Gender, race, and the economic statuses of students are directly associated with students’ achievement in mathematics and reading in urban schools (Southworth, 2010; Lubienski & Crane, 2010). The achievement gap associated with these factors has persisted in the American education system for 59 years since the Brown vs. Board of Education decision. Moreover, it has been 47 years since the Coleman Report of 1966 (Southworth 2010), which implicated race and income as predictors of student achievement.

Over the years, educational policies have changed in the United States. The introduction of the “No Child Left Behind Act” in 2000 led to staffing each school with highly qualified teachers in the hope of reducing it (Konstantopoulos & Chung, 2011). However, evidence in related literature illustrates that race, economic status, and gender continue to impact a student’s achievement (Van de Gaer et al, 2008).

The present study will investigate the roles that race, gender, and students’ economic statuses play on whether or not a student passes or fails the End of Course Test Scores (EOCTs) in Biology in a suburban high school in the southeastern United States.

Search Strategies

For the content of this paper, the Google scholar search engine was used in the initial searches of utilized. All resources were published between January 1990 and June 2012.  The articles were identified through a comprehensive search of four electronic databases: Academic Search Complete (EBSCO), Education Journals (ProQuest), Omnifile Full Text (Wilson), and Research Library (ProQuest). After an initial search, additional sources were found by searching the bibliographies of the chosen articlesThe search terms used during the computer-based searches included: gender effect on learning and achievement; parental income and student achievement; race and student achievement; income, race, and EOCT scores; students’ learning outcomes; achievement; achievement gap, student and/or pupil.

To be eligible for this review, the article had to meet the following four criteria: 1) include race, gender, and family income as a predictor of student achievement; 2) been published in a peer-reviewed journal; 3) be published between 2000 and 2012; and 4) Only studies published in English were reviewed.  In addition, the study must assess the effect of gender, race, and family income on student achievement.

Research questions:

Is race a statistically significant predictor of a student passing the end of course test (EOCT) in Biology?

Is gender a statistically significant predictor of a student passing the end of course test (EOCT) in Biology?

Is parental income a statistically significant predictor of a student passing the end of course test (EOCT) in Biology?

Null hypotheses:

Race is not a statistically significant predictor of a student passing the end of course test (EOCT) in Biology.

Gender is not a statistically significant predictor of a student passing the end of course test (EOCT) in Biology.

Parental income is not a statistically significant predictor of a student passing the end of course test (EOCT) in Biology.

Data Collection

The data used in this authentic project was collected from the DeKalb County Public School’s website on student achievement. The database consists of student achievement scores in Biology, Mathematics, Physical Science, and Writing as measured by the End of Course Test (EOCT). I selected only the data pertaining to the names of students that I teach this semester in the Instructional Data Management System (IDMS Database).  The information available via IDMS was incomplete due to missing information. To fill in the gaps of missing information, I developed a survey for my students to complete which included questions pertaining to:  race, gender, and parents-income [measured by whether they received free and reduced lunch or not (see Appendix I)]. The pre-existing data and the survey data were both examined for accuracy and for filling in the information gap that existed between them.

The data collected was used to determine whether gender, race, and a student’s family income are predictors of their EOCT score in Biology. Various literature (Southworth, 2010; Van de Gaer et al, 2008 & Lubienski et al, 2010 & Dulaney & Banks, 1994, Desimone, L. 1994, Patterson, Kupersmidt, & Vaden, 1990) published in regards to a student’s achievement in urban schools has linked gender, race, and student’s economic statuses to the student’s achievement. However, few studies have looked extensively into these predictors in suburban high schools. Therefore this authentic study will assist to culminate the existing information gap on the effect that race, gender, and family income has in the American suburban schools.

Method

            The authentic study used both existing and survey data. The study participants’ population included a total of 90 high school students of whom 50 were males and 40 were females. The racial distribution of the student participants included; 28 Hispanics, 35 African Americans, 21 Caucasians, and 6 Asians. Of the total 90 students surveyed, 55 did not receive free and/or reduced lunches while the remaining 35 did. The analysis solely included Biology EOCT scores taken in the Fall semester of the 2012-2013 school year.

