Google Workspace for Data Collection
Author: Jenee Vickers Johnson, M.Ed, BCBA; info@ciddl.org
Data-based decision making (DBDM) is the process by which professionals collect, graph, and analyze observational data to inform instructional decisions. DBDM is associated with improved decision making and student outcomes. Traditionally, DBDM involved pencil paper data collection and graphing on equal interval line graphs. However, many private applied behavior analysis companies use automated data-collection programs on mobile devices or computers for collecting, graphing, and sharing data with relevant stakeholders. These programs have promising features but may be prohibitively expensive or incompatible with certain methods of data collection. Although robust programs are available (e.g., Datafinch Catalyst, Central Reach), these programs require costly group membership fees that are beyond the resources of individual teachers. With some modifications, Google Workspace may be a practical alternative to support DBDM for special education professionals.
Steps for Using Google Workspace to Support DBDM:
1) Create a Google Form for data collection.
2) Create individual items for data collection. Make the first item a short answer response for data collector initials and make this item required. This allows for analysis of discrepancies in student performance across professionals and can help detect errors in data collection. Notably, a field with a date is not required because Google Forms entries are automatically dated and time stamped.
3) If collecting data on IEP goals or benchmarks, write the measurable goal/benchmark and criteria in the “question” field. Write directions for data collection in the item description.
4) Match the item type with the data measurement method.
Measurement Type | Google Forms Item Type | Graph Type |
Count | For first trial/cold probes, use a multiple choice grid with correct/incorrect options: For trial-by-trial data use a checkbox grid with columns for each trial: | · Cumulative record of mastered targets · Equal interval line graph of percent correct or number correct
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Frequency | This type of data collection will require the supplemental use of a timer. The short answer option with validation set to “whole number” is best for this data type: | · Equal interval line graph of count |
Duration or Latency | This type of data collection will require the supplemental use of a timer. The short answer option with validation set to “whole number” is best for this data type: | · Equal interval line graph of time |
Interval Recording (partial, whole, momentary time sampling) | Create a checkbox grid with rows for each interval and columns for each target behavior. Intervals can be specific times of day or just numbered intervals of a specific time (e.g., rows are numbered 1-5 and each represent a 15 second interval). | · Summative analysis: Bar graph of frequency of behavior per interval, bar graph of number or percent of intervals per each target behavior. · Formative analysis: Equal interval line graph with percent or number of intervals with or without behavior. Separate data paths for each behavior.
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Task Analysis | Multiple choice grid with rows for each step in the task analysis and columns with options for correct/incorrect (or +/-): | · Cumulative record with number of mastered steps, equal interval line graph with percent correct of total task analysis. |
5) Share the Google Form with professionals who will be collecting data on IEP goals/benchmarks (e.g., related service, paraprofessionals, and general education teachers).
6) Click the “responses” tab to view summary results. This tab shows summaries that can be helpful for certain data types but will not likely help with formative data analysis or DBDM.
7) Click on the green square icon to view data in sheets. Use conditional formatting to make rules to detect mastery (e.g. highlight when three consecutive rows say “correct” for an item). Use simple formulas to convert data to the desired form for graphing purposes.
8) Share graphs with individuals who support the student (e.g., BCBAs, reading specialists, related services).
9) Set a reminder to graph and analyze data on a regular frequency (e.g., every three days). Establish rules for determining when to make instructional changes based on data. Consider using goal lines and trend lines to assist with instructional decision-making.
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