2001 NCES MIS Conference Mohawk Regional Information Center Online Data Analysis at the School...

2001 NCES MIS Conference

Mohawk Regional Information Center

Online Data Analysis at the School Building Level

Meeting Local Needs

Donald D. Peasley, Program Information and Improvement Specialist

Joan Sotherden, Director




Goals for Today• Define Who Wants the Data Analysis• Define What they need from the Data

– Assessing Existing Data

– Assessing Important Questions

• Identify the Planning Needed for the Collection, Organization, Analysis, Interpretation, and Utilization of Data at the Local Level

• Define Cultural Considerations



Data Based Decision Making . ..



Data, Data Analysis and Technology . . .

• “When you work with technology, you’re really working with people, their social attitudes, their superstitions, and their rules”– Jorg Haake, German National Research Center

for Information Technology



Why Base Decisions on the Data?

• Mandated Accountability

• Need for Documented Growth and Improvement of Learning Outcomes

• Market Forces

• Increased Expectations of Clientele

• Opportunities Afforded by Technology



Who Needs Data Analysis at the Local Level?

• Primary Audience:– Principals– Superintendents, Central Office Admin.

• Secondary Audience:– Department Chairs– Teachers– School Boards– State Education Department



What is the Client Need that Drives Data Analysis Requests?

• Federal Reporting

• State Reporting

• Regional Accountability—Comparisons

• Local Instructional Improvement Decisions

• Self Preservation!!!



Planning and Assessing Needs for A Locally

Based Data Analysis System • What’s the big picture?

School/Community Information

Student Information

Teacher /Instructional Information

Parent/Family

InformationStudent Performance/

Success



Planning and Assessing Needs for a Locally Based Data Analysis System

• What are your primary questions?

• Example—Is retention related to race or gender?

• What is the grade distribution broken down by race, gender?

• What is the attendance rate broken down by race, gender?



What is the grade distribution broken down by race, gender?Attempt One

Name of Measurement=REGENTS SEQUENTIAL COURSE I EXAM

Regents Score

85 or A bove

65 Through 84

56 Through 64

Less Than 55

Co

un

t

80

70

60

50

40

30

20

10

Gender

Female

Male

Regents Score * Gender * Name of Measurement Crosstabulation

38 74 112

33.9% 66.1% 100.0%

22 19 41

53.7% 46.3% 100.0%

35 43 78

44.9% 55.1% 100.0%

29 22 51

56.9% 43.1% 100.0%

124 158 282

44.0% 56.0% 100.0%

Count

% within Regents Score

Count


Count


Count


Count


Less Than 55

56 Through 64

65 Through 84

85 or Above

RegentsScore

Total

Name of MeasurementREGENTS SEQUENTIALCOURSE I EXAM

Female Male

Gender

Total

Chi-Square Tests

9.613a 3 .022

9.676 3 .022

282

Pearson Chi-Square

Likelihood Ratio

N of Valid Cases

Name of MeasurementREGENTS SEQUENTIALCOURSE I EXAM

Value df

Asymp.Sig.

(2-sided)

0 cells (.0%) have expected count less than 5. The minimum expected count is18.03.

a.



Question 2: What is the relationship of course grades to state exit exams?—Attempt One

Correlations

1.000 .872** .856** -.380**

. .000 .000 .000

16703 3819 11155 16236

.872** 1.000 .996** -.314**

.000 . .000 .000

3819 4048 7 3991

.856** .996** 1.000 -.323**

.000 .000 . .000

11155 7 13039 10839

-.380** -.314** -.323** 1.000

.000 .000 .000 .

16236 3991 10839 21333

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Final Mark

Regents Exam

Local Exam

Total Absences

Final MarkRegentsExam

LocalExam

TotalAbsences

Correlation is significant at the 0.01 level (2-tailed).**.

