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115Journal of Urban Education: Focus on Enrichment________________________________________________________________________________________________________path model was created to display the relationshipsbetween and among variables.POPULATIONThe total population for this study consisted of 131high schools serving 9th grade students within TexasRegion IV school districts. The schools are located inurban, suburban, and rural locations within the citylimits of Houston, Texas. The majority of the schoolshave similar socioeconomic status. However, manydiffer in terms of school characteristics such as studentethnicity and teacher ethnicity. The schools chosen forthe study demonstrate varying levels of achievementon the 9th grade Reading TAKS test.SAMPLING PROCEDURESThe schools chosen for this study were selected basedon a convenience sample. A convenience sample wasbest suited for this study because the data needed toexplore the research questions is made readily availablethrough the Texas Education Agency website. Thisand computing the path coefficients for each of thestructural models. Linear equations represent thestructural equations while the path coefficients arestandardized beta coefficients.DESCRIPTIVE ANALYSESTable 1 reports the overall mean and standard deviationfor each of the measures in the study. A meanvalue indicates the overall average percent of a schoolcharacteristic in the study. School characteristics includedstudent and teacher ethnicity, teacher gender,and years of teaching experience. For the one hundredand thirty one schools represented in the study theaverage percent of European American students wasabout 35%. The standard deviation for EuropeanAmerican students was about 28%. This indicated alarge disparity in the percentage of European Americanstudents at the different schools in the study. EuropeanAmerican teachers on average representedabout 69% of the schools sampled. The standard deviationfor European American teachers was about 25%.This also indicated that a large disparity existed in theTable 2 Pearson Product-Moment Correlations of School CharacteristicsEffect White Students White Teachers Gender Female Teaching ExperienceTAKS Reading -.104 .017 .040 -.145White Students 1 .836** .275* -.219White Teachers .836** 1 1 -156Teaching Experience .217* -.156 -.085 1**Correlation significant at the -.01 level (2-tailed); * Correlation significant at the 0.05 level (2-tailed)public access information system permits researchersto extrapolate data from the Academic Excellence IndicatorReports for use in educational studies. In thismanner, the confidential information for each student,such as name, date of birth, age, and test scores, remainsconfidential while still permitting the researcherto use school reported data to conduct valuable research.STATISTICAL ANALYSISThe statistical procedures performed consisted of calculatingthe correlations between each of the variables,schools population of European American teachers.The average percentage of female teachers was about60%. The standard deviation for female teachers wasabout 7%. This indicated a small disparity existed inthe percentage of female teachers at the differentschools in the study.Finally, teachers with five or more years of teachingexperience represented about 27% of the schools sampled.The standard deviation for teachers with five orfewer years of teaching experience was about 7%. Thisalso indicated that a small disparity existed in the percentageof teachers with five or fewer years of teachingexperience in the schools represented in the study.
Journal of Urban Education: Focus on Enrichment116________________________________________________________________________________________________________CORRELATIONAL RESULTSTable 2 reports the results of the zero-order correlationsamong five constructs. The final results of thebivariate correlations produced two statistically significantrelationships at the .01 level and two at the .05level. The following relationships were significant atthe .01 level: the percentage of European Americanstudents and European American students (r =.863)and European American teachers and female teachers(r =.275). At the .05 level the following relationshipswere significant: the percentage of European Americanstudents and female teachers (r =.217) and EuropeanAmerican students and teachers with five orfewer years of teaching experience (r =-.219).There were no significant relationships between thepercentage of African-American students that passedThe results from the computations of the path coefficientsfor the causal model are presented in Figurebelow. The four research questions presented in thisstudy investigated the relationships among independentand dependent variables. To address these questions,two areas were examined. These included ameasure of fitness for each structural equation and theindividual effect of each independent variable on thedependent variable.The good of fit or measure of fitness of each structuralequation measures the amount of variance in the dependentvariable that is explained by the structuralequation. An R-square value or coefficient of determinationwas computed for each structural equation. Inorder to measure the individual effect of each independentvariable on the dependent variable, the correlationcoefficient was decomposed into direct, indirect,total effect, and non-causal effects between two variables.The results of the findings are reported in Table3 below.Table 3 Decomposition of the Effects from Path Model (R=.09)Effect Correlation Direct Indirect Total Effect Non-CausalWhite Students -.104 -.466** 0 -.446 -.103White Teachers .017 .370** 0 -.370 -.353Gender Female -.040 -.030 0 -.030 -.075
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