Creating an account

AnalyZ Solutions uses a secure, industry-standard authentication system to manage user accounts. No payment details are ever required.

Logging in and out

To log in, visit analyz.solutions or click Login from the landing page. Your email and password are the only credentials needed.

To log out, click the Log out button in the top-left corner of the sidebar inside the app. Your session is terminated immediately and all in-memory data is cleared.

Data security

Guest mode vs. registered account

Quantitative data

Use the sidebar on the left. Make sure the mode toggle at the top of the sidebar is set to Quantitative.

Multi-sheet Excel files

If your Excel file has more than one sheet containing data, AnalyZ will detect this and show a sheet picker in the sidebar. Select the sheet you want and click Load selected sheet.

Demo datasets

If you don't have data ready, use one of the demo buttons in the sidebar:

• Test data without labels — a 300-row survey dataset (age, income, gender, education, score, region, employment).
• Test data with labels — the same dataset with descriptive variable labels already applied.

Clearing loaded data

Click the ✕ button next to the filename in the sidebar. This removes the dataset from the session. All analysis results are cleared too.

Connecting via API

AnalyZ can pull data directly from survey platforms — no file download needed. Supported platforms include KoBo Toolbox, ODK Central, REDCap, Google Sheets, and any custom REST endpoint returning JSON or CSV.

Qualitative data

Switch the mode toggle in the sidebar to Qualitative. Then use the file uploader to load transcript files.

Top navigation bar

The sticky bar at the top of every page contains the main navigation controls:

Home screen

The home screen presents two mode cards: Quantitative Analysis and Qualitative Analysis. Click either to enter that mode. Once in a mode, the home screen shows the relevant module cards (five quantitative, four qualitative). Click any card to open that module. If no dataset or transcript is loaded, modules will show a prompt to upload data first.

Quantitative modules

Qualitative modules

AI and sharing modules

Guided Analysis wizard

Available at any time from the Guided Analysis button in the top navigation bar. It helps you identify the right analysis when you are unsure where to start — or when you want confirmation that you are using the right approach for your goal.

The wizard works in three stages:

Goals covered include: describing data, comparing groups, predicting an outcome, exploring relationships, validating a scale, building a chart or dashboard, cleaning data, calculating sample size, understanding qualitative data, finding patterns across transcripts, mixed-methods analysis, and producing a data story.

Sub-sample filtering

Most quantitative analysis modules have a 🔍 Filter data panel at the top. Expand it to add one or more conditions (e.g. gender == Female, age > 30). All analysis in that module will run on the filtered subset only. An info banner confirms the active filter. Remove conditions to return to the full dataset.

Saving a session

AnalyZ saves both quantitative and qualitative work together in a single .analyz.json file. You can switch between modes within the same session and save everything at once.

Uploading a session file into the Quant or Qual picker instead

If you drop a .analyz.json file into the regular Upload button rather than Restore Session, AnalyZ still recognizes it — but only restores the matching half of the file:

• Via the Quantitative upload button: restores your dataset and any dashboard charts, but not transcripts or API connections.
• Via the Qualitative upload button: restores transcripts and coding only, nothing quantitative.

You'll see a status message noting what else is in the file and pointing you to Restore Session if you want everything at once. This is intentional — it lets you pull just the piece you need without disturbing what's already loaded elsewhere in the app.

Saving locally on this device

Separate from downloading a session file, AnalyZ can optionally keep a working copy of your session in this browser's local storage, so it's still there if you close the tab and come back. This is off by default and never sent to our servers either way.

⬇️ Downloading your data

Separate from session saving, the sidebar also has a ⬇ Download Data section that lets you export the current (possibly modified) dataset in your preferred format: CSV, Excel (.xlsx), Stata (.dta), or SPSS (.sav). Variable labels and value labels are embedded in the Stata and SPSS exports.

Data View is your first stop after loading a file. Use it to confirm everything loaded correctly and to get familiar with your variables before running any analysis.

The data table

Use the row range slider at the top of the controls bar to set which rows to display. Drag the left handle to set the start row and the right handle to set the end row — for example, drag to rows 100–300 to inspect a specific section of a large dataset. The label beside the slider shows the active range (e.g. 1–50). A missing value indicator appears automatically — a green badge if the dataset is complete, or an orange warning with a count if there are missing cells.

