Advanced Uses of SWIS Dataand SWIS Facilitation Skillspresented by Susan Barrett

Anne Todd, Nadia Sampson, and Celeste Rossetto Dickey, University of Oregon

Steve Romano, Marla Dewhirst, Susan Barrett, Jerry Bloom, Kelly Davis, Rachel Freeman, and Rob Horner

SWIS Facilitator Trainers

Seth May, Megan Amedo, & Mary Green, University of Oregon

June 2007www.swis.org

Goals Review for teaching teams to use the Big 5 reports Recommend use of SWIS individual student data to jump start an

FBA for a student

Review of roles and responsibilities Update

new features within SWIS Definitions, extra information

SWIS integration with district packages SAMI VISA License Agreement

Review and summarize Ethnicity Reports Getting connected

SWISTM Facilitation is an Eight Step Process

1. Complete Readiness Tasks.2. Submit License Agreement and School Information

Form.3. Setting Up for Swift at SWIS™ Training.4. Conduct Swift at SWIS™ Training.5. Provide Follow Up Support. 6. Maintenance.7. Annual SWIS™ Facilitator Boosters.8. SWIS™ License Renewal Process.

Your challenges should you decide to except them……

Use your resources to make their job easier Use your skills to teach others your skills

You want to work yourself out of ‘the job’ so that you can move on to the next school/

district/region/state

Teach others to keep it going in your absence

Teach others to keep it going in each others absences

SYST

EMS

PRACTICES

DATASupportingStaff Behavior

SupportingDecision-Making

SupportingStudent Behavior

SW PositiveBehaviorSupport

OUTCOMES

Social Competence,Academic Achievement, and Safety

Improving Decision-Making

Problem SolutionFrom

To ProblemProblem-solving

InformationSolution

Problem-solving Steps1. Define the problem(s)

– Analyze the data2. Define the outcomes and data sources for measuring the outcomes3. Consider 2-3 options that might work4. Evaluate each option:

– Is it safe?– Is it doable?– Will it work?

5. Choose an option to try6. Determine the timeframe to evaluate effectiveness7. Evaluate effectiveness by using the data

– Is it worth continuing? – Try a different option? – Re-define the problem?

Key Features of Effective Data Systems

Data are accurate Data are very easy to collect Data are used for decision-making

Different decisions require different data sources at different times

Don’t collect it unless you are going to use it

Data are available when decisions need to be made Data collectors must see the information used for

decision-making… make it valuable for them to collect

SWISTM summary 05-06 (Majors Only)1668 schools, 838,184 students

Grade Range

Number of Schools

Number of Students

Mean ODRs per 100 per school day

K-6 1010 439,932Mean = 435

.37 (sd=.50)

6-9 312 205,129Mean = 657

1.01 (sd=1.06)

9-12 104 102,325Mean = 983

1.16 (sd=1.37)

K-(8-12) 239 90,198Mean = 377

1.09 (sd=1.56)

Standard Deviation for non-statisticians Normal distribution: Bell curve

most of examples in set of data are close to the average, with a few examples tend to be from one extreme to the other

Standard Deviation: a statistic that tells you how tightly all the various examples

are clustered around the mean in a set of data. When the examples are pretty tightly bunched together and the bell-shaped curve is steep, the standard deviation is small. When the examples are spread apart and the bell curve is relatively flat, that tells you that you have a relatively large standard deviation.

One standard deviation away from the mean in either direction on the horizontal axis accounts for somewhere around 68 percent of the people in this group.

Standard deviation is a measure of variation in a distribution of data

Red represents one standard deviation from the mean (about 68% of data set

Red & green represent two standard deviations from the mean (about 95% of data set)

Red, green & blue represent three standard deviations from the mean (about 99% of data set)

Interpreting Office Referral Data:Is there a problem?

Absolute level (depending on size of school) SWIS data summaries per 100 students

Trends Peaks before breaks? Gradually increasing trend across year?

Compare levels to last year Improvement?

Teach Teams a ‘Using Data Routine’

Teach a routine Examine data during the first 10-15 min of each meeting Teach the basic questions to ask:

How many ODRs? (Do we have a problem?) What types of problem behaviors are most common? Where, When, Who?

Use problem solving strategy Define problem Identify 2-3 solutions Talk about which solutions will work, are doable, fair Determine the solution to try

What is the smallest change we could make that would improve student behavior?

Determine next steps How will we know if our efforts have been successful? Who do we need to share the data with and when?

