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Source: American Evaluation Association Annual Conference

Resulting in 4 citations.

1.	Merola, Stacey
1.	Timing of Dropout Decisions: Rethinking the ABCs Presented: Washington DC, American Evaluation Association Annual Conference, October 2013 Cohort(s): NLSY97 Publisher: American Evaluation Association Keyword(s): Achievement; Dropouts; Social Environment Permission to reprint the abstract has not been received from the publisher. In this paper we demonstrate the benefits of using longitudinal data to understand the reasons why students dropout and the timing of their dropout decisions. Many early-warning systems are being developed to identify students at-risk for dropout. Most of these systems focus on the "ABC's": attendance, behavior, and course grades, with much of emphasis of interventions focusing on improving student achievement. Data from the National Longitudinal Survey of Youth 1997 (NLSY97), were analyzed using survival analysis to assess the timing of dropout as related to reasons for dropping out. We found that even though students who exhibit difficulties in academics are often prioritized for dropout prevention services, academic problems do not precipitate dropout as quickly as other interpersonal and social factors. Students with academic difficulties stayed in school longer than all other types of dropouts, and students with interpersonal issues (i.e., those who indicated that school was too dangerous) were the first to exit.
	Bibliography Citation Merola, Stacey. "Timing of Dropout Decisions: Rethinking the ABCs." Presented: Washington DC, American Evaluation Association Annual Conference, October 2013.

2.	Porowski, Allan Passa, Aikaterini
2.	Charting a Student's Educational Career Using the National Longitudinal Survey of Youth (NLSY): Timing and Sequencing of Risk Factors among Dropouts Presented: Washington DC, American Evaluation Association Annual Conference, October 2013 Cohort(s): NLSY97 Publisher: American Evaluation Association Keyword(s): Bullying/Victimization; Crime; Dropouts; Family Environment; Grade Point Average (GPA)/Grades; Household Income; Sexual Activity; Substance Use Permission to reprint the abstract has not been received from the publisher. The National Longitudinal Survey of Youth (NLSY97) is a publicly-available data source that includes a nationally-representative set of U.S. residents who were between the ages of 12 and 16 in 1997. There are now 14 rounds of annual data on this sample, offering ample opportunities for long-term tracking of student risk factors and outcomes. In this presentation, the authors will provide the results of an inquiry designed to take a holistic view of a dropout's educational career. In particular, the authors will identify key inflection points in a dropout's academic career (from middle school through high school) where risk factors manifested themselves, and present the sequencing of those risk factors. Risk factors investigated include the initiation of alcohol use, initiation of tobacco use, initiation of marijuana use, grades in school, family dissolution, changes in household income, crime victimization, sexual intercourse, and crime. The investigation will help identify the root causes of dropout.
	Bibliography Citation Porowski, Allan and Aikaterini Passa. "Charting a Student's Educational Career Using the National Longitudinal Survey of Youth (NLSY): Timing and Sequencing of Risk Factors among Dropouts." Presented: Washington DC, American Evaluation Association Annual Conference, October 2013.

3.	Uekawa, Kazuaki
3.	The Influence of Family Structure on Social Outcomes Presented: Washington DC, American Evaluation Association Annual Conference, October 2013 Cohort(s): NLSY97 Publisher: American Evaluation Association Keyword(s): Crime; Dropouts; Family Structure; Health Factors; Sexual Activity; Substance Use; Transition, Adulthood Permission to reprint the abstract has not been received from the publisher. The National Longitudinal Survey of Youth (NLSY97) provides a wealth of data on youth as they transition into adulthood, including information about their family structure. In this presentation, the author will present results of his investigation of the relationship between a youth's family structure (i.e., intact, blended, divorced, and never-married families) on a variety of social outcomes including dropout, substance abuse, sexual behaviors, health habits, and crime. This investigation will include both a summary of the literature on the influence of family structure on social outcomes, as well as the results of quantitative analyses to describe the marginal influences of family structure on each type of outcome. Implications of these findings will be discussed, especially as they relate to family engagement.
	Bibliography Citation Uekawa, Kazuaki. "The Influence of Family Structure on Social Outcomes." Presented: Washington DC, American Evaluation Association Annual Conference, October 2013.

4.	Uekawa, Kazuaki
4.	The Use of Receiver Operating Characteristic (ROC) Curve Analysis for the Prediction of Educational Outcomes: Lessons Learned from the National Longitudinal Survey of Youth (NLSY) Presented: Washington DC, American Evaluation Association Annual Conference, October 2013 Cohort(s): NLSY97 Publisher: American Evaluation Association Keyword(s): Dropouts; Educational Outcomes; Modeling Permission to reprint the abstract has not been received from the publisher. Receiver Operating Characteristic (ROC) Curve Analysis can be applied to the prediction of educational outcomes that are dichotomous in nature. Such outcomes include grade retention, dropout, college enrollment, or college graduation. The ROC Curve Analysis is used often in medical science where, given the values of a continuous variable (e.g., blood pressure, hormone level), prediction is made for the dichotomous outcome (e.g., diabetes, pregnancy). Based on a pair of diagnostic statistics, sensitivity and specificity, the analysis helps derive a cut point for the predictor variables such that the prediction result will be optimized. Using publicly available educational databases as examples (e.g., NLSY97, NELS88), the authors will show how the analysis can be implemented in educational systems. For example, this method can be used to predict dropouts for an early warning system, or help superintendents predict retention rates. We will conclude the presentation by discussing this method's strengths and weaknesses as a tool for educational intervention.
	Bibliography Citation Uekawa, Kazuaki. "The Use of Receiver Operating Characteristic (ROC) Curve Analysis for the Prediction of Educational Outcomes: Lessons Learned from the National Longitudinal Survey of Youth (NLSY)." Presented: Washington DC, American Evaluation Association Annual Conference, October 2013.

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