Showing value labels

Click Show value labels above the table. Any cell with a label mapping will display as 1 (Male) instead of just 1. Click again to return to raw values. When a dataset has value labels (e.g. imported from Stata or SPSS), this toggle is switched on automatically — so you see decoded labels by default and can turn them off to inspect raw codes.

Column details

Expand the Column details section below the table for a full inventory of every variable:

Editing variable labels

Expand Edit variable labels at the bottom of the page. Select a variable, type a descriptive label (e.g. "Age of respondent in years"), and click Save label. Labels appear throughout the app wherever that variable is used. Leave the field blank and click Save to clear a label.

Column Inspector

Click any column header in the data table to open the Column Inspector — a slide-in panel on the right side of the screen. It shows the full distribution of that column alongside key statistics, so you can explore your data without running a formal analysis.

Click a different column header to switch instantly. Click the same header again, or the ✕ button, to close the panel. The Inspect tab on the right edge of the screen is always visible and lets you open or hide the drawer at any time.

Categorical columns

The inspector shows a horizontal bar chart with one bar per distinct value, sorted by frequency. Each bar displays the count and percentage inline. Click any bar to filter the data grid to rows containing that value — or exclude it. Reset removes the filter; Exclude all hides every value at once. Filters applied here are view-only and do not modify your dataset.

Numeric columns

The inspector shows a binned histogram. Use the Bins slider to adjust the number of bins (5 to 40) and reshape the distribution in real time. A yellow dashed line marks the mean and a blue dashed line marks the median — when they diverge noticeably, the distribution is skewed. Key statistics are shown above the chart: valid count, missing count, unique values, range, mean, median, and standard deviation.

Changing variable type from the inspector

The inspector includes a Variable type row showing the current type — continuous or categorical — with an (auto) suffix when the type has been inferred rather than set explicitly. Click Change to expand the type editor inline. Once saved, the type badge updates immediately and the distribution chart adjusts to reflect the new classification. The change propagates to all analytical modules — Descriptive Analysis, Inferential, Visualization — for the remainder of the session.

Data Management gives you a full set of data preparation tools without writing any code. A live row / column / missing-value count is shown at the top of each action screen. Every action is undoable (up to 10 steps).

Variable Types

AnalyZ auto-detects variable types. Numeric columns with more than 10 unique values are treated as continuous; those with 10 or fewer unique values are treated as categorical.

Use Variable Types to override this for individual columns — for example, a Likert scale coded 1–5 has more than 10 possible values if recoded but represents categories. Getting types right is important because they control which tests and chart options appear throughout the app.

You can also change the type directly from the Column Inspector in Data View — click a column header, then click Change in the Variable type row. Both routes update the same setting.

Create Variable

Add a new column to the dataset using one of four methods:

• Formula — arithmetic using existing columns (e.g. score / age)
• Constant — a fixed value for all rows
• Conditional (if/else) — e.g. if score > 50 then "Pass" else "Fail"
• Sum / Mean of items — aggregate multiple scale items into a composite score

Give the new variable a name, choose its data type, and optionally add a label and value labels before clicking Create.

Recode Variable

Map old values to new values — for example, collapse age groups or reverse-score Likert items. Select the source variable, add value mappings row by row (old value → new value), give the result a name, and click Apply. The original variable is preserved; the recoded version is added as a new column.

Rename Variable

Select a variable and type its new name. The rename is reflected everywhere in the app immediately.

Delete Variables / Delete Observations

Delete Variables removes one or more columns. A preview shows what the dataset will look like before you confirm.

Delete Observations removes rows matching a filter condition you define (e.g. age < 18).

Manage Duplicates

Identifies fully duplicated rows, or duplicates based on a key column you choose. Preview the duplicates, then remove them keeping either the first or last occurrence.

🔗 Merge Data

Join a second dataset to the current one using a common key variable. Choose a join type:

📥 Append Data

Stack a second dataset on top of the current one (row-wise). Columns present in one dataset but not the other are filled with missing values. Useful for combining data collected in batches (e.g. survey wave 1 and wave 2).

🔀 Split Column

Splits a text column into multiple new columns using a delimiter you specify (comma, space, semicolon, pipe, tab, or any custom string). Each part becomes a separate column named colname_A, colname_B, colname_C, and so on, inserted immediately after the source column.