The BIG 5 How often

where what

when

who

How often ?Where ?When ?Who ?What ?

What is summary for:

1. High school of 850 students? 2. High School of 1825 students?

3. Elem. school of 625 students? 4. Middle School of 625 students?

Office Discipline Referrals per Day per Month

0

2

4

6

8

10

12

14

16

Sept Oct Nov Dec Jan Feb March April May June

# p

er d

ay p

er 1

00 s

tud

ents

Series1

Office Discipline Referrals per Day per Month

0

2

4

6

8

10

12

14

Sept Oct Nov Dec Jan Feb March April May June

# p

er d

ay p

er 1

00 s

tud

ents

Series1

What is summary for: 1. High school of 850 students? 2. High School of 1825 students?

3. Elem. school of 625 students? 4. Middle School of 625 students?

Trevor Test Middle School

565 students

Grades 6,7,8

0

20

40

60

80

100

Num

ber

of S

tudents

1 2 3 4 5 6 7 8 9 10111213141516171819

Number of Referrals

Students per Number of Referrals

0

5

10

15

20

Ave R

efe

rrals

per

Day

Sept Oct Nov Dec Jan Feb Mar Apr May Jun

School Months

Office Referrals per Day per MonthThis Year

0

10

20

30

40

50

Num

ber

of R

efe

rrals

Lang Achol ArsonBombCombsDefianDisruptDressAgg/fgtTheftHarassProp D Skip Tardy Tobac Vand Weap

Types of Problem Behavior

Referrals per Prob Behavior

0

20

40

60

80

Num

ber

of O

ffic

e R

efe

rrals

Bath RBus A Bus Caf ClassComm Gym Hall Libr Play G Spec Other

School Locations

Referrals by Location

0

10

20

30

40

50

Num

ber

of R

efe

rrals

7:00 7:30 8:00 8:30 9:00 9:30 10:0010:3011:00 11:3012:0012:30 1:00 1:30 2:00 2:30 3:00 3:30

Time of Day

Referrals by Time of Day

Cafeteria Class Commons Hall

12:00

Lang.

Defiance

Disrespect

Harrass Skip

Summarize the big 5

Is there a problem? If no, what will we do to sustain our efforts? If yes, is problem definable or do we need

more information? More information? What is needed? Definable problem? What is the problem?

Next steps How will we know if it is working? When will we review the data?

Langley Elementary School

478 Students

K-5

What do you do when?

A problem is being discussed at a meeting without looking at data/information…

Data is being reviewed and the problem being defined doesn’t have team members confidence in data or the problem being defined

Data is being reviewed, the team continues to look for things that need fixing… you notice that their rates are way below the national SWIS average

•Data Entry

Who What When

Ginger/ Doug Office Referrals End of each day

Ginger/ Doug Integrity Report and Accuracy Checks 4th tues of each month

•Report Generation

Who What When

SW PBS

Student Services Team

Administration/ Counseling

BIG 5

Individual student report

Custom report of all students referrals

Defined as needed

2nd tues/month

Thursday afternoons

As needed

Data Entry and Reporting Schedule

Teach Teams a ‘Using Data Routine’

Teach a routine Examine data during the first 10-15 min of each meeting Teach the basic questions to ask:

How many ODRs? (Do we have a problem?) What types of problem behaviors are most common? Where, When, Who?

Use problem solving strategy Define problem Identify 2-3 solutions Talk about which solutions will work, are doable, fair Determine the solution to try

What is the smallest change we could make that would improve student behavior?

Determine next steps How will we know if our efforts have been successful? Who do we need to share the data with and when?

Tailoring SWIS to Individual School Needs: “Extra Info” and “Comments” Elaborate an Existing Problem Behavior Category

E.g. Harassment (sexual, racial, religious) E.g. Weapon (gun, knife, other)

Defining a ‘track’, small learning community, or homeroom

When selecting Extra Info categories remember core features of all Categories Operationally defined

Can see it and hear it Mutually Exclusive

No overlap between definitions Exhaustive

Everything is included

Categories for Harassmentseparate form and content

Which set(s) of categories are acceptable?

