Glossary

Key terms and concepts used throughout the Askalot platform.

Entities

Project

A top-level container for organizing survey research. Groups related questionnaires and campaigns.

Questionnaire

A survey definition that references a QML file. Contains questions, logic, and flow rules. Questionnaires are templates - they define what to ask.

Campaign

An active data collection effort. Links one questionnaire to a pool of respondents. Campaigns define who to survey and track progress.

Respondent

A survey target - the person being surveyed. Has demographic data (age, gender, location) and contact information. Respondents access surveys via magic links without platform login.

User

A platform operator who can log in. Users have roles (admin, manager, interviewer, designer) that determine their permissions. Users are not survey targets.

Interviewer

A User with the interviewer role who facilitates surveys on behalf of respondents. Interviewers help respondents complete surveys but don't answer questions themselves.

Survey

An individual survey session. Links a specific respondent to a questionnaire, optionally within a campaign. Tracks progress and stores answers.

Survey Concepts

Direct Mode

Survey execution where the respondent completes the survey independently, typically via a magic link sent by email.

Field Interview Mode

Survey execution where an interviewer facilitates the survey on behalf of the respondent. The interviewer operates the interface and records the respondent's answers.

Campaign-Generated Survey

A survey created through the campaign invitation workflow. Linked to a campaign for progress tracking and reporting.

Ad-Hoc Survey

A survey created on the spot without campaign association. Used for testing, training, or opportunistic data collection. Not tracked in campaign statistics.

Magic Link

A unique, time-limited URL that allows a respondent to access their survey without logging in. Contains encrypted survey and respondent identifiers.

QML Concepts

QML (Questionnaire Markup Language)

A YAML-based language for defining questionnaires. Specifies questions, answer options, branching logic, and validation rules.

Block

A logical grouping of Items within a questionnaire. Blocks are used to break complex questionnaires into smaller logical units. Items within a Block can reference other Items in the same Block, but not Items outside the Block. This scoping enables validation and analysis of questionnaire logic.

Item

One unit of request to the Respondent. Item types include:

• Comment: Display-only text with no outcome (informational)
• Question: Single outcome (one answer)
• QuestionGroup: List of outcomes (multiple related answers)
• MatrixQuestion: An n × m matrix of outcomes (grid of answers)

Outcomes of an Item share the same domain constraints (the possible range of values).

Control

The input type for an Item: radio buttons, dropdown, checkbox, slider, text field, etc. Multi-outcome Items (QuestionGroup, MatrixQuestion) have multiple controls of the same type.

Precondition

A list of logical formulas that must all be satisfied to show an Item. If no Precondition is specified, it defaults to True (always show). Based on previous answers or computed values.

Postcondition

A list of logical formulas that must all be satisfied to accept a response for an Item. If no Postcondition is specified, it defaults to True (always accept). Used for validation.

CodeBlock

A Python block executed after an Item is answered (the outcome has a value) and the response has been validated by the Postcondition. Used for computed values and complex logic.

Campaign Concepts

Workload Assignment

Distribution of respondents among interviewers in field interview campaigns. Each interviewer gets a subset of respondents to survey.

Invitation Status

Tracks whether a respondent has been invited, has started, or has completed their survey.

Response Rate

The percentage of respondents who completed their survey out of the total invited.

Sampling Concepts

Respondent Pool

A collection of respondents selected for a specific campaign. Pools are typically generated from a Sampling Strategy to match target demographic distributions. Unlike strategies, pools are not meant for reuse—respondents who have participated in research are generally not selected again to respect their time and avoid survey fatigue.

Sampling Strategy

A reusable configuration that defines target demographic distributions for respondent selection. Specifies stratification factors, target sample size, and selection algorithm. The same strategy can generate fresh pools for different campaigns, each time selecting new respondents who match the demographic criteria.

Stratification Factor

A demographic dimension (age, gender, location, etc.) with target proportions for sampling. For example, a gender factor might target 48% male, 50% female, 2% other.

Oversample Factor

A multiplier applied to target sample size to compensate for expected non-response. An oversample factor of 1.2 means selecting 20% more respondents than the target.

Selection Algorithm

The method used to select respondents from the pool. Options include:

• Greedy: Selects respondents that best improve demographic balance at each step
• Random Constrained: Randomly selects while respecting demographic constraints

Quality Metric

A measure of how well the sample matches target demographic distributions:

• RMSE: Root Mean Square Error of proportion deviations
• MAE: Mean Absolute Error of proportion deviations
• Chi-Square: Statistical test for distribution fit
• Max Deviation: Largest single proportion deviation

Data Analysis Concepts

Medallion Architecture

A data processing pattern with three stages of increasing refinement:

1. Bronze: Raw survey responses extracted directly from completed surveys
2. Silver: Cleaned and weighted data after post-stratification
3. Gold: Final refined dataset ready for export and analysis

Bronze Dataset

The first stage of the medallion architecture. Contains raw survey responses with respondent demographics but no statistical adjustments. Created by extracting completed surveys from a campaign.

Silver Dataset

The second stage of the medallion architecture. Contains weighted responses after post-stratification (raking) to correct for sampling bias. Created by applying raking to a Bronze dataset.

Gold Dataset

The final stage of the medallion architecture. Contains refined data ready for export. May include field transformations (renaming, reordering, filtering). Created from Silver or Bronze datasets.

Raking (Post-Stratification Weighting)

An iterative proportional fitting algorithm that calculates respondent weights to align sample demographics with target population distributions. Corrects sampling bias when actual response rates differ from targets.

Respondent Weight

A multiplier assigned to each response during raking. Under-represented demographics receive weights > 1.0; over-represented demographics receive weights < 1.0. Applied during statistical analysis.

Export Formats

Supported output formats for datasets:

• CSV: Universal comma-separated values
• XLSX: Excel format with summary and schema sheets
• SPSS: Statistical package format with variable and value labels
• Parquet: Efficient columnar format with full metadata

AI-Assisted Features

Campaign Wizard

A multi-step guided interface for creating campaigns. Walks users through project selection, questionnaire upload, sampling strategy, pool generation, and campaign launch with recommendations at each step.

Chat Interface

A conversational AI interface for campaign operations. Allows natural language commands like "create a campaign for customer satisfaction research" and executes operations through MCP tools.

Persona Profile

A demographic and behavioral template used for synthetic response generation. Defines characteristics like age range, income level, and response style. Examples: "Young Professional", "Family-Oriented", "Retired Senior".

Synthetic Response Generation

AI-powered simulation of survey responses based on persona profiles. Used for testing data analysis pipelines, validating questionnaire logic, and generating realistic test data.

MCP Tools

Model Context Protocol tools that expose platform operations to AI assistants. Enable programmatic access to create projects, manage campaigns, complete surveys, and analyze data through a standardized interface.

Platform Components

SirWay

The survey execution platform. Respondents and interviewers use SirWay to complete surveys.

Targetor

The campaign management platform. Managers use Targetor to create campaigns, import respondents, and track progress.

Armiger

The questionnaire design environment. Designers use Armiger to create and validate QML files with AI assistance and SMT validation.

Balansor

The data analysis platform. Analysts use Balansor to process survey responses through the medallion architecture, apply post-stratification weighting, and export refined datasets.

Portor

The API gateway and MCP server. Provides REST endpoints and Model Context Protocol tools for programmatic access to all platform operations. Powers the AI Chat Interface.

Roundtable

The administration platform. Manages users, organizations, and platform settings. Hosts the Campaign Wizard and Chat Interface for AI-assisted operations.