Use Preview split before applying to verify how the delimiter will divide the values. Useful for columns such as "A B C", "Yes, No, Maybe", or multi-response fields encoded as delimited strings.

Reshape Data

Converts your dataset between wide and long formats — the two fundamental data structures in analysis:

↩ Undo

The ↩ Undo button in the sidebar rolls back the last data management action. Up to 10 steps of history are maintained. The dataset, variable labels, and value labels are all restored together.

Descriptive Analysis has five analysis types presented as clickable cards on the module home screen. Cards are ordered to match a typical analysis workflow. Click any card to open that analysis. Use the Back button to return to the card selection. Use the optional 🔍 Filter data panel to restrict any analysis to a sub-group.

🔍 Data Quality

Run a full audit of your dataset before analysis. For every variable, the check reports: missing value count and percentage, duplicate values, whether the column is constant (only one unique value), and outliers (values beyond 3 standard deviations, for numeric columns). Results are colour-coded — red for high missing (>30%), orange for moderate missing (10–30%), and green (OK) for clean columns.

Run this first, before any other analysis, to catch data quality issues that could distort your results.

Frequency

Shows how often each value appears in a variable, with counts, percentages, valid percentages, and cumulative percentages.

🔲 Cross-Tabulate

Produces a contingency table showing how two categorical variables relate to each other — counts with row and column percentages. A chi-square test of independence is included automatically.

Select a Row variable and a Column variable (both should be categorical), then click Generate.

📋 Table

Creates a professional grouped summary table — means and standard deviations for selected continuous variables, broken down by a categorical grouping variable. This is the "Table 1" (characteristics table) commonly seen in academic papers.

Summary Statistics

Produces a full numeric profile for selected continuous variables:

• Mean, median, mode
• Standard deviation, variance, range
• Minimum, maximum, Q1, Q3
• Skewness and kurtosis

Inferential Analysis is organised into four groups. Select a group, then choose the specific test. Every result includes an automated plain-language interpretation.

Test groups & available tests

Means

Independent t-test

What it does: Compares the means of two independent groups to determine whether the difference is statistically significant.

What you will see: Group means, mean difference, t-statistic, degrees of freedom, p-value, 95% confidence interval, and Cohen's d effect size.

One-sample t-test

Tests whether a sample mean differs significantly from a known or hypothesised value. Enter the Test value (the benchmark you want to compare against).

Paired t-test

Compares two measurements from the same subjects (e.g. pre-test and post-test scores). Select the two numeric variables representing each measurement.

One-way ANOVA

What it does: Compares means across three or more groups simultaneously, testing whether at least one group differs from the others.

What you will see: ANOVA table, F-statistic, p-value, eta-squared (η²) effect size, and Tukey HSD post-hoc pairwise comparisons when the overall test is significant.

Two-way ANOVA

Tests the effect of two categorical factors on a numeric outcome, including their interaction. Select a dependent variable and two grouping variables.

Percentages

Chi-square test

What it does: Tests whether two categorical variables are independent — i.e. whether the distribution of one variable differs across categories of the other.

What you will see: A contingency table showing observed and expected counts, the chi-square statistic (χ²), degrees of freedom, p-value, and Cramér's V effect size.

Z-test for Proportion

Tests whether an observed proportion differs significantly from a benchmark (e.g. is the approval rate significantly different from 50%?). Enter the variable, the value to count as a "success," and the benchmark proportion.

Correlation

Pearson Correlation

What it does: Measures the strength and direction of the linear relationship between two or more numeric variables.

What you will see: A correlation matrix with r values, significance stars, and p-values for each pair.

Spearman Correlation

What it does: Rank-based correlation suitable for ordinal data or when the normality assumption cannot be met.

What you will see: Spearman's rho (ρ) correlation matrix with significance indicators.

Regression

Linear Regression

What it does: Models the relationship between one or more predictor variables and a continuous numeric outcome, showing which predictors have a significant effect and by how much.

What you will see: Coefficient table (β, standard error, t-statistic, p-value, 95% CI), R², adjusted R², F-test, and plain-language interpretation per predictor.

Logistic Regression

What it does: Predicts the probability of a binary outcome (yes/no, pass/fail, dropout/retained) from one or more predictors.

