Research in Middle Level Education A Dropout Prediction Model That Highlights Middle Level Variables A Dropout Prediction Model A Dropout Prediction Model That Highlights Middle Level Variables. Belcher, D C. & Hatley, R. V 2017. Citation Key: Belcher2017doi abstract bibtex In a rapidly changing informational society the concern with high school dropouts is becoming more pronounced. The lackof low-skill job positions and increases in the poverty-level population require intensive study of students at-risk. The study reported in this article investigated the potential of identij5jmg and predicting who would drop out of school and who would persist to graduation. Data on 22 variables, typically available in a student's cumulative record, for two cohort classes of dropouts and persisters were collected. All 22 variables were sign\$cantly different for those who dropped out and those who persisted to graduation. Stepwise logistical regression determined the most parsimonious bestfit of six variables in early identifica-tion of potential dropouts withjive of the six being variables associated with student performance and behavior while in the junior high/middle school. These six variables, in combination, classified correctly 84.7 percent of the students as dropouts (80.5% accuracy) or persisters (86.4% accuracy).
@article{belcherResearchMiddleLevel2017,
title = {Research in {Middle} {Level} {Education} {A} {Dropout} {Prediction} {Model} {That} {Highlights} {Middle} {Level} {Variables} {A} {Dropout} {Prediction} {Model} {A} {Dropout} {Prediction} {Model} {That} {Highlights} {Middle} {Level} {Variables}},
issn = {1082-5541},
doi = {10.1080/10825541.1994.11670032},
abstract = {In a rapidly changing informational society the concern with high school dropouts is becoming more pronounced. The lackof low-skill job positions and increases in the poverty-level population require intensive study of students at-risk. The study reported in this article investigated the potential of identij5jmg and predicting who would drop out of school and who would persist to graduation. Data on 22 variables, typically available in a student's cumulative record, for two cohort classes of dropouts and persisters were collected. All 22 variables were sign\$cantly different for those who dropped out and those who persisted to graduation. Stepwise logistical regression determined the most parsimonious bestfit of six variables in early identifica-tion of potential dropouts withjive of the six being variables associated with student performance and behavior while in the junior high/middle school. These six variables, in combination, classified correctly 84.7 percent of the students as dropouts (80.5\% accuracy) or persisters (86.4\% accuracy).},
urldate = {2017-10-06},
author = {Belcher, D Christopher and Hatley, Richard V},
year = {2017},
note = {Citation Key: Belcher2017},
}
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