Case Study

Measuring Behavior

Introduction

Data collection is a critical part of understanding student behavior and making informed decisions. A systematic process for collecting and analyzing behavioral data reduces subjectivity and helps educators more easily identify patterns in student behavior. By consistently and accurately measuring student behavior, educators can:

• Monitor student progress over time
• Identify patterns related to when, where, and how behavior occurs
• Determine if behavioral supports and interventions have been effective
• Decide if an intervention should be continued, adapted, or stopped
• Communicate behavioral progress to students, parents, and other school professionals

Operationally Defining a Behavior

Before collecting any data, educators need to define the target behavior (i.e., the specific interfering behavior in need of change) and the replacement behavior (i.e., an appropriate alternative behavior that serves the same function as the target behavior). Educators might collect data on the student’s engagement in either or both behaviors.

To facilitate consistent and accurate data collection, educators must develop an operational definition—a clear and specific description of the behavior to be measured. This definition should begin with a broad statement describing the behavior in terms that are:

• Observable: Details what the behavior looks and sounds like, not what the student might be thinking or feeling
• Measurable: Describes the behavior in such a way that it can be counted or timed
• Actively Stated: Specifies what the student does rather than what they fail to do

It is also helpful to include specific examples and non-examples of the behavior as part of the operational definition. These additional details help clarify the definition by illustrating what constitutes a behavior when collecting data. Below are examples of operational definitions for a student’s target and replacement behaviors.

Target Behavior	Definition Mandy uses classroom materials or school property in a manner other than their intended purpose, resulting in potential or actual damage.	Examples Snapping pencils in half Ripping pages out of a textbook Carving marks into the surface of a desk	Non-Examples Erasing a mistake on a worksheet Doodling on paper during free time Accidentally dropping a ruler on the floor
Replacement Behavior	Definition Mandy manipulates designated objects kept in a calm-down kit at her desk.	Examples Squeezing a stress ball Tearing designated strips of paper Rubbing a Velcro strip with her thumb	Non-Examples Throwing a stress ball across the room Leaving her desk to look for a different fidget tool Scraping a fidget across the wall and chipping off the paint

Selecting a Data Collection Method

Educators can use a variety of methods to collect behavioral data. However, the best method will depend on the unique student, behavior, and context. As such, educators should be knowledgeable about multiple options and select a method that:

• Aligns with a student’s behavior as operationally defined
• Captures the frequency, intensity, or other dimensions of the behavior as accurately as possible
• Is sensitive enough to show when the behavior has changed
• Is feasible for the educator to use in the classroom

In addition to selecting an appropriate method, educators must also decide:

• Who will collect data
• Where data will be collected
• How often data will be collected
• How data will be documented, stored, and graphed
• When and how progress will be communicated

Each case study includes multiple STAR Sheets and cases.

About the Strategy

Direct Behavior Rating (DBR) is a method of data collection that combines observation and rating scales. When using this method, educators observe the student during regularly scheduled periods and rate the extent to which the student demonstrates a target or replacement behavior using a predetermined scale.

What the Research and Resources Say

• DBR produces valid measurements of student behavior, yielding similar results to traditional direct observation methods (Chafouleas et al., 2012b; Fabiano et al., 2017; Smith et al., 2018).
• DBR is particularly efficient because an educator can monitor a student’s behavior while simultaneously carrying out regular teaching responsibilities (Briesch et al., 2016).
• When behaviors are operationally defined and observers are trained, DBR can capture consistent measurements across multiple observers and settings (Briesch et al., 2013; Casale et al., 2021; Chafouleas et al., 2012a).
• Because DBR effectively represents behavioral changes over time, it is a useful tool for progress monitoring (Chafouleas et al., 2012b; Fabiano et al., 2017; Hustus et al., 2018; Matta et al., 2020; Smith et al., 2018).
• The table below highlights categories of behaviors that can be effectively measured using DBR.