What you will see: Odds ratios, coefficient table, model fit statistics (log-likelihood, AIC, pseudo-R²), and classification accuracy.

Ordinal Regression

For ordinal outcomes (e.g. a 5-point satisfaction scale used as a dependent variable). Uses a proportional odds model.

Multinomial Regression

For nominal outcomes with three or more categories. Compares each category to a reference category you select.

Hierarchical Regression

Build a regression model in sequential blocks to test how much each additional set of predictors improves explanatory power (ΔR²).

Multilevel Regression

What it does: Models outcomes for observations that are nested within groups — for example, patients within clinics, students within schools, or households within villages. Accounts for the fact that observations in the same group tend to be more similar to each other than to observations in other groups, which standard regression ignores.

Outcome types supported: Continuous (EM-REML), Binary/logistic (PQL, reports odds ratios or log-odds), Count/Poisson (PQL, reports incidence rate ratios). Toggle between odds ratios and coefficients for binary outcomes using the pill button above the results table.

What you will see: Fixed effects table (coefficients, SE, z or t statistic, p-value, 95% CI), variance components (σ²_u, σ²_e, ICC at each level), model fit statistics (AIC, BIC, log-likelihood), group-level random intercept BLUPs, and an ICC verification panel.

📖 Reading the output

All tests use standard significance thresholds: *** p < .001, ** p < .01, * p < .05, ns p ≥ .05. Each result section ends with a plain-language interpretation paragraph suitable for direct use in a report.

The Visualization module opens with a card selection screen. Choose Single Chart to build and explore one chart at a time, or Multi-chart Dashboard to build multiple charts side by side and view them together. After selecting a mode, a tab bar at the top allows switching between the two modes without going back to the card screen. Use the Back link beside the tabs to return to the card selection.

Available chart types

Comparisons

Trends

Part-of-whole

Relationships & Patterns

Distributions

Building a chart

Editing a chart

Expand the 🎨 Edit chart panel below any chart to customise:

• Chart title, X-axis label, Y-axis label
• Font size and text colour
• Bar / marker colour
• Background colour
• Axis minimum and maximum values

Click Apply changes to update the chart. Changes persist for the session.

Multi-chart dashboard

Build a first chart and click + Add to dashboard. Build a second chart and add it. AnalyZ switches automatically to dashboard mode, laying charts out in a responsive grid. You can continue adding charts.

Cross-filtering

In the dashboard view, clicking a bar, pie slice, or data point cross-filters all other charts simultaneously — the selected value is highlighted and other values are dimmed. This lets stakeholders explore patterns across charts interactively. Hold Ctrl / Cmd and click to select multiple values across multiple charts at once. Click the same point again to clear the filter.

AI-assisted chart interpretation

Each chart in the dashboard has an AnalyZense button that generates a plain-language interpretation of that specific chart — what the pattern means, what stands out, and what it might imply. The AI works from the chart's summary data, never your raw dataset.

Sharing the dashboard

Click Share dashboard to publish the dashboard as a live link. Anyone with the link can view and interact with the dashboard in their browser — including cross-filtering — with no AnalyZ account required. The link can be revoked at any time from the dashboard view.

Downloading the dashboard

Click Download as HTML to save the entire dashboard as a single interactive HTML file. This file can be opened in any browser, shared as an email attachment, or embedded in a website. Cross-filtering works in the downloaded file without any internet connection.

Add to Data Canvas

Any chart in the builder or dashboard can be sent to a Data Canvas story using the Add to Canvas button. A modal opens where you write the slide headline and choose the destination canvas. The slide is added in the background — you continue working in the dashboard without losing your place. See the Data Canvas guide for how to build and publish a narrative story.

Downloading charts

Three export formats are available below every chart: PNG (high-resolution static image), JPG (compressed static image), and HTML (fully interactive — zoom, hover, pan).

This module is for researchers using multi-item scales. The module opens to three clickable cards — choose your analysis and click the card to open it. Use Back to return to the card selection. Results can be exported to Excel or Word.

Cronbach's Alpha

What it does: Measures internal consistency — whether a set of scale items all reliably measure the same underlying construct.

What you will see: The overall alpha coefficient, an inter-item correlation matrix, and item-total statistics including alpha if item deleted for each item.

