collecting data carol ann davis, ed. d. university of washington [email protected]

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Collecting Data Carol Ann Davis, Ed. D. University of Washington [email protected] u

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Page 1: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Collecting Data

Carol Ann Davis, Ed. D.

University of Washington

[email protected]

Page 2: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Did you know?

Students whose teachers monitor progress regularly and frequently have higher rates of learning as compared to students whose teachers do not collect data.

Page 3: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

What are the purposes of measurement?Why should I collect data?

• To communicate the child’s progress to others

• To provide feedback to the student

• To determine the effectiveness of the instruction

• To determine if it is necessary to change our instruction

Page 4: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Why collect data?

Monitoring the effects of instruction requires some form of systematic documentation or feedback.

Collecting data helps teachers

accurately gauge children’s progress

decide how to change instruction

Page 5: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Where do I begin?

It all starts with the student’s written objectives Who Behavior – observable description of the

behavior Condition – describes under what condition

you expect the behavior to occur Criteria – the level of performance expected

Page 6: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Given a 5th grade reading passage, Joseph will read 80 words per minute across three reading passages.

Given three different reading materials (e.g, flashcards, books, signs in the community), Gitit will read 50 functional sight words (see list) with 100% accuracy.

When given a task that is new and difficult, Neil will request assistance/help at least once during the activity without engaging in challenging behavior.

Page 7: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Heart rate can be monitored 24 hours a day with a machine but this is not efficient

More efficient to take a resting heart rate

What are other ways to make data collection efficient?

Page 8: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Types of data collection

Anecdotal Recording Permanent product Observation

Page 9: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

How do I know when to use observation system?

You want specific information about a behavior and you cannot obtain a written product

You can count the behavior (has a beginning and end) OR

The behavior is well defined

Page 10: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

How do I know which measurement system to use?

It should be matched to the target behavior/objective

It represents the behavior and progress or lack of progress

It needs to be simple and linked to instruction

Page 11: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Data Collection Systems

Event Recording – number of times a behavior occurs

Time sampling – estimate of the number of times/length of a behavior

Rate – number of times a behavior occur within a time period

Duration – how long behaviors last

Latency – time between instruction and the student beginning the response

Page 12: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Event Recording Number of times behavior occur

How many hits How many correct How many turn-takes

Used with discrete behaviors Good if behaviors occur during a specific time

Strength Weakness

minimal material needed (pencil/paper) if behavior occurs at high rate it is difficult to count

Easy to collect

Can be used in conjunction with time and opportunities

if behavior occurs for extended periods (out of seat behavior)

can be misleading if number of opportunities varies across data sessions

Page 13: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

//// //// //// ///

//// //// //// //// ///

Basic Data Sheet for Event Recording

Student: ________________________________Observer: _______________________________Behavior: _______________________________

1810:00 – 10:153/16

2310:00 – 10:153/15

Total Occurrences

Notations of OccurrenceTime

Start Stop

Date

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 14: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Student: Jack Date:9/30/02Behavior: Striking head with hand or fist.

Initials Activity Count Total

D.J. Arrival / / / / 4

D.J. Small Group / 1

D.J. Transition 2 / / / / / / / / / 9

F.W. Playcourt 0

F.W. Transition 3 / / / / / 5

J.D. Circle 0

J.D. Transition 4 0

J.D. Snack 0

D.J. Transition 5 / / / / / / / / / / / / / / 14

D.J. Bathroom / / / / / / / / / 9

D.J. Transition 6 0

F.W. Free Choice / / / / / / / 7

F.W. Transition 7 / / / / / / / / / / / / / / 14

J.D. Closing Circle / / / / / 5

J.D. Departure / / / 3

Page 15: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Event Recording with Controlled Presentations

14 Place spoon in mouth √ √ √ √ √ √

13 Bring spoon to mouth Ø Ø Ø Ø √ √

12 Lift spoon √ √ √ √ √ √

11 Scoop √ √ √ √ √ √

10 Place spoon in bowl Ø √ √ √ Ø √

9 Lift spoon Ø Ø Ø Ø √ √

8 Place carton on table Ø Ø Ø √ Ø Ø

7 Pour milk in bowl √ √ √ √ √ √

6 Lift carton Ø Ø Ø Ø Ø √

5 Open milk carton Ø Ø Ø √ √ √

4 Place box on table Ø Ø √ √ √ √

3 Pour cereal in bowl Ø Ø Ø Ø Ø Ø

2 Lift box √ √ √ √ √ √

1 Open cereal box Ø Ø Ø Ø √ √

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 16: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Rate

The number of times a behavior occurs divided by the amount of time. Words read per minute Math problems worked per minute Words typed per minute

Use when the opportunity to respond varies in time

Strength Weakness

Has no floor or ceiling Not familiar to most people

Page 17: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Percentage

The number of times a behavior occurred given a total number of opportunities

Advantages Disadvantages

Easy to calculate Does not take much to skew if small n

Convert to common measurement

Does not tell us about fluency

Page 18: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Interval and Time Sampling Recording

