# Few things are more feared than statistical analysis.

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1 DATA ANALYSIS AND THE PRINCIPALSHIP Data-driven decision making is a hallmark of good instructional leadership. Principals and teachers can learn to maneuver through the statistcal data to help create goals and strategies for change and improvement. BY THEODORE B. CREIGHTON Few things are more feared than statistical analysis. Many school leaders make decisions about instructional leadership by using their intuition and shooting from the hip, rather than considering data collection and data analysis. But meaningful information can be gained only from a proper analysis of data, and good decisions are based on this thoughtful process of inquiry and analysis. School districts across the United States collect and maintain many forms of educational data (e.g., attendance rates and standardized and criterion-referenced test scores); nevertheless, most schools use the collection of these data to satisfy administrative requirements, not to assess and evaluate school improvement. Standardized test scores are generally reviewed only briefly until the local newspaper calls. Average daily attendance is reported to state education agencies, then filed away. Educators rarely examine these data to assess the quality of teaching and learning at their schools. To most educators, statistics means endless calculations and formula memorization. Statistics is perceived as the formal domain of advanced mathematics and as a course taught by professors who desire to make their students lives as painful as possible. Such courses usually focus on formal proofs of mathematical theorems and the derivation of statistical formulas. Contrary to popular opinion, if someone has passed a high school course in elementary algebra, he or she has the knowledge and skills required for understanding statistical analysis. Educators fears of statistics likely relate to a variety of factors, but principal and teacher preparation programs must accept the fact that the presentation of statistics in education probably lacks four important components. First, it does not emphasize the relevance of statistics to the dayto-day lives of principals and teachers. Second, it does not fully integrate current technology into the teaching and 52 P RINCIPAL L EADERSHIP

2 learning of statistics. Third, few (if any) statistics courses are designed for students enrolled in education leadership or teacher education programs. Fourth, many statistics courses taught in colleges of education focus on inferential statistics as a tool for conducting research projects and dissertations. Far less time is spent on statistical strategies that might help principals improve their skills in problem analysis, program and student evaluation, data-based decision making, and report preparation. Relevance Students in traditional statistics courses frequently ask, When will I ever use this stuff? Most statistics courses at the college level are taught as hard-core math, devoid of powerful and practical applications relevant to school administration and student learning. Many educators seem to realize the importance of relevance in instructional courses but have been slow to incorporate the same level of practical application to statistics and research courses. Technology According to McNamara and Thompson (1996), applied educational statistics instruction must move away from the traditional conception of statistics as mathematical theory and approximate administrator and teacher preparation programs. The advance of technology and the large MAY/JUNE

4 ANOVA MATRIX, INSTRUCTIONAL STRATEGY BY CONTENT AREA Math Reading Science Cooperative Learning math with cooperative learning reading with cooperative learning science with cooperative learning Direct Instruction Scores for students in math with direct instruction Scores for students in reading with direct instruction science with direct instruction Each cell in Figure 1 contains a separate group of students tested with the same dependent variable (a standardized test). The ANOVA allows us to test the mean differences between these six instructional situations. We are looking for any statistically significant difference among the six groups. Figure least one course in tests and measurements or statistics. I have four graduate degrees myself and can recall no class discussion of what to do with assessment information in planning how to help students do better. I have come to the conclusions that such courses are taught by researchers as though they are preparing researchers. As a result, the emphasis is on esoteric experimental design which can t be replicated in a normal school setting. (P. 22) Holcomb continues by quoting Gerald Bracey (1997), internationally recognized as an expert on the understanding of education statistics: Many of the university professors who create and use statistics are more comfortable using them than they are teaching other human beings what they mean. And in all too many instances, statistics are taught in a theoretically rarefied atmosphere replete with hardto-understand formulas and too few examples to the daily life of education practitioners. MAIN EFFECT DUE TO TYPE OF INSTRUCTIONAL STRATEGY Cooperative Learning Direct Instruction Math Reading Science Note: * represents test mean scores of 200 for each of math, reading, and science utilizing cooperative learning strategies and mean scores of for each of math, reading, and science using the direct instruction strategy. Direct instruction is represented by the solid black line, and cooperative learning is represented by the dashed line. Figure 2 Using Data Analysis in the Principal s Office Example One. Data analysis and statistics that foster school improvement must be more understandable and useful to educators in schools. For example, analysis of variance (ANOVA) is a procedure for evaluating the mean differences between two or more samples, similar to a t test. The difference between an ANOVA and a t test is that the t test is an appropriate procedure for no more than two samples. The ANOVA permits comparison among many variables at one time, allowing for much more flexibility. And educators must look at many variables (e.g., socioeconomic status, limited English, and special education). Let s start off with what seems to be an oxymoron: the formulas and calculations of the ANOVA are quite complicated, but the concept of the ANOVA is simple. Further, if someone has a conceptual understanding of the process of the ANOVA, he or she can use one of many user-friendly computer software programs to perform the procedure with a few keyboard strokes. Suppose we want to look at the effects of two instructional strategies: cooperative learning and direct instruction. But we realize that the results might vary according to the variety of courses taught. In other words, maybe cooperative learning strategies work better in math classes than in language classes. Figure 1 shows two instructional strategies and three subject areas, forming six groups (samples) of students to be evaluated. You can guess what our questions might be. Is there any difference between the two instructional strategies? Does perhaps one strategy work better in math instruction whereas another works better in science? We might find evidence to support the use of cooperative learning in all three content areas (main effect). If this were the case, there would be no interaction between the two instructional strategies. MAY/JUNE

6 tion, the writing scores would give us reason to suggest purchaising new instructional materials for that subject area. Conclusion Collecting data without purpose is meaningless. Using the many different kinds of data collected in schools to help make decisions legitimizes the goals and strategies educators create for change and improvement. These data help us identify groups of students who are improving and groups of students who are not and help to identify the reasons. Data-driven decision making and instructional leadership must go hand in hand. It is no secret that our profession is under careful scrutiny by our national and state legislators, communities, and, yes, the universities. I cannot imagine a better time for us to consistently use data to improve decision making in our schools for the benefit of our students. Yes, it is time consuming and, in some cases, costly. But what s our choice? Until we begin to seriously evaluate and analyze the data that exist in our schools, our profession will continue to be scrutinized and questioned with regard to student achievement and quality teaching and learning. Evidence indicates that public schools are losing some of our market share to private schools, vouchers, charter schools, and some emerging for-profit enterprises. We must discontinue the practice of making decisions based upon intuition and gut feelings. References Bracey, G Understanding education statistics: It s easier (and more important) than you think. Arlington, Va.: Educational Research Service. Creighton, T. B Schools and data: The educator s guide for using data to improve decision making. Thousand Oaks, Calif.: Corwin Press. Holcomb, E. L Getting excited about data: How to combine people, passion, and proof. Thousand Oaks, Calif.: Corwin Press. McNamara, J. F., and Thompson, D. P Teaching statistics in principal preparation programs. International Journal of Educational Reform 5 (3): Theodore B. Creighton is a former principal and superintendent. He is professor for the Department of Educational Leadership at Sam Houston State University in Huntsville, Tex. PL Advertisement MAY/JUNE

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