Value Added in CPS

What is value added? A measure of the contribution of schooling to student performance Uses statistical techniques to isolate the impact of schooling from other factors Focuses on how much students improve from one year to the next

Demographic adjustments Value added makes adjustments for demographics of schools and classrooms Adjustments determined by relationships between growth, student characteristics Adjustments measure partial differences in growth across groups district-wide

Some schools with low percent meet/exceed are high value-added schools in which students grow

Value added in many domains Annual state assessments –Focus on year-to-year student improvement Short-term assessments –Focus on short-term student improvement –Potentially faster turnaround High school assessments –Explore/PLAN/ACT, for example –Focus on improvement in high school

Value added in CPS Based on ISAT for grades 3 through 8 Analyzes students ISAT scores, demographics, and schools attended Schools and classrooms where students improve more (relative to similar students) identified as high value added Extra ISAT points gained by students at a school/classroom on average relative to observably similar students across district

Alternative understanding Average student gain on ISAT relative to district average, with adjustments for: –Shape of the test scale (Prior ISAT score) –Grade level –Gender, mobility, free/reduced-price lunch, race/ethnicity, disability, language proficiency, homelessness, other-subject pretest –Enrollment in multiple schools or classrooms

Regression model (in English) Posttest Pretest Post-on-Pre Link Student Characteristics School and Classroom Effects Unobserved Factors = x + ++ Value Added

Student characteristics Gender Race/ethnicity Free or reduced-price lunch Language proficiency (by Access score) Disability (by disability type) Mobility Homelessness Other-subject pretest

Why include student characteristics? One goal of value-added analysis is to be as fair as possible We want to remove the effect of factors that were not caused by the school during the specific period we are evaluating

Examples Curriculum Classroom teacher School culture Math pull-out program at school Structure of lessons in school Safety at the school Value added reflects the impact of these factors Examples Student motivation English Language Learner Status At home support Household financial resources Learning disability Prior knowledge These factors need to be measured and isolated Related to the school Not related to the school What do we want to evaluate?

Controlling for other factors Students bring different resources to the classroom. These factors can affect growth, so we want to remove the effects of these non- school factors. Examples Student motivation English Language Learner Status At home support Household financial resources Learning disability Prior knowledge These factors need to be measured and isolated Not related to the school

Controlling for other factors In order to include a characteristic in the model, we must have data on that characteristic for all students. Some characteristics are harder to measure and collect than others. The data that we do have available can tell us something about the effect of data we would like to have.

Controlling for other factors For example, we can use free or reduced- price lunch as a substitute for our ideal data about household finances in our calculations. What we want Household financial resources What we have Free or reduced-price lunch Related data

Adjustments are based on real data Why is it important that VARC uses student test scores to calculate adjustment factors? –We do not have a preconceived notion of which student subgroups will grow faster than others –We want to be as fair as possible when evaluating school performance –Student subgroups perform differently on each subject area from year-to-year –We want our adjustments to apply specifically to the situation we are evaluating

Multiple regression Measures effects of each variable on posttest controlling for all other variables –Effect of pretest on posttest controls for student characteristics, schools –Effects of student characteristics on posttest control for pretest, schools –Effects of schools (value added) on posttest control for pretest, student characteristics All effects measured simultaneously

Dosage Accounts for students changing schools and classrooms Students enrolled in a school or classroom for a fraction of a year get a fractional dose of the schools or classrooms effect Apportions student growth among schools and classrooms enrolled in the same year

Pretest measurement error Pretest measures student attainment in previous year with measurement error –Models that ignore this will bias in favor of high-attainment schools and classrooms Measurement error is accounted for in VA model to correctly account for pretest –Using approaches in Fuller (1987) –Ensures against bias

Models that correct for measurement error avoid biasing in favor of schools and classrooms with high initial scores

Value added model All of these features ensure that value added reflects the results of schooling on student achievement Value added uses the data available to measure the impact of schools and classrooms as accurately, fairly, and realistically as possible

Work in progress Classroom-level value added –Measures student growth within classrooms Differential effects value added –Measures growth among students of a particular group (ELL, disability, etc.) in a school or classroom Value added from other assessments –Scantron (short-term) –Explore/PLAN/ACT (high school)