Behavior measured	Citations
Academic engagement (e.g., raising a hand, engaging with materials, making on-topic comments)	Casale et al., 2021; Chafouleas et al., 2013; Chafouleas et al., 2012b; Fabiano et al., 2017; Hustus et al., 2018; Kilgus et al., 2014; Johnson et al., 2016; Matta et al., 2020; Smith et al., 2018
Disruptive behavior (e.g., throwing objects, calling out, cursing)	Chafouleas et al., 2012b; Chafouleas et al., 2013; Daniels et al., 2021; Hustus et al., 2018; Kilgus et al., 2014; Johnson et al., 2016; Matta et al., 2020; Smith et al., 2018; Wickerd & Hulac, 2017
Respectful behavior (e.g., following directions, prosocial interactions, making polite requests)	Fabiano et al., 2017; Kilgus et al., 2014; Johnson et al., 2016

Steps for Implementation

After identifying and operationally defining the behavior to be measured, educators can use the steps below to collect DBR data.

1. Create an observation schedule: Observations should occur during times when the behavior is expected to happen and on a regular schedule (e.g., every day during math class, every time students are working independently). The length of observation periods can vary based on context and the behavior being monitored. Observation periods could be as brief as 15 minutes or as long as the entire school day.

2. Select or develop a rating scale: Ratings are typically scored on a numerical scale to measure one of the following dimensions:
   • Percentage of time the student was engaged in the behavior (0%–100%)
   • Intensity of the student’s behavior across the observation period (e.g., 0 = no behavior observed, 3 = mild, 7 = moderate, 10 = severe)

3. Conduct observations: Using the observation schedule developed in Step 1, the educator monitors the student’s defined behavior while continuing their usual teaching activities.
4. Rate and record the behavior’s occurrence: As soon as the observation period is over (e.g., at the end of a lesson, after recess), the educator marks the best estimate of the behavior’s occurrence using the scale created in Step 2. Ratings can be documented on paper forms, computerized systems, or mobile apps.
5. Organize the data: The data should be organized in a way that supports interpretation and decision-making. For example:
   • Numerical data from multiple days or weeks can be entered into a simple table or chart for comparison.
   • Plotting the data on a line graph supports visual analysis of changes in the amount, severity, or consistency of the behavior over time. (For more information, review the Graphing Data STAR Sheet.)

Tips for Implementation

• To collect more objective data and avoid bias, create a DBR scale that uses numbers (e.g., 0%–100%) to measure the extent of a behavior rather than a categorical scale (e.g., never, rarely, sometimes, often, always). Because collecting DBR data is designed to be quick and nondisruptive while teaching, rely on mental notes during the lesson and quickly record your rating immediately afterward.
• For the most accurate data, assign a consistent person to observe and rate the student’s behavior during each class period or activity. For example, have the math teacher always record DBR data during math class, rather than having a paraeducator record on some days and the math teacher on others.
• Add brief written notes about the nature of the observed behavior alongside the numerical rating to provide additional description or context.
• Calculate the average DBR rating across days or time periods to reveal patterns of behavior.

  Average Calculation

  Average = sum of all ratings ÷ number of ratings

  Example: Average = (60% + 80% + 70% + 40% + 60%) ÷ 5 = 62%

About the Strategy

Systematic direct observation (SDO) requires an observer to precisely measure how often a target or replacement behavior occurs or how long it lasts. Data can be captured using one of the following four methods.

• Frequency or event recording—documenting the number of times a behavior occurs within a given period (e.g., the number of times a student calls out during a one-hour lesson)
• Interval recording—documenting whether a behavior occurred during brief periods or intervals (e.g., the number of one-minute intervals in which the student was out of their seat for any portion of time)
• Duration recording—documenting how long a student engages in a behavior (e.g., time spent on task during independent work)
• Latency recording— documenting the amount of time that elapses between when a direction or prompt is given and when the student begins the behavior (e.g., time between giving a student an assignment and the student beginning to work)

What the Research and Resources Say

• When observers use consistent definitions and procedures, SDO data provide a clearer picture of behavior than informal observations or memory-based reports (Lane & Ledford, 2014; LeBlanc et al., 2020).
• Although frequency and duration recording are considered the most accurate SDO methods, they might not be the most feasible in typical classroom settings because they require an observer’s undivided attention (Alberto et al., 2022; Briesch et al., 2018; Ledford et al., 2015).
• In a busy classroom, interval recording can be a more practical way to approximate the frequency or duration of a behavior (Yoder et al., 2018; Zakszeski et al., 2017).
• Interval recording is most accurate when intervals are short. While intervals between 15 and 20 seconds are commonly recommended, intervals should be no longer than three minutes (LeBlanc et al., 2020; Ledford et al., 2015; Zakszeski et al., 2017).

