School Systems Use Data to Research Paper

Excerpt from Research Paper :

For example, with respect to the leadership support area developed by Bryk, Korkmaz (2006) reports that in some cases, collecting primary data are required to make informed decisions, particularly with respect to human resource decisions. In his study of 842 teachers working in 42 elementary schools, Korkmaz operationalized the dimensions in this area as described in Table 2 below.

Table 2

Operationalization of Elementary School Climate and Leadership Dimensions



Instruments Used

School vision

This dimension was defined as being a top-down process that begins with the elementary school principal and purposely extends to all school stakeholders. The two important elements of effective leadership identified were:

1. Building up positive interpersonal relations; and,

2. Developing a school vision.

To measure the perceptions of teachers working in these schools about the vision in their schools, the study used the Robustness Semantic Differential (RSD), developed by Licata and Willower (1978) (see proforma copy at Appendix a).

Organizational health

This dimension measures the perception of teachers concerning the health of their schools. Some ways to measure this dimension include the extent to which:

1. Teachers working in schools see their school's success in building positive communication with its environment as a strong characteristic of their school.

2. Schools provide a learning conducive atmosphere.

3. Schools are flexible and can respond to changes in their environments to achieve their organizational goals and promote the common values of the educators.

4. Technical, managerial and institutional levels are in harmony wherein students, teachers and principals respond to the school vision.

5. Teachers are committed to the school vision and see that their colleagues are working towards a better future.

First developed by Hoy and Miskel (1991), the Organizational Health Inventory (OHI) was used in the study. A middle school form of the OHI scale was published by Hoy and Tarter (1997) and Hoy and Sabo (1998); the OHI developed by Licata and Harper (2001) was comprised of 33 items across 6 subscales representing approximately 77% of the cumulative variance. Relatively high alpha reliability coefficients have been established for these subscales. In addition, the instrument used by Licata and Harper (2001) for their research Organizational Health and Robust School Vision was also used in the study.

Source: Modified from Korkmaz, 2006

Based on his analysis of the data collected to measure these aspects of the leadership pillar, Korkmaz identified a significant relationship between elementary school teachers, organizational health and a robust school vision. According to Korkmaz, "As a result of multiple regression analysis, it was found that collegial leadership and academic emphasis related to school health and the resource support subscale were related to the robust school vision" (2006, p. 15). In order to place these statistical results in context, Korkmaz also suggested an explanation for this relationship. In this regard, this researcher noted that, "Statistics used to collect data and analyze the findings seem to support the hypothesis of the study that there is an important positive relationship between teachers' perception of organizational health and their perception of robust school vision" (2006, p. 15). This researcher is also quick to qualify these findings in other ways as well while still drawing some important and relevant conclusions. In this regard, Korkmaz concluded that, "It can comfortably be stated then that where technical, managerial and institutional levels are in harmony in a middle school, there is a healthy professional atmosphere. Probably, a school with such an atmosphere meets its needs and directs its potential energy towards the realization of its mission" (2006, p. 15). This same approach has been used by...
...463). In order to make the maximum use of the data collected for these types of studies, identifying others ways to analyze existing data to inform practice just makes good business sense. In this regard, Korkmaz provides the following suggestions for further research:

1. School health could be compared to the managerial style of the school (e.g., School-Based Management) or to determine whether there is a mutual relationship between managerial style and school health;

2. The manner in which managerial style affects the organizational health of the school could be investigated; and,

3. The effect of a robust school health on students' success can be studied.

The need to use this type of data analysis to develop meaningful findings, though, will likely require the addition of other data sets that can help identify potential cause and effect relationships and eliminate false leads. For this purpose, Korkmaz (2006) recommends using the instruments described in Table 2 above or comparable instruments with established reliability and validity to evaluate the entire range of factors that may contribute to school health. In sum, Korkmaz concludes that, "By studying types of relationships between school health and organizational conflict, the results can be used to develop school health. To develop a school vision, teachers' perceptions of principals' effectiveness can be measured and the results could be used to discover the possible positive relationship between school health and school vision" (2006, p. 15). This analysis, though, is not a static affair but rather requires administration every few years in order to ensure that its findings remain relevant and on-point (Korkmaz, 2006).

Other sources of data that can be used to implement strategies and drive instruction can be databases provided by the National Assessment for Educational Progress (NAEP) that provide a wide range of elementary school-related performance data that can help identify general trends in performance gaps. For instance, using the NAEP database, Philipp (2008) examined problem areas in mathematics instruction at the elementary school level across the country. According to Philipp, "The way most students are learning mathematics in the United States is problematic. In particular, students learn to manipulate mathematical symbols without developing the underlying conceptual meanings for the symbols" (2008, p. 7).

Design and Methodology

This type of study requires a qualitative research design supplemented by quantitative data sets such as standardized test results and other performance measures already being used, as well as the results of any primary research conducted to replace, augment or otherwise inform the analytical process. Based on the qualitative research design selected, a purposive sampling technique would be a suitable methodology for collecting the data needed for the assessment of each of the five pillars described by Bryk. According to Neuman (2003), purposive sampling is "a type of nonrandom sample in which the researcher uses a wide range of methods to locate all possible cases of a highly specific and difficult to reach population" (p. 542).

An exploratory data collection strategy would be used to collect the statistical data needed for this type study, with as many relevant data sources being located and integrated into the study as possible. All statistical data would be analyzed using Excel or SPSS Version 11.0 for Windows (Student Version) and the results of this analysis qualitatively synthesized. By focusing on an individual school, the results that are generated by this research approach may not be generalizable beyond a single school, but the framework that was used for the analytical process in transferable in a wholesale fashion to any academic setting.


Rationale in support of a qualitative design.

The literature was consistent in showing that strictly quantifiable measures are most useful for the specific purposes for which they were collected, but this information can be used in other ways to gain insights into strengths and weaknesses of a given elementary school as well. Typically, though, in order to develop truly original and significant contributions to the body of knowledge, researchers have been required to include a qualitative element in their analysis as well. Indeed, within the five indicators developed by Bryk and described above exists the entire range of possible outcomes, making some type of subjective analysis an important component to understand the context in which these outcomes were achieved. Therefore, simply "counting the beans" will not be sufficient to developing a robust and thorough understanding of the results of any data analysis that is used that can be provided through the inclusion of a qualitative approach.

How the research design and methodology inform the questions of the study.

Because every elementary school is unique in many ways, there is no "one-size-fits-all" approach available that is best suited for every school; however, the five-pillar framework developed by Bryk can be used as a generic framework in which the most important dimensions of elementary school instruction can be measured depending on the specific needs of the schools that are involved.

How this study might contribute to the body of knowledge on this topic.

Despite the introduction of a number of different methods for measuring the effectiveness of academic instruction, there has been far less attention directed at how data of other types…

Sources Used in Documents:


Bryk, a.S. (2010). Organizing schools for improvement: Research on Chicago school improvement indicates that improving elementary schools requires coherent, orchestrated action across five essential supports. Phi Delta Kappan, 91(7). 23-25.

Korkmaz, M. (2006). The relationship between organizational health and robust school vision in elementary schools. Educational Research Quarterly, 30(1), 14-16.

Licata, J.W. & Harper, G.W. (1999). Healthy schools, robust schools and academic emphasis as an organizational theme. Journal of Educational Administration, 37(5), 463 -- 475.

Neuman, W.L. (2003). Social research methods: Qualitative and quantitative approaches, 5th ed. New York: Allyn & Bacon.

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