View
213
Download
0
Category
Preview:
Citation preview
Collecting and Analyzing Data to Inform ActionCollecting and Analyzing Data to Inform Action
Stage 2: A theory of action for your project
Stage 2: A theory of action for your project
Exploring research and best practices to provide a strong rationale for the design of your projectWhat 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 projectWhat 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?
Creating a roadmapCreating 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?
Stage 3: Implementing the action & collecting data
Stage 3: Implementing the action & collecting data
Simultaneously implementing your improvement strategy/strategies and collecting data to examine the effect the action is havingWhat 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 havingWhat is happening?Why is it happening?What impact is it having?
Types of dataTypes 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
Categories of dataCategories of data
Informal or formalDemographic data – looking at
subgroups of studentsGenderEthnicitySocioeconomic statusParent employmentAttendance rates
Informal or formalDemographic data – looking at
subgroups of studentsGenderEthnicitySocioeconomic statusParent employmentAttendance rates
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)
Perceptual data/qualitative data – attitudes, beliefsSurveysInterviewsObservationsAnecdotal records
Perceptual data/qualitative data – attitudes, beliefsSurveysInterviewsObservationsAnecdotal records
Selecting data sourcesSelecting 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
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 sourcesMultiple perspectives on the research
teamMultiple 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 sourcesMultiple perspectives on the research
teamMultiple data collection points
Tips from “What is Data Anyway?”
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
Stage 4: Reflecting on data and planning informed actionStage 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?
Processes of data analysisProcesses of data analysis
Pattern analysisTriangulationDisaggregationDiscussion
Pattern analysisTriangulationDisaggregationDiscussion
Sagor’s generic analysis questions
Sagor’s generic analysis questions
ACR Question 1: What did we do?Time allocatedLook for patternsCreate a timeline
ACR Question 1: What did we do?Time allocatedLook for patternsCreate a timeline
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 contextDisaggregation
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 contextDisaggregation
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)
Planning for informed action
Planning for informed action
Data-based decisionmakingLevels 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 decisionmakingLevels 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
Telling the story of your Action Research journeyTelling the story of your Action Research journey
Action Research Project Summary Form – complete by April 15 Effective Schools Research Network
summariesExamples on the WVCPD website
PLA Session III, April 24 & 25, Morgantown
Action Research Project Summary Form – complete by April 15 Effective Schools Research Network
summariesExamples on the WVCPD website
PLA Session III, April 24 & 25, Morgantown
Recommended