Predicting Heuristic Decisions in Child Welfare: A Neural Network Exploration.
Behavior analysts have long recognized the benefits of closely following their data; however, the data we are following may be moving faster than the tools we have to accurately analyze and predict future behaviors. This problem even saturates behavior-analytic investigations that focus on the evalu...
| Published in: | Behavior & Social Issues Vol. 30; no. 1; pp. 194 - 209 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Published: |
Springer Nature
2021
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=155639283&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 155639283 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10649506 G26 jtl: Behavior & Social Issues issn: 10649506 maglogo: N pubinfo: dt: 2021 vid: 30 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 155639283 10.1007/s42822-021-00047-1 ppf: 194 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.2MB tig: atl: Predicting Heuristic Decisions in Child Welfare: A Neural Network Exploration. aug: au: Ninness, Chris Yelick, Anna Ninness, Sharon K. Cordova, Wilma affil: Behavioral Software Systems, 2207 Pinecrest Dr, 75965, Nacogdoches, TX, USA Florida Institute for Child Welfare, Florida State University, Tallahassee, FL, USA Texas A&M University–Commerce, Commerce, TX, USA Stephen F. Austin State University, Nacogdoches, TX, USA su: Child welfare Behavior analysts Decision making Artificial neural networks Self-organizing maps sug: subj: Child welfare Behavior analysts Decision making Artificial neural networks Self-organizing maps keyword: Architectures Cross-validation Deep neural networks Prediction Self-organizing map Architectures Cross-validation Deep neural networks Prediction Self-organizing map ab: Behavior analysts have long recognized the benefits of closely following their data; however, the data we are following may be moving faster than the tools we have to accurately analyze and predict future behaviors. This problem even saturates behavior-analytic investigations that focus on the evaluation of complex data related to public policy issues in areas such as poverty, geriatrics, and child welfare practice. In the face of this research enigma, there exists a more powerful and precise set of classification and prediction platforms for researchers in the behavioral sciences. In this article, we describe a combination of neural network strategies that predict child welfare professionals' decision making. Extending the data analysis from the Yelick and Thyer (2019) study, we employed our current version of the Kohonen self-organizing map in conjunction with our deep neural network as a strategy for identifying participants who were at high probability for making heuristic decisions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|