Steps for Implementation

After identifying and operationally defining the behavior to be measured, use the steps below to collect SDO data.

1. Choose the appropriate method: The selected recording method must be appropriate for the nature of the behavior being measured.
   • Frequency or event recording—use for relatively brief behaviors with a clear beginning and end (e.g., shouting, hand raising, kicking)
   • Interval recording—use for behaviors that occur frequently or continuously (e.g., off-task behavior, academic engagement), or for when it is not feasible to count or time every instance of the behavior
   • Duration recording—use for sustained behaviors when the amount of time spent engaging in the behavior is the primary concern (e.g., crying episodes, attention to task, tantrums)
   • Latency recording—use for capturing the time between an instruction being given and the behavior beginning or ending (e.g., delayed transitions, delayed response to directions)
2. Create an observation schedule: Observations should occur during times when the behavior is expected to happen and should last long enough to capture multiple instances of the behavior.
3. Select or develop a recording form: The documentation system needs to align with the method selected in Step 1. Data can be documented on paper forms, computerized systems, or mobile apps.

4. Identify and train observer(s): One or more individuals (e.g., paraeducator, school psychologist) can conduct observations while the educator goes about usual instruction. Observers should be trained to understand the behavioral definition and the procedures for data collection.
5. Conduct observations and record data: Using the observation schedule developed in Step 2, the observer watches the student and records instances of the defined behavior throughout the observation session.
6. Organize the data: The data should be organized in a way that supports interpretation and decision-making. For example:
   • Numerical data from multiple days or weeks can be entered into a simple table or chart for comparison.
   • Plotting the data on a line graph supports visual analysis of changes in the amount, severity, or consistency of the behavior over time. (For more information, review the Graphing Data STAR Sheet.)

Tips for Implementation

• To simplify the collection of data, try using one of these strategies:
  • Frequency data—Make tally marks on paper, use a small handheld counter, or put a handful of small items (e.g., pennies, paper clips) in one pocket and move one item to the other pocket each time the behavior occurs.
  • Interval data—Use a stopwatch, smartphone app, or vibrating wearable device to signal the end of an interval and prompt you to record data.
  • Duration or latency data—Use a digital stopwatch to collect data in minutes and seconds; using a wall clock or wristwatch will not be as precise.
• Consider using technology tools (e.g., smartphone timers, mobile tally counter apps, web-based form-building tools) to make SDO data collection more feasible in a busy classroom.
• If you are unsure whether the behavior occurred (e.g., the student walks behind a bookshelf and cannot be seen), do not attempt to guess what happened. Document only what can be directly seen or heard.
• Add brief written notes about the nature of the observed behavior alongside the numerical data to provide additional description or context.
• Calculate rates, percentages, or averages as appropriate to allow comparisons of data across observation periods of differing lengths. The chart below provides helpful calculations for each SDO method.

Method	Calculation	Formula	Example
Frequency	Rate of the behavior	Rate = frequency count ÷ length of observation	25 callouts ÷ 1.5 hours = 16.7 callouts per hour
Interval	Percentage of intervals in which the behavior occurred	Percentage = number of intervals with behavior ÷ total number of intervals x 100	42 intervals with off-task behavior ÷ 60 total intervals x 100 = 70% of intervals with behavior
Duration	Percentage of time engaged in the behavior	Percentage = minutes engaged in the behavior ÷ total minutes observed x 100	6 min out of seat ÷ 20 min observed x 100 = 30% of time engaged in target behavior
Duration	Average length of behavioral instances	Average = sum of lengths ÷ number of instances	(2.2 min + 6.1 min + 3.5 min + 5.0 min) ÷ 4 instances = average length of 4.2 min
Latency	Average elapsed time	Average = sum of latency lengths ÷ number of instances	(23 sec + 90 sec + 49 sec) ÷ 3 instances = average elapsed time of 54 sec

About the Strategy

Scatterplot recording is a visual method used to analyze behavioral patterns over time. In this approach, the school day is divided into equal time intervals, and the educator records whether a given target or replacement behavior occurred during each interval.