Provides estimate of actual number of times or how long a behavior occurs Needs time to teach and collect data Can be used for discrete or continuous (talking with peers)

behaviors Measurement of duration can be estimated

Strength: Weakness:

EASY - record once regardless of number of occurrences

Easy to teach and collect data

May underestimate the behavior

Page 19: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Interval and Time Sampling Recording

How:

1. Identify a sample amount of time that is representative of the behavior you are trying to collect.

2. Divide the total amount of time spent in the observation into short intervals (typically 1 minute)

3. Record (+/-) if behavior occurred during the interval

Page 20: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Interval Recording

Partial-interval – the behavior does not consume the whole interval You are going to miss something Less accurate when behavior is ______ occurring

Whole interval – the behavior is continuous and occurs across intervals Does not give recorder a break

Page 21: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Time Sampling (Momentary time sampling)

Usually in minutes rather than seconds Behavior is observed at the end of the interval A mark is noted only if the behavior is observed at

the moment the behavior is observed Intervals might be averaged to make sure the

student does not figure out observation schedule More practical for teaching and data collection Accurate for behaviors which are frequent or longer

in duration

Page 22: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

5 Minute Intervals

5 10 15 20 25 30

+ + + + - -

35 40 45 50 55 60

+ + - + - +

(8/12) x 100 = 66.66%

Behavior occurred at the end of 67% of the intervals during a 60-minute time period

Page 23: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Coding Form for Multiple Students

10 20 30 40 50 60

Tony

Al

Ellen

Austin

Mary

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 24: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Coding Form For Multiple Behaviors

Sessions

1 2 3 4 5 6 7 8 9 10

H √ √ √ √ √F √C √ √ √V √ √ √ √

H = Head RollingF = Hand FlappingC = Finger ContortionsV = High-Pitched Vocalizations

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 25: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

http://www.youtube.com/watch?v=pv5zWaTEVkI

Page 26: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Duration Recording

Use when behavior of concern varies in length of time

How: a. Record when behavior begins

b. Record when behavior ends

Summarize: Average or total

Strength: Weakness:

tells how many times and how long

behaviors must have clear beginning and end

Page 27: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Latency Recording

Time between the end of the instruction and when the student begins to perform the response.

Use when interested in how long it takes a student to begin performing a requested behavior

How: 1. Record the time after the end of the instruction2. Record the time when the student begins to perform the

response

Page 28: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

What type of data would you need to collect if you were interested in:

How long a student engaged in social interaction

How many times a student initiated communication (e.g., requests for objects)

How many words a student read per minute

How many words spelled correctly

Page 29: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Levels of Assistance

Variation of event recording Used instead of binary scoring system Records multiple levels of performance

Page 30: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Task Analytic Recording

The process of breaking a given task down into specific steps

Collecting data on which steps are completed

Page 31: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

What data collection system should be used?

When given a request to “do this…”, J will imitate 8 motor actions using objects/materials with 80% accuracy on 3 out of 4 opportunities

Before leaving the bathroom, J will independently follow all the steps in washing hands routine 4 out of 5 opportunities with 100% accuracy

During group lab time, J will increase engagement with structured activities from 30 seconds to 3 minutes 4 out of 5 opportunities

When given a reading passage at the 3 grade level, L will read 100 words per minute with 100% accuracy over 3 data sessions.

Page 32: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Measurement must be

Objective (replicable) Valid (measure what is supposed to be

measured) Reliable (measured the same way each time)

Page 33: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

What are threats to validity?

Reactivity The effects on the individual’s behavior

produced by the assessment procedures themselves

Page 34: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Remedies to threats of validity

Involve participant observers Observe covertly Minimize interactions Become part of the environment Use a

second observer Observer drift

Page 35: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Measures may be reliable but not valid

Using a ruler to measure our foot is not a valid measure of shoe size

BUT you will get a reliable (the same) measure every time

Measures cannot be valid if there are not reliable

Using a shoe guide that gives us different measurements every time may not be measuring what it is says it is measuring.

Page 36: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Reliability

The insurance that the data collector is colleting data on what he or she intended to collect

Page 37: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Gross Calculation

Smaller # of occurrencesof the behavior

Larger # of occurrences of the behavior

X 100

Page 38: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Gross Calculation

Smaller # of minutes

Larger # of minutes

X 100

Page 39: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Gross Calculation

Agreements

Agreements + Disagreements

X 100

Page 40: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

What should be on the data sheet?

Place for student’s name, date, teacher’s name

Place to list the skill to which you are working

Target behavior

Place to record the counts

Place to summarize the information

Page 41: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

How will I manage all of the data sheets?

Use one data form to collect information on many different target behaviors

Mom + +

Dad + +

In + +

Out - -

stop + -

nickel - -

penny + -

dime + +

quarter + +

$1 + +

Page 42: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Stimulus Prompt Test

1.00

Nickel

Penny

Dime

Quarter

Stimulus Prompt Test

Sequence 1

Sequence 3

Sequence 2

Sequence 1

Sequence 3

Stimulus Prompt Test

120

140

130

170

190

Stimulus Criteria

Small group 1:00

Computer 4:00

Leisure 2:00

Computer 4:00

Gym 3:00

Stimulus Prompt Test

Connect 4

Jinga

Kerplunk

Uno

puzzle

Stimulus Prompt Test

Turn takes

Stimulus Prompt Test

1:00

4:00

7:00

10:00

5:00

Stimulus Prompt Test

Mom

Dad

In

Stop

Out

Picture of Prompt Test

Jumping

Running

Kicking

Sitting

Pointing

Page 43: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Incorporate your data collection form with the classroom schedule

Activity Skill Data collection

8:30 – 9:15

1. Number of minutes engaged

2. Number recognition

3. Sight word recognition

____ mins. engaged

Page 44: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Data Collection for a group of students

Child Child Child Child Child

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Program:

prompt__________

Page 45: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

During lunch, before or after school, Joseph will initiate to a peer at least 5 times for 3 data sessions.

What are the considerations? What type of data should be collected?

Page 46: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

How often should I collect data?

Often enough to use it to guide your instruction. If you collect data 1X per month, then you would not be

ready to make a decision about changing instruction before 3 months

You do not have to collect data on every occurrence of the target behavior, data simply must be representative of the target behavior.

Page 47: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Collect data more frequently for:

High-priority target behaviors

Target behaviors that require ongoing decisions (e.g., reading sets of words)

Page 48: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

How do I collect data for objectives that are written too big?

Break the larger goal into smaller steps focusing on the target behavior.

Example: Given three different reading materials (e.g,

flashcards, books, signs in the community), Gitit will read 50 functional sight words (see list) with 100% accuracy.

Page 49: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Given three different reading materials (e.g, flashcards, books, signs in the community), Gitit will read 5 functional sight words (see list) with 100% accuracy.

Mom + +

Dad + +

In - +

Out + +

stop + -

8 out of 10 words read correctly = 80% (for this set of words)

Program: Functional sight words

= 80%

Page 50: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Group Activity

Go back to your defined behavior and written objective and decide on a measurement system that is most appropriate for that behavior

Describe why you choose that system

Page 51: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Conversion of Data

Need to convert raw data into a more representative number

Frequency data that is collected with different number of opportunities -- percentage

Frequency data when varying amounts of time were available – rate

Page 52: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Interval or Time Sampling is reported in terms of number of intervals the behavior occurred or percent intervals the behavior occurred

Duration or Latency is reported in terms of number of seconds, minutes, etc…

Page 53: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Graphing

Summary of raw data into a readable format

A picture says a thousand words

Clear, easy to interpret and everyone on same page

Page 54: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Line graphs

Data are graphed at the appropriate intersections

Page 55: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Cumulative graphs

The number of occurrences are added to the previous session

This approach provides the total number of responses

Graphs must always trend upward

Page 56: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Bar graphs or Histograms

Data is plotted using a bar to represent the occurrences of the behavior

Page 57: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Summary of Data Conversion Process

Type of Recording Data Conversion

Permanent Product * Report number of occurrences

if both time and opportunities to respond are constant

Event * Report percentage if time is constant (or not of concern) and opportunities vary.

* Report rate if both time (which is of concern) and opportunities vary, OR if time varies and opportunities are constant.

Interval * Report number of intervals if constant

Time Sampling * Report percentage of intervals

during or at the end of which behavior occurred.

Duration * Report number of seconds/minutes/hours

for which the behavior occurred.

Latency * Report number of seconds/minutes/hours

between antecedent stimulus and onset of behavior.

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 58: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Basic Components of a Line Graph

a. Ordinate label e. Continuity breakb. Ordinate f. Data pointc. Abscissa label g. Abscissad. Data path

Sessions

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

109876543210

L A B E L

A

B C

DE F

G

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 59: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Permanent Product- Data to Graph

Graphed as a number of items If the number of opportunities is constant

or percentage of items If the number of opportunities varies

Page 60: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Calculate the percentage

Divide the number of correct responses by the total number of responses and multiply the result by 100

Number of correct responses

X 100= %

Total number of responses

Page 61: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Computing Rate

A rate of correct responding is computed by dividing the correct responses by the time taken for responding:

# correct

Correct Time =

Time

Computing a rate of error may be done by dividing the number of errors by the time:

Errors

Rate of Error =

Time

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.

Page 62: Collecting Data Carol Ann Davis, Ed. D. University of Washington cadavis1@u.washington.edu

Graphing Rate Data

Student: StevenBehavior: Packet AssemblyObservation Period: Vocational training at Red Cross

Day Number Completed

Amount of Time

Rate per Minute

Monday 45 30’ 1.5

Wednesday

40 25’ 1.6

Friday 45 25’ 1.8

Tuesday 40 20’ 2.0

Thursday 50 25’ 2.0

Monday 48 20’ 2.4

Wednesday

54 20 2.7

Alberto & TroutmanApplied Behavior Analysis for Teachers, 7e

Copyright ©2006 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458

All rights reserved.