Ãå±±ÂÖ¼é

This section presents brief overviews of the data and methods for generating the maps and graphs in the project. For more detailed information, please contact Dr. Mark W. Olofson or Dr. David S. Knight.

Standardized Test Data

Over the years presented in this project (1994-95 to 2014-15), the state of Texas has administered three different versions of statewide standardized exams. Students have taken the Texas Assessment of Academic Skills (TAAS), the Texas Assessment of Knowledge and Skills (TAKS), and the State of Texas Assessments of Academic Readiness (STAAR) exams. For each year, we use as the source for achievement data the assessment that was most widely taken by Texas students. For more historical information about standardized tests in Texas, click .

The Texas Education Agency released both raw scores of these tests and scaled scores, which allow for comparisons of student achievement over time. We convert the scaled scores into units of standard deviations. This process of “standardizing” or “z-scoring” test scores is a common process wherein all values are rescaled based on the mean and the standard deviation of the set of values. Test scores were standardized within each year, grade, subject area, and test form. This allows for scores to be compared across years. The scaled scores, rather than the raw scores, were used for standardization. For more information about scaling in Texas exams, click .

Interpretation of Achievement Values

Values for standardized test scores and gaps are scaled by standard deviation (SD). Although interpretations vary, a difference in 0.20 to 0.25 SD can be interpreted as one instructional year. For example, if the gap between FRL and non-FRL students is 0.45, this can be interpreted as about two years of instruction.

Achievement Gap Trends

The values for the trends in the achievement gaps were generated by running a regression with the gap value as the dependent variable and the year as the independent variable. The reported trend can be interpreted as the change in the gap associated with the change in one year. The R-squared values for these regressions were also reported. The R-squared value can be interpreted as an indication of how well the regression line approximates the data. As R-squared approaches 1, the values of the regression line approaches a perfect fit for the data.

Attendance Data

The values for attendance in this project were calculated as the decimal percentage of days missed with regard to the number of days the student was eligible to attend. Attendance data was taken from the school where the student had the most number of eligible days for that school year.

Interpretation of Attendance Data

Values for attendance and attendance gaps are scaled by standard deviation. Although there is some variation across grade levels, a difference of 0.14 to 0.18 SD can be interpreted as one full school day. For example, if the gap between ELL and non-ELL students is 0.30, this can be interpreted as two days absent over the course of the school year.

Demographic Characteristics

The demographic characteristics used in this analysis were reported by the schools to the Texas Education Agency. For more information on these measures, see the .