What the Research and Resources Say

• Plotting scatterplot data can help reveal relationships between a behavior and a time of day, an activity, a social grouping, a physical environment, the presence of certain people, or a combination of such variables (Alberto et al., 2022).
• Scatterplot recording is most practical for easily observable, high-intensity behaviors (e.g., loud vocalizations, property destruction) that occur sporadically throughout the school day (Delgado et al., 2017; Robinson et al., 2019).
• Scatterplot data are especially useful when a behavior’s cause is not immediately obvious through informal observation (Robinson et al., 2019).
• Educators can collect scatterplot data while they are teaching, without disrupting classroom routines (Alberto et al., 2022).
• In the functional behavioral assessment (FBA) process, educators can use scatterplot data to pinpoint peak times for the behavior. Then direct observations can be scheduled during those periods to gather deeper insights (Anderson et al., 2015).

Steps for Implementation

After identifying and operationally defining the behavior to be measured, use the steps outlined below to collect scatterplot data.

1. Create an observation schedule: The school day should be divided into consistent intervals of time (e.g., 15 minutes, 30 minutes). It is often beneficial to align these intervals with the instructional schedule, which allows recording to occur during natural transitions.
   • Use shorter intervals for behaviors that occur frequently.
   • Use longer intervals for behaviors that occur less often but are intense or dangerous (e.g., elopement, meltdowns) that rarely occur.
     x

     elopement

     glossary

2. Select or develop a recording form: Recording forms are typically set up as a grid with columns representing days of the week and rows representing time intervals. Forms should also include a key to indicate whether and how often the behavior was observed, as illustrated in the sample scatterplot recording form below.

3. Conduct observations: Throughout the school day, the educator monitors the student’s defined behavior while continuing usual teaching activities.
4. Record the behavior’s occurrence: At the end of each time interval, the educator marks whether the behavior occurred on the recording form created in Step 2. Data can be marked on paper forms, computerized systems, or mobile apps.
5. Organize the data: The data should be organized in a way that supports interpretation and decision-making. The visual recording sheets often make it easy to see patterns that reflect when the behavior does and does not occur.

Tips for Implementation

• For more detailed data, consider using a key to further clarify the behavior’s frequency. For example, you might use a slash mark to indicate that the behavior occurred once during an interval but mark an X if it happened two or more times.
• To avoid confusion, clearly mark intervals in which the student was not observed and differentiate it from intervals when the behavior did not occur.
• Set a timer for the interval length or an alarm for the ending time of each interval as a reminder to mark the recording sheet.
• To summarize the data, calculate the percentage of intervals during which the behavior was observed for each day of the week or across a full week. For example:
  • On Monday, the behavior occurred in 8 out of 12, or 67%, of intervals.
  • Across a week, the behavior occurred in the 9:30 a.m.–10:00 a.m. interval on 3 out of 5, or 60%, of the days.
• Once a reliable pattern has emerged in the scatterplot data, consider recording more detailed antecedent-behavior-consequence (ABC) data during the times of day the behavior is most likely to occur. ABC data can help educators more fully understand what antecedent might be triggering and what consequence might be reinforcing the behavior.

About the Strategy

Graphing data offers a visual picture of a student’s progress over time that is easier to interpret than a list of numbers or a stack of data recording sheets. Well-constructed graphs allow for ongoing analysis of progress and support communication among educators, students, families, and other professionals.

Although there are numerous types of graphs, line graphs are most often used to represent systematic direct observation (SDO) and Direct Behavior Rating (DBR) data. When using line graphs, educators plot data points over a given period, connect the adjacent plotted data points to create a line, and then interpret the shape of the connected line. Because scatterplot recording forms are inherently visual, it is usually not necessary to create an additional graph.

What the Research and Resources Say

• Although raw data can be difficult to interpret on its own, organizing data points on a graph helps educators visualize trends and make data-based decisions (Briesch et al., 2018; Davis & Akers, 2023).
• Visual analysis is most efficient when the graph is simple, uncluttered, and clearly labeled (Alberto et al., 2022; Kubina et al., 2021).
• Multiple school-based professionals (e.g., teachers, school psychologists, behavior analysts) should be involved in interpreting graphed data and making data-based decisions (Belmonte-Mulhall & Harrison, 2023; Jimenez et al., 2016).

