Correlation for All Schools

EOIMeanSDNPearson r Value
Math70.7116.75175.204*
Reading74.2512.14175.221*
Biology48.4415.57175.256*
Note. *Significant at the .05 level


Taking NBCT as the independent variable, and Biology, Math, and Reading as the dependent variables, we can perform basic regression and ANOVA analysis for statistical significance, the results of which are summarized in Tables 2-4.

Table 2
NBCT vs. Biology

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

45.250

1.463

30.923

.000

NBCTs

124.048

35.641

.256

3.480

.001

Note. (a) Dependent Variable: Biology; (b) R Square = .073; (c) F = 12.114


From the F and t-tests above, we can conclude at the 5% significance level and at a p-level .001 that NBCTs are a statistically significant predictor of Biology test scores. This means that students in schools with a greater proportion of NBCTs tended to score higher on EOI tests in Biology.


Table 3
NBCT vs. Math

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

67.972

1.595

42.620

.000

NBCTs

106.611

38.844

.204

2.745

.007

Note. (a) Dependent Variable: Math; (b) R Square = .036; (c) F = 7.533.


From the F and t-tests above, we can conclude at the 5% significance level and at a p-level .007 that NBCTs are a statistically significant predictor of Math test scores. This means that students in schools with a greater proportion of NBCTs tended to score higher on EOI tests in Math.

Table 4
NBCT vs. Reading

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

72.100

1.151

62.630

.000

NBCTs

83.707

28.039

.221

2.985

.003

Note. (a) Dependent Variable: Reading; (b) R Square = .049; (c) F = 8.913


From the F and t-tests above, we can conclude at the 5% significance level and at a p-level .003 that NBCTs are a statistically significant predictor of Reading test scores. This means that students in schools with a greater proportion of NBCTs tended to score higher on EOI tests in Reading.

However, it is not sufficient to state that a relationship exists between NBCTs and test scores; it is also necessary to show that there are no underlying factors driving the relationship. In our study, several factors like Locale, School Size, or Title 1 status might be correlated with both NBCTs and test scores. In that case, such factors may drive the relationship between NTCBs and test scores, and any appearance of a correlation might have been due to the underlying cause. In order to address the concern, the following ANOVA tests conclusively showed that no such general relationship exists between NBCT and Locale or Title 1 status. Although there exists a relationship between NBCT and school size, a relationship does not exist between test scores and school size (however, a relationship does exist between title 1 status, and locale and test scores). Because the two cases in which scores and outside force were correlated were exactly the two cases in which outside force and NBCT were not correlated (and vice versa), we can safely state that no such outside force is driving the relationship between NBCT and student test scores.


Table 5
NBCT vs. Locale

N

Mean

Std. Deviation

Std. Error

city

21

.0348

.03750

.00818

suburb

23

.0365

.03366

.00702

town

59

.0231

.03125

.00407

rural

72

.0218

.02994

.00353

Total

175

.0257

.03210

.00243

The analysis of the data revealed some marginal differences in means, particularly among the town/rural section as compared to city/suburb. The F-test results are F(3, 171) = 1.948, p = .124. Our p-level is .124 versus our alpha-level of .05; therefore, the test is not statistically significant enough to reject the null that Locale does not affect the NBCT.

Table 6
NBCT vs. Size

N

Mean

Std. Deviation

Std. Error

1A

30

.0100

.02051

.00374

2A

26

.0154

.02387

.00468

3A

27

.0256

.03566

.00686

4A

30

.0250

.03432

.00627

5A

31

.0345

.02803

.00503

6A

31

.0416

.03725

.00669

Total

175

.0257

.03210

.00243

In this comparison, the results revealed that there are significant differences between the NBCT rates and size, and we should expect to see that fact reflected in our F-test: F(5, 169) = 4.348, p = .001. Our p-level is .001 versus our alpha-level of .05; therefore, the test is statistically significant enough to reject the null that Size does not affect the NBCT.

