Example Correlation Analysis for ABC High School (Scenario #1)
Step #1: Organize your teacher observation/practice data, demographic data, teacher level measure data, and any
other data deemed relevant (i.e. format an Excel Spreadsheet).
 Note that sample data for ABC High School has been populated within the Building Summary of Teacher
Ratings spreadsheet
Step #2: Locate the School Performance Profile (SPP) for your building using the School Performance Profile
website.
 For our ABC High School example, we are providing a printout of details from the SPP website. The SPP
score we will use is 54.1.
Step #3: Determine the average observation/practice rating for all your teachers within your building.
 Using the provided spreadsheet, the average for High School ABC is 2.16.
Step #4: Use the Connectedness with SPP chart to help determine a possible level of connectedness between the
average observation/practice rating for all teachers in your building and the teacher-level measure - School
Performance Profile (SPP).

On the Connectedness with SPP chart, identify the range where your average teacher observation/practice
rating falls. In our example, the 2.16 average teacher observation/practice rating falls within the 1.50 –
2.49 range.

Using the average teacher observation/practice range identified above, identify the range where your SPP
score falls on the Connectedness with SPP chart. In our example, the 54.1 SPP score falls within the below
60 range.

Using the SPP range identified above, determine a possible level of connectedness. In our example, we can
explore possible sources of evidence that may indicate a limited level of connectedness between the average
teacher observation/practice rating and SPP.
The 2.16
average
teacher
observation
/practice
rating falls
within this
range
Is there evidence
that may
indicate a
limited level of
connectedness?
The 54.1
SPP score
falls
within
this range
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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Step #5: Repeat steps #3 and #4 for subsets of teachers in your building.

For our ABC High School example, we are going to analyze subsets of those teachers that have associated
PVAAS scores. To do this, we need to conduct three independent sorts of our sample Building Summary of
Teacher Ratings spreadsheet (sort by ELA-Literature, Math-Algebra I, and Science-Biology teachers that
have associated PVAAS scores).
Prior to any sorting, it is strongly recommended that you create a backup file with this information
(select File  Save As from the Menu bar). This backup file becomes the master electronic copy of your
completed spreadsheet. That way you retain an original copy in case you run into any difficulties
sorting.

First we are going to sort by ELA-Literature teachers with associated PVAAS scores
o
Highlight rows 7 through 47. Note that it is important to include the header row (row #7) and all
the rows that have associated teacher data.
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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o
Once highlighted, select Data  Sort from the menu bar. A window should be displayed that
enables you to select the column you wish to sort by. Select “Teacher Specific Measure Growth PVAAS Reading (percent).”
Data 
Sort Menu
Select the
PVAAS Reading
for the sort.
o
The resulting sort places all teachers with Teacher Specific Measure Growth - PVAAS Reading
scores at the top of the spreadsheet. Note that if there are ELA teachers who do not teach courses
with associated PVAAS scores, they are not included in this sort. Also note that we just applied a
sort of the data. This means data for all teachers still remain and our average teacher
observation/practice rating is still 2.16.
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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Teachers with
associated
PVAASReading scores
are at the top of
the list.

Now we need to determine the average observation/practice rating for the subset of teachers with
associated PVAAS – Reading scores. This will require you to highlight, and then delete rows representing all
teachers that do not have associated PVAAS – Reading scores.
o
In our example, we need to highlight and delete rows 16 – 47 of the sorted spreadsheet. Once rows
are highlighted, you can select the “Delete” button from the top ribbon.
Delete Button
Non PVAASReading rows
are highlighted
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o
Note that after the rows are deleted, we are left with only those teachers with associated PVAAS –
Reading scores. Also the spreadsheet has a recalculated average of teacher observation/practice
ratings, which is now 2.10. The average PVAAS score for reading is 62.00.
The result is a sort that will be beneficial in later analysis. Thus, we are now going to “Save As”
this as a new file called “Teacher Ratings – PVAAS Reading.”
Average PVAAS reading score
Average teacher rating for ELALiterature teachers with
associated PVAAS reading scores

The same process should be repeated for math and science teachers with associated PVAAS scores. Go back
to your master file and conduct the various sorts repeating the procedures outlined above (this time for
PVAAS-math and PVAAS-Science).
o
The resulting screens captures are provided below. Note that the average teacher
observation/practice rating is 2.53 with an average PVAAS score of 50.00 for Math – Algebra I. The
average teacher observation/practice rating is 1.90 with an average PVAAS score of 62.00 for
Science-Biology.
Average PVAAS math score
Average teacher rating for Math –
Algebra I teachers with
associated PVAAS math scores
Average PVAAS science score
Average teacher rating for
Science-Biology teachers with
associated PVAAS science scores
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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Step #6: Compare possible levels of connectedness between teacher observation/practice ratings and SPP scores for
all the teachers (step #4) and the various subsets (step #5).

Place appropriate data from the previous steps in an Overview Table.

