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AddMaple Glossary

This glossary defines key terms and concepts used throughout the AddMaple platform and documentation.

Core Platform Terms

AddMaple

A browser-based data analysis platform that processes data locally without uploading to the cloud. Uses modern web APIs (File API, Worker API) to read and analyze files directly in your browser.

Project

A workspace containing your dataset and all associated analyses, charts, dashboards, and reports.

Dataset

The raw data file you upload and analyze within AddMaple. Can be CSV, XLSX, SAV, JSON, Parquet, or AddMaple file formats.

Column Store

AddMaple's proprietary data structure designed for fast pivoting, binning, and filtering of data within the browser.

Data Types & Detection

Multiple Choice (MC)

Categorical columns with a limited set of text results. Appears with turquoise bars on the chart dashboard. Used for single-answer survey questions or categorical data.

Multiple Choice Plus (MC+)

Columns where there are multiple results per row. Appears with blue bars. Used for multi-select survey questions, tags, or any data where multiple values can be assigned to one record.

Numeric

Automatically detected numeric data (numbers, currency, percentages). Appears with green bars. AddMaple automatically creates bins/buckets for visualization in histograms.

Opinion Scale

Special detection for opinion scales including both numeric scales (1-10) and text-based answers (Very Important, Somewhat Important, etc.). Allows display of Likert charts.

Date/Datetime

Automatically detected date and time columns. Enables time-based analysis and special date binning features.

Text

Free text answers or responses. Future improvements will include full text search and automated tagging.

Unique

Columns with different values for each row, typically identifiers. Cannot be filtered or pivoted but can be viewed in table or raw data view.

Currency

Automatically detected currency columns with currency symbols. Can be filtered and viewed like numeric columns.

Percent

Automatically detected percentage columns containing numbers followed by "%" signs.

Data Processing & Preparation

Binning

The process of grouping numeric or date values into ranges for easier analysis and visualization.

Number Binning

  • Auto (Freedman-Diaconis rule): Statistically optimal bin width
  • Equal Frequency: Bins with similar counts
  • Fixed Width: Consistent interval bins
  • Custom: Manually defined ranges and labels

Date Binning

  • Auto: Optimal time-based bins
  • Calendar Periods: Year, Quarter, Month, Week, Day, Hour
  • Fixed Intervals: Every X minutes/hours/days/weeks
  • Custom Breakpoints: User-defined date boundaries

Weighting

Applying respondent weights to survey data for more accurate analysis. Adjusts results to better represent target populations when certain groups are over- or under-represented.

Column Grouping

Combining similar columns with overlapping categories to analyze their totals together. Useful for aligning data across similar questions.

Data Cleaning

Process of preparing data for analysis, including merging categories, renaming columns, and handling missing values.

Analysis & Visualization

Pivot/Pivoting

The process of cross-tabulating data by selecting two columns to analyze their relationship. Creates pivot charts and tables showing how one variable relates to another.

Filter/Filtering

Narrowing down your dataset to focus on specific segments or values. Can be applied by clicking bars in charts, using the sentence builder, or from table views.

Aggregation

Summarizing data using statistical measures:

  • Total/Sum: Adding up values
  • Average/Mean: Calculating the mean
  • Median: Finding the middle value
  • Count Unique: Counting distinct values

Chart Dashboard

The main interface showing all your data as interactive charts. Each column type appears with color-coded tiles (turquoise for MC, blue for MC+, green for numeric, etc.).

Pivot Chart

Interactive charts created when you pivot two columns together. Show relationships between variables with statistical summaries.

Likert Chart

Special chart type for opinion scale data, showing responses in a scale format. Created by pivoting opinion scale columns or grouping similar columns.

Histogram

Chart type for numeric data showing the distribution of values across bins or ranges.

Horizontal Bar Chart

Default chart type for categorical data, showing categories as horizontal bars with values.

Box Plot

Statistical visualization showing quartiles, median, and outliers for numeric data.

Scatter Plot

Chart showing relationship between two numeric variables, often with regression lines.

Statistical Analysis

Chi-Square Test

Statistical test determining significant relationships between two categorical columns. Provides p-value, Cramér's V (relationship strength), and chi-square statistic.

T-Test

Statistical test comparing means between two groups. Automatically performed when pivoting a numeric column with a categorical column having exactly 2 categories.

Correlation

Measures relationship strength between two numeric variables:

  • Pearson Correlation: For normally distributed data
  • Spearman Correlation: For non-normally distributed data

Regression Analysis

  • Linear Regression: Relationship between numeric variables
  • Logistic Regression: Relationship between numeric and binary variables
  • Multivariate Regression: Planned future feature

ANOVA (Analysis of Variance)

Statistical test comparing means across multiple groups.

Kruskal-Wallis Test

Non-parametric alternative to ANOVA for non-normally distributed data.

P-Value

Statistical measure indicating the probability that observed relationships are due to chance. Lower values indicate stronger evidence of real relationships.

