Keywords: Equity, Standardized Assessment, Evaluation
Paper Description: This quantitative study uses a least-squares regression model to determine the predictive power of socio-economic factors on district-level student achievement on new, Common Core-aligned standardized assessments. We posit that educators may use our methodology and model to control for socio-economic factors, and more equitably compare school district performance.
Purpose
We used a least-squares regression model to determine the predictive power of socio-economic status (SES) on student achievement on Common Core-aligned standardized assessments in the State of Connecticut. The purpose of this case study is to: 1) Determine extent to which SES explains the differences between school district student achievement on Common Core-aligned assessments; and 2) Share a model that controls for SES of districts to more fairly examine and compare district performance on Common Core-aligned assessments. Perspective(s)/Theoretical Framework Since the Coleman Report (1966), over fifty years of research has connected indicators of SES to indicators of student academic achievement (White, 1982; Siren, 2005). Quantitative studies document statistically significant correlation between SES and student achievement on the SAT and ACT (Zwick, 2004), the National Assessment of Educational Progress (McGraw, R., Lubienski, S., & Strutchens, M., 2006), the Programme International for Student Assessment (Perry, L., & McConney, A., 2003), and state-level mastery assessments (Duncan, G, & Murnane, R., 2011). In 2009, the National Governor’s Association (NGA) commissioned the development of academic standards. In 2010, the authors of the Common Core State Standards (CCSS) published the College and Career Readiness Standards. By the end of 2013, more than 45 states and territories had adopted the CCSS. Concurrent with the adoption of the CCSS, two consortiums began developing assessments aligned to the CCSS. The Partnership for Assessment of Readiness for College and Career (PARCC) and the Smarter Balanced Assessment Consortium (SBAC) developed standardized assessments aligned to the CCSS for students in grades 3-8 and high school. The Common Core-aligned assessments replaced state-based mastery assessments. Results from the SBAC and PARCC standardized assessments are now available, making it possible to examine the shared variance between student achievement on the Common Core-aligned assessments and SES of families and communities. Methods/Modes of Inquiry/Data Sources/Analysis This study used the mean Vertical Scale Score (VSS) on the Smarter Balanced Assessment (SBA) administered in spring of 2016 in the State of Connecticut in grades three through eight in Mathematics and English Language Arts (ELA). …show more content…
To be included in the database, we determined that the Local Educational Agency (LEA) must be a Connecticut public school district for which we can collect SBA assessment data and SES data at both the grade level and the district level. We set the minimum number of test-takers at 60. We used a least-squares regression model to determine the predictive power of two socio-economic factors on student achievement. The outcome variable is the mean student scale score on the SBA, and the predictive variables are two measures of SES. First, we performed a simple regression model for each grade level and subject area for SBA to determine the predictive power of one SES variable, the percentage of students qualifying for either free or reduced-price lunch (FRPL). We then tested the hypothesis that adding a district-level variable to the model would improve …show more content…
We fit both a straight and curvilinear model. The adjusted r-squared for each grade level and subject area represents the percentage of variance explained by the model. This approach allowed us to answer the primary research question:
To what extent does SES explain the differences between school district student achievement in the State of Connecticut on Common
Core-aligned assessments?
Results and Conclusions
We begin by reporting the descriptive statistics that show the percent of districts and students that are the subject of analysis.
[Insert Table 1]
We then present the measures of central tendency by grade for both subjects on the SBA.
[Insert Table 2]
The main research question dealt with determining the strength of the relationship between SES and student achievement on SBA. We first illustrate the correlation between SBA and FRPL by reporting the Pearson r values for all grade levels and both subject areas.
[Insert Table 3]
Next, we added the community EAR variable to the model.
[Insert Table 4]
In all cases, adding in the EAR variable increased the strength of the relationship at a statistically significant level. The predictive variables EAR and FRPL are statistically
Paper Description: This quantitative study uses a least-squares regression model to determine the predictive power of socio-economic factors on district-level student achievement on new, Common Core-aligned standardized assessments. We posit that educators may use our methodology and model to control for socio-economic factors, and more equitably compare school district performance.
Purpose
We used a least-squares regression model to determine the predictive power of socio-economic status (SES) on student achievement on Common Core-aligned standardized assessments in the State of Connecticut. The purpose of this case study is to: 1) Determine extent to which SES explains the differences between school district student achievement on Common Core-aligned assessments; and 2) Share a model that controls for SES of districts to more fairly examine and compare district performance on Common Core-aligned assessments. Perspective(s)/Theoretical Framework Since the Coleman Report (1966), over fifty years of research has connected indicators of SES to indicators of student academic achievement (White, 1982; Siren, 2005). Quantitative studies document statistically significant correlation between SES and student achievement on the SAT and ACT (Zwick, 2004), the National Assessment of Educational Progress (McGraw, R., Lubienski, S., & Strutchens, M., 2006), the Programme International for Student Assessment (Perry, L., & McConney, A., 2003), and state-level mastery assessments (Duncan, G, & Murnane, R., 2011). In 2009, the National Governor’s Association (NGA) commissioned the development of academic standards. In 2010, the authors of the Common Core State Standards (CCSS) published the College and Career Readiness Standards. By the end of 2013, more than 45 states and territories had adopted the CCSS. Concurrent with the adoption of the CCSS, two consortiums began developing assessments aligned to the CCSS. The Partnership for Assessment of Readiness for College and Career (PARCC) and the Smarter Balanced Assessment Consortium (SBAC) developed standardized assessments aligned to the CCSS for students in grades 3-8 and high school. The Common Core-aligned assessments replaced state-based mastery assessments. Results from the SBAC and PARCC standardized assessments are now available, making it possible to examine the shared variance between student achievement on the Common Core-aligned assessments and SES of families and communities. Methods/Modes of Inquiry/Data Sources/Analysis This study used the mean Vertical Scale Score (VSS) on the Smarter Balanced Assessment (SBA) administered in spring of 2016 in the State of Connecticut in grades three through eight in Mathematics and English Language Arts (ELA). …show more content…
To be included in the database, we determined that the Local Educational Agency (LEA) must be a Connecticut public school district for which we can collect SBA assessment data and SES data at both the grade level and the district level. We set the minimum number of test-takers at 60. We used a least-squares regression model to determine the predictive power of two socio-economic factors on student achievement. The outcome variable is the mean student scale score on the SBA, and the predictive variables are two measures of SES. First, we performed a simple regression model for each grade level and subject area for SBA to determine the predictive power of one SES variable, the percentage of students qualifying for either free or reduced-price lunch (FRPL). We then tested the hypothesis that adding a district-level variable to the model would improve …show more content…
We fit both a straight and curvilinear model. The adjusted r-squared for each grade level and subject area represents the percentage of variance explained by the model. This approach allowed us to answer the primary research question:
To what extent does SES explain the differences between school district student achievement in the State of Connecticut on Common
Core-aligned assessments?
Results and Conclusions
We begin by reporting the descriptive statistics that show the percent of districts and students that are the subject of analysis.
[Insert Table 1]
We then present the measures of central tendency by grade for both subjects on the SBA.
[Insert Table 2]
The main research question dealt with determining the strength of the relationship between SES and student achievement on SBA. We first illustrate the correlation between SBA and FRPL by reporting the Pearson r values for all grade levels and both subject areas.
[Insert Table 3]
Next, we added the community EAR variable to the model.
[Insert Table 4]
In all cases, adding in the EAR variable increased the strength of the relationship at a statistically significant level. The predictive variables EAR and FRPL are statistically