Table 7
NBCT vs. Title

N

Mean

Std. Deviation

Std. Error

Title I

54

.0217

.03232

.00440

Non-Title I

121

.0275

.03197

.00291

Total

175

.0257

.03210

.00243

As a group, Title I schools exhibited no significant positive correlation, but non-Title I schools exhibited a significant positive correlation with student academic achievement as measured by the test scores. For Title I schools, 2.2% of the teachers were NBCT compared to 2.8% in Non-Title I schools. Non-Title I schools generally had higher mean test scores, but only a slightly higher frequency of NBCTs. There are more than twice as many non-Title I schools in our sample of high schools throughout the state. Further analysis showed that NBCT is not only correlated, but can also be identified as a statistically significant causal factor in student test scores. The difference in means here is rather small when compared to standard error: F(1, 173) = 1.244, p = .266. Our p-level is .266 versus our alpha-level of .05; therefore, the test is not statistically significant enough to reject the null that Title 1 status does not affect the influence of NBCTs. The ANOVA tests concerning student scores and outside factors (locale, size, title 1 status) are located in the appendix.
Further statistical analyses were undertaken to investigate the influence of Title I status versus non-Title I status in the particular academic subjects of Math, Reading, and Biology.

Table 8
Title I and Non-Title I Schools

Title I

Mean

SD

N

Pearson

r Value

Title I

Math

66.33

20.28

54

.090

Reading

68.77

13.71

54

.079

Biology

43.31

16.66

54

.117

NBCTs

.022

.032

54

Non-Title I

Math

72.67

14.59

121

.261*

Reading

76.70

10.54

121

.282*

Biology

50.73

14.55

121

.309*

NBCTs

.028

.032

121

*Significant at the .05 level


As can be seen in Table 8, when the data for the comparison of Title I and non-Title I schools is disaggregated by subject, the results confirm those presented in Table 7. Across the board, in all subjects, student academic achievement in Math, Reading, and Biology were all significantly correlated to NBCTs in non-Title I schools.

Table 9
Statistics for Locale

Locale

Mean

SD

N

Pearson

r Value

city

NBCTs

.035

.038

21

Biology

36.76

19.57

21

.356

Reading

64.07

18.30

21

.314

Math

58.29

22.38

21

.419

suburb

NBCTs

.037

.034

23

Biology

56.61

15.93

23

.576*

Reading

80.52

11.60

23

.510*

Math

79.28

14.33

23

.395

town

NBCTs

.023

.0313

59

Biology

47.90

13.26

59

.115

Reading

73.90

10.33

59

.131

Math

68.53

15.57

59

.118

rural

NBCTs

.022

.0299

72

Biology

49.68

14.14

72

.263*

Reading

75.51

9.64

72

.222

Math

73.39

14.29

72

.171

*Significant at the .05 level


When the data was analyzed by Locale, only suburban schools (in Reading and Biology) and rural schools (in biology) showed a positive and significant correlation between the number of NBCTs and EOI scores. No clear pattern emerged in this particular data analysis and categorization, with the exception of a decrease in the number of NBCTs as the locale changed from city schools to rural schools. The relative number of schools in each category may also have influenced the results, with rural schools having the lowest number of NBCTs. For these classifications, the total percentages of NBCTs were: city 3.5%; suburb 3.7%; town 2.3%; and, rural 2.2%.