Measures of Low income, Race, Gender, and Academic Achievement

            For the purpose of this research paper, low income was defined as the percent of students who received free or reduced lunch. The definition was consistent with past research on measures of low income in secondary education in America (Abbott & Joireman, 2001). Race was categorized into four groups: African American/Black, Caucasians, Hispanics, and Asians. Students chose the race category that they belonged to based upon their own understanding of racial identity. Student gender data was extracted directly from the IDMS website. Furthermore, academic achievement was categorized into two groups; students who passed the EOCT or those who did not.

Data Analysis

            The data collected in the authentic study were analyzed using SPSS statistical software. Binary regression was used to perform the analysis. The data was first entered manually into the SPSS statistical program. There was no continuous data in any of the predictors’ outcomes variables. Therefore all data was entered as categorical data. Since all the predictors’ variables contained categorical data, there were no linearity or multicollinearity assumptions to be met before the binary regression statistics were run. In addition, since the participants in the IDMS database were randomly selected, I assumed independence of errors. The significant level was decided based upon the p values. If the p value was greater than 0.05, then the null hypothesis was rejected. And, if the p value was less than 0.05, then the null hypothesis was accepted.

Results

            The logistic regression results indicated that gender and race were not statistically significant predictors of a student having passed the EOCT exam in Biology. The parental income, however, was a significant predictor, p < .001 and therefore its null hypothesis was rejected (see Table 1). Based on the results of the logistic regression, both null hypotheses for gender and racial identity were accepted since the associated p values were greater than .05, at .315 and .257 respectively. The summaries of findings are the following:

• The odds of a male student having passed the EOCT in Biology were 1.74 times greater than that of the odds of a female student passing the EOCT in Biology; however, the odds was not statistically significant, p values was greater than .05.
• The odds of a student who was not receiving free and reduced lunch having passed the EOCT in Biology was 9.37 times greater than that of a student who was receiving free and/or reduced lunch. The odds for the predictor were statistically significant, p < .001.
• The odds of a student who was an African American passing the EOCT in Biology was .53 times less greater than the odds of a Caucasian student passing the EOCT test in Biology; however, the odds was not statistically significant, p value was greater than .05.
• The odds of student who was Hispanic passing the EOCT test in Biology was .19 times less greater than the odds of a Caucasian student passing the EOCT test in Biology; however, the odds was not statistically significant, , p value was greater than .05.

 

Table 1

Logistic Regression Predictors of a Student’s Passing Biology EOCT test in Biology

 

Variable	B	OR	95% CIs for OR
Constant	0.50
Parental Income
Did not Received   Free Lunch	2.24**	9.37	[2.61, 33.58]
Receiveda
Gender
Femalea
Male	0.55	1.74	[.59,   5.15]
Racial Identity                   African     Americans	-.63	.56	[.04,   6.64]
Hispanics	–    1.67	1.88	[.02,   2.41]
Caucasiansa
Nagelkerke   R2	.32
χ2(5)	25.25***

• Note. N = 90
• p < .05*, p < .01**, p < .001***.
• ^a Reference category.

 

 

 

Tables Associated with the Logistic Analysis are Listed Below:

Table 1           

Case Processing Summary
Unweighted Casesa		N	Percent
Selected Cases	Included in Analysis	90	100.0
	Missing Cases	0	.0
	Total	90	100.0
Unselected Cases		0	.0
Total		90	100.0
a. If weight is in effect, see classification table for the   total number of cases.

