ecm regression analysis
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NEHA NEHRA | AYAPPARAJ SKS | ADITYA NATHIREDDY | VIBEESH CS
API (Annual Performance Indicator) : Elementary School
ECONOMETRICS PROJECT PRESENTATION
Agenda API – Introduction Key features of the API Business Objective Overall Data Sanity Check Regression Model Inferences Recommendations
API: An Introduction• The API is a single number, ranging from a low of 200 to a high of 1000, which reflects a school’s, an Local Educational Agencies (LEA) , or a student group’s performance level, based on the results of statewide assessments. Its purpose is to measure the academic performance and improvement of schools.
• The API is calculated by converting a student’s performance on statewide assessments across multiple content areas into points on the API scale. These points are then averaged across all students and all tests. The result is the API. An API is calculated for schools, LEAs, and for each student group with 11 or more valid scores at a school or an LEA.
API: Key features• The API is based on an improvement model. The assessment results from one year are compared to assessment results from the prior year to measure improvement.
• The API is used to rank schools. A school is compared to other schools statewide and to 100 other schools that have similar opportunities and challenges.
• The API is a cross-sectional look at student achievement. It does not track individual student progress across years but rather compares snapshots of school or LEA achievement results from one year to the next.
• The API is currently a school-based requirement only under state law. However, API reports are provided for LEAs in order to meet federal requirements under the federal Elementary and Secondary Education Act (ESEA).
To find the factors that have most influence on the performance of elementary schools in California, from 400 elementary schools from the California Department of Education's API 2000 dataset.
Overall Data Sanity Check : Missing Value Treatment
Dataset consists of 400 school and 21 Variables
Imputing mobility,acs_k3 and acs_46 with column wise mean 18.25 , 18.55 , 29.69 respectively
Pct free meals impute by category wise mean. Refer Notes below
Imputing 19 values of avg_ed as 0 since variables not_hsg, hsg, some_col,col_grad and grad_school are 0 parents are neither high school graduates neither went to some college or school
Overall Data Sanity Check : Outlier Treatment
ACS-K3: 6 values are negative , this is only for district number 140. This shows there is some manual input error as class size cannot be negative
We would take absolute values for the same Variable Full Percentage of full time teachers : Values which are less than
1 as the percentage figures are between 1 -100. Multiply those with 100
Regression Model
API = (869.12*Intercept)+(2.27*grad_sch)-(3.67*meals)-(41.21*yr_rnd)
Model Significance:
1) Adjusted R2 is 81.35% where 81.35% of variability in API is being explained by variables grad_sch , meals and yr_rnd
2) The variables are significant at 99% significant level and VIF less than 1.8
3) This model has MAPE of 8.15
Variable 1 Inferences : Parent Grad School 1) This is a positive indicator on the API.
If grad_sch increases by 1 the API increases by 2.77
2) A graduate parent plays a strong and
a role of a contributor in the life of his/her child
3) They help in the academics arena as
well as creating an overall outlook towards life
4) They would be able to understand the
gap areas well and help their child in the improvement areas
5) They focus on inculcating the
discipline and right virtues that help in development of the child thereby API
Variable 2 Inferences : Percentage of Free Meals 1) This is a negative indicator on the
API .If meals increases by 1 the API decreases by 3.67
2) The access of free meals is a sign
of poor background of the students who are deprived of basic necessities.
The main concern for these students is to get food as they cannot afford the same
3) The academic performance
suffers as a result for the same 4) A large expenditure would go in
arranging of these meals thereby reducing budget on other key parameters like hiring of teachers etc
Variable 3 Inference : Year Round School 1) This is a negative indicator on the API .If
meals increases by 1 the API decreases by 41.21
2) Students in a year round schooling attend school the same number of days as in traditional school (180 Days) but former has several short vacations instead of one long vacation
3) This is a huge burden for schools to
manage the maintenance of infrastructure and teachers. This would reduce the budget planned for other activities
4) Students can’t plan for other activities
which they can learn during long holidays like summer intern-ships, hobby classes thereby impacting student's performance
Recommendations 1) Year Round Schools have an add on
pressure for all the sides schools, teachers and students. Proper incentives to school focusing on teachers development and curriculum planning.
2) Focus on guiding schools on proper
budget planning and track the expenditure spent on various aspects. This will ensure proper utilization o the funds.
3) Meals that are provided must be healthy
and good quality. 4) Graduate Parents must be brought in
panel as part of guest lectures, workshop . This would help in boosting the morale of the students and guiding the poor students in performing better.
Thank You
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