Table 10
Statistics for School Size

Size

Mean

SD

N

Pearson

r Value

1A

NBCTs

.010

.021

30

Biology

47.07

13.71

30

.483*

Math

70.41

15.37

30

.195

Reading

72.53

9.73

30

.454*

2A

NBCTs

.015

.024

26

Biology

46.34

14.85

26

-.084

Math

69.49

16.89

26

-.100

Reading

75.92

10.60

26

.079

3A

NBCTs

.026

.036

27

Biology

48.41

13.43

27

.124

Math

70.96

17.59

27

-.099

Reading

73.81

11.85

27

-.163

4A

NBCTs

.025

.034

30

Biology

47.43

17.25

30

.250

Math

70.35

14.68

30

.444*

Reading

72.86

12.84

30

.174

5A

NBCTs

.035

.028

31

Biology

45.10

17.93

31

.181

Math

63.92

18.35

31

.185

Reading

70.18

13.85

31

.385*

6A

NBCTs

.042

.037

31

Biology

55.87

14.11

31

.412*

Math

78.96

15.13

31

.469*

Reading

80.32

11.58

31

.363*

*Significant at the .05 level


When the schools were grouped by size, only the smallest and largest classifications showed more than one significant and positive correlation. In fact, the other groups showed some negative correlations. In this state, school size classifications are determined by the Secondary School Activities Association (SSAA) based on enrollment numbers provided by the schools. 6A populations ranged from 4,395-1,259 average daily attendance, 5A from 1,240-667, 4A from 661-490, 3A from 489-378, 2A from 377-244, and 1A from 243-104. The two smallest classifications were not included because of limitations presented by the effect size, the presence of a large number of private schools, and because virtually none had any NBCTs. For these classifications, the total percent of NBCT’s are: 1A, 1.0%; 2A, 1.5%; 3A, 2.6%; 4A, 2.5%; 5A, 3.5%; and, 6A, 4.2%.



Discussion
The results of the statistical analyses of this study revealed several notable findings, which are expanded upon in this section. A more detailed comparison of the EOI mean scores indicated that the biology EOIs were the lowest overall, and non-Title I high schools showed higher EOI scores than Title I schools (see appendix). No significant differences of EOI means emerged from the breakdown of the data by other factors, with the exception that suburban high schools showed higher mean EOI scores than other categories of locale. The locale of schools yielded no clear pattern as to the relationship between NBCTs and EOIs, yet when grouped by size, the smallest (1A) and largest schools (4A, 5A, and 6A) showed a positive effect.

However, medium size schools showed a negative correlation between the number of NBCTs and EOIs. It is possible that this negative correlation (and positive correlation for 1A Schools) was caused by the effect size of the smaller faculties and student populations within the small schools, increased emphasis on NBCT certification, and other factors, such as socioeconomics and parental education and involvement in the larger schools. Analyzing data by locale yielded few correlations between EOIs and NBCTs, except for the suburban category. Again, this may be due to superior socioeconomic status associated with suburban schools as well as increased resources in suburban schools in general. In addition, because of the rural nature of the state, many more schools were classified as rural relative to schools classified as suburban.

The disparity between the Title I and non-Title I schools is intriguing and requires further investigation (see appendix). It is possible that non-Title I schools demonstrated a stronger apparent relationship between NBCTs and student achievement as measured by EOI scores because of socioeconomic and parental education levels, as well as other factors that were not addressed in this analysis. It is significant to note that although the percentage of the NBCTs for these two categories were similar (2.8% for non-Title I versus 2.2% for Title I schools), the EOI score means were consistently higher for the non-Title I schools. In addition, many of the non-Title I high schools have feeder middle schools with Title I designation and middle schools were not included in this analysis.

Despite the variation seen when schools were placed in specific groups for comparison, the overall positive impact of a higher proportion of NBCTs in a given faculty on students’ EOIs is unmistakable in the results of the analysis. Further causal analysis is necessary to investigate the potential impact on student EOIs from other critical factors, such as SES, which must be addressed separately. However, our results show a general positive relationship between number of NBCTs and student performance in the measures that we chose to incorporate in our analysis. Specifically, the proportion of NBCTs predicts test scores for all three of our investigated subjects: biology, reading, and mathematics. Furthermore, the significance levels are very low, which implies a strong rejection of the null and supports no doubt that NBCT is a significant factor for high school student test scores. The presence of NBCT does not change the results of the data anlayses when different locales are taken into account. However, the presence of NBCT changes with differences in school size. In other words, school size is a potential reason for differing student scores. Last, NBCT does not change over Title I status, which means that whether or not a school is Title I has no bearing on whether or not it is NBCT.

What does this mean? Our goal is to prove that NBCT is the cause of test score differences, as opposed to locale, size, and Title I status. As long as NBCT is not related to a variable, that variable cannot cause the test score difference. Therefore, we can automatically rule out locale and Title I status as “driving” the correlation between NBCT and scores. Furthermore, the only variable with which NBCT is related (school size), is not related to test scores. In other words, school size has no effect on student test scores, so obviously the differences we observed in NBCT with regard to test scores is not caused by school size. In short, our conclusion is that the presence of NBCTs is a predictor of test scores, and that none of the three primary factors forwarded by the study (locale, size, or Title I status) are potential problems. In other words, our first conclusion, that NBCTs predict test scores, is very strong.

