Our prediction accuracy ranges from 62% to over 80% of districts in a state, depending on the grade level and subject tested

(a) percentage of families in a community with income over $200,000 a year, (b) percentage of people in a community in poverty, and (c) percentage of people in a community with bachelor’s degrees. Those three variables accurately predicted results for 78% of our samples.

The 2011 grade seven math model of best fit included the (a) percentage of people in a community with advanced degrees, (b) percentage of people in a community without a high school diploma, and (c) percentage of lone parent households.

the results of state standardized tests are strongly influenced by non-school factors.

complimentary

e.g., Darnell, 2015 Darnell, B. (2015). The value of Iowa school district demographic data in explaining school district ITBS/ITED 3rd and 11th grade language arts and mathematics scores. Seton Hall University Dissertations and Theses (ETDs). Paper 2075.; Maylone, 2002 Maylone, N. (2002). The relationship of socioeconomic factors and district scores on the Michigan educational assessment program tests: An analysis. Unpublished, Eastern Michigan University, Ypsilanti, Michigan.; Sackey, 2014 Sackey, A. N. L. (2014). The influence of community demographics on student achievement on the Connecticut mastery test in mathematics and English language arts in grade 3 through 8. Seton Hall University Dissertations and Theses (ETDs). Paper 2010.; Tienken, 2016 Tienken, C. H. (2016). Standardized test results can be predicted, so stop using them to drive education policymaking. In C. Tienken & C. Mullen (Eds.), Education policy perils: Tackling the tough issues (pp. 157–185). Philadelphia, PA: Taylor Francis Routledge.).

the percentage of students in a school or district who will score proficient or above on state standardized tests in language arts and mathematics at the district level can be predicted, with a good deal of accuracy, by using only community- and family-level demographic variables found in U.S. census data

Research indicates that children from low-SES households and communities develop academic skills more slowly compared to children from higher SES groups (Morgan, Farkas, Hillemeier, & Maczuga, 2009). Initial academic skills are correlated with the home environment, where low literacy environments and chronic stress negatively affect a child’s preacademic skills. The school systems in low-SES communities are often underresourced, negatively affecting students’ academic progress (Aikens & Barbarin, 2008). Inadequate education and increased dropout rates affect children’s academic achievement, perpetuating the low-SES status of the community. Improving school systems and early intervention programs may help to reduce these risk factors, and thus increased research on the correlation between SES and education is essential.

the financial well-being of a family stands in for other more important variables such as race and family level of education.

Now comes research that reveals that a simple demographic study of the wealth of the parents could have accurately predicted the outcomes, no desks or test packets needed.

In his report, published by Education Next, Peterson cited a 2011 Brookings Institution study that found that the direct impact of family income on math scores, once factors such as race and parental education are factored out of the equation, is just 6.4 percent of a standard deviation.

But when it comes to wealth and educational outcomes, common knowledge has it right: on average, kids from wealthy families do significantly better than kids from poor families. Household wealth is associated with IQ1and school achievement,2 and that phenomenon is observed to varying degrees throughout the world.3 Household wealth is asso-ciated with the likelihood of a child graduating from high school4 and attending college.5 With a more fine-grained analysis, we see associations with wealth in more basic academic skills like read-ing achievement6 and math achievement.7 And the association with wealth is still observed if we examine even more basic cogni-tive processes

Usingonlycommunitydemographicfactors,thisstudysuccessfullypredictedasmuchas73%(11thgradeLanguageArts)oftheactual2010ITBS/ITEDscoresandasmuchas69%(11thgradeMathematics)oftheactual2010ITBS/ITEDscores.

the percentage of students in a school or district who will score proficient or above on state standardized tests in language arts and mathematics at the district level can be predicted, with a good deal of accuracy, by using only community- and family-level demographic variables found in U.S. census data (e.g., Darnell, 2015 Darnell, B. (2015). The value of Iowa school district demographic data in explaining school district ITBS/ITED 3rd and 11th grade language arts and mathematics scores. Seton Hall University Dissertations and Theses (ETDs). Paper 2075. ; Maylone, 2002 Maylone, N. (2002). The relationship of socioeconomic factors and district scores on the Michigan educational assessment program tests: An analysis. Unpublished, Eastern Michigan University, Ypsilanti, Michigan. ; Sackey, 2014 Sackey, A. N. L. (2014). The influence of community demographics on student achievement on the Connecticut mastery test in mathematics and English language arts in grade 3 through 8. Seton Hall University Dissertations and Theses (ETDs). Paper 2010. ; Tienken, 2016 Tienken, C. H. (2016). Standardized test results can be predicted, so stop using them to drive education policymaking. In C. Tienken & C. Mullen (Eds.), Education policy perils: Tackling the tough issues (pp. 157–185). Philadelphia, PA: Taylor Francis Routledge. ).

Education officials and governors from more than 40 states essentially volunteered their public school students, parents, teachers, and school administrators to participate in various standardized testing programs that met the requirements set forth in the RTTT grant application and other state-developed accountability guidelines. The mandated tests must align to the Common Core State Standards (CCSS) (National Governors Association Center for Best Practices [NGA] & Council of Chief State School Officers [CCSSO], 2010) in mathematics and English language arts, or other state-adopted curriculum standards that conformed to the college and career readiness definitions and mandates set forth in RTTT.

the results of state standardized tests are strongly influenced by non-school factors