Regents Exam

120100806040200-20

Fin

al M

ark

120

100

80

60

40

20

0 Rsq = 0.7603



Question 3: What is the Effect of Attendance on Achievement? Attempt One

Correlations

1.000 .872** .856** -.380**

. .000 .000 .000

16703 3819 11155 16236

.872** 1.000 .996** -.314**

.000 . .000 .000

3819 4048 7 3991

.856** .996** 1.000 -.323**

.000 .000 . .000

11155 7 13039 10839

-.380** -.314** -.323** 1.000

.000 .000 .000 .

16236 3991 10839 21333

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Pearson Correlation

Sig. (2-tailed)

N

Final Mark

Regents Exam

Local Exam

Total Absences

Final MarkRegentsExam

LocalExam

TotalAbsences

Correlation is significant at the 0.01 level (2-tailed).**.

Total Absences

140120100806040200

Fin

al M

ark

120

100

80

60

40

20

0 Rsq = 0.1440



Do you know how you measure the answers to your questions?

• What will the report look like?



General Considerations in any Data Analysis Project

• What are the “business rules” for the organization?– (what are the important questions????)

• Where is the data now?– (what format is the data in?)– (what “shape” is the data in?)

• What constitutes “valid” data?– (when you deem the data to be “good”?)

• How often should the data be updated?– (what is the “refresh” cycle?)

• Who gets to use the data– (what are the security policies?)



A Gateway to Performance Data



Attempt Two—Preformatted Answers to Primary Questions



Attempt Two, Cont.



Attempt Two, Cont.

Final Course Averages and Regents Exam Scores - US History & Government - 1999

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

US History &Government

3.2% 33.6% 34.4% 16.8% 12.0%

Regents 1.6% 24.2% 30.6% 16.1% 27.4%

100-90 89-80 79-70 69-65 Below 65



Attempt Two, Cont.

Total Yearly Absences and Total Number of Courses Failed by Grade, 2000 School Year,

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

Total Yearly Absences

None

Failed One Course

Failed Two Courses

Failed Three or More Courses

None 85.90% 84.38% 52.94% 80.00% 28.57%

Failed One Course 7.69% 6.25% 17.65% 13.33% 28.57%

Failed Two Courses 2.56% 3.13% 11.76% 6.67% 14.29%

Failed Three or More Courses 3.85% 6.25% 17.65% 0.00% 28.57%

0-5 6-9 11-14 15-19 20+

PERIOD Final Course Average GR 10

%

TOTAB

Failures



Planning for a Data Analysis System

• Are you sure that you are getting the most out of your existing student management system?

• Do you know how to use your student management system?

• What data exists in a system now?

• What reports do you use now?

• What reports could you use now?

• Who is going to enter this data?

• Who is going to verify and maintain this data?

• Who is going to use this data?

• Are you sure you are not duplicating efforts?

• It only looks easy



Delivering and Supporting A Data Analysis System

• Student, Financial, Personnel System(s) Report Functionality

• OLAP Access to Key Performance Indicators

• The Data Storage Shed….

• Ad-Hoc Analysis Capability***

• Training on Access

• Training on Interpretation

• Training on Data Management

• Regional collaboration

• Statewide collaboration



Hints for Success

• Involve Teachers as Leaders and Planners

• Start Somewhere

• Promise Low, Deliver High

• It’s OK to Say “I don’t know”

• Get Help (and pay for it) when you need it

• “I’m from BOCES and I’m here to help”

• Involve Teachers as Leaders and Planners



Top Ten Data Analysis Tools

1. Ad-Hoc Procedures2. Spreadsheets--The Duct Tape of Data

Analysts3. Frequency Distributions4. Average, Median, Standard Deviation5. Bar Charts (see frequency distributions)6 thru 10. Crosstabs tables and 2 and 3 Way

Bar Charts



Try our site…cognos.moric.org—log on as guest

Contact: [email protected]@mohawk.moric.org

Thank You!!!!

2001 NCES MIS Conference Mohawk Regional Information Center Online Data Analysis at the School...

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