Q methodology

Q methodology is a workhorse method in policy research, environmental psychology and stakeholder viewpoint analysis — wherever the question "what shared positions exist in this group?" matters more than "how many people hold each position?". Respondents Q-sort a set of statements onto a forced-distribution pyramid, and factor analysis identifies the latent viewpoints that organise the sorted data.

tickStat is a survey and experiment platform built by academic researchers, for academic researchers, and Q methodology is one of the disciplines it was designed to support natively. The full Q-sort workflow lives on a single platform: designing the Q-set and the consensus pyramid, the drag-and-drop sorting interaction (mobile-friendly), and a one-click Factor Analysis with varimax rotation and publication-ready interpretation reports.

Why tickStat for Q methodology¶

Drag-and-drop sorting that works on mobile. Most Q-sort implementations were built for desktop with touch as an afterthought. tickStat's sorter is responsive from the first version, so participants can complete the study from a phone — important for fieldwork outside computer-equipped lab settings.
Consensus Pyramid dashboard. Visualise the forced-distribution structure (column counts, statement positions) in real time as the data comes in. Spot pyramid violations, monitor study balance and decide when you have enough data to estimate factors.
One-click Factor Analysis with varimax rotation. Eigenvalues, factor loadings, defining sorts and factor scores computed inside the platform — no round-trip to a separate stats package required for the first cut. Export the underlying per-respondent sorts for any custom analysis you want to run downstream.
Factor interpretation report, ready for publication. The platform generates a textual summary of each factor (consensus statements, distinguishing statements, defining sorts) that drops straight into the results section of a paper.
Reproducible and open. Q-set, instructions, sorting interface, factor extraction settings — every parameter is exposed and documented. Reproducing a study or sharing it with a co-author means sharing the survey definition, not a screenshot of a wizard.
Multilingual fielding in 14 languages. Run the same Q study across countries with a single survey definition.
GDPR and EU AI Act compliant. Important for academic researchers running publication-grade studies under the current regulatory regime.

What's on this page¶

Below: how to design the Q-set, configure the forced-distribution pyramid, run the sort interaction, monitor progress in the Consensus dashboard, and run the integrated Factor Analysis with varimax rotation.

If you are new to the platform, start with the Getting started guide.

This question type implements Q methodology. To configure it, first create a matrix with radio-type columns. Then, in the Q question configuration, map the matrix answers to the bins presented in the Q model. Below is an image showing how this question is displayed:

Note: In this interface, the user can move yellow cards to the bottom of the stack by clicking on them, and cards can be dragged from any position to any other position.

Configuration:

As an example, first configure a matrix where the user selects items they agree with, disagree with, or feel neutral about. The answer titles in the matrix should match the bin labels in the Q model question.

The Q model question requires the following configuration:

Matrix Segmentation Question: The source matrix configured previously.
Rows: Number of rows in the pyramid.
Columns: Number of columns in the pyramid.
Direction of the matrix: Accepted values are "up" or "down," depending on the desired display orientation.
Literal bags: Titles for the source bins where items are initially presented for the user to move into the pyramid. Example: En desacuerdo;Neutras;De acuerdo
Literal columns: Text displayed in the pink rectangles. Example: ; ; ; ; ; ;
Detailed column literal: Text displayed below the pink rectangles. Example: Muy en desacuerdo;;;Ni de acuerdo ni en desacuerdo;;;Muy de acuerdo
Source of the bags: This important configuration links matrix columns to Q model bins. For example, 1,2;3;4,5 means that matrix items selected as answer 1 or 2 go to the first bin, items selected as answer 3 go to the second bin, and items selected as answer 4 or 5 go to the third bin.

To omit a label, simply leave it as a space.

After saving the configuration, a pyramid based on the specified rows and columns appears, where you need to set the items for each column. The configuration will look like the following image:

For interpreting the report related to this question type, consult the "Info qModel" Excel tab.

Visual Results Dashboard¶

Q Model questions include an integrated visual results dashboard, accessible from the Analysis menu (the Q-Model Analysis entry, which appears whenever the survey contains a Q-Model question). If the survey has a single Q-Model question the dashboard opens directly; if it has several, you first pick the question to analyse. The dashboard offers three analysis modes:

Consensus Pyramid: Shows an aggregate view of where all respondents placed each card on average. Cards are color-coded by consensus level: - Green (High consensus, σ < 1.3): Most respondents placed the card in the same position. - Yellow (Medium, σ 1.3–1.5): Some variation in placement. - Red (Low/Polarized, σ > 1.5): Respondents disagree significantly about this card.

Hover over any card to see its exact mean position, standard deviation, and number of respondents.

Card Distribution: Click any card from the left panel to see a horizontal bar chart showing how respondents distributed that card across all columns. The list is sorted by consensus level, making it easy to identify the most agreed-upon and most polarized items.

Factor Analysis: Runs a Q-Methodology factor analysis using R (requires the qmethod R package). This identifies groups of respondents who share similar viewpoints: 1. Select the number of factors (or use Auto to let the system decide based on Kaiser criterion). 2. Click "Run Analysis" — the system samples up to 800 respondents, computes correlations, extracts factors via PCA with varimax rotation, and identifies distinguishing and consensus statements. 3. Results show factor profiles (Q-score pyramids per factor), the number of respondents per factor, and variance explained. 4. The Statements Analysis tabs show consensus statements (items all groups agree on) and distinguishing statements per factor (items that differentiate each group). 5. Click "Generate Interpreted Report" to produce a Word document (.docx) with an AI-generated interpretation of the results, including factor profile images and data tables.

The dashboard counter, the Factor Analysis and the Interpreted Report all consider only respondents who completed the entire survey (status Done). Pyramid placements from respondents who finished the Q Model question but later abandoned the survey are excluded so that statistics and factor groupings are not distorted by partial data.

Respondent filter bar: Above the dashboard there is a collapsible Respondent filters bar that lets you segment the analysis by demographic or screening answers (e.g. age range, region, gender, study group). Pick one or more filterable questions, choose the values you want to keep, and press Apply. The Consensus Pyramid, the Card Distribution and the Factor Analysis all recompute on the matching subset, and the stats card updates to show how many respondents are included. Clear all restores the full Done dataset. Whenever you change the filter the previous Factor Analysis results are cleared so you re-run the analysis on the new subset, and the Interpreted Report (Word) generated afterwards describes the same filtered subset. Available filter questions: radio, checkbox, single-choice and predefined-list (listaValores) demographic items.

Definitions: - Columna (Column): Matrix column index. - Fila (Row): Matrix row index. Note: Rows are numbered from bottom to top.