Michael A. Lawson, PhD Tania Alameda-Lawson, PhD Elizabeth ... · Michael A. Lawson, PhD Tania...

Michael A. Lawson, PhD Tania Alameda-Lawson, PhD

Elizabeth Anderson, PhD Binghamton University

Contact Info: [email protected]

In view of the resource and time constraints facing schools:

◦ What student data should be collected to inform the development of Full-Service Community Schools?

◦ How can student data be used to inform service delivery and program planning?

◦ How should we think about analyzing our results?

In the last six months, have you… 2010 Data 2011 Data Chnge

YES NO YES NO +/-

felt very sad for two weeks or longer? 0.32 0.68 0.28 0.72 -0.04

hurt yourself on purpose? 0.08 0.92 0.07 0.93 -0.01

felt hopeless for two weeks or longer? 0.26 0.74 0.27 0.73 +0.01

felt helpless, or felt that you had problems too big to solve? 0.49 0.51 0.4 0.6 -0.09

Gotten in a fight on school property? 0.27 0.73 0.21 0.79 -0.06

Missed school for reasons other than illness? 0.25 0.75 0.24 0.76 -0.01

Wanted to leave school for reasons other than illness 0.27 0.73 0.26 0.74 -0.01

(USED FOR PERFORMANCE MEASUREMENT !!)

In the last six months, have you… 2010 Data 2011 Data Chnge

YES NO YES NO +/-

felt very sad for two weeks or longer? 0.32 0.68 0.28 0.72 -0.04

hurt yourself on purpose? 0.08 0.92 0.07 0.93 -0.01

felt hopeless for two weeks or longer? 0.26 0.74 0.27 0.73 +0.01

felt helpless, or felt that you had problems too big to solve? 0.49 0.51 0.4 0.6 -0.09

Gotten in a fight on school property? 0.27 0.73 0.21 0.79 -0.06

Missed school for reasons other than illness? 0.25 0.75 0.24 0.76 -0.01

Wanted to leave school for reasons other than illness 0.27 0.73 0.26 0.74 -0.01

(USED FOR PERFORMANCE MEASUREMENT !!)

These analyses don’t show us how many kids positively endorse some or all of these indicators

of mental health

Hopeless Hurt s Self Cuts Class Aggression

Hopeless - .54 .32 .21

Hurts Self .54 - .23 .31

Cuts Class .32 .23 - .61

Aggression .21 .31 .61 -

Correlational analyses often do not give practitioners a good sense of how different “variables” present themselves for real kids, in real

schools.

Allow us to understand what (typical) combinations of risk, vulnerability, and/or strengths exist in student populations.

Allow us to model how different combinations of vulnerability/risk factors may reflect different “profiles” of risk.

Allow us to think about how we can develop service structures which are tailored to unique school communities and/or contexts.

Social-Emotional

Risk

Internalizing Symptoms

(Depression)

Externalizing Symptoms

(Aggression)

Avoidance

Victimization Risk

Perpetrator

Risk

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sad hurt nohope nohelp bullied teased schfear fearbfor fearaft fight mean insult

2ndResponse Category 1st Response Category

Our statistical analyses yielded five distinct risk profiles for each level of schooling (elementary, middle, and high school) ◦ See “Appendix” in Conference Materials for more

information or you can contact the presenters.

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No Risk (38%)


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Perpetrators (13.6%)


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Multiple Risk (7.7%)


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Depressed-Victim-Perpetrator (17.3%)


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Victim-Perpetrator (23.2%)


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401 403 404 407 410 412 414 416 417 420 422

Variation by School

Multiple Risk (7.7%) Dep-Vict-Perp (17.3) Vict-Perp (23.2) Perp (13.6%) No Risk (38%)

One Size Does Not Fit All!!

Using Your Assigned “School Profile”:

◦ What services or what configurations of services are

needed to respond to each of the risk profiles in your school?

◦ In view of the proportion of students assigned to each profile, what social and human resources are available to support student needs?

◦ What additional resources and/or partnerships are needed to address the needs and/or challenges implicated by the data?