Steps for Implementation

1. Create a line graph. The grid for the line graph must be set up to align with the observation schedule and the nature of the data collected. It should include labels for both axes:
   • The x-axis—the horizontal line at the bottom of the graph—represents time (e.g., days, observation sessions, class periods).
   • The y-axis—the vertical line on the left side of the graph—represents the quantitative measurement of the behavior (e.g., rate, percentage of intervals, percentage of observation session, DBR rating).
2. Plot the data on the graph. Data is transferred to the graph by plotting a point to represent the behavioral occurrence at each time. For example, if frequency data show that a student engaged in a target behavior seven times during Observation Session 1, a point would be plotted at (1, 7) on the graph. A solid line is then drawn to connect each point to the next.
3. Analyze the data. Once the line graph has been constructed, it can be visually analyzed to identify:
   • Level—the behavior’s overall magnitude (e.g., high, moderate, low)
   • Trend—whether the behavior is increasing, decreasing, or staying the same over time
   • Variability—how consistent or fluctuating the rate of the behavior is

   Based on this visual analysis, educators can determine the effectiveness of current supports and interventions and make adaptations if needed.

Tips for Implementation

• To obtain an accurate reflection of student performance, aim for at least six to eight data points before analyzing the graph. Daily behavior can fluctuate due to factors like illness or fatigue, making a greater number of data points essential to minimize outliers and related misinterpretation.
• To facilitate ongoing visual analysis of emerging trends, set up the graph early and plot data points as they are collected, rather than waiting to graph a large batch of data.
• If data are being collected on multiple behaviors simultaneously and both use the same type of measurement, represent both on the same line graph. Use a different geometric symbol (e.g., circle, triangle, square) for each behavior, and provide a key to clearly denote which line represents which behavior. Review the example below.

• Consider using spreadsheet software or web-based tools to transform raw data into line graphs. Such tools can be much more efficient than graphing by hand and reduce the likelihood of human error.

Background

Scenario

Teagan rarely finishes her classwork because she is frequently out of her seat wandering the classroom. During a typical lesson, she will walk away from her desk to look at a display across the room, drift over to the cubbies to rummage through her backpack, and make multiple trips to the pencil sharpener. These trips add up quickly, leaving her with barely any time to look at her assignments.

Teagan’s teacher, Ms. Lowe, feels like she is constantly telling Teagan to return to her desk and get back to work. Monitoring and redirecting Teagan takes a great deal of Ms. Lowe’s time and energy, especially because she does not have a paraeducator in the classroom. Ms. Lowe is frustrated, but she realizes that she doesn’t have a clear, objective picture of Teagan’s behavior. Before she can decide how to best intervene, she needs to collect data to help her understand the nature and severity of the behavior. Recognizing the difficulty of doing so while actively teaching, she contacts the school’s behavior specialist to collaborate on an observation and data collection plan.

Strategies

• Systematic Direct Observation
• Graphing Data

Assignment

1. Read the Introduction and the STAR Sheet for the strategy listed above.
2. Define the four types of SDO data.
   1. Identify which type of SDO data you would recommend to Ms. Lowe for measuring Teagan’s out-of-seat behavior. Explain why.
   2. Identify a type of SDO data that would be inappropriate for measuring Teagan’s out-of-seat behavior. Explain why.
3. Ms. Lowe develops a line graph to visually organize the data collected using the SDO method chosen in Question 2a. When setting up the graph, describe how she should label the x-axis and the y-axis and what each would represent.

Background

Scenario

Rohan, a student in Ms. Clark’s second-grade class, loves animals, nature, and playing sports. He has always seemed to enjoy school but has started to display more academic difficulty this year. Ms. Clark is growing increasingly concerned that Rohan seems to be finding ways to avoid his classwork, such as putting his head down on the desk, spending extended time looking for materials, and even dropping to the floor and refusing to work.

When the principal asks Ms. Clark about Rohan’s behavior, she explains that it doesn’t happen all day or during all activities, but she can’t quite pin down a pattern. Together, they decide that collecting data in a scatterplot could help determine if the behavior is tied to a specific subject, activity, or time of day.

They write the following operational definition of Rohan’s behavior:

Ms. Clark records data on Rohan’s behavior during all instructional times for one week, as shown below.

Student: Rohan

Target Behavior: Rohan engages in actions unrelated to the assigned instructional task for at least 30 seconds.

