PFT1981 | R Documentation |
Data from Project Follow Through (PTF) in public school education. There are 49 DMUs (school sites) in PFT and 21 DMUs in Non-Follow Through (NFT). Authors consider 3 outputs (Y) and 5 inputs (X).
data("PFT1981")
Data frame with 70 rows and 10 columns. Definition of inputs (X) and outputs (Y):
Total Reading Scores (as measured by the Metropolitan Achievement Test).
Total Math Scores (total mathematics score by the Metropolitan Achievement Test.
Total Coopersmith Scores (Coopersmith self-esteem inventory, intended as a measure of self-esteem).
Education level of mother (as measured in terms of percentage of high school graduates among female parents).
Occupation Index (highest occupation of a family member according to a pre-arranged rating scale).
Parental Visit Index (representing the number of visits to the school site).
Counseling Index (parent counselling index calculated from data on time spent with child on school-related topics such as reading together, etc.).
Number of Teachers (number of teachers at a given site).
PFT or NFT.
Vicente Coll-Serrano (vicente.coll@uv.es). Quantitative Methods for Measuring Culture (MC2). Applied Economics.
Vicente Bolos (vicente.bolos@uv.es). Department of Business Mathematics
Rafael Benitez (rafael.suarez@uv.es). Department of Business Mathematics
University of Valencia (Spain)
Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through", Management Science, 27(6), 668-697. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1287/mnsc.27.6.668")}
make_deadata
, model_basic
# Example 1. Replication of results in Charnes, Cooper and Rhodes (1981)
data("PFT1981")
# selecting DMUs in Project Follow Through (PFT)
PFT <- PFT1981[1:49, ]
PFT <- make_deadata(PFT,
dmus = 1,
inputs = 2:6,
outputs = 7:9 )
eval_pft <- model_basic(PFT,
orientation = "io",
rts = "crs")
eff_pft <- efficiencies(eval_pft)
# Example 2. Replication of results in Charnes, Cooper and Rhodes (1981)
data("PFT1981")
# selecting DMUs in Non-Follow Through (NFT)
NFT <- PFT1981[50:70,]
NFT <- make_deadata(NFT,
dmus = 1,
inputs = 2:6,
outputs = 7:9 )
eval_nft <- model_basic(NFT,
orientation = "io",
rts = "crs")
eff_nft <- efficiencies(eval_nft)
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