 

 

 Table 2

Dependent Variable Encoding
Original Value	Internal   Value
Failed	0
Passed	1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3

 

 

Categorical Variables Codings
		Frequency	Parameter   coding
			(1)	(2)	(3)
Race	Hispanic	28	1.000	.000	.000
	Black/African American	35	.000	1.000	.000
	Caucasian	21	.000	.000	1.000
	Asian	6	.000	.000	.000
Students_Who_Recieve_Free_lunc	Does Not Receive Free and Reduced Lunch	55	1.000
	Receive Free and Reduced Lunch	35	.000
Student_gender	Female	40	1.000
	Male	50	.000

 

Table 4

 

Omnibus Tests of Model Coefficients
		Chi-square	df	p
Step 1	Step	25.254	5	.000
	Block	25.254	5	.000
	Model	25.254	5	.000

 

 

 

 

Table   5     Model Summary
Step	-2   Log likelihood	Cox   & Snell R Square	Nagelkerke   R Square
1	87.882a	.245	.342
a. Estimation terminated at iteration number 5 because parameter   estimates changed by less than .001.

 

 

Table 6

Classification Table
	Observed		Predicted
			Student_EOCT_Scores		Percentage   Correct
			Failed	Passed
Step 1	Student_EOCT_Scores	Failed	19	10	65.5
		Passed	8	53	86.9
	Overall Percentage				80.0
The   cut value is .500

 

 

 

 

 

 

 

 

 

 

Table 7

 

 

Variables in the Equation
		B	S.E.	Wald	df	p	Exp(B)	95%   CIs EXP(B)
								Lower	Upper
Step 1a	Gender(1)	.555	.553	1.008	1	.315	1.742	.590	5.146
	Parent_Income(1)	2.237	.651	11.805	1	.001	9.369	2.614	33.575
	Race			4.041	3	.257
	Race(1)	-1.673	1.301	1.653	1	.199	.188	.015	2.405
	Race(2)	-.630	1.288	.239	1	.625	.533	.043	6.644
	Race(3)	-1.609	1.417	1.289	1	.256	.200	.012	3.218
	Constant	.498	1.215	.168	1	.682	1.645
a. Variable(s) entered on step 1: Gender, Parental_Income, Race.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References.

Desimone, L. (1990). Linking parent involvement with student achievement: Do race and income matter? The Journal of Educational Research, 93(1), 11-30.

Dulaney, C., & Banks, K. (1994). Racial and gender gaps in academic achievement (E&R Report No. 94.10). Raleigh, NC: Wake County Public Schools System, Dept. of Evaluation and Research. (ERIC Document ED380198)

Konstantopoulos, S. & Chung, V. (2011). Teacher effects on minority and disadvantaged students’ grade 4 achievements. Journal of Education Research, 104(2), p73-86.

Lubienski, S. T., & Crane, C. C. (2010). Beyond Free Lunch: Which family background measure matter? Education Policy Analysis Archives, 18(11), p1-39.

Petterson, C. J., Kupersmidt, J. B., & Vaden, N. A. (1990). Income levels, gender, ethnicity, and household composition as predictors of children’s school based competence. Child Development, 61, 486-494.

Southworth, S. (2010). Examining effects of school composition on North Carolina student achievement over time. Education Policy Analysis Archives, 18(29), p1-42.

Van de gaer, E., Pustjens, H. & Van Damme, J. (2008). Mathematics participation and mathematics achievement across secondary school: The Role of Gender. Sex Roles, 59(7/8), p568-685. DOI: 10.1007/s11199-008-9455-x

 

 

 

 

Appendix I

 

Student Survey:

Predictors of Students’ Scores on the Biology and Physical Science EOCTs

 

Ethnicity and/or Race

Check the box next to the correct term that correctly describes your race or ethnicity.

• o Black/African American
• o Latino/Hispanic
• o White
• o Asian

 

Parents’/Guardian’s Income

Do you receive free and/or reduced lunch or breakfast or both?

• o Yes o No

Gender

Check the box that correctly describes your gender.

• o Male
• o Female

EOCT Scores

Be very honest in reporting your EOCT score for the following subjects. Check the appropriate box.

a) I passed my Biology EOCT the first time I took it.

• o Yes   o No

b) I passed my Physical Science EOCT the first time I took it.

• o Yes   o No