A Sexual Racial Religious Other

B Sexual Verbal Physical Gender

C Verbal only Physical only Verbal and Physical

D With weapon With Gang With intent to harm

Categories for Weaponsseparate form and content

Which set(s) of categories are acceptable? A

Gun Knife Club Hand gun Gang

B Gun Knife Club

C Bomb Physical Threat Intimidation

D Hand gun Rifle Short knife (< 3 in) Long knife (> 3 in) Other

Report Options

To record Build new categories Add new categories to ODR form Train faculty and staff to enter data Train faculty to use the reports for decision making Train data entry person

Custom Reports Advanced Options Activate “Show” extra info Run Custom Report

45 students (7.6%) and 117 referrals 589 total enrollment

8 students 6+ referrals

1.3%

37 students 2-5

6.2%

38 students (6.4%) and 146 referrals total enrollment = 589

8 students 6+

1.3%

30 students 2-5

5%

Using SWIS for Individual Student Intervention Design Assumption

The design of behavior support is most efficient and effective when based on basic functional behavioral assessment information [who, what, where, when, why]

Who engaged in problem behavior What problem behavior(s) were performed Where (what conditions/situation) are

associated with problem behavior Why do problem behaviors occur (e.g. what is

the maintaining function?)

Individual Student

Referrals by student report (Who)

Individual Student Report (What, Where, When, Why)

Use these data to build preliminary hypothesis statement(s).

Use the data to identify additional information needed from FBA Interviews.

Context/Setting Problem Behavior Maintaining SR+

Use SWIS Data To Define Preliminary Hypothesis Statements/ Other Info Needed

Mark Banks (2003-2004) Grade 7

Compare the challenges Mark presented last year with the challenges he presents this year.

What is the current hypothesis statement? What other information would you want?

SettingContext

Problem Behavior

Maintaining Reinforcer

Mark Banks

Time

Location Motivation

Average per day per month Problem Behavior

Mark Banks (04-05)

Setting BehaviorMaintaining Reinforcer

Commons Area AggressionHarassment

Obtain Attention

Tardy Escape Task9:45 am Math

Allie PierceAverage per day per month Problem Behavior

Location Motivation

Time

Allie Pierce (03-04)(If you were to conduct an observation, when would you observe Allie?)

Setting Behavior Maintaining Reinf

8:30Class10:45 class

Skipping class Avoid Work

Willie LomanAverage per day per month Problem Behavior

LocationMotivation

Time this year

Willie Loman (May, 03-04)

Setting Behavior Maintaining Reinf

Bus Zone Inappropriate language

To get peer attention

Use Hypothesis Statements

Verify Hypotheses FACTS Interviews Direct Observation Formal Functional Analysis

Combine with Wrap Plan/Person Centered Plan Design Support

Prevent access to problem context/conditions Teach new, replacement skills Place problem behavior on extinction Exaggerate rewards for appropriate behavior Improve consequences for problem behavior Establish safety systems Establish system for on-going data collection

Roles & Responsibilities

SWIS™ Management Team = SMSWIS™ Facilitator = SFSWIS™ User = SU

_____Provide ongoing assistance to SWIS™ Facilitators_____Provide ongoing assistance to schools using SWIS™_____Complete and submit a License Agreement and School Information Form_____Complete the SWIS™ Readiness Checklist_____Respond to questions from school personnel using SWIS™_____Collaborate with a SWIS™ Facilitator to adopt SWIS™_____Send passwords over email_____Provide training on how to use data for decision-making_____Enter data into a SWIS™ account_____Sign the License Agreement_____Attend team meetings and offer coaching for using the data for decision-making _____Log in and preview school account monthly to check for data entry accuracy

Roles & Responsibilities cont.

SWIS™ Management Team = SMSWIS™ Facilitator = SFSWIS™ User = SU

_____Maintain password confidentiality_____Make changes to school SWIS™ accounts _____Check and update billing information_____Update Facilitator contact information_____Use the SWIS™ User’s Manual for problem-solving_____Contact SWIS™ Management Team with questions about SWIS _____Change a school’s Facilitator_____Pay for SWIS™ account_____Check www.swis.org for updates_____Set up SWIS™ accounts in a timely manner_____Sign the School Information Form_____Use a referral form and referral procedure that are compatible with SWIS™_____Problem-solve SWIS™ bugs

What potential problems do you anticipate and what can you do to prevent them from occurring?

What steps will you take so that this transition happens smoothly for the school?

You are taking over some schools from another SWIS™ Facilitator who has retired or moved to a different position:

Someone at the school contacts you and wants to know how they can delete a referral. They are not listed as a SWIS™ user and they are not on the SWIS™ team. Before telling them how to do it:

What concerns might you have?

How will you help the team define the problem and come up with solutions?