The key question we attempted to address, “Does the number of NBCTs correlate to a higher EOI score for students,” seems to have been answered in the broadest sense by the data analysis. However, disaggregation of the data raises key questions pertaining to the influence of NBCTs on students, particularly those that attend school in rural and urban settings. From our perspective, these results sound a call for more effort and incentives to be put forth by policy makers, school boards, and administrators toward encouraging secondary teachers to become NBC. The overall positive impact of having more NBCTs in school faculties has been documented by studies in various states through quantitative and qualitative methodologies. The data from the present study and analysis demonstrated a similar finding in high schools. Despite mixed results when the data were disaggregated, there is no indication (all other factors being equal or controlled for) that the presence of more NBCTs in a school has any type of negative impact on student achievement.

Consequently, NBC could be viewed as an adjunct to other approaches and environments that enhance student achievement such as increased parental involvement. In fact, the NBPTS Research Pocket Card (2008) acknowledged,

More than 150 studies have examined National Board Certification. The vast majority found NBCTs make a significantly measurable impact on teacher performance, student learning, engagement and achievement. While some of the results are mixed, most are positive about National Board Certification accomplishments and its potential for improving education nationwide. (p. 1)

Although the process of NBC is an expensive, demanding, and time-consuming process for the teachers who undertake it, it appears that the benefits for our school systems, and thus for our students and communities, outweigh the costs. Given that our results suggest that the percentage of NBCTs has a campus-wide effect on student achievement, future studies should investigate this pattern further. Because of the many unanswered questions raised by this study, we cannot posit the conclusion that NBCTs influence all students at all locales in the same manner. The findings of the study clearly show that having NBCTs on a campus do not always yield positive correlations to achievement (EOIs), and future studies should investigate why this is the case. For example, researchers should consider such possible mitigating factors as campus and department leadership roles of NBCTs and the number of NBCTs to which students are exposed at the elementary and middle level. More research is needed to examine the influence of NBCTs on those schools with children at the highest risk. Schools in socioeconomically depressed areas (in this state, rural and urban schools make up the majority of these schools) underperformed even when compared to higher SES schools with the presence of NBCTs. In addition, this research only looked at overall NBCT frequency on each campus. In the future, a more specific study should break down the analysis in terms of actual subjects taught by NBCTs. The state EOI means were substantially lower for biology than they were for reading and math. Is this a factor of the number of NBCTs, and if so, will increasing the number of NBCTs improve science achievement?

Further qualitative case studies may be carried out that determine how NBC has affected individual teachers’ teaching philosophies and practices as well as their students’ achievement and attitudes toward their education. Such connections are especially urgent in light of the federal No Child Left Behind Act of 2001 (NCLB), which requires all states to establish state academic standards, tests, and accountability measures that meet federal requirements for monitoring the adequate yearly progress of schools. Furthermore, additional effort in exploring the differences found in schools’ Title I status, size, and locale in this study should be initiated as well. This may include analyzing a potential relationship between frequency of NBCTs and the state’s Academic Performance Index (API). The API was created to measure the performance and progress of a school or district based on several factors that are believed to contribute to overall educational success. The possible scores range from 0 to 1,500. The factors used in the calculation of an API score include the state’s School Testing Program (as measured by student success on state achievement tests), school completion (including attendance, dropout, and graduation rates), and academic excellence (including ACT scores and participation, Advanced Placement [AP] credit, and college remediation rates in reading and mathematics). An analysis involving APIs may give a more complete picture of the overall affect that NBCTs have on high school campuses. Similarly-focused research should include elementary and middle schools.

