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School counselor dispositions as predictors of data usage.(Report)

Professional School Counseling

| June 01, 2009 | Holcomb-McCoy, Cheryl; Gonzalez, Ileana; Johnston, Georgina | COPYRIGHT 2009 American School Counselor Association. This material is published under license from the publisher through the Gale Group, Farmington Hills, Michigan.  All inquiries regarding rights should be directed to the Gale Group. (Hide copyright information)Copyright

This article examined school counselor dispositions (e g,, general self-efficacy, counselor, self-efficacy, openness to change, commitment to counseling improvement/professional development) that predict data usage among K-12 professional school counselors. For the study, 130 professional school counselors from Maryland and Virginia completed the School Counselor Attribute and Data Usage Survey. Results indicated that school counselor self-efficacy and general self-efficacy beet predict school counselor data usage.

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Public school education is in an era of accountability, high-stakes testing, and standards-based reform (Carnoy, Elmore, & Sisken, 2003; Hamilton, Stecher, & Klein, 2002). Equity and accountability mandates (e.g., No Child Left Behind Act) have made it imperative that educators base decisions on accurate and meaningful data about student progress and achievement (Creighton, 2001). As such, school counselors are now being asked to document their programs' effectiveness and to target students and student groups who need the most assistance (Johnson & Johnson, 2003; Poynton & Carey, 2006; Studer, Oberman, & Womack, 2006). Although school counselors have historically used needs assessments as a means of targeting student needs, there is a current movement in the profession to include data-based decision making in the context of accountability and standards-based reform. This shift to more data-driven school counseling programs requires that school counselors utilize and generate more than the traditional enumerative data that document such things as the number of student contacts, the number of parent or teacher consultations, or the number of guidance classes taught (Stone & Dahir, 2004). School counselors are now being asked to document and use data to illustrate their effectiveness in the context of academic achievement, attendance, and other student outcomes (e.g., retention rates, graduation rates). Even the ASCA National Model[R] (American School Counselor Association, 2005) states that comprehensive school counseling programs should be data-driven and that the use of data should be integral to ensuring that every student receives the benefits of school counseling programs.

As stated previously, the effective use of data by district and school personnel is increasingly identified as a central tenet in school restructuring and improvement (Earl & Katz, 2002). Johnson (2002) examined many uses of data as a major force in building school and district capacity to equitably educate students and reduce achievement gaps. Her analysis of data usage in low-performing schools provided many examples of the effectiveness of using data as a means to change conditions and practices that act as barriers to equitable student achievement. While schools collect a wide array of data (e.g., demographic data, performance data, program data), it has been reported that data are rarely used effectively to promote increased student achievement (Herman & Gribbons, 2001; Love, 2004). Reasons for the lack of data usage in schools include educators' lack of training, cultural resistance, and fear of reprisal (Lachat & Smith, 2004).

Although many pre-service school counseling training programs have sought to integrate data usage skills into their training, many counselor educators and school counseling supervisors have realized that training alone does not create an effective "data-using school counselor." Because there has been limited research that examines why some school counselors use data as a component of their school counseling programs and others do not, we examined school counselor dispositions as possible predictors of data usage.

DISPOSITIONS

The literature on dispositions is grounded in the fields of philosophy and psychology. Goleman's (1995) model of emotional intelligence and Perkins, Tishman, Ritchhart, Donis, and Andrade's (2004) connections between neurological, experiential, and reflective intelligence acknowledge the impact of dispositions on people's thinking and judgments. Katz (1993) defined dispositions as patterns of behaviors that are exhibited frequently and intentionally in the absence of coercion. In the literature, there are numerous terms associated with dispositions. These terms range from tendencies and values to habits-of-mind and behaviors (Ritchhart, 2001). Nevertheless, dispositions are often used to describe trends in behaviors and attitudes rather than possessing a set of skills or knowledge. For instance, one might have a caring disposition but lack the skills and knowledge of "counseling." Katz reminded us that individuals can acquire knowledge and skills but not have the disposition to use them.

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