1 / 18

Data Analysis Concepts & Terms

Data Analysis Concepts & Terms. Concepts. Data Analysis Concepts & Terms. Triangulation Data Analysis Terms & Techniques Data Sources. Triangulation. What is it ? Why is it important?. Triangulation: A Multidimensional View. Triangulation. What is it?

malissa-economos
Download Presentation

Data Analysis Concepts & Terms

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Analysis Concepts & Terms Concepts

  2. Data AnalysisConcepts & Terms • Triangulation • Data Analysis Terms & Techniques • Data Sources

  3. Triangulation • What is it? • Why is it important?

  4. Triangulation: A Multidimensional View Triangulation • What is it? • Using multiple data sources, data collection procedures, and analytic procedures. • Why is it important? • It can ensure a more accurate view that will help in making more effective decisions.

  5. Data Analysis Model and Process When using a process to analyze data it is important to practice a multidimensional view. Triangulation Triangulation: A Multidimensional View

  6. Data Analysis Techniques to Review: Examples of Data Analysis Techniques • Collecting and reviewing baseline data • Discuss / define student data points • Disaggregating student data and digging deeper • The Data Analysis Model and Process • Graphing and visually displaying data to share with teachers, campuses and district staff

  7. Examples of Data Analysis Techniques Baseline Data: Facts / Characteristics Definition Initial student (assessment) information and data that is collected prior to program interventions and activities. • It can be used later to provide a comparison for assessing the interventions impact / success. • Usually collected at the: • BOY, MOY, EOY. Examples Non-examples Baseline data Data: Readiness Inventories, ACP Tests, ISIP, ITBS, Fluency Probes, Texas Middle School Fluency Assessment (TMSFA), TAKS. • Unspecific or • non-measurable item.

  8. Examples of Data Analysis Techniques Student Data Point: Definition Facts / Characteristics A data point is one score on a graph or chart, which represents a student’s performance at one point in time. Can be collected at different intervals (daily, weekly, monthly). Can be plotted on a graphical display. Trends and patterns can be observed. Examples Non-examples Student data point • Unspecific or • non-measurable item.

  9. Disaggregating student data and digging deeper: Examples of Data Analysis Techniques • Disaggregating data involves separating student-learning data results into groups of data sets by race/ethnicity, language, economic level, and or educational status. • Normally student achievement data are reported for whole populations, or as aggregate data. When data is disaggregated, patterns, trends and other important information are uncovered.

  10. Examples of Data Analysis Techniques • Why is it important? • By looking at data by classrooms in a school, by grade levels within a school or district, or by schools within in a district; disaggregated data can tell you more specifically what is affecting student performance. Disaggregating student data and digging deeper:

  11. Examples of Data Analysis Techniques • Why is it important? • Disaggregators allow the ability to focus in on a particular group of students and to compare them with a reference group. • For example, a campus may want to see how the Limited English Proficient (LEP) students are performing relative to other students. Disaggregating student data and digging deeper:

  12. Examples of Data Analysis Techniques Disaggregators can include the following: • Race • Ethnicity • Gender • Special Education Status • Lunch Status (Income Level) • English Proficiency (LEP) • Grade • Attendance Rates • Retention • Current and Prior Programs, Supports, and Interventions Example: • Fourth-grade African American, White, Hispanic, Native American, and Asian students’ performance in math.

  13. Examples of Data Analysis Techniques Practice a consistent process to analyze data such as: The Data Analysis Model and Process

  14. Examples of Data Analysis Techniques Further information over The Data Analysis Model and Process, tools and resources can be found at: http://www.dallasisd.org/Page/12258

  15. Graphing and visually displaying data to share with teachers, campuses and district staff Examples of Data Analysis Techniques • Data Walls can: • Create visual displays of data, and student / teacher progress toward goals • Build a shared vision of campus and teacher ownership and awareness toward goals

  16. Graphing and visually displaying data to share with teachers, campuses and district staff Examples of Data Analysis Techniques • Data Walls can: • Facilitate team engagement and learning • Create visuals that anchor teachers and campuses work and can be shared with other audiences

  17. Student Data Specific Examples of Student Data: Data Sources • Assessments • Academic Behavior • On-Track /Graduation • College Readiness • Course Enrollment • Demographics • Elementary (PK-5): • ISIP, ITBS/Logramos, STAAR, TAKS, Readiness Inventory, Interim Assessments • Secondary (6-12): • Readiness Inventory, Interim Assessment, Writing Assessment, ACP, TAKS/STAAR, Texas Middle School Fluency Assessment (TMSFA), Fast ForWord Reading Progress Indicator (RPI), EOC, Readistep, PSAT

  18. Examples of Campus Data & Locations: Data Sources • AEIS – Academic Excellence Indicator System : http://ritter.tea.state.tx.us/perfreport/aeis/ • AYP – Adequate Yearly Progress : http://www.tea.state.tx.us/ayp/ • District performance standards and campus information found in Dallas ISD Campus Data Packets: http://mydata.dallasisd.org/SL/SD/cdp.jsp

More Related