Time	Activity	Mon	Tues	Wed	Thurs	Fri	% of Days
8:30–8:45	Morning work	Did Not Occur	Did Not Occur	Did Not Occur	/	Did Not Occur	20%
8:45–9:00	Morning meeting	Did Not Occur	Did Not Occur	Did Not Occur	Did Not Occur	Did Not Occur	0%
9:00–9:15	ELA whole group	/	/	Did Not Occur	Occurred 2 or More Times	/	80%
9:15–9:30	ELA whole group	Occurred 2 or More Times	/	/	Occurred 2 or More Times	Occurred 2 or More Times	100%
9:30–9:45	ELA small group	Did Not Occur	Did Not Occur	Did Not Occur	/	Did Not Occur	20%
9:45–10:00	ELA small group	Did Not Occur	/	Did Not Occur	/	Did Not Occur	40%
10:00–10:15	ELA independent work	Occurred 2 or More Times	Occurred 2 or More Times	Occurred 2 or More Times	Occurred 2 or More Times	Occurred 2 or More Times	100%
12:00–12:15	Math whole group	Did Not Occur	Did Not Occur	Did Not Occur	/	Did Not Occur	20%
12:15–12:30	Math whole group	Did Not Occur	Did Not Occur	Did Not Occur	Occurred 2 or More Times	/	40%
12:30–12:45	Math small group	Did Not Occur	Did Not Occur	Did Not Occur	/	Did Not Occur	20%
12:45–1:00	Math stations	/	Did Not Occur	Did Not Occur	Occurred 2 or More Times	/	60%
1:15–1:30	Science	Did Not Occur	Did Not Occur	Did Not Occur	Did Not Occur	Did Not Occur	0%
1:30–1:45	Science	Did Not Occur	Did Not Occur	Did Not Occur	Did Not Occur	Did Not Occur	0%
1:45–2:00	Science	Did Not Occur	Did Not Occur	Did Not Occur	/	Did Not Occur	20%
2:00–2:15	Independent reading	Occurred 2 or More Times	Occurred 2 or More Times	Occurred 2 or More Times	Occurred 2 or More Times	Occurred 2 or More Times	100%
% of Intervals		33%	33%	20%	80%	40%	Empty

Possible Strategies

• Direct Behavior Rating
• Systematic Direct Observation
• Scatterplot Recording

Assignment

1. Read the Introduction and the STAR Sheets for the strategies listed above.
2. Which method did Ms. Clark use for data collection? Why do you think she selected this method?
3. Look at the chart displaying Rohan’s data.
   1. What do the rows (labeled top to bottom) represent?
   2. What do the columns (labeled left to right) represent?
   3. How many times did the target behavior occur during Math Stations on Friday?
   4. How many times did the target behavior occur between 8:45 a.m. and 9:00 a.m. on Tuesday?
4. Based on the data, when did Rohan’s target behavior occur most often?

Background

Scenario

Ava is a kindergarten student with autism who spends most of her school day in an inclusive classroom. Although Ava can communicate using one- and two-word utterances, she primarily uses picture cards to communicate her wants and needs. Ava has recently begun to demonstrate significant behaviors that disrupt the learning environment and pose safety concerns. She yells “No!,” tears up worksheets, and occasionally pinches or hits peers. Ava’s teacher, Mrs. Pritchard, and full-time paraeducator, Ms. Cox, have noticed that these behaviors seem to occur most frequently when Ava is asked to transition from one activity to another.

Ava’s individualized education program (IEP) team initiated a functional behavioral assessment (FBA) to explore the function of Ava’s behavior. They wrote the following operational definition of the behavior:

The information gathered from informal assessments and antecedent-behavior-consequence (ABC) data suggest that the function of the behavior is to avoid the termination of a preferred activity. The team decides to introduce a visual schedule and a visual timer to support Ava during transitions, but they first need to collect baseline data on the behavior.

Possible Strategies

• Systematic Direct Observation
• Direct Behavior Rating
• Scatterplot Recording

Assignment

1. Read the Introduction and the STAR Sheets for the strategies listed above.
2. Describe how the operational definition of Ava’s target behavior is observable, measurable, and actively stated.
3. Identify which of the three possible data collection strategies you would use to collect data on Ava’s behavior.
   1. Explain why you chose this method.
   2. Explain the specific steps Ava’s team would need to take to prepare for collecting data using this method.
   3. Explain how the team could organize and visually analyze the data once collected.