You are working with a school that is K-8. They have asked you to help them meet readiness requirements. Some questions have arisen about how many SWIS™ accounts the school might need. They have one administrator, two school-wide teams (one for K-5 and one for 6-8), they are housed in one building. They report data to the NCES as one school:How will you help the school

determine how many SWIS™ accounts they may need for decision-making?

You are doing your monthly data accuracy check in one of the school accounts that you facilitator and notice that the school far exceeds the number of referrals per 100 students that is average for the school type and size:Is there a problem? If so,

how will you help the team define the problem and find possible solutions?

If there isn’t a problem, why?

Supporting SWIS Schools

Readiness Work with team at the school

Licensing License Agreement and School Information Form Data sharing Annual updates (SIF & passwords)

Teaching, coaching, & on-going support Data entry Using the data for decision making Updating accounts annually

SAMISWIS Account Management Interface

SAMI allows Facilitators to make changes to their SWIS™ schools ONLINE without sending in a School Information Change Form.

http://sami.swis.org http://samibeta.swis.org School Management School Summary

Integrity report Report status

Billing Summary Facilitator information updates

District Integration Update ASIST

a software application that builds a bridge from eSIS discipline data to SWIS accounts.

Data entry into eSis On a 24 hour schedule, ASIST will transfer eSIS data to SWIS.

S-Dex A software application that builds a bridge from SWIS to district

databases Data entry into SWIS

Beta testing with ten school districts in Oregon School Interoperability Framework (SIF)

Spring 2007 Download and upload from district databases Anticipated to be used nationwide to link software applications

New FeaturesDefinitions updatedCICOVISA

New Problem Behavior Categories

Problem behaviors Minors

Dress Code Tardy Information and other Electronic Technology

Violations Majors

Inappropriate displays of affection Information and other electronic technology violations Gang Affiliation Display (next SWIS update)

Student uses gesture, dress, and/or speech to display affiliation with a gang.

Schools within schools

What decisions need to be made for whole school?

What decisions need to be made for small schools within large school?

What reports are needed for: ‘Parent’ school? School within school?

Check In Check Out

CICO data collection and reporting application

Must have a SWIS account Development and deployment timeline

Spring ’07: development and beta testing Fall ’07: further testing November ’07: determine next steps for

deployment

Credit Card Payment

SWIS can now be paid for with a Charge Card Currently, transaction over the phone Future: Online Visa payment

Thank you to Jody & Megan for making this happen!

Ethnicity Reports

Rationale The power of information The risks and ethics of dis-proportionality

Format Multiple reports are needed for decision-

making

Ethnicity Reports

Key Questions What proportion of enrolled students in school are from

each ethnicity?

What proportion of referrals are contributed from students in each ethnicity?

What proportion of students with at least one referral are from each ethnicity?

What proportion of students within each ethnicity have received at least one office discipline referral?

Enthicity % Enrolled vs % Referrals

0

10

20

30

40

50

60

70

Native Asian Black Latino Pacific White Unkown NotListed

Pe

rce

nta

ge

s

% Enrolled % Referrals

Figure 1: (a) % of students enrolled, (b) % of ODRs by ethnicity

Enthicity % Enrolled vs % Students

0

10

20

30

40

50

60

70

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

es

% Enrolled % Students Ref

Figure 2: Proportion of all students with referrals by student ethnicity.

% of students within each ethnicity with referrals

0

2

4

6

8

10

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

e o

f st

ud

ents

Figure 3: Proportion of students within ethnicity who have at least one ODR.

Ethnicity Report Analysis #1

Enthicity % Enrolled vs % Referrals

0

10

20

30

40

50

60

70

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

es

% Enrolled % Referrals

Enthicity % Enrolled vs % Students Referred

0102030

40506070

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

es

% Enrolled % Students with Referrals

% of students within each ethnicity with referrals

0

5

10

15

20

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

e o

f st

ud

ents

Ethnicity Analysis #2

Enthicity % Enrolled vs % Referrals

0

10

20

30

40

50

60

70

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

es

% Enrolled % Referrals

Enthicity % Enrolled vs % Students Referred

0

10

20

30

40

50

60

70

Native Asian Black Latino Pacific White Unkown NotListed

Per

cen

tag

es

% Enrolled % Students with Referrals

% of students within each ethnicity with referrals

0

5

10

15

20

Native Asian Black Latino Pacific White Unkown NotListed

Pe

rce

nta

ge

of

stu

de

nts

Ethnicity Analysis #3

Resources

www.pbis.orgwww.swis.orgwww.pbssurveys.orgwww.pbismaryland.org