data analysis for assuring the quality of your cosf data 1

Data Analysis for Assuring the Quality of your COSF Data

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What are these numbers??

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OSEP reporting requirements: the outcomes Percentage of children who

demonstrated improved:

1.Positive social emotional skills (including positive social relationships)

2.Acquisition and use of knowledge and skills (including early language/ communication [and early literacy])

3.Use of appropriate behaviors to meet their needs

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OSEP reporting categories

Percentage of children who:

a.Did not improve functioning

b.Improved functioning, but not sufficient to move nearer to functioning comparable to same-aged peers

c. Improved functioning to a level nearer to same-aged peers but did not reach it

• Improved functioning to reach a level comparable to same-aged peers

e. Maintained functioning at a level comparable to same-aged peers

3 outcomes x 5 “measures” = 15 numbers 4

Getting to progress categories from the COSF ratings

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Functioning

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Entry

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Entry Exit

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Entry Exit

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Key Point

• The OSEP categories describe types of progress children can make between entry and exit

• Two COSF ratings (entry and exit) are needed to calculate what OSEP category describes a child progress

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How changes in ratings on the COSF correspond to reporting categories a - e

e. % of children e. % of children who who maintainmaintain functioning at functioning at a level a level comparable to comparable to same-aged same-aged peerspeers

• Rated 6 or 7 at Rated 6 or 7 at entry; ANDentry; AND

• Rated 6 or 7 at Rated 6 or 7 at exitexit

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Entry Exit

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Entry Exit

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Entry Exit

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How changes in ratings on the COSF correspond to reporting categories a - e

d. % of children d. % of children who improve who improve functioning to functioning to reachreach a level a level comparable to comparable to same-aged same-aged peerspeers

• Rated 5 or Rated 5 or lower at lower at entry; ANDentry; AND

• Rated 6 or 7 Rated 6 or 7 at exitat exit

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Entry Exit

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How changes in ratings on the COSF correspond to reporting categories a - e

c. % of children c. % of children who improved who improved functioning to functioning to a level a level nearernearer to same aged to same aged peers, but did peers, but did not reach itnot reach it

• Rated higher Rated higher at exit than at exit than entry; ANDentry; AND

• Rated 5 or Rated 5 or below at exitbelow at exit

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Entry Exit

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Entry Exit

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How changes in ratings on the COSF correspond to reporting categories a - e

b. % of children b. % of children who who improvedimproved functioning, but functioning, but not sufficient to not sufficient to move nearer to move nearer to same aged same aged peerspeers

• Rated 5 or lower Rated 5 or lower at entry; ANDat entry; AND

• Rated the same Rated the same or lower at exit; or lower at exit; ANDAND

• ““Yes” on the Yes” on the progress progress question (b)question (b)

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Entry Exit

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Entry Exit

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Entry Exit

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Entry Exit

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How changes in ratings on the COSF correspond to reporting categories a - e

a. % of children who a. % of children who did not improvedid not improve functioningfunctioning

• Rated lower at Rated lower at exit than entry; exit than entry; OROR

• Rated 1 at both Rated 1 at both entry and exit; entry and exit; ANDAND

• Scored “No” on Scored “No” on the progress the progress question (b)question (b)

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Entry Exit

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Entry Exit

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The ECO Calculator can be used to translate COSF entry and exit ratings to the 5 progress categories for federal reporting

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Promoting quality data through data analysis

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Promoting quality data through data analysis

• Examine the data for inconsistencies

• If/when you find something strange, what might help explain it?

• Is the variation because of a program data? Or because of bad data?

(at this point in the implementation process, data quality issues are likely!)

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The validity of your data is questionable if…

The overall pattern in the data looks ‘strange’– Compared to what you expect– Compared to other data– Compared to similar

states/regions/agencies

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COSF Ratings – Outcome 1 Entry data (fake data)

Rating Statewide

1 30

2 42

3 51

4 60

5 10

6 10

7 032

COSF Ratings – Outcome 1 Entry data (fake data)

Rating Statewide

1 30 (15%)

2 42 (20%)

3 51 (25%)

4 60 (30%)

5 10 (5%)

6 10 (5%)

7 0 (0%)33

Frequency on Outcome 1 – Statewide (fake data)

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COSF Ratings – Outcome 1 Entry data (fake data)

Rating Agency 1

Agency 2

Agency 3

Agency 4

1 3 1 1 2

2 4 1 2 2

3 5 2 3 3

4 6 3 2 4

5 1 4 5 4

6 1 5 5 4

7 0 4 2 1 35

COSF Ratings – Outcome 1 Entry data (fake data)

Rating Group 1 Group 2 Group 3 Group 4

1 15% 5% 5% 10%

2 20% 5% 10% 10%

3 25% 10% 15% 15%

4 30% 15% 10% 20%

5 5% 20% 25% 20%

6 5% 25% 25% 20%

7 0% 20% 10% 5%36

Questions to ask when looking at data

• Do the data make sense?– Am I surprised? – Do I believe the data? Some of it? All of it?

• If the data are reasonable (or when they become reasonable), what might they tell us?

• When we believe the data, how can we use it for program improvement?

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Using data for program improvement

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Plan (vision) Program characteristics

Child and family outcomes

Implement

Check(Collect and analyze data)

ReflectAre we where we

want to be?

Continuous Program Improvement

Using data for program improvement = EIA

•Evidence•Inference

•Action

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Evidence

• Evidence refers to the numbers, such as“45% of children in category b”

• The numbers are not debatable

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Inference• How do you interpret the #s?• What can you conclude from the

#s?• Does evidence mean good news?

Bad news? News we can’t interpret?

• To reach an inference, sometimes we analyze data in other ways (ask for more evidence)

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Inference• Inference is debatable -- even

reasonable people can reach different conclusions

• Stakeholders can help with putting meaning on the numbers

• Early on, the inference may be more a question of the quality of the data

Explaining variation• Who has good outcomes = • Do outcomes vary by

•Region of the state?•Amount of services received?•Type of services received?•Age at entry to service?•Level of functioning at entry?•Family outcomes?•Education level of parent?

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Action

• Given the inference from the numbers, what should be done?

• Recommendations or action steps• Action can be debatable – and

often is• Another role for stakeholders• Again, early on the action might

have to do with improving the quality of the data

Working Assumptions

• There are some high quality services and programs being provided across the state

• There are some children who are not getting the highest quality services

• If we can find ways to improve those services/programs, these children will experience better outcomes

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Questions to ask of your data

• Are ALL services high quality?

• Are ALL children and families receiving ALL the services they should in a timely manner?

• Are ALL families being supported in being involved in their child’s program?

• What are the barriers to high quality services?

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Program improvement: Where and how

– At the state level – TA, policy

– At the agency level – supervision, guidance

– Child level -- modify intervention

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Key points

• Evidence refers to the numbers and the numbers by themselves are meaningless

• Inference is attached by those who read (interpret) the numbers

• You have the opportunity and obligation to attach meaning

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Plan (vision) Program characteristics

Child and family outcomes

Implement

Check(Collect and analyze data)

ReflectAre we where we

want to be?

Is there a problem?

Why is it happening?

What should be done?

Is it being done?

Is it working?

Tweaking the System

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Continuous means…

•…….the cycle never ends..the cycle never ends.

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