Background

Scenario

Cyrus, a high school sophomore, has struggled with maintaining safe, productive habits during his chemistry lab blocks. Because these labs involve hazardous materials and strict procedures, Cyrus’s teacher, Mr. Ramirez, wants to prioritize increasing his safe and on-task behavior, including handling materials appropriately, remaining at his designated station, and following written instructions.

To track Cyrus’s progress, Mr. Ramirez decides to use a DBR scale representing the estimated proportion of the total class time that Cyrus spends safely engaged in his tasks. He begins by collecting baseline data for one week:

• Monday (Day 1): 35% of the lab period spent safely on task
• Tuesday (Day 2): 50% of the lab period spent safely on task
• Wednesday (Day 3): 20% of the lab period spent safely on task
• Thursday (Day 4): 45% of the lab period spent safely on task
• Friday (Day 5): 30% of the lab period spent safely on task

To address the low baseline data, Mr. Ramirez implements an intervention at the start of Week 2. Cyrus is given a step-by-step checklist of lab safety rules, setup instructions, and cleanup tasks. Mr. Ramirez continues to collect DBR data over the next five days:

• Monday (Day 6): 55% of the lab period spent safely on task
• Tuesday (Day 7): 65% of the lab period spent safely on task
• Wednesday (Day 8): 70% of the lab period spent safely on task
• Thursday (Day 9): 75% of the lab period spent safely on task
• Friday (Day 10): 70% of the lab period spent safely on task

Possible Strategies

• Direct Behavior Rating
• Graphing

Assignment

1. Read the Introduction and the STAR Sheets for the strategies listed above.
2. Provide two reasons why DBR is an effective way for Mr. Ramirez to collect data on Cyrus’s behavior.
3. Construct a line graph of Cyrus’s progress monitoring data, making sure to include all relevant components and labels. Download a blank graph.
4. Visually analyze the graph.
   1. What do you notice in terms of level, trend, and variability?
   2. How does Cyrus seem to be responding to the intervention?

Background

Scenario

Mr. Smith teaches history at Hamilton High School. Because his students have a wide range of reading abilities, he often reads a paragraph from the text aloud and then asks listening comprehension questions. During this question-and-answer period, students are required to raise their hands and wait to be called on.

Daniela, a new transfer student, frequently talks to peers and writes notes to friends during this activity. When she does respond to questions, she blurts out the answer without waiting to be called on. Though her answers are always correct, Mr. Smith is frustrated with her behavior.

Concerned about this pattern of behavior, Mr. Smith asks the school psychologist, Mrs. Patel, to observe Daniela and record data on her behavior. She decides to observe for 10 to 20 minutes each day and collect frequency data on the number of times Daniela calls out as well as interval data on Daniela’s off-task actions. Mrs. Patel collects data for a one-week baseline period (review data samples below).

Frequency Data: Summary Table

Behavior: Daniela verbally states a response or answer to a question without first seeking and receiving explicit permission to speak.

Date	Time Start	Time End	Total Time	Tally	Total Occurrences	Rate
3/13/20xx	2:15 p.m.	2:25 p.m.	10 min		4	4 ÷ 10 min = 0.4/min
3/14/20xx	2:16 p.m.	2:26 p.m.	10 min		5	Empty
3/15/20xx	2:15 p.m.	2:35 p.m.	20 min		8	Empty
3/16/20xx	2:17 p.m.	2:27 p.m.	10 min		6	Empty
3/17/20xx	2:15 p.m.	2:35 p.m.	20 min		11	Empty

Interval Data: Summary Table

Behavior: Daniela directs attention to materials, activities, or peer conversations that are unrelated to the current instructional task for 3 consecutive seconds or longer.

Date	Time Start	Time End	% of intervals
3/13/20xx	2:15 p.m.	2:25 p.m.	60%
3/14/20xx	2:16 p.m.	2:26 p.m.	75%
3/15/20xx	2:15 p.m.	2:35 p.m.	63%
3/16/20xx	2:17 p.m.	2:27 p.m.	70%
3/17/20xx	2:15 p.m.	2:35 p.m.	Empty

Directions: Circle “+” for occurrence of behavior and “–” for nonoccurrence of behavior. Count the number of intervals during which the behavior occurred. Divide this number by the total number of intervals and multiply by 100 to determine the percentage of intervals during which the behavior occurred.