Exploratory Data Analysis
This website focuses on the application of Exploratory Data Analysis (EDA) in creating Functional Behavior Analysis (FBA) to understand and address behaviors effectively. It emphasizes the importance of EDA in identifying critical variables, patterns, and relationships within behavioral data. Through systematic data collection using a Functional Behavior Observation (FBO) form, the site facilitates the analysis of antecedents, behaviors, and consequences. Additionally, it highlights how data visualization and statistical analysis can reveal insights, helping in making data-driven decisions for designing interventions. This platform serves as a comprehensive tool for behavior analysts, educators, and related professionals, leveraging EDA techniques for enhanced behavior analysis.
Create a Functional Behavior Observation Form
A Functional Behavior Observation (FBO) form serves as a tool in behavior analysis, designed for collecting information on the antecedents (events or situations preceding behavior), the behavior itself, and the consequences (events or situations following the behavior). You can create any behavior, antecedent, and function of choice. Systematic data collection using the FBO form allows behavior analysts and educators to analyze patterns and relationships between antecedents, behavior, and consequences.
Data Visualization
EDA facilitates the visual representation of data through plots, graphs, and charts. These visualizations enable you to identify patterns and trends, making discovering potential relationships and correlations between variables easier.
Statistical Analysis
EDA often involves statistical techniques to summarize and analyze data. Applying descriptive statistics, correlation analysis or other relevant methods allows for quantifying relationships between variables and identifying possible factors contributing to the behavior. Data is processed through algorithms used for data manipulation and analysis, which allow for efficient handling of structured data. These algorithms are used among data scientists, analysts, and developers working with tabular data.