There is still much to determine with regard to the best ways to prepare our teachers at the beginning and throughout their careers in the classroom. Our study, among others, attempts to elucidate the effectiveness of the methods currently available to prepare teachers and describe their relative efficacies. This research is critical for our students because they deserve to be exposed to the best teachers possible, increasing their chances of current and future academic and personal success. Our study was limited to one state, one level of teacher education, and one set of student outcomes. However, in conjunction with other current and future studies, we can go beyond a narrow snapshot of the efficacy of teacher certification to a more complete and comprehensive canvas of how to most effectively prepare our teachers and successfully educate our students.


Appendix

Set 1

In the first series of tests, the null hypothesis is that there is no distinction across Title I status in the scores of the students. In other words, we would expect that a student in title 1 schools would receive the same math score as a student in non-title 1 schools.


Test 1
ANOVA, testing for differences between Title 1/Non-Title 1 schools in Math Score:

ANOVA

Math

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

1499.423

1

1499.423

5.480

.020

Within Groups

47336.737

173

273.623

Total

48836.160

174

Means

N

Mean

Std. Deviation

Std. Error

Title I

54

66.3315

20.27984

2.75974

Non-Title I

121

72.6686

14.58861

1.32624

Total

175

70.7131

16.75314

1.26642

From the analysis above, the non-title 1 students perform better at mathematics than the title 1 students, and at a statistically significant level. Our p-value of .020 is lower than our alpha-level of .05, allowing us to reject the null hypothesis that no differences exist between the two samples.


Test 2
ANOVA, testing for differences between Title 1/Non-title 1 schools in Reading Score:

ANOVA

Reading

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

2347.234

1

2347.234

17.433

.000

Within Groups

23293

173

134.643

Total

25640.416

174

Means

N

Mean

Std. Deviation

Std. Error

Title I

54

68.7704

13.71024

1.86573

Non-Title I

121

76.6992

10.53990

.95817

Total

175

74.2526

12.13914

.91763

From the analysis above, the non-title 1 students perform better at reading than the title 1 students, and at a statistically significant level. Our p-value of .000 is lower than our alpha-level of .05, allowing us to reject the null hypothesis that no differences exist between the two samples.


Test 3
ANOVA, testing for differences between Title 1/Non-title 1 schools in Biology Score:

ANOVA

Biology

Sum of Squares

df

Mean Square

F

Sig.

Between Groups

2051.472

1

2051.472

8.849

.003

Within Groups

40107.648

173

231.836

Total

42159.120

174

Means

N

Mean

Std. Deviation

Std. Error

Title I

54

43.3148

16.66181

2.26739

Non-Title I

121

50.7273

14.54705

1.32246

Total

175

48.4400

15.56579

1.17666

From the analysis above, the non-title 1 students perform better at Biology than the title 1 students, and at a statistically significant level. Our p-value of .003 is lower than our alpha-level of .05, allowing us to reject the null hypothesis that no differences exist between the two samples.


Set 2

Biology/Math/Reading vs Locale

The null hypothesis is that the locale should have no influence on student scores in Biology/Math/Reading. We will perform a cross sample ANOVA as well as Hukey-post hoc to compare across the different samples.

MEANS

N

Mean

Std. Deviation

Std. Error

Biology

city

21

36.7619

19.57014

4.27056

suburb

23

56.6087

15.92807

3.32123

town

59

47.8983

13.25960

1.72625

rural

72

49.6806

14.13897

1.66629

Total

175

48.4400

15.56579

1.17666

Math

city

21

58.2857

22.38339

4.88446

suburb

23

79.2783

14.33039

2.98809

town

59

68.5339

15.57387

2.02754

rural

72

73.3875

14.29299

1.68445

Total

175

70.7131

16.75314

1.26642

Reading

city

21

64.0667

18.29668

3.99266

suburb

23

80.5174

11.58956

2.41659

town

59

73.9034

10.32830

1.34463

rural

72

75.5083

9.64200

1.13632

Total

175

74.2526

12.13914


ANOVA

Sum of Squares

df

Mean Square

F

Sig.