Principal Component Analysis (PCA)

What it does: Reduces a large set of variables into a smaller number of uncorrelated components that capture the most variance. Use when your goal is to simplify your data, remove multicollinearity, or create composite scores for use in regression or other analyses.

What you will see: A scree plot, component loadings table, and total variance explained per component.

Exploratory Factor Analysis (EFA)

What it does: Uncovers the latent factors underlying a set of observed variables. Unlike PCA, EFA assumes that your observed variables (e.g. questionnaire items) are caused by a smaller number of unobserved constructs (factors). Use it when you want to understand the theoretical structure beneath your items.

What you will see: Factor loadings table, eigenvalues, variance explained, a scree plot, and a factor correlation matrix (for oblique rotations).

Sampling brings together three planning tools used before or independently of any loaded dataset: Sample Size Estimation, Power Analysis, and Draw a Sample. Start on the landing screen by choosing the tool you need, then select the specific calculator or design.

📐 Sample Size Estimation — organised by study design

Calculators are grouped by what your study is actually doing, not by sampling technique. Choose the group, then the specific design, enter your parameters, and click Calculate sample size.

Cross-sectional

A single round of data collection — one flexible calculator that adapts to your needs rather than several separate ones. Choose what you're estimating and how you'll select participants; everything else adjusts automatically.

Also included: optional population size (finite population correction) and expected non-response rate, which inflates the final recruitment target.

Measuring change

Study designs built around detecting a difference — before vs. after, or one group vs. another. Because they exist to detect an effect, these calculators already incorporate statistical power as part of their formula.

Change in Proportion offers two designs: two independent groups (the classic formula) or same group, before/after (a paired design, which typically needs a smaller sample because each participant acts as their own comparison — the higher the correlation between the two measurements, the greater the saving).

Other study designs

⚙️ Common parameters

📖 Reading the output

Each calculator shows:

• The primary sample size in large text (e.g. per arm for RCT, total to recruit for Cross-sectional)
• Secondary figures such as the pre-non-response minimum, per-stratum allocations, or number of clusters needed
• An interpretation panel with colour-coded guidance and any design-specific caveats
• The formula used — shown in monospace at the bottom for transparency and citation

⚡ Power Analysis — eight statistical tests

Choose the test matching your planned analysis, then switch between two modes: enter a sample size to see the achieved power, or enter a target power to find the required sample size.

Use Power Analysis when you want to test a specific statistical test not covered by the Sample Size Estimation calculators above, or when you want to explore power across a range of sample sizes rather than get a single figure.

After calculating, click Show power curve to see a chart of power vs. sample size for small, medium, and large effects — helpful for understanding the trade-off between sample size and detection sensitivity.

🎯 Draw a Sample

Upload your sampling frame — a CSV or Excel file listing every unit in your population — and have AnalyZ select your sample directly.

1. Upload your frame (CSV, XLS, or XLSX). AnalyZ shows the row and column count once loaded.
2. Choose a design: Simple Random, Systematic, Stratified, Cluster, or Probability Proportional to Size (PPS).
3. Set your sample size and any design-specific parameters — a strata column and allocation method for stratified; a cluster column and number of clusters for cluster or PPS; a size measure column for PPS.
4. Optionally set a random seed to make the draw reproducible.
5. Click Draw sample. Review the preview table, then download as CSV or Excel — either the selected rows alone, or the full frame with a _selected flag column added.

The Qualitative module is organised into four sub-modules. Load your transcripts first (see Loading Data → Qualitative data), then open Qualitative Analysis from the home screen. Use the buttons below to jump directly to any sub-module's guide.

Sense Making brings together the analyses you have run across any module — quantitative tests, qualitative codes, visualisations — and uses AI to synthesise them into a single coherent interpretation. Instead of describing each result individually, the AI identifies patterns, connections, and contradictions across your full body of evidence.

How it works

Projects

A project is a named collection of analyses grouped for a specific synthesis. Key facts:

• You can have multiple projects — one per research question, study, or deliverable.
• Each project holds up to 20 analyses. A warning appears at 15; the button is blocked at 20.
• Projects are private to your account — no other user can see them.
• Deleting a project permanently removes all its analyses and conversation history.
• You can copy an analysis from one project to another from inside the project workspace.

