Predictors of Categorical At-Risk High School Dropouts

Article excerpt

For educators and counselors concerned with the well-being of society, school, family, and, particularly, the individual student, identifying the predictors of high school failure is a critical task. By identifying predictors early, counselors and other school personnel may be able to generate effective prevention and intervention strategies. A great deal of research has been conducted in an effort to identify factors that contribute to dropping out of school before high school graduation (Rumberger, 1983; Suh, 2001; Valdivieso, 1986; Vallerand, Fortier, & Guay, 1997; Velez, 1989). Variables that influence school dropout appear to come from various domains, such as individual, family, and school (Chavez, Belkin, Hornback, & Adams, 1991 ; National Collaboration for Youth, 1989; Wells, 1990). Many researchers who are interested in the issue of school dropout have attempted to identify students who are, in fact, at increased risk of dropping out. Some researchers (e.g., Janosz, Blanc, Boulerice, & Tremblay, 2000) have categorized at-risk students as those who exhibit academic, behavioral, or attitudinal problems that lead to school dropout. For our study, the term at-risk refers to aspects of a student's background and environment that may lead to a higher risk of her or his educational failure.

Most of the research on at-risk students uses models with multiple variables that influence at-risk behavior. For example, Battin-Pearson et al. (2000) claimed that a comprehensive model that includes multiple problematic sources for an individual is likely to better predict early high school dropout. Although such comprehensive models provide useful information for helping to understand why adolescents drop out, they are too broad to generate a guide that is focused enough to allow for the development and implementation of effective interventions. In addition, when intervention is delayed until multiple problems are manifested, intensive efforts may be needed, and the impact of the intervention strategy may be reduced. Thus, when it comes to reducing dropout, counselors and other professionals need to identify single models that can be used earlier in the educational process to guide intervention.

The purpose of this study was to identify and compare different factors that contribute to school dropout rates among three groups of at-risk students, thereby facilitating the implementation of effective dropout prevention strategies. We selected for analysis three at-risk categories that are frequently identified as strong predictors for school dropout. They are low socioeconomic status (SES), poor academic achievement, and suspension from school.

Low SES is one of the most frequently cited predictors of school dropout (Bradby, Owings, & Quinn, 1992; Gruskin, Campbell, & Paulu, 1987; McMillen & Kaufman, 1997; Orr, 1987; Weis, Farrar, & Petrie, 1989). Gruskin et al. noted that "when socioeconomic factors are controlled, the differences across racial, ethnic, geographic, and other demographic lines blur" (p. 5). Orr also pointed out that educational and socioeconomic backgrounds together are the strongest determinants of whether a student will drop out of school. Orr's statement posited that along with low SES, poor academic achievement is one of the strongest predictors in the etiology of dropout. Battin-Pearson et al. (2000) agreed that poor academic achievement is the strongest predictor of dropout. Deviant behavior has also been well documented to have a direct impact on high school dropout rate. Deviant behaviors are often expressed as disruptive school behaviors and frequent delinquent behaviors that increase the risk of school dropout for many students (Farmer & Payne, 1992; Gruskin et al., 1987; Reyes, 1989; Tindall, 1988). In the current study, suspension was considered to be symptomatic of deviant behaviors.

On the basis of the aforementioned theoretical perspectives, this study attempted to identify the most significant contributing factors to school dropout by categorizing students according to membership in a particular at-risk group. …


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