Biology

Between Groups

4526.790

3

1508.930

6.857

.000

Within Groups

37632.330

171

220.072

Total

42159.120

174

Math

Between Groups

5725.724

3

1908.575

7.570

.000

Within Groups

43110.436

171

252.108

Total

48836.160

174

Reading

Between Groups

3202.242

3

1067.414

8.135

.000

Within Groups

22438.174

171

131.217

Total

25640.416

174

Multiple Comparisons

Tukey HSD

Dependent Variable

(I) Locale

(J) Locale

Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

Biology

city

suburb

-19.84679*

4.47750

.000

-31.4635

-8.2300

town

-11.13640*

3.76957

.019

-20.9164

-1.3564

rural

-12.91865*

3.67915

.003

-22.4641

-3.3732

suburb

city

19.84679*

4.47750

.000

8.2300

31.4635

town

8.71039

3.64670

.083

-.7509

18.1716

rural

6.92814

3.55315

.211

-2.2904

16.1467

town

city

11.13640*

3.76957

.019

1.3564

20.9164

suburb

-8.71039

3.64670

.083

-18.1716

.7509

rural

-1.78225

2.60511

.903

-8.5411

4.9766

rural

city

12.91865*

3.67915

.003

3.3732

22.4641

suburb

-6.92814

3.55315

.211

-16.1467

2.2904

town

1.78225

2.60511

.903

-4.9766

8.5411

Math

city

suburb

-20.99255*

4.79232

.000

-33.4261

-8.5590

town

-10.24818

4.03462

.057

-20.7159

.2195

rural

-15.10179*

3.93785

.001

-25.3184

-4.8852

suburb

city

20.99255*

4.79232

.000

8.5590

33.4261

town

10.74436*

3.90310

.033

.6179

20.8709

rural

5.89076

3.80299

.411

-3.9760

15.7575

town

city

10.24818

4.03462

.057

-.2195

20.7159

suburb

-10.74436*

3.90310

.033

-20.8709

-.6179

rural

-4.85360

2.78828

.306

-12.0877

2.3805

rural

city

15.10179*

3.93785

.001

4.8852

25.3184

suburb

-5.89076

3.80299

.411

-15.7575

3.9760

town

4.85360

2.78828

.306

-2.3805

12.0877

Reading

city

suburb

-16.45072*

3.45739

.000

-25.4208

-7.4806

town

-9.83672*

2.91075

.005

-17.3886

-2.2849

rural

-11.44167*

2.84093

.000

-18.8124

-4.0709

suburb

city

16.45072*

3.45739

.000

7.4806

25.4208

town

6.61400

2.81587

.091

-.6917

13.9197

rural

5.00906

2.74364

.265

-2.1092

12.1274

town

city

9.83672*

2.91075

.005

2.2849

17.3886

suburb

-6.61400

2.81587

.091

-13.9197

.6917

rural

-1.60494

2.01159

.855

-6.8240

3.6141

rural

city

11.44167*

2.84093

.000

4.0709

18.8124

suburb

-5.00906

2.74364

.265

-12.1274

2.1092

town

1.60494

2.01159

.855

-3.6141

6.8240

*The mean difference is significant at the 0.05 level.

In general, the suburbs score the highest. Many, if not most, of the cross comparisons are significant, and the overall null that locale does not matter is thoroughly rejected at a p-value of .000 vs our alpha level of .05.


Set 3

Biology/Math/Reading vs. Size

The null hypothesis is that the size should have no influence on student scores in Biology/Math/Reading. We will perform a cross sample ANOVA as well as Hukey-post hoc to compare across the different samples.

Means

N

Mean

Std. Deviation

Std. Error

Biology

1A

30

47.0667

13.70611

2.50238

2A

26

46.3462

14.84572

2.91149

3A

27

48.4074

13.42861

2.58434

4A

30

47.4333

17.25205

3.14978

5A

31

45.0968

17.93201

3.22068

6A

31

55.8710

14.11321

2.53481

Total

175

48.4400

15.56579

1.17666

Math

1A

30

70.4133

15.36821

2.80584

2A

26

69.4885

16.89171

3.31274

3A

27

70.9630

17.59257

3.38569

4A

30

70.3533

14.68405

2.68093

5A

31

63.9161

18.34906

3.29559

6A

31

78.9581

15.12860

2.71718

Total

175

70.7131

16.75314

1.26642

Reading

1A

30

72.5333

9.73332

1.77705

2A

26

75.9231

10.59782

2.07840

3A

27

73.8148

11.84852

2.28025

4A

30

72.8567

12.84345

2.34488

5A

31

70.1839

13.84462

2.48657

6A

31

80.3161

11.58076

2.07997

Total

175

74.2526

12.13914

.91763

         

ANOVA

Sum of Squares

df

Mean Square

F

Sig.

