Using correlational and prediction data to enhance student achievement in K-12 schools: a practical application for school counselors

Professional School Counseling, June, 2006 by William B. Ware, John P. Galassi

Correlational data and regression analysis provide the school counselor with a method to describe growth in achievement test scores from elementary to high school. Using Microsoft Excel, this article shows the reader in a step-by-step manner how to describe this growth pattern and how to evaluate interventions that attempt to enhance achievement and to reduce the achievement gap among ethnic groups.

Juanita Smith, a sixth-grade counselor at Cesar Chavez Middle School in Hopeville, NC, has just returned from her first team meeting of the year with the four teachers who make up the sixth-grade Superstars team. As was the case last year, the teachers noted that their students didn't seem to be handling the transition from the smaller single-teacher, self-contained classes in elementary school to the multiple teachers, longer class periods, and changing classes that they were encountering in middle school. The students didn't seem to feel attached to the school and constantly complained that they wished they could go back to their old school. Academically, they seemed uninvolved and easily distracted, and the teachers thought that this response was particularly evident among the boys. The teachers worried that this lack of involvement would be reflected in disappointing scores on the standardized end-of-grade tests, just like last year. Because the goal of the school's student improvement plan is academic success for all students and closing the achievement gap between minority and White students, the teachers had asked Juanita whether the degree to which students felt attached to the school could impact their learning and ultimately predict their growth in achievement scores. If so, then they wanted to know what could be done right now to change how students felt about school.

INTRODUCTION

In this article, we show how Juanita and other school counselors can use available data and both regression and correlational analyses to answer the questions that she has been asked. Our demonstration will employ Microsoft Excel, a common software program that is frequently bundled with both Windows- and Macintosh-based computers. The demonstration will proceed in a step-by-step manner so that it can be generalized to analyzing related data by counselors who may not feel especially confident in their data analysis skills.

By way of review, the reader may recall that the relationship between two variables such as achievement (end-of-grade scores, or EOG) and sense of belonging (feeling attached to school or a sense of community or relatedness in school) is frequently described by a correlation coefficient. Specifically, the correlation coefficient can vary between 1.00 and -1.00. A value of 1.00 means that there is a perfect positive linear relationship between the two variables so that the more attached that students feel to the school, the higher their EOG test scores will be. Conversely, a correlation of -1.00 would indicate a perfect negative linear relationship between belonging and EOG test scores such that a high attachment is associated with low test scores. Finally, a correlation of 0.00 means that there is no linear relationship between a student's feelings of attachment or belonging to school and EOG test scores.

If the two variables are positively correlated, we might speculate that a counseling or educational intervention that increases students' levels of attachment to school also might raise their test scores. However, we could not be certain of that as the correlation only tells us that the high scores on the two variables tend to go together and not necessarily that raising scores on one of them will cause an increase in scores on the other variable. If there is a causal relationship between the two variables, we do not know the direction of the effect. That is, attachment to school may be the cause of achievement, achievement may be the cause of attachment to school, or it is also possible that the two correlated variables have a common cause. Despite our lack of certainty, the speculation that increasing students' feelings of attachment might result in greater commitment to learning and therefore higher achievement is worth pursuing, and we show the reader how to do that later in the article.

In order to answer the teachers' question about the extent to which feeling attached to school is related to academic growth, we need to briefly review some information about regression analysis. In North Carolina as in other states, students are assessed in a number of academic domains at several points in time. Within a domain (e.g., math), we are interested in describing students' patterns of growth, both where they started and the rate of change. Once we have described the growth pattern for students, we then can look at the relationship between growth rate and sense of belonging. But belonging is not the only variable (correlate) about which the teachers were concerned. Teachers also expressed concern that gender and ethnicity might also be related to students' responses to the elementary-middle school transition. A few even confided to Juanita that parent involvement in the school might be an important factor in students' sense of belonging to the school. In addition, the students' test scores in math during elementary school were available to Juanita, who had every reason to believe that their past pattern of achievement on these tests might be described with regression analysis. Specifically, Juanita thought that, after plotting the achievement scores for a student as a function of "time," she could use regression analysis to describe the growth in achievement pattern in math for each student. In this article, we show the reader how that can be completed. We then show how to examine the relationships between rate of growth and other variables such as sense of belonging, gender, ethnicity, and parent involvement.