1. Collecting and Analyzing Data to Inform Action

2. Stage 2: A theory of action for your project Exploring research and best practices to provide a strong rationale for the design of your project What is known about the context where your team will implement improvement strategies? What is present in the professional knowledge base? What do we know from our own experiences, our practical knowledge base? Exploring research and best practices to provide a strong rationale for the design of your project What is known about the context where your team will implement improvement strategies? What is present in the professional knowledge base? What do we know from our own experiences, our practical knowledge base?

3. Creating a roadmap Start with your achievement targets – what are the actions your team will need to take to achieve improvement? What specific actions need to be taken related to each target? Who is involved with each action? When should actions occur? Will multiple actions need to be taken simultaneously? Is there a sequence of events that should be followed? If problems are encountered, what types of remedial steps should be taken? Start with your achievement targets – what are the actions your team will need to take to achieve improvement? What specific actions need to be taken related to each target? Who is involved with each action? When should actions occur? Will multiple actions need to be taken simultaneously? Is there a sequence of events that should be followed? If problems are encountered, what types of remedial steps should be taken?

4. Stage 3: Implementing the action & collecting data Simultaneously implementing your improvement strategy/strategies and collecting data to examine the effect the action is having What is happening? Why is it happening? What impact is it having? Simultaneously implementing your improvement strategy/strategies and collecting data to examine the effect the action is having What is happening? Why is it happening? What impact is it having?

5. Types of data Quantitative data – numeric, results, “what” Tests Checklists Surveys Forced-choice Qualitative data – narrative, descriptive, “why” or “how” Interviews Observations Document analysis Open-ended Quantitative data – numeric, results, “what” Tests Checklists Surveys Forced-choice Qualitative data – narrative, descriptive, “why” or “how” Interviews Observations Document analysis Open-ended

6. Categories of data Informal or formal Demographic data – looking at subgroups of students Gender Ethnicity Socioeconomic status Parent employment Attendance rates Informal or formal Demographic data – looking at subgroups of students Gender Ethnicity Socioeconomic status Parent employment Attendance rates

7. Achievement or outcome data Teacher tests Text tests State tests Standardized tests (ACT, SAT) Program or process data – what does your school do and how does it do it? Programs (Reading First) Services and interventions (ESL, Counseling, AP) Philosophical approaches (teaming, cooperative learning) Achievement or outcome data Teacher tests Text tests State tests Standardized tests (ACT, SAT) Program or process data – what does your school do and how does it do it? Programs (Reading First) Services and interventions (ESL, Counseling, AP) Philosophical approaches (teaming, cooperative learning)

8. Perceptual data/qualitative data – attitudes, beliefs Surveys Interviews Observations Anecdotal records Perceptual data/qualitative data – attitudes, beliefs Surveys Interviews Observations Anecdotal records

9. Selecting data sources Rely heavily on existing or readily available data Include data that can be collected while teachers are facilitating learning Maximize the value for students of monitoring their own progress Rely heavily on existing or readily available data Include data that can be collected while teachers are facilitating learning Maximize the value for students of monitoring their own progress

10. Validity – does the data measure or describe what you are trying to achieve? Reliability – is the data accurate? Triangulation – a process of corroboration (Sagor, page 93) Multiple data sources Multiple perspectives on the research team Multiple data collection points Validity – does the data measure or describe what you are trying to achieve? Reliability – is the data accurate? Triangulation – a process of corroboration (Sagor, page 93) Multiple data sources Multiple perspectives on the research team Multiple data collection points

11. Tips from “What is Data Anyway?” Do no harm Collect feasible data Don’t collect data if you’re not going to use it Data is everybody’s business Collect data frequently Analyze data as you collect it Keep asking questions Don’t jump to conclusions; don’t jump to solutions Do no harm Collect feasible data Don’t collect data if you’re not going to use it Data is everybody’s business Collect data frequently Analyze data as you collect it Keep asking questions Don’t jump to conclusions; don’t jump to solutions

12. Stage 4: Reflecting on data and planning informed action Have your team look at data and ask: What does this data see to tell us? What does it not tell us? What else do we need to know? What action might we need to take? What can we celebrate? Have your team look at data and ask: What does this data see to tell us? What does it not tell us? What else do we need to know? What action might we need to take? What can we celebrate?

13. Processes of data analysis Pattern analysis Triangulation Disaggregation Discussion Pattern analysis Triangulation Disaggregation Discussion

14. Sagor’s generic analysis questions ACR Question 1: What did we do? Time allocated Look for patterns Create a timeline ACR Question 1: What did we do? Time allocated Look for patterns Create a timeline

15. ACR Question 2: What changes occurred regarding the achievement targets? Look at trends (may use basic descriptive statistics: mean, median, mode, standard deviation for numeric data; coding by categories and looking at frequencies for qualitative data) Consider the context Disaggregation ACR Question 2: What changes occurred regarding the achievement targets? Look at trends (may use basic descriptive statistics: mean, median, mode, standard deviation for numeric data; coding by categories and looking at frequencies for qualitative data) Consider the context Disaggregation

16. ARC Question 3: What was the relationship between actions taken and any changes in performance on the targets? Assertions based on empirical data (your findings) and intuition (practitioner knowledge) ARC Question 3: What was the relationship between actions taken and any changes in performance on the targets? Assertions based on empirical data (your findings) and intuition (practitioner knowledge)

17. Planning for informed action Data-based decisionmaking Levels of decisions What to do tomorrow? What changes to make in instruction? What changes to make in the program? What resources should be allocated to support the work? Iterative process Data-based decisionmaking Levels of decisions What to do tomorrow? What changes to make in instruction? What changes to make in the program? What resources should be allocated to support the work? Iterative process

18. Telling the story of your Action Research journey Action Research Project Summary Form – complete by April 15 Effective Schools Research Network summaries Examples on the WVCPD website PLA Session III, April 24 & 25, Morgantown Action Research Project Summary Form – complete by April 15 Effective Schools Research Network summaries Examples on the WVCPD website PLA Session III, April 24 & 25, Morgantown