Biology

Between Groups

2259.290

5

451.858

1.914

.094

Within Groups

39899.830

169

236.094

Total

42159.120

174

Math

Between Groups

3586.784

5

717.357

2.679

.023

Within Groups

45249.376

169

267.748

Total

48836.160

174

Reading

Between Groups

1877.812

5

375.562

2.671

.024

Within Groups

23762.604

169

140.607

Total

25640.416

174

Multiple Comparisons

Tukey HSD

Dependent Variable

(I) Size

(J) Size

Mean Difference (I-J)

Std. Error

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

Biology

1A

2A

.72051

4.11708

1.000

-11.1478

12.5888

3A

-1.34074

4.07603

.999

-13.0907

10.4092

4A

-.36667

3.96731

1.000

-11.8032

11.0699

5A

1.96989

3.93519

.996

-9.3741

13.3139

6A

-8.80430

3.93519

.226

-20.1483

2.5397

2A

1A

-.72051

4.11708

1.000

-12.5888

11.1478

3A

-2.06125

4.22194

.997

-14.2318

10.1093

4A

-1.08718

4.11708

1.000

-12.9555

10.7811

5A

1.24938

4.08613

1.000

-10.5297

13.0285

6A

-9.52481

4.08613

.188

-21.3039

2.2543

3A

1A

1.34074

4.07603

.999

-10.4092

13.0907

2A

2.06125

4.22194

.997

-10.1093

14.2318

4A

.97407

4.07603

1.000

-10.7759

12.7240

5A

3.31063

4.04477

.964

-8.3492

14.9705

6A

-7.46356

4.04477

.440

-19.1234

4.1963

4A

1A

.36667

3.96731

1.000

-11.0699

11.8032

2A

1.08718

4.11708

1.000

-10.7811

12.9555

3A

-.97407

4.07603

1.000

-12.7240

10.7759

5A

2.33656

3.93519

.991

-9.0074

13.6805

6A

-8.43763

3.93519

.270

-19.7816

2.9063

5A

1A

-1.96989

3.93519

.996

-13.3139

9.3741

2A

-1.24938

4.08613

1.000

-13.0285

10.5297

3A

-3.31063

4.04477

.964

-14.9705

8.3492

4A

-2.33656

3.93519

.991

-13.6805

9.0074

6A

-10.77419

3.90280

.069

-22.0248

.4764

6A

1A

8.80430

3.93519

.226

-2.5397

20.1483

2A

9.52481

4.08613

.188

-2.2543

21.3039

3A

7.46356

4.04477

.440

-4.1963

19.1234

4A

8.43763

3.93519

.270

-2.9063

19.7816

5A

10.77419

3.90280

.069

-.4764

22.0248

Math

1A

2A

.92487

4.38439

1.000

-11.7140

13.5638

3A

-.54963

4.34068

1.000

-13.0625

11.9632

4A

.06000

4.22491

1.000

-12.1191

12.2391

5A

6.49720

4.19070

.632

-5.5833

18.5777

6A

-8.54473

4.19070

.325

-20.6252

3.5358

2A

1A

-.92487

4.38439

1.000

-13.5638

11.7140

3A

-1.47450

4.49606

.999

-14.4353

11.4863

4A

-.86487

4.38439

1.000

-13.5038

11.7740

5A

5.57233

4.35144

.795

-6.9715

18.1162

6A

-9.46960

4.35144

.254

-22.0135

3.0743

3A

1A

.54963

4.34068

1.000

-11.9632

13.0625

2A

1.47450

4.49606

.999

-11.4863

14.4353

4A

.60963

4.34068

1.000

-11.9032

13.1225

5A

7.04683

4.30739

.576

-5.3701

19.4637

6A

-7.99510

4.30739

.433

-20.4120

4.4218

4A

1A

-.06000

4.22491

1.000

-12.2391

12.1191

2A

.86487

4.38439

1.000

-11.7740

13.5038

3A

-.60963

4.34068

1.000

-13.1225

11.9032

5A

6.43720

4.19070

.642

-5.6433