School #1

Vict-Perp (15%)

No_Risk (38%)

Perp (10%)

Dep-Vic-Perp (27%)

Mult._Risk (10%

School#2

Vict-Perp (18%)

No_Risk (41%)

Perp (7%)

Dep-Vic-Perp (19%)

Mult._Risk (15%)

School#3

Vict-Perp (27%)

No_Risk (34%)

Perp (5%)

Dep-Vic-Perp (26%)

Mult._Risk (8%)

School #4

Vict-Perp (23%)

No_Risk (31%)

Perp (13%)

Dep-Vic-Perp (29%)

Mult._Risk (12%)

Tier 3 (5%): Intensive

Tier 2 (15%): Targeted Supports

Tier 1 (80%): Universal Supports

Tier 3 (22%):

Intensive



Multiple Risk Depressed-Victim-Perpetrators

No Risk

Victim-Perpetrators Perpetrators

Tier 3 (22%):

Intensive



Multiple Risk Depressed Victim Perpetrators

No Risk


Intensive Case Management (SW Interns)

Group Work Team Mentoring Family Outreach

Olweus Program Bystander Support (Awareness & Response)

Tier 3 (22%):

Intensive



Multiple Risk Depressed Victim Perpetrators

No Risk


Intensive Case Management (SW Interns)

Group Work Team Mentoring Family Outreach

Olweus Program Bystander Support (Awareness & Response)

Social, Educational, Health & Wellness PROMOTION ACTIVITY

Assumes that the construct/phenomenon of interest is “categorical” instead of continuous. ◦ Continuous variables:

Things that we have more or less of (e.g. “You are smarter than me.”—you are “more” intelligent)

◦ Categorical variables:

Phenomena which differ with respect to “quality.”

Having a “Type A” personality entails having qualitatively different personality features than other types

(People with Type A personalities, don’t have “more” personality per se, but they do have a different personality “Type”)

55 Schools in 10 School Districts in Broome County in Upstate New York.

Surveys were administered to 50% of students in each school.

(Ultimately 1490 7th And 8th Graders were surveyed)

Four variables were used to measure internalizing symptoms (depression).

In the past six months: (SAD) Felt very sad for two weeks or longer. (HURT) Hurt yourself on purpose (NOHOPE) Felt hopeless for two weeks or

longer. (NOHELP) Felt helpless, or felt that you had

problems that were too big to solve ◦ 1=“yes” ◦ 0= “no”

Two variables were used to measure student risk for victimization.

In the last 30 days:

(BULLIED) Has someone bullied, been mean, or threatened you at school.

(TEASED) Have other students made fun of you, insulted, or teased you at school

◦ 1=“yes”

◦ 0= “no”

Three variables were used to measure avoidance behaviors.

In the past 30 Days:

(SCHFEAR) How many times have you not gone to school because you felt you would not be safe at school?

(FEARBFOR) How many times have you not gone to school because you felt you would not be safe on your way to school?

(FEARAFT) How many times have you not gone to school because you felt you would not be safe on your way home from school?

◦ 0=None ◦ 1=Once

◦ 2= Twice or More

One variable was used to measure student aggression.

In the past year:

(FIGHT) How many times have you been in a physical fight on school property?

◦ 0=None

◦ 1=Once

◦ 2=Twice or More

Two variables were used to measure risks of bullying behavior:

In the past 30 days:

(MEAN) Said mean or threatening things to another student.

(INSULT) Made fun of other students, or insulted or teased them at school

◦ 1=“yes”

◦ 0= “no”

Each Risk Profile Model is built iteratively ◦ We start with a one-class solution and then build

successively until such time that there is no improvement in model fit. (BIC and LMR-LRT, e.g. Nylund, 2007)

For each level of student (Elementary, Middle and High School) a five class solution best fit the data.

Each “class-level reliability” was very high (>.88); Entropy>.88; providing important confidence in the models.

Michael A. Lawson, PhD Tania Alameda-Lawson, PhD Elizabeth ... · Michael A. Lawson, PhD Tania...

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