Statement of Purpose & Principles
of Effective Data Use
"Learning takes place neither in isolation, nor only at school. Multiple measures must be considered and used to understand the multifaceted world of learning from the perspective of everyone involved"
(Bernhardt, 2004, p. 10)
(Bernhardt, 2004, p. 10)
Simply put, data is information. In the classroom, teachers use data to glean specific information regarding how to improve and what to improve on. Data shows students what is and what is not working. It is also used to give administration evidence of teacher effectiveness and student achievement. In the school and classroom setting, this information is used to improve student learning and achievement through careful analysis and application of the information gathered from data. This helps the school and/or teacher understand:
- the changes in which the school and community are experiencing
- the needs of students, parents, teachers, school, and community
- how well current processes meet each stakeholders' needs
- if all subgroups of students are being well-served
- the academic gaps between the desired and actual results
- the root causes for these gaps
- the types of education programs and/or processes that beed to be implemented to lessen the gaps and meet the needs of all stakeholders
- how well these new programs and/or processes are being conducted to meet the needs of the targeted group(s). (Bernhardt, 2004, p. 2).
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Qualitative
vs.
Quantitative Data
![Picture](/uploads/1/1/7/8/11781499/9680626.png?437)
Once data is collected, it is highly important to recognize what kind of data this information is displaying - QUANTITATIVE or QUALITATIVE. All data can be categorized into these two types. "Quantitative and qualitative techniques provide a tradeoff between breadth and depth, and between generalizability and targeting to specific (sometimes very limited) populations." (http://www.nsf.gov/pubs/2002/nsf02057/nsf02057_4.pdf - p. 43). Depending on the purpose of collecting the data, one might utilize one or the other, although, a combination of these two types provides a stronger set of information. In order to use these types effectively, independently and together, one must understand the components to each.
Qualitative Data
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Qualitative data involves descriptors and opinions. This kind of data can be observed and collected through surveys, interviews, group meetings, or general perceptions. When presenting qualitative data, vague terms are often used such as "greater amount", "more likely", "less often", etc. The emphasis with qualitative data is more on explaining behavior than counting numbers and it usually involves an anecdotal set of information regarding "big ideas". The open-ended questions that are so common with qualitative data best answer the questions "How?" and "Why?" rather than "Who?", "What?", and "When?".
Strengths
Strengths
- Complements and offers additional insights to the numbers presented with quantitative data
- Provides detailed information to explain complex issues
- Allows for multiple methods to be used for gathering data on sensitive issues
- Findings usually can not be generalized to the study population or community
- More difficult to analyze
- Generality does not offer concrete, objective data pieces
Quantitative Data
![Picture](/uploads/1/1/7/8/11781499/8494819.png?246)
Quantitative data deals with numbers. Unlike qualitative data, this type of data can be and is measured. "Data collected through quantitative methods are often believed to yield more objective and accurate information because they were collected using standardized methods, can be replicated, and, unlike qualitative data, can be analyzed using sophisticated statistical techniques" (p. 44 http://www.nsf.gov/pubs/2002/nsf02057/nsf02057_4.pdf). This information can be counted mathematically and usually contains statistical numeric evidence. Quantitative data often reflects analysis of numbers including mean, median, mode, average, highest, and lowest (among others) in a set of information. This type of data often answers questions such as "Who?", "What?", and "When?".
Strengths
Strengths
- Involves objective, inarguable facts
- Relatively easy to analyze
- Data is typically very consistent, accurate and reliable
- May not explain the trends in the data
- Difficult to understand context of program activities
- Data may not be robust enough to explain complex issues
Four Measures of Data
According to Bernhardt, there are four major measures of data that should be used when considering the analysis of student achievement. These four measures include demographics, perceptions, student learning, and school process. Analysis of these four measures of data "provide a powerful picture that will help us understand the school's impact on student achievement" (Bernhardt, 1998, p. 1). When combining these categories, a much richer wellspring of information is gleaned and "helps us to define the questions we want to ask, and focuses us on what data are necessary in order to find the answers" (Bernhardt, 1998, p. 1).
Demographics: This data provides descriptive information about the school community and identifies group characteristics such as gender, ethnicity, grade level, etc. Teachers often have little to no control over this set of data, however, it is important to observe these trends "for purposes of prediction and planning" (Bernhardt, 1998, p. 1).
Perceptions: This data helps determine the opinions of valuable stakeholders such as students, parents, administrators, teachers, etc., regarding the learning environment. Perception data can be collected through surveys, questionnaires, interviews, and observations. This category of data is helpful to know how to improve the system and informs about future possibilities.
Student Learning: This data is purely academic based. It explains how students are performing academically compared to the identified criterion-referenced standards.
School Processes: These are the different programs that are implemented within a school that affect classroom performance (RTI, speech, special education, etc.). This is what "defines what teachers are doing to get the results that we are getting" (Bernhardt, 1998, p. 3). The strategies that teachers use in the classroom also fall under this measure of collecting data.
Demographics: This data provides descriptive information about the school community and identifies group characteristics such as gender, ethnicity, grade level, etc. Teachers often have little to no control over this set of data, however, it is important to observe these trends "for purposes of prediction and planning" (Bernhardt, 1998, p. 1).
- Example question: How many students are enrolled in your school this year?
- Example question: How much of your class is male or female?
Perceptions: This data helps determine the opinions of valuable stakeholders such as students, parents, administrators, teachers, etc., regarding the learning environment. Perception data can be collected through surveys, questionnaires, interviews, and observations. This category of data is helpful to know how to improve the system and informs about future possibilities.
- Example question: What are parent, student, and staff opinions of the learning environment?
- Example question: How do students feel about reading in school?
Student Learning: This data is purely academic based. It explains how students are performing academically compared to the identified criterion-referenced standards.
- Example question: How did students at your school score on the state test?
- Example question: What percentage of students met proficiency on the final exam?
School Processes: These are the different programs that are implemented within a school that affect classroom performance (RTI, speech, special education, etc.). This is what "defines what teachers are doing to get the results that we are getting" (Bernhardt, 1998, p. 3). The strategies that teachers use in the classroom also fall under this measure of collecting data.
- Example question: What special programs are operating in your school this year?
- Example question: It could be found that 80% of students with a 504 plan have behavior plans as well.
Within these four measures of data collection, one can identify both qualitative and quantitative data sets. For example, there can be qualitative and quantitative data under demographics, under students learning, etc. One of these measures alone is not necessarily 'better' than another, it simply provides different information. Depending on the desired purpose for collecting data, the appropriateness of each measure may be emphasized differently. This being said, all data is not created equally because the purpose may differ from question to question. If one wants to answer the question, "How do parents think we meet the needs of students?", the data collected from measures 'school processes' and 'perception' would be the most appropriate data set to analyze. Bernhardt emphasizes the importance of using key questions to guide and build data sets so that there is a clear focus for analysis (Bernhardt, 1998, p. 5). One thing can be certain is, the more measures that are used over time, and not until all four measures are used, "are we able to answer questions that will predict if the actions, processes, and programs that we are establishing will meet the needs of all students" (Bernhardt, 1998, p. 4).