18.5177

6A

-8.60473

4.19070

.317

-20.6852

3.4758

5A

1A

-6.49720

4.19070

.632

-18.5777

5.5833

2A

-5.57233

4.35144

.795

-18.1162

6.9715

3A

-7.04683

4.30739

.576

-19.4637

5.3701

4A

-6.43720

4.19070

.642

-18.5177

5.6433

6A

-15.04194*

4.15621

.005

-27.0230

-3.0608

6A

1A

8.54473

4.19070

.325

-3.5358

20.6252

2A

9.46960

4.35144

.254

-3.0743

22.0135

3A

7.99510

4.30739

.433

-4.4218

20.4120

4A

8.60473

4.19070

.317

-3.4758

20.6852

5A

15.04194*

4.15621

.005

3.0608

27.0230

Reading

1A

2A

-3.38974

3.17724

.894

-12.5488

5.7693

3A

-1.28148

3.14556

.999

-10.3492

7.7862

4A

-.32333

3.06167

1.000

-9.1492

8.5025

5A

2.34946

3.03688

.972

-6.4049

11.1039

6A

-7.78280

3.03688

.112

-16.5372

.9716

2A

1A

3.38974

3.17724

.894

-5.7693

12.5488

3A

2.10826

3.25816

.987

-7.2840

11.5006

4A

3.06641

3.17724

.928

-6.0926

12.2254

5A

5.73921

3.15336

.456

-3.3510

14.8294

6A

-4.39305

3.15336

.731

-13.4832

4.6971

3A

1A

1.28148

3.14556

.999

-7.7862

10.3492

2A

-2.10826

3.25816

.987

-11.5006

7.2840

4A

.95815

3.14556

1.000

-8.1096

10.0259

5A

3.63094

3.12144

.854

-5.3672

12.6291

6A

-6.50131

3.12144

.301

-15.4995

2.4969

4A

1A

.32333

3.06167

1.000

-8.5025

9.1492

2A

-3.06641

3.17724

.928

-12.2254

6.0926

3A

-.95815

3.14556

1.000

-10.0259

8.1096

5A

2.67280

3.03688

.951

-6.0816

11.4272

6A

-7.45946

3.03688

.143

-16.2139

1.2949

5A

1A

-2.34946

3.03688

.972

-11.1039

6.4049

2A

-5.73921

3.15336

.456

-14.8294

3.3510

3A

-3.63094

3.12144

.854

-12.6291

5.3672

4A

-2.67280

3.03688

.951

-11.4272

6.0816

6A

-10.13226*

3.01188

.012

-18.8146

-1.4499

6A

1A

7.78280

3.03688

.112

-.9716

16.5372

2A

4.39305

3.15336

.731

-4.6971

13.4832

3A

6.50131

3.12144

.301

-2.4969

15.4995

4A

7.45946

3.03688

.143

-1.2949

16.2139

5A

10.13226*

3.01188

.012

1.4499

18.8146

*The mean difference is significant at the 0.05 level.

These results are a bit mixed. In general, there are few differences across schools. The big difference exists in 6A and 5A being significantly different at the .05 significance level in their individual cross-comparison. The overall null, that size does not matter, is rejected in Math (.023 P level) and Reading (.024 P level) but not in Biology (.094 P level) when comparing to the standard alpha level of .05

Consequently, while we can state that size has an effect on scores, the effect is ambiguous, and the only pinpoint effect that was statistically significant was 6A vs 5A in Math and Reading.


Acknowledgements

Special thanks to John Hathcoat of the University of Oklahoma-Tulsa for his assistance and feedback, and Scott Goldman of the Oklahoma State Department of Education for providing data sets.

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