During the workshop, several presentations focused on research identifying the precursors of dropping out. In this chapter, we first discuss the findings from this research and then describe ways that data systems can be developed to incorporate these indicators and used to develop intervention strategies.

RESEARCH ON PRECURSORS TO DROPPING OUT

A considerable body of research exists on precursors to dropping out, but research findings are not entirely definitive, and the advice they offer has evolved over time (see, e.g., literature reviews in Gleason and Dynarski, 2002; Jerald, 2006; National Research Council, 2001). Early research suggested that certain social and family background factors were associated with an increased risk of dropping out, such as being poor, minority, from a single-parent family, or from a family with low educational attainment or low support for education (Barro and Kolstad, 1987; Eckstrom et al., 1987; Haveman, Wolfe, and Spaulding, 1991; Mare, 1980, National Center for Education Statistics, 1990, 1992; Natriello, McDill, and Pallas, 1990; Rumberger, 1995). In the 1980s, researchers began questioning the role of individual factors—in part because these variables are beyond the control of school systems—and research was designed to identify school-related factors associated with dropping out (Whelage and Rutter, 1986, cited in Jerald, 2006). This research documented that although individual demographic factors are related to dropping out, students’ educational experiences are equally important. These studies showed that students who dropped out reported that they disliked school and found it boring and not relevant to their needs; had low achievement, poor grades, or academic failure; or had financial needs that required them to work full-time (ERIC Digest, 1987; Jerald, 2006; Jordan, Lara, and McPartland, 1999). Other research has identified school-related factors associated with lower dropout rates, including high schools with smaller enrollments, more supportive teachers, positive relationships among students and school staff, and a more rigorous curriculum (Croninger and Lee, 2001; Lee and Burkham, 2000; McPartland and Jordan, 2001).

The late 1980s and early 1990s brought concerted efforts to develop intervention programs designed to prevent at-risk students from dropping out. These programs were supported, in part, by federal grants from the School Dropout Demonstration Assistance Program. Federally funded evaluations of these efforts examined the effectiveness of the approaches the programs used for identifying at-risk students. These reviews found that the approaches tended to misclassify students, resulting in programs serving students who would not have dropped out and failing to serve students in most need of preventive services (Dynarski, 2000; Gleason and Dynarski, 1998, 2002). In these studies, Gleason and Dynarski reported that many of the variables used to identify at-



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