Referring back to the Connectedness with SPP chart and using our SPP score of 54.1, we can determine
possible levels of connectedness between teacher observation/practice ratings and SPP:
*
that example
* Note
highlighted in red
shows how to
read the chart for
Math – Algebra I.
The same
approach applies
for the other
subsets

Referring to the Connectedness with SPP chart, we can determine possible levels of
connectedness
All Teachers (2.16)  Evidence May Indicate a Limited Connectedness with SPP
PVAAS – ELA – Literature subset (2.10)  Evidence May Indicate a Limited Connectedness with SPP
* PVAAS – Math – Algebra I subset (2.53)  Evidence May Indicate a Poor Connectedness with SPP
PVAAS – Science subset (1.90)  Evidence May Indicate a Limited Connectedness with SPP
Is there evidence indicating differences in possible levels of connectedness when comparing the average of all
teacher observation/practice ratings and SPP versus the subset(s) of teacher observation/practice ratings and SPP?
o
In our example, it appears that for our PVAAS Math – Algebra I subset we may have a possible poor
level of connectedness with SPP versus a possible limited level of connectedness for the other subsets.
Let’s now consider possible levels of connectedness among teacher observation/practice ratings (all
teachers/subsets) and other teacher-level measures (PVAAS, Elective Data) to see if this helps to explain
these differences.
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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o
Here is the analysis for possible levels of connectedness with PVAAS:
*
*Note that example
highlighted in red
show how to read
the charts for
Math – Algebra I.
Referring to the Connectedness with PVAAS chart, we can determine possible levels of
connectedness:
PVAAS – ELA – Literature (2.10)  Evidence May Indicate a Good Connectedness with PVAAS
– Math – Algebra I (2.53)  Evidence May Indicate a Poor Connectedness with PVAAS
* PVAAS
PVAAS – Science (1.90)  Evidence May Indicate a Good Connectedness with PVAAS
The same
approach applies
for the other
subsets.

Based upon the results, we see varying levels of possible connectedness within the subsets when considering the
possible connectedness with PVAAS. Now we need to look for plausible explanations. Possible guiding
questions to assist in this analysis include:





o
What trends are you discovering?
Are these the types of relationships you would expect to see? Why or why not?
Which trends are the most positive and/or the most problematic? And why?
What evidence would define plausible root causes?
How does data inform your thinking about how to best support school and/or district goals?
We found a possible poor connectedness level between the Math – Algebra I subset of average teacher
observation/practice ratings and SPP, as well as with PVAAS.

The ratings of the teachers could in fact be accurate, but they are teaching a poorly aligned
curriculum to the standards as demonstrated by the lack of static achievement (Keystone) at
32.1% advanced or proficient and growth (PVAAS) at 50.0.

The ratings of the teachers may not be accurate due to various factors including personal
relationships, strong personalities, and experience.

When looking at the SPP calculations for Other Academic Indicators, we see an attendance rate
of 90.12. This indicates that students are regularly attending school; however, academic
achievement is lacking, especially in math. Perhaps teachers are rated highly based on
observations of classrooms where students are engaged; however, the content being delivered is
not aligned to the PA Core Standards and/or inadequate pedagogies are being used?

Other plausible explanations…
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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o
We found a possible limited connectedness level between the ELA-Literature subset of average teacher
observation/practice ratings and SPP, but a possible good connectedness level between the ELALiterature subset and PVAAS scores.

The possible good connectedness level between the ELA – Literature average teacher
observation/practice ratings and PVAAS could be due to the curriculum being aligned to the
standards, resulting in a stronger link between what is tested and what is taught.

The ELA department has two teachers with two or fewer years of experience. These two
teachers also have the highest PVAAS scores. This could be due to their more recent exposure
to the standards and instructional practices designed to improve student achievement (such as
student engagement). Also note that both of these teachers have been rated as quality
instructors, which aligns to their student’s growth on PVAAS.
ELA-Literature teachers
with two or fewer years of
experience.


Five of the eight ELA-Literature teachers have PVAAS scores of 61 or lower, which could
support the assumption that the curriculum is not aligned and/or not being taught. Or researchbased effective pedagogies are not being used in the classroom to improve instruction and thus
student achievement.

Other plausible explanations…
Step #7: Identify possible opportunities where your analysis may help inform comprehensive planning,
principal SLOs, etc.
o
The PVAAS Math – Algebra I subgroup has been identified with a possible poor connectedness level
with SPP and PVAAS. Therefore, it might be important to include the PVAAS Math – Algebra I
subgroup in the development of strategic interventions to increase student achievement. This could be
accomplished through a Principal SLO and/or Teacher SLOs for this subset.
o
The PVAAS ELA-Literature subset was identified with a possible limited connectedness level with SPP
and a possible good connectedness level with PVAAS. However, the PVAAS score is close to the lower
margin in the “good” range. Therefore, opportunities could exist to work with these teachers in the
analysis of the data and develop strategic interventions to increase student achievement. Perhaps
reviewing the pedagogies of the two newest teachers would be beneficial since they have the two highest
PVAAS scores?
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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
Step #8: Based upon the conversation, the supervising administrator will assign a rating (0, 1, 2 or 3)
relative to the principal/school leader’s knowledge, understanding and intended application of
evidence/data presented.
o

Referring back to the Performance Level Descriptor Chart, the supervising administrator (in collaboration
with the principal), let’s consider the following for our example:

Degree  The principal / school leader demonstrates the ability to disaggregate teacher
observation/practice ratings and teacher-level measures, as well as conducts an analysis to
determine plausible connections among the data.

Quality  The principal / school leader cites plausible causes for the connections among
teacher observation/practice ratings and teacher-level measures. Also the principal / school
leader articulates why the plausible connections may have occurred among teacher
observation/practice ratings and teacher-level measures.

Plan  General plans for increasing student performance based upon the analysis of teacher
observation/practice ratings and teacher-level measures have been provided. Opportunities exist
to further define timelines and implementation strategies for the incorporation into a Principal
SLO.

Therefore, the correlation rating for this principal / school leader is a “2” based upon a general
action plan as opposed to a plan that includes specificity.
Step #9 - The correlation rating is then entered into the Principal Rating Tool.
Enter 0, 1, 2, or 3 here
Select the “Correlation
Data” tab on the Principal
Rating Tool
Example Correlation Analysis for ABC High School (07/14/14): © Pennsylvania Department of Education, 2014
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