Interpretation modes

Choose a mode in the project workspace before interpreting. The mode persists and can be changed at any time — switching mode and re-interpreting is one click.

Context field

The context field (in the project workspace) tells the AI what this project is about. It is optional but significantly improves interpretation quality. Good context includes:

• The research question or objective
• How the data was collected (e.g. "survey of 300 B2B clients")
• Who the findings are for
• Any hypotheses you want tested or confirmed

Context is saved to the project automatically when you click 💾 Save context.

Refining the interpretation

After the initial interpretation, a Refine interpretation panel appears below the synthesis. Type a plain-language instruction and the AI rewrites the interpretation incorporating it — the refined version replaces the original. Examples:

• "What might explain the contradiction between the t-test result and the qualitative themes?"
• "Focus more on the findings for the female sub-group."
• "Summarise the three most important findings in one paragraph."
• "What are the limitations of this analysis?"

The AI rewrites the full interpretation incorporating your instruction, drawing on the original analyses and any context you provided. The revised version replaces the previous one in the project.

Exporting the interpretation

Click ⬇ Export to Word in the synthesis view. The downloaded .docx file includes:

• Project title and interpretation mode
• Research context (if provided)
• Numbered list of all analyses included, with module type and user notes
• Full AI interpretation

Privacy and the AI

The AI receives no raw data. It only sees what you explicitly added to the project: aggregated statistics, auto-generated interpretation text from each module, and any notes you wrote. Your dataset stays in your browser session and is never transmitted to the AI.

Data Canvas — turns your analysis outputs into a scrollable, shareable narrative. Where the dashboard lets people explore data interactively, Data Canvas tells the story of what the data means for a non-technical audience — donors, community members, board members, or anyone who needs to understand findings without doing the analysis themselves.

Open Data Canvas from the home page card or the sidebar navigation.

Slide types

Adding slides from analysis modules from analysis modules

Every analysis output across all modules has an Add to Canvas button below the export buttons. Clicking it opens a modal where you:

• Write the slide headline — the plain-language insight title
• Choose which canvas to send it to (or create a new canvas inline)
• Confirm — the slide is added in the background while you continue working

For participant quotes: in the Thematic Coding module, each coded segment has an Add Quote to Canvas button. The quote text, speaker label, and code are carried across automatically.

Building in the canvas

AnalyZense AI features

Public viewer

The public story is available at /story-building/story/[slug]. Readers see:

• The story title, description, and AnalyZ branding at the top
• Each slide in sequence — numbered insight, headline, visual (chart / table / quote / infographic), and context paragraph
• Side navigation dots on desktop for jumping between insights
• A floating previous / next bar when there are multiple slides
• Charts remain fully interactive — hover, zoom, and pan work in the viewer

No account, no login, no software required to view. The link works on mobile and desktop.

Canvas settings

• Title and description — shown at the top of the public viewer
• Custom slug — set a short word for the URL (e.g. /story-building/story/q3-evaluation). Must be unique across all public stories.
• Public / Private toggle — switch a published story back to private at any time. The link stops working immediately.
• Delete canvas — permanently removes all slides and the public link

Privacy and the AI

All Data Canvas AI features work from the slide content you have built — headlines, context text, chart summaries, and table data visible in the canvas. Your raw dataset is never sent to the AI. Participant quotes are sent as written text only — no metadata from your dataset accompanies them.

Tables

Every analysis module that produces a results table includes export buttons below the output. Tables can be downloaded as:

• Excel (.xlsx) — multiple sheets when there are several result tables
• Word (.docx) — formatted document ready for insertion into a report

Charts

All charts in the Visualization module and frequency charts in Descriptive Analysis can be downloaded as:

• PNG — high-resolution static image (ideal for papers and slide decks)
• JPG — compressed static image
• HTML — fully interactive chart that can be opened in any browser, shared, or embedded

💾 Dataset

Use ⬇ Download Data in the sidebar to export your current (possibly modified) dataset. Available formats:

💼 Sessions

Use 💾 Save Session in the sidebar to download a complete snapshot of your work as a .analyz.json file — dataset, labels, variable type overrides, connected API sources, dashboard charts, and (for qualitative sessions) all transcripts and coded segments. Click 📂 Restore Session at any time to load that file back in and resume your exact session.