Effect Size

Measures the practical significance of statistical relationships:

  • Cramér's V: For categorical relationships (0-1 scale)
  • Cohen's d: For mean differences

Dashboard & Reporting

Dashboard

A collection of pages containing charts, text, images, and KPIs arranged for storytelling and sharing findings.

Page

Individual canvas within a dashboard containing various items. Tabs can be reordered by dragging.

Item Types

Components that can be added to dashboard pages:

  • Text: Written content
  • Section: Content dividers
  • Call Out: Highlighted information
  • Image: Visual content
  • Video: Video content
  • Charts: Saved analyses from My Collection

My Collection/Insights

Storage area for saved charts and notes. Items can be organized into Collections and added to dashboards.

Collections

Folders for organizing Insights by theme (e.g., "Q3 Feedback", "Launch Readout").

Report

Document containing selected charts and analyses for presentation or sharing.

Publishing

Making dashboards publicly accessible via shareable links. Can include password protection and explorable data options.

Explorable Data

Feature allowing dashboard viewers to interact with underlying data by exploring selected columns.

Public Link

Shareable URL for published dashboards that can be accessed without AddMaple accounts.

Text Analysis & AI Features

AI Summary

Automated analysis of text columns using AI to generate summaries of responses. Processing time varies by dataset size.

Thematic Coding

Process of categorizing text responses into themes or codes for analysis. Can be done manually or with AI assistance.

AI Chart Explanation

AI-powered summaries explaining what current charts show and their key insights.

Clean with AI

Automated feature for cleaning, renaming, ordering, and merging categories in legends.

Text Search

Feature for filtering text responses by specific words or phrases.

User Interface Elements

Sentence Builder

The top black menu bar that displays applied filters and pivots in plain English. Interactive interface for adding filters, pivots, and aggregations.

Legend

Interface for managing how categories appear in charts:

  • Rename categories
  • Reorder categories
  • Merge categories
  • Assign colors
  • Hide/show categories
  • Toggle between Ordered vs Independent categories

More Menu

Context menu providing additional options for charts, columns, and analyses.

Column Settings

Interface for configuring individual column properties including binning, types, and display options.

Manage Columns

Central interface for configuring all columns in a project, including types, binning, and other settings.

Project Settings

Configuration area for project-wide settings including color presets, weighting, and data management.

Data Views

Graph View

Default view showing all columns as interactive charts on the chart dashboard.

Table View

Tabular display of data with options to add/remove columns, sort, filter, and explore individual rows.

Row-by-Row View

Individual record view showing complete responses with comparison to overall dataset. Useful for reading free-text responses and understanding context.

Raw Data View

Direct display of original data without processing or formatting.

Export & Sharing

Export Formats

  • CSV: Comma-separated values
  • XLSX: Excel format
  • JSON: JavaScript Object Notation
  • Parquet: Columnar storage format
  • AddMaple Fast: Optimized for fast loading
  • AddMaple Compressed: Smallest file size

PowerPoint Export

Export charts and analyses to PowerPoint presentations.

Excel Crosstabs

Export pivot tables to Excel format with proper formatting.

Public Sharing

Sharing dashboards and reports via public links with optional password protection.

Technical Terms

Browser-Based Processing

All data analysis happens locally in your browser without uploading to cloud servers.

File API

Modern web API allowing browsers to read files directly from your computer.

Worker API

Web API enabling background processing for large datasets without blocking the user interface.

Columnar Storage

Data storage format optimized for analytical queries and fast aggregation.

Effective Sample Size

Adjusted sample size accounting for weighting schemes in statistical calculations.

Degrees of Freedom

Number of values free to vary in statistical calculations.

Expected Values

Theoretical counts expected if no relationship existed between variables (used in chi-square tests).

Outlier Handling

Statistical methods for identifying and managing extreme values that might distort analysis.

Welch-Satterthwaite Degrees of Freedom

Method for calculating degrees of freedom in weighted t-tests.

Kish Effective Sample Size

Method for adjusting sample size calculations when using survey weights.

Workflow Terms

Save/Add to Dashboard

Process of saving charts or analyses to My Collection for later use in dashboards.

Swap Columns

Feature allowing you to switch which column is being pivoted vs. which is doing the pivoting.

Related Columns

Columns that AddMaple suggests as potentially related based on data patterns.

Cross-tabulation

Statistical table showing relationships between two categorical variables.

Segmentation

Process of dividing data into meaningful groups for analysis and comparison.

Demographic Analysis

Analysis focusing on population characteristics like age, gender, income, education, etc.

Response Rate

Percentage of contacted individuals who completed a survey or provided data.

Sample Size

Number of observations or responses in your dataset.

Confidence Level

Statistical measure of certainty in results (typically 95% or 99%).

This glossary covers the main terms and concepts used throughout AddMaple. For detailed explanations and step-by